From: khemendra kumar Date: Fri, 24 Dec 2021 10:58:13 +0000 (+0530) Subject: removed Example Apps X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=commitdiff_plain;h=refs%2Fchanges%2F82%2F4582%2F1;p=ealt-edge.git removed Example Apps Signed-off-by: khemendra kumar Change-Id: I298462f51143db8d76f9e81bba8e9a648cb0507a --- diff --git a/example-apps/PDD/deployment/pcb-dep.yaml b/example-apps/PDD/deployment/pcb-dep.yaml deleted file mode 100644 index c825478..0000000 --- a/example-apps/PDD/deployment/pcb-dep.yaml +++ /dev/null @@ -1,34 +0,0 @@ ---- -apiVersion: apps/v1 -kind: Deployment -metadata: - name: pcb-ui-deployment -spec: - replicas: 2 - selector: - matchLabels: - app: pcbui - template: - metadata: - labels: - app: pcbui - spec: - containers: - - name: pcbui - image: ealtedge/pcbui - ports: - - containerPort: 80 ---- -apiVersion: v1 -kind: Service -metadata: - name: pcbui-service -spec: - type: NodePort - selector: - app: pcbui - ports: - - protocol: "TCP" - port: 8000 - targetPort: 80 - nodePort: 30002 diff --git a/example-apps/PDD/deployment/setup.md b/example-apps/PDD/deployment/setup.md deleted file mode 100644 index b19a298..0000000 --- a/example-apps/PDD/deployment/setup.md +++ /dev/null @@ -1,29 +0,0 @@ - -# Pre-Setup (Before Application Integration) -Step 1: docker login to akraino dockerhub -username/pwd - ealtedge/Huawei_akraino - -or - -pull images manually -docker pull ealtedge/pcbdefdet-be -docker pull ealtedge/pcbui - - -Step 2: pretrained model for defect detection -- copy pretrained model to Host machine where PCB containers are running - 1. cloen model from gitlab git clone https://gitlab.com/gauravagrawal/ealtedge/-/tree/master/PCB-Def-Det-Models - 2. create folder /root/pcb-def-det/model in host machine. - 3. scp all files in models cloned to above path. - -Step 3: iput images for defect detection -- image are copied to container images currently. if want to use different image, need to follow below steps - 1. download application code and go example-apps/PDD/pcb-defect-detection/input_image - 2. copy the input image to one of folder. - 3. build the docker "docker build -t ealtedge/pcbdefdet-be ." and push to docker hub - 4. now new images will be avaible in container for detection - 5. in future this can be enhance to feed from UI - -- Some Pre-Checks - - Check that ports are not already occupied - - UI running on "30002" node port of VM where containers are running. diff --git a/example-apps/PDD/frontend-src/Dockerfile b/example-apps/PDD/frontend-src/Dockerfile deleted file mode 100644 index b8b6991..0000000 --- a/example-apps/PDD/frontend-src/Dockerfile +++ /dev/null @@ -1,40 +0,0 @@ -# FROM node:current-slim as node -# WORKDIR /usr/src/app -# COPY package.json . - -# RUN npm cache clean --force -# RUN npm install -# RUN npm audit fix - -# COPY . . - -# RUN npm run build --prod - -# EXPOSE 4200 -# CMD [ "npm", "start" ] -#COPY . . -#RUN npm run build --prod - -# Stage 1 - -#FROM node:10-alpine as build-step - -FROM node:12.9 as build-step - -RUN mkdir -p /app - -WORKDIR /app - -COPY package.json /app - -RUN npm install - -COPY . /app - -RUN npm run build --prod - -#Stage 2 - -FROM nginx:1.17.1-alpine - -COPY --from=build-step /app/dist/ealtedge /usr/share/nginx/html diff --git a/example-apps/PDD/frontend-src/angular.json b/example-apps/PDD/frontend-src/angular.json deleted file mode 100644 index 725ec04..0000000 --- a/example-apps/PDD/frontend-src/angular.json +++ /dev/null @@ -1,153 +0,0 @@ -{ - "$schema": "./node_modules/@angular/cli/lib/config/schema.json", - "version": 1, - "newProjectRoot": "projects", - "projects": { - "ealtedge": { - "projectType": "application", - "schematics": { - "@schematics/angular:component": { - "style": "scss" - } - }, - "root": "", - "sourceRoot": "src", - "prefix": "app", - "architect": { - "build": { - "builder": "@angular-devkit/build-angular:browser", - "options": { - "outputPath": "dist/ealtedge", - "index": "src/index.html", - "main": "src/main.ts", - "polyfills": "src/polyfills.ts", - "tsConfig": "tsconfig.app.json", - "aot": false, - "assets": [ - "src/favicon.ico", - "src/assets" - ], - "styles": [ - "./node_modules/bootstrap/dist/css/bootstrap.min.css", - "src/styles.scss" - ], - "scripts": [ - "node_modules/particles.js/particles.js" - ] - }, - "configurations": { - "production": { - "fileReplacements": [ - { - "replace": "src/environments/environment.ts", - "with": "src/environments/environment.prod.ts" - } - ], - "optimization": true, - "outputHashing": "all", - "sourceMap": false, - "extractCss": true, - "namedChunks": false, - "aot": true, - "extractLicenses": true, - "vendorChunk": false, - "buildOptimizer": true, - "budgets": [ - { - "type": "initial", - "maximumWarning": "4mb", - "maximumError": "5mb" - }, - { - "type": "anyComponentStyle", - "maximumWarning": "2mb", - "maximumError": "2kb" - } - ] - } - } - }, - "serve": { - "builder": "@angular-devkit/build-angular:dev-server", - "options": { - "browserTarget": "ealtedge:build", - "proxyConfig": "proxy-conf.json" - }, - "configurations": { - "production": { - "browserTarget": "ealtedge:build:production" - } - }, - "scripts": [ - "node_modules/particles.js/particles.js", - "node_modules/chart.js/dist/Chart.js" - ] - }, - "extract-i18n": { - "builder": "@angular-devkit/build-angular:extract-i18n", - "options": { - "browserTarget": "ealtedge:build" - } - }, - "test": { - "builder": "@angular-devkit/build-angular:karma", - "options": { - "main": "src/test.ts", - "polyfills": "src/polyfills.ts", - "tsConfig": "tsconfig.spec.json", - "karmaConfig": "karma.conf.js", - "assets": [ - "src/favicon.ico", - "src/assets" - ], - "styles": [ - "./node_modules/bootstrap/dist/css/bootstrap.min.css", - "src/styles.scss", - "node_modules/@fortawesome/fontawesome-free/scss/fontawesome.scss", - "node_modules/@fortawesome/fontawesome-free/scss/solid.scss", - "node_modules/@fortawesome/fontawesome-free/scss/regular.scss", - "node_modules/@fortawesome/fontawesome-free/scss/brands.scss", - "node_modules/angular-bootstrap-md/assets/scss/bootstrap/bootstrap.scss", - "node_modules/angular-bootstrap-md/assets/scss/mdb.scss", - "node_modules/animate.css/animate.css" - ], - "scripts": [] - } - }, - "lint": { - "builder": "@angular-devkit/build-angular:tslint", - "options": { - "tsConfig": [ - "tsconfig.app.json", - "tsconfig.spec.json", - "e2e/tsconfig.json" - ], - "exclude": [ - "**/node_modules/**" - ] - } - }, - "e2e": { - "builder": "@angular-devkit/build-angular:protractor", - "options": { - "protractorConfig": "e2e/protractor.conf.js", - "devServerTarget": "ealtedge:serve" - }, - "configurations": { - "production": { - "devServerTarget": "ealtedge:serve:production" - } - } - }, - "deploy": { - "builder": "@angular/fire:deploy", - "options": {} - } - } - } - }, - "defaultProject": "ealtedge", - "cli": { - "analytics": "7f67dc91-d9ae-45b6-9289-13885740c4f1" - } -} \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/browserslist b/example-apps/PDD/frontend-src/browserslist deleted file mode 100644 index 8084853..0000000 --- a/example-apps/PDD/frontend-src/browserslist +++ /dev/null @@ -1,12 +0,0 @@ -# This file is used by the build system to adjust CSS and JS output to support the specified browsers below. -# For additional information regarding the format and rule options, please see: -# https://github.com/browserslist/browserslist#queries - -# You can see what browsers were selected by your queries by running: -# npx browserslist - -> 0.5% -last 2 versions -Firefox ESR -not dead -not IE 9-11 # For IE 9-11 support, remove 'not'. \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/e2e/protractor.conf.js b/example-apps/PDD/frontend-src/e2e/protractor.conf.js deleted file mode 100644 index 73e4e68..0000000 --- a/example-apps/PDD/frontend-src/e2e/protractor.conf.js +++ /dev/null @@ -1,32 +0,0 @@ -// @ts-check -// Protractor configuration file, see link for more information -// https://github.com/angular/protractor/blob/master/lib/config.ts - -const { SpecReporter } = require('jasmine-spec-reporter'); - -/** - * @type { import("protractor").Config } - */ -exports.config = { - allScriptsTimeout: 11000, - specs: [ - './src/**/*.e2e-spec.ts' - ], - capabilities: { - 'browserName': 'chrome' - }, - directConnect: true, - baseUrl: 'http://localhost:4200/', - framework: 'jasmine', - jasmineNodeOpts: { - showColors: true, - defaultTimeoutInterval: 30000, - print: function() {} - }, - onPrepare() { - require('ts-node').register({ - project: require('path').join(__dirname, './tsconfig.json') - }); - jasmine.getEnv().addReporter(new SpecReporter({ spec: { displayStacktrace: true } })); - } -}; \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/e2e/src/app.e2e-spec.ts b/example-apps/PDD/frontend-src/e2e/src/app.e2e-spec.ts deleted file mode 100644 index 6404366..0000000 --- a/example-apps/PDD/frontend-src/e2e/src/app.e2e-spec.ts +++ /dev/null @@ -1,23 +0,0 @@ -import { AppPage } from './app.po'; -import { browser, logging } from 'protractor'; - -describe('workspace-project App', () => { - let page: AppPage; - - beforeEach(() => { - page = new AppPage(); - }); - - it('should display welcome message', () => { - page.navigateTo(); - expect(page.getTitleText()).toEqual('Welcome to ealtedge!'); - }); - - afterEach(async () => { - // Assert that there are no errors emitted from the browser - const logs = await browser.manage().logs().get(logging.Type.BROWSER); - expect(logs).not.toContain(jasmine.objectContaining({ - level: logging.Level.SEVERE, - } as logging.Entry)); - }); -}); diff --git a/example-apps/PDD/frontend-src/e2e/src/app.po.ts b/example-apps/PDD/frontend-src/e2e/src/app.po.ts deleted file mode 100644 index 5776aa9..0000000 --- a/example-apps/PDD/frontend-src/e2e/src/app.po.ts +++ /dev/null @@ -1,11 +0,0 @@ -import { browser, by, element } from 'protractor'; - -export class AppPage { - navigateTo() { - return browser.get(browser.baseUrl) as Promise; - } - - getTitleText() { - return element(by.css('app-root h1')).getText() as Promise; - } -} diff --git a/example-apps/PDD/frontend-src/e2e/tsconfig.json b/example-apps/PDD/frontend-src/e2e/tsconfig.json deleted file mode 100644 index 39b800f..0000000 --- a/example-apps/PDD/frontend-src/e2e/tsconfig.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "extends": "../tsconfig.json", - "compilerOptions": { - "outDir": "../out-tsc/e2e", - "module": "commonjs", - "target": "es5", - "types": [ - "jasmine", - "jasminewd2", - "node" - ] - } -} diff --git a/example-apps/PDD/frontend-src/karma.conf.js b/example-apps/PDD/frontend-src/karma.conf.js deleted file mode 100644 index dabd5cd..0000000 --- a/example-apps/PDD/frontend-src/karma.conf.js +++ /dev/null @@ -1,32 +0,0 @@ -// Karma configuration file, see link for more information -// https://karma-runner.github.io/1.0/config/configuration-file.html - -module.exports = function (config) { - config.set({ - basePath: '', - frameworks: ['jasmine', '@angular-devkit/build-angular'], - plugins: [ - require('karma-jasmine'), - require('karma-chrome-launcher'), - require('karma-jasmine-html-reporter'), - require('karma-coverage-istanbul-reporter'), - require('@angular-devkit/build-angular/plugins/karma') - ], - client: { - clearContext: false // leave Jasmine Spec Runner output visible in browser - }, - coverageIstanbulReporter: { - dir: require('path').join(__dirname, './coverage/ealtedge'), - reports: ['html', 'lcovonly', 'text-summary'], - fixWebpackSourcePaths: true - }, - reporters: ['progress', 'kjhtml'], - port: 9876, - colors: true, - logLevel: config.LOG_INFO, - autoWatch: true, - browsers: ['Chrome'], - singleRun: false, - restartOnFileChange: true - }); -}; diff --git a/example-apps/PDD/frontend-src/ng.log b/example-apps/PDD/frontend-src/ng.log deleted file mode 100644 index 62e7c13..0000000 --- a/example-apps/PDD/frontend-src/ng.log +++ /dev/null @@ -1,19 +0,0 @@ -nohup: ignoring input - -> demoedgex@0.0.0 start /home/root1/eliot2910/eliot/blueprints/common/eliot-ui/frontend-src -> ng serve --proxy-config proxy.conf.json --host=0.0.0.0 - -An unhandled exception occurred: Port 4200 is already in use. Use '--port' to specify a different port. -See "/tmp/ng-ZJRaj8/angular-errors.log" for further details. -npm ERR! file sh -npm ERR! code ELIFECYCLE -npm ERR! errno ENOENT -npm ERR! syscall spawn -npm ERR! demoedgex@0.0.0 start: `ng serve --proxy-config proxy.conf.json --host=0.0.0.0` -npm ERR! spawn ENOENT -npm ERR! -npm ERR! Failed at the demoedgex@0.0.0 start script. -npm ERR! This is probably not a problem with npm. There is likely additional logging output above. - -npm ERR! A complete log of this run can be found in: -npm ERR! /home/root1/.npm/_logs/2019-11-19T09_35_26_086Z-debug.log diff --git a/example-apps/PDD/frontend-src/package-lock.json b/example-apps/PDD/frontend-src/package-lock.json deleted file mode 100644 index 5933735..0000000 --- a/example-apps/PDD/frontend-src/package-lock.json +++ /dev/null @@ -1,17804 +0,0 @@ -{ - "name": "ealtedge", - "version": "0.0.0", - "lockfileVersion": 1, - "requires": true, - "dependencies": { - "@angular-devkit/architect": { - "version": "0.803.23", - "resolved": "https://registry.npmjs.org/@angular-devkit/architect/-/architect-0.803.23.tgz", - "integrity": "sha512-BRDbnmdULrle2l7WFZHEW/OAwS8RRg08+jiNG3gEP0BxDN6QMNMKmWhxmX67pgq3e/xMvu2DH0z71mAPNtJDAw==", - "dev": true, - "requires": { - "@angular-devkit/core": "8.3.23", - "rxjs": "6.4.0" - }, - "dependencies": { - "@angular-devkit/core": { - "version": "8.3.23", - "resolved": "https://registry.npmjs.org/@angular-devkit/core/-/core-8.3.23.tgz", - "integrity": "sha512-y++LN6R/fu+obPUKEMDSKZ5FzeWN5rV0Z8vrdC+uF02VJLv/5QI/dUx3ROKFzJO3m2LU6EAuo5b/TLAPq4ving==", - 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} - } -} diff --git a/example-apps/PDD/frontend-src/package.json b/example-apps/PDD/frontend-src/package.json deleted file mode 100644 index c48b527..0000000 --- a/example-apps/PDD/frontend-src/package.json +++ /dev/null @@ -1,83 +0,0 @@ -{ - "name": "ealtedge", - "version": "0.0.0", - "scripts": { - "ng": "ng", - "start": "ng serve --proxy-config proxy-conf.json --host=0.0.0.0", - "build": "ng build", - "test": "ng test", - "lint": "ng lint", - "e2e": "ng e2e", - "postinstall": "ngcc" - }, - "private": true, - "dependencies": { - "@angular/animations": "^9.1.0", - "@angular/cdk": "^9.2.0", - "@angular/common": "^9.1.0", - "@angular/compiler": "^9.1.0", - "@angular/core": "^9.1.0", - "@angular/forms": "^9.1.0", - "@angular/localize": "^9.1.0", - "@angular/material": "^9.2.0", - "@angular/platform-browser": "^9.1.0", - "@angular/platform-browser-dynamic": "^9.1.0", - "@angular/router": "^9.1.0", - "@fortawesome/fontawesome-free": "^5.13.0", - "@ng-bootstrap/ng-bootstrap": "^5.1.5", - "@types/chart.js": "^2.9.16", - "angular-bootstrap-md": "^9.0.0", - "angular-captcha": "^3.7.4", - "angular-svg-icon": "^8.0.0", - "animate.css": "^3.7.2", - "bootstrap": "^4.4.1", - "chart.js": "^2.9.3", - "cors": "^2.8.5", - "hammerjs": "^2.0.8", - "jqwidgets-ng": "^9.0.0", - "maven": "^4.7.0", - "mvn": "^3.5.0", - "ng-recaptcha": "^5.0.0", - "ng-terminal": "^2.3.0", - "ngx-bootstrap": "^5.3.2", - "ngx-markdown": "^8.2.1", - "ngx-md": "^8.1.6", - "ngx-permissions": "^7.0.3", - "ngx-toastr": "^11.3.0", - "ngx-webstorage": "^4.0.1", - "node": "^12.14.1", - "particles.js": "^2.0.0", - "rxjs": "~6.5.4", - "sessionstorage": "^0.1.0", - "tslib": "^1.11.1", - "yaml": "^1.7.2", - "zone.js": "~0.10.3" - }, - "devDependencies": { - "@angular-devkit/architect": "^0.803.23", - "@angular-devkit/build-angular": "^0.901.0", - "@angular/cli": "~9.1.0", - "@angular/compiler-cli": "^9.1.0", - "@angular/language-service": "^9.1.0", - "@types/cors": "^2.8.6", - "@types/jasmine": "~3.3.8", - "@types/jasminewd2": "^2.0.8", - "@types/node": "^8.10.59", - "codelyzer": "^5.2.1", - "fuzzy": "^0.1.3", - "inquirer": "^6.5.2", - "inquirer-autocomplete-prompt": "^1.0.1", - "jasmine-core": "~3.4.0", - "jasmine-spec-reporter": "~4.2.1", - "karma": "^4.4.1", - "karma-chrome-launcher": "~2.2.0", - "karma-coverage-istanbul-reporter": "~2.0.1", - "karma-jasmine": "~2.0.1", - "karma-jasmine-html-reporter": "^1.5.1", - "lite-server": "^2.5.4", - "protractor": "~5.4.0", - "ts-node": "~7.0.0", - "tslint": "~5.15.0", - "typescript": "~3.8.3" - } -} diff --git a/example-apps/PDD/frontend-src/proxy-conf.json b/example-apps/PDD/frontend-src/proxy-conf.json deleted file mode 100644 index 20d7cb9..0000000 --- a/example-apps/PDD/frontend-src/proxy-conf.json +++ /dev/null @@ -1,20 +0,0 @@ -{ - "/tempstatus": { - "target": "http://localhost:3001", - "secure": false - }, - "/settemplimit": { - "target": "http://localhost:3001", - "secure": false - }, - "/api/*": { - "target": "http://localhost:3001", - "secure": false, - "logLevel": "debug", - "changeOrigin": true - }, - "/postDeploy": { - "target": "http://localhost:3001/postDeploy", - "secure": false - } -} \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/app/app-routing.module.ts b/example-apps/PDD/frontend-src/src/app/app-routing.module.ts deleted file mode 100644 index 89cfd96..0000000 --- a/example-apps/PDD/frontend-src/src/app/app-routing.module.ts +++ /dev/null @@ -1,67 +0,0 @@ -import { NgModule } from '@angular/core'; -import { Routes, RouterModule, ActivatedRouteSnapshot, RouterStateSnapshot } from '@angular/router'; -import { HomeComponent } from './home/home.component'; - -import { PcbComponent } from './pcb/pcb.component'; -import { PcbimagesComponent } from './pcbimages/pcbimages.component'; -import { PcboutputimagesComponent } from './pcboutputimages/pcboutputimages.component'; - - - - -const routes: Routes = [ - - { - path: '', - component: HomeComponent - // canActivate: [AuthGuard] - }, - { - path: 'akrainowiki', - component: HomeComponent, - resolve: { - url: 'externalUrlRedirectResolver' - }, - data: { - externalUrl: 'https://wiki.akraino.org/display/AK/ELIOT%3A+Edge+Lightweight+and+IoT+Blueprint+Family?src=contextnavpagetreemode' - } - }, - { - path: 'pcb', - component: PcbComponent, - }, - { - path: 'displayimage', - component: PcbimagesComponent, - }, - { - path: 'results', - component: PcboutputimagesComponent, - }, - { - path: '**', - redirectTo: '' - } -]; - -@NgModule({ - imports: [RouterModule.forRoot(routes, { - scrollPositionRestoration: 'enabled' - })], - exports: [RouterModule], - providers: [ - { - provide: 'externalUrlRedirectResolver', - useValue: (route: ActivatedRouteSnapshot, state: RouterStateSnapshot) => - { - // window.location.href = (route.data as any).externalUrl; - window.open((route.data as any).externalUrl); - debugger; - console.log(route.url); - route.url[0].path=""; - - } - } -] -}) -export class AppRoutingModule { } diff --git a/example-apps/PDD/frontend-src/src/app/app.component.html b/example-apps/PDD/frontend-src/src/app/app.component.html deleted file mode 100644 index 4c8e43d..0000000 --- a/example-apps/PDD/frontend-src/src/app/app.component.html +++ /dev/null @@ -1,132 +0,0 @@ - - - - - - - - - -
- -
\ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/app/app.component.scss b/example-apps/PDD/frontend-src/src/app/app.component.scss deleted file mode 100644 index 7e34fea..0000000 --- a/example-apps/PDD/frontend-src/src/app/app.component.scss +++ /dev/null @@ -1,29 +0,0 @@ -// .main { -// .menu { -// .options { -// .home { -// font-size: 25px; -// } -// .others { -// // padding: 0 14px; -// } - -// } -// } -// .sidemenu { -// mat-sidenav-container { -// height: 112vh; -// } -// } -// } - -.main { - margin: 0 auto; - padding-left: 0px; - padding-top: 0px; - height: auto; -} - -body { - margin: auto; -} \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/app/app.component.spec.ts b/example-apps/PDD/frontend-src/src/app/app.component.spec.ts deleted file mode 100644 index 5b20a0e..0000000 --- a/example-apps/PDD/frontend-src/src/app/app.component.spec.ts +++ /dev/null @@ -1,35 +0,0 @@ -import { TestBed, async } from '@angular/core/testing'; -import { RouterTestingModule } from '@angular/router/testing'; -import { AppComponent } from './app.component'; - -describe('AppComponent', () => { - beforeEach(async(() => { - TestBed.configureTestingModule({ - imports: [ - RouterTestingModule - ], - declarations: [ - AppComponent - ], - }).compileComponents(); - })); - - it('should create the app', () => { - const fixture = TestBed.createComponent(AppComponent); - const app = fixture.debugElement.componentInstance; - expect(app).toBeTruthy(); - }); - - it(`should have as title 'ealtedge'`, () => { - const fixture = TestBed.createComponent(AppComponent); - const app = fixture.debugElement.componentInstance; - expect(app.title).toEqual('ealtedge'); - }); - - it('should render title in a h1 tag', () => { - const fixture = TestBed.createComponent(AppComponent); - fixture.detectChanges(); - const compiled = fixture.debugElement.nativeElement; - expect(compiled.querySelector('h1').textContent).toContain('Welcome to ealtedge!'); - }); -}); diff --git a/example-apps/PDD/frontend-src/src/app/app.component.ts b/example-apps/PDD/frontend-src/src/app/app.component.ts deleted file mode 100644 index 9df7fc6..0000000 --- a/example-apps/PDD/frontend-src/src/app/app.component.ts +++ /dev/null @@ -1,18 +0,0 @@ -import { Component } from '@angular/core'; -import { ToolbarService } from './toolbar/toolbar.service'; - -@Component({ - selector: 'app-root', - templateUrl: './app.component.html', - styleUrls: ['./app.component.scss'] -}) -export class AppComponent { - title = 'EliotUI'; - - constructor(public toolbarService: ToolbarService) {} - - ngOnInit() { - this.toolbarService.show(); - } - -} diff --git a/example-apps/PDD/frontend-src/src/app/app.module.ts b/example-apps/PDD/frontend-src/src/app/app.module.ts deleted file mode 100644 index e8c7cc7..0000000 --- a/example-apps/PDD/frontend-src/src/app/app.module.ts +++ /dev/null @@ -1,137 +0,0 @@ -import { BrowserModule } from '@angular/platform-browser'; -import { NgModule } from '@angular/core'; - -import { AppRoutingModule } from './app-routing.module'; -import { AppComponent } from './app.component'; -import { EaltserviceService } from './ealtservice.service'; - -import {MatExpansionModule} from '@angular/material/expansion'; -import {MatFormFieldModule} from '@angular/material/form-field'; -import {MatInputModule} from '@angular/material/input'; -import { FormsModule, ReactiveFormsModule } from '@angular/forms'; -import {MatCardModule} from '@angular/material/card'; -import { MatSnackBarModule } from '@angular/material/snack-bar'; -import {MatMenuModule} from '@angular/material/menu'; - -import { HttpClientModule } from '@angular/common/http'; - -// import { MatIconModule, MatSidenavModule, MatListModule, MatButtonModule } from '@angular/material'; -import { MatToolbarModule } from '@angular/material/toolbar'; -import { MatSidenavModule } from '@angular/material/sidenav'; -import { MatListModule } from '@angular/material/list'; -import { MatButtonModule } from '@angular/material/button'; -import { MatIconModule } from '@angular/material/icon'; -import {MatPaginatorModule} from '@angular/material/paginator'; -import {MatTableModule} from '@angular/material/table'; -import {MatSelectModule} from '@angular/material/select'; -import {MatTooltipModule} from '@angular/material/tooltip'; - -import {MatCheckboxModule} from '@angular/material/checkbox'; - -import { BrowserAnimationsModule } from '@angular/platform-browser/animations'; -import { MarkdownModule } from 'ngx-markdown'; -import { NgxMdModule } from 'ngx-md'; - -import {MatDialogModule} from '@angular/material/dialog'; -import {MatRadioModule} from '@angular/material/radio'; - -import { BotDetectCaptchaModule } from 'angular-captcha'; -import { NgTerminalModule } from 'ng-terminal'; - -import { NgxPermissionsModule } from 'ngx-permissions'; - -import {MatSlideToggleModule} from '@angular/material/slide-toggle'; -import { jqxChartModule } from 'jqwidgets-ng/jqxchart'; -import { MDBBootstrapModule } from 'angular-bootstrap-md'; -import { ChartsModule, WavesModule } from 'angular-bootstrap-md'; -import * as cors from "cors"; - - -import 'hammerjs'; -import 'particles.js'; -import 'chart.js'; -import { HomeComponent } from './home/home.component'; -import { ToolbarComponent } from './toolbar/toolbar.component'; -import { ToastrModule } from 'ngx-toastr'; - -import {NgbModule} from '@ng-bootstrap/ng-bootstrap'; -import { CarouselModule } from 'ngx-bootstrap/carousel'; - -import {MatTabsModule} from '@angular/material/tabs'; -import {MatStepperModule} from '@angular/material/stepper'; -import { MenuItemComponent } from './menu-item/menu-item.component'; -import { PcbComponent } from './pcb/pcb.component'; -import { PcbimagesComponent } from './pcbimages/pcbimages.component'; -import { PcboutputimagesComponent } from './pcboutputimages/pcboutputimages.component'; - - -@NgModule({ - declarations: [ - AppComponent, - HomeComponent, - ToolbarComponent, - MenuItemComponent, - PcbComponent, - PcbimagesComponent, - PcboutputimagesComponent - ], - - imports: [ - BrowserModule, - AppRoutingModule, - MatExpansionModule, - BrowserAnimationsModule, - MatFormFieldModule, - MatInputModule, - FormsModule, - MatCardModule, - MatToolbarModule, - MatIconModule, - MatSidenavModule, - MatListModule, - MatButtonModule, - HttpClientModule, - MatSnackBarModule, - MatMenuModule, - MatTableModule, - MatPaginatorModule, - MatSelectModule, - MatTooltipModule, - ReactiveFormsModule, - ToastrModule, - BotDetectCaptchaModule, - NgbModule, - CarouselModule.forRoot(), - MatTabsModule, - MatStepperModule, - MatCheckboxModule, - NgTerminalModule, - MarkdownModule, - NgxMdModule, - // NgxFloatButtonModule, - MatDialogModule, - NgxPermissionsModule.forRoot(), - MatRadioModule, - MatSlideToggleModule, - jqxChartModule, - ChartsModule, - WavesModule, - // MDBBootstrapModule - MDBBootstrapModule.forRoot() - // ParticlesModule - ], - - exports: [], - - providers: [ - EaltserviceService, - ], - - entryComponents: [ - ], - - bootstrap: [AppComponent] - -}) -export class AppModule { -} diff --git a/example-apps/PDD/frontend-src/src/app/datainterface.ts b/example-apps/PDD/frontend-src/src/app/datainterface.ts deleted file mode 100644 index 8182710..0000000 --- a/example-apps/PDD/frontend-src/src/app/datainterface.ts +++ /dev/null @@ -1,25 +0,0 @@ -export interface nodeDetails { - eliotNodes: nodinfo[]; -} - -export interface nodinfo { - nodeName: string; - nodeStatus: string; - nodeRole: string; - age: string; - version: string; - internalIp: string; - externalIp: string; - osImage: string; - kernel: string; - containerRuntime: string; -} - -export interface uploadImageData { - uploadFile: any; -} - -export interface pcbdetectObject { - -} - diff --git a/example-apps/PDD/frontend-src/src/app/ealtservice.service.ts b/example-apps/PDD/frontend-src/src/app/ealtservice.service.ts deleted file mode 100644 index dd0a7e4..0000000 --- a/example-apps/PDD/frontend-src/src/app/ealtservice.service.ts +++ /dev/null @@ -1,82 +0,0 @@ -import { Injectable } from '@angular/core'; -import { HttpClient, HttpHeaders, HttpParams, HttpResponse } from '@angular/common/http'; -import { Observable,throwError } from 'rxjs' -import { timer, Subscription, pipe } from 'rxjs'; -import { switchMap } from 'rxjs/operators'; - -import { delay } from 'rxjs/operators'; - -import { nodeDetails } from './datainterface'; - - -import { retry,catchError } from 'rxjs/operators'; -import { v4 as uuid } from 'uuid'; - - - -@Injectable({ - providedIn: 'root' -}) -export class EaltserviceService { - - - private baseUrl = 'http://localhost:30281/'; - - private imageUploadUrl = this.baseUrl+'uploadimageinput'; - - private nodes_url = './../assets/data/nodes.json'; - private packageUploadUrl = this.baseUrl+'uploadimageinput'; - - private historyIdUrl = this.baseUrl+'history/files'; - - private outputImageUrl = this.baseUrl - - private inputImageUrl = this.baseUrl - - private pcbDetectUrl = this.baseUrl - - constructor(private http:HttpClient) { - } - - httpOptions = { - headers: new HttpHeaders({ - 'Content-Type':'application/json' - }) - } - - getNodesInfoo(): Observable { - return this.http.get(this.nodes_url); - } - - postHistoryId(data): Observable { - return this.http.get(this.historyIdUrl, {params: data} ); - } - - postDeploymentPackage(data): Observable { - return this.http.post(this.packageUploadUrl, data); - } - - postInputImages(data): Observable { - return this.http.post(this.imageUploadUrl, data) - } - - getOutputImage(): Observable { - debugger; - this.outputImageUrl = this.baseUrl + 'v1/pcb/resultimage'; - return this.http.get(this.outputImageUrl); - } - - getInputImage(data): Observable { - debugger; - this.inputImageUrl = this.baseUrl + 'v1/pcb/preview/' + data; - // return this.http.get(this.monitorImageUrl); - return this.http.get(this.inputImageUrl); - } - - pcbDetect(data): Observable { - debugger; - this.pcbDetectUrl = this.baseUrl + 'v1/pcb/detection/' + data; - return this.http.get(this.pcbDetectUrl); - } - -} diff --git a/example-apps/PDD/frontend-src/src/app/home/home.component.html b/example-apps/PDD/frontend-src/src/app/home/home.component.html deleted file mode 100644 index 64e12eb..0000000 --- a/example-apps/PDD/frontend-src/src/app/home/home.component.html +++ /dev/null @@ -1,83 +0,0 @@ -
-
- -
- -
- -
-
- -
- - -
-

About us

-

Enterprise Applications on Lightweight 5G Telco Edge

-

Enterprise Application on Lightweight 5G Telco Edge is an Akraino approved blueprint family, - which intends to make a complete ecosystem for 5G Telco Edge Enterprise level platform. - Platform which can be leveraged by various Telecom operators to give value added services to end users. -

- - -
- - -

Ealt-Edge High Level Features

-
  • Lightweight MEP Solution
    • -
  • Autonomous MEP Edge Sites.
    • -
  • Unified Portal for platform management and for App developers.
    • -
  • Sandbox with SDKs and tools chains for MEC app developers
    • -
  • Heterogeneous deployment on Multi-Arch.
    • -
  • ETSI MEC Compliant.
    • -
    -
    -
    - -
    -
    - -
    - -
    - -
    -
    - © Copyright EALT-EDGE Akraino. All Rights Reserved -
    -
    -
    -
    \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/app/home/home.component.scss b/example-apps/PDD/frontend-src/src/app/home/home.component.scss deleted file mode 100644 index 1341928..0000000 --- a/example-apps/PDD/frontend-src/src/app/home/home.component.scss +++ /dev/null @@ -1,289 +0,0 @@ - - -.main { - display: block; - width: 100%; - overflow:hidden; - - .carousel{ - display:block; - color: black; - img { - width: 100% - } - } -} - -h3,h1,h5,h2 { - color: black; -} - - - - -/** -* Template Name: Flexor - v2.0.0 -* Template URL: https://bootstrapmade.com/flexor-free-multipurpose-bootstrap-template/ -* Author: BootstrapMade.com -* License: https://bootstrapmade.com/license/ -*/ - -/*-------------------------------------------------------------- -# General ---------------------------------------------------------------*/ -body { - font-family: "Open Sans", sans-serif; - color: #313030; - } - - a { - color: #ff5821; - } - - a:hover { - color: #ff7e54; - text-decoration: none; - } - - h1, h2, h3, h4, h5, h6 { - font-family: "Raleway", sans-serif; - } - - - /*-------------------------------------------------------------- - # Sections General - --------------------------------------------------------------*/ - section { - padding: 60px 0; - overflow: hidden; - } - - .section-bg { - background-color: #fff9f7; - } - - .section-title { - text-align: center; - padding-bottom: 30px; - } - - .section-title h2 { - font-size: 32px; - font-weight: 500; - margin-bottom: 20px; - padding-bottom: 0; - font-family: "Poppins", sans-serif; - color: #635551; - } - - .section-title p { - margin-bottom: 0; - } - - - - /*-------------------------------------------------------------- - # About - --------------------------------------------------------------*/ - .about .icon-boxes h4 { - font-size: 18px; - color: #7f6d68; - margin-bottom: 15px; - } - - .about .icon-boxes h3 { - font-size: 28px; - font-weight: 700; - color: #554945; - margin-bottom: 15px; - } - - .about .icon-box { - margin-top: 40px; - } - - .about .icon-box .icon { - float: left; - display: flex; - align-items: center; - justify-content: center; - width: 64px; - height: 64px; - border: 2px solid #ffcbba; - border-radius: 50px; - transition: 0.5s; - background: #fff; - } - - .about .icon-box .icon i { - color: #696969; - font-size: 32px; - } - - .about .icon-box:hover .icon { - background: #0ab8e4; - border-color: #f5f5f5; - } - - .about .icon-box:hover .icon i { - color: #fff; - } - - .about .icon-box .title { - margin-left: 85px; - font-weight: 700; - margin-bottom: 10px; - font-size: 18px; - } - - .about .icon-box .title a { - color: #343a40; - transition: 0.3s; - } - - .about .icon-box .title a:hover { - color: #07afc5; - } - - .about .icon-box .description { - margin-left: 85px; - line-height: 24px; - font-size: 14px; - } - - .about .video-box { - background: url("./../../assets/images/kubecon-1.png") no-repeat; - background-size: cover; - min-height: 500px; - } - - .about .play-btn { - width: 94px; - height: 94px; - background: radial-gradient(#ff5821 50%, rgba(255, 88, 33, 0.4) 52%); - border-radius: 50%; - display: block; - position: absolute; - left: calc(50% - 47px); - top: calc(50% - 47px); - overflow: hidden; - } - - .about .play-btn::after { - content: ''; - position: absolute; - left: 50%; - top: 50%; - transform: translateX(-40%) translateY(-50%); - width: 0; - height: 0; - border-top: 10px solid transparent; - border-bottom: 10px solid transparent; - border-left: 15px solid #fff; - z-index: 100; - transition: all 400ms cubic-bezier(0.55, 0.055, 0.675, 0.19); - } - - .about .play-btn::before { - content: ''; - position: absolute; - width: 120px; - height: 120px; - -webkit-animation-delay: 0s; - animation-delay: 0s; - -webkit-animation: pulsate-btn 2s; - animation: pulsate-btn 2s; - -webkit-animation-direction: forwards; - animation-direction: forwards; - -webkit-animation-iteration-count: infinite; - animation-iteration-count: infinite; - -webkit-animation-timing-function: steps; - animation-timing-function: steps; - opacity: 1; - border-radius: 50%; - border: 5px solid rgba(255, 88, 33, 0.7); - top: -15%; - left: -15%; - background: rgba(200, 94, 84, 0); - } - - .about .play-btn:hover::after { - border-left: 15px solid #ff5821; - transform: scale(20); - } - - .about .play-btn:hover::before { - content: ''; - position: absolute; - left: 50%; - top: 50%; - transform: translateX(-40%) translateY(-50%); - width: 0; - height: 0; - border: none; - border-top: 10px solid transparent; - border-bottom: 10px solid transparent; - border-left: 15px solid #fff; - z-index: 200; - -webkit-animation: none; - animation: none; - border-radius: 0; - } - - @-webkit-keyframes pulsate-btn { - 0% { - transform: scale(0.6, 0.6); - opacity: 1; - } - 100% { - transform: scale(1, 1); - opacity: 0; - } - } - - @keyframes pulsate-btn { - 0% { - transform: scale(0.6, 0.6); - opacity: 1; - } - 100% { - transform: scale(1, 1); - opacity: 0; - } - } - - - /*-------------------------------------------------------------- - # Footer - --------------------------------------------------------------*/ - #footer { - color: #313030; - font-size: 14px; - background: #fff; - } - - #footer .social-links a { - font-size: 18px; - display: inline-block; - background: #ff5821; - color: #fff; - line-height: 1; - padding: 8px 0; - margin-right: 4px; - border-radius: 50%; - text-align: center; - width: 36px; - height: 36px; - transition: 0.3s; - } - - #footer .social-links a:hover { - background: #74f6ff; - color: #fff; - text-decoration: none; - } - - #footer { - background: black; - color: white; - } \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/app/home/home.component.spec.ts b/example-apps/PDD/frontend-src/src/app/home/home.component.spec.ts deleted file mode 100644 index 490e81b..0000000 --- a/example-apps/PDD/frontend-src/src/app/home/home.component.spec.ts +++ /dev/null @@ -1,25 +0,0 @@ -import { async, ComponentFixture, TestBed } from '@angular/core/testing'; - -import { HomeComponent } from './home.component'; - -describe('HomeComponent', () => { - let component: HomeComponent; - let fixture: ComponentFixture; - - beforeEach(async(() => { - TestBed.configureTestingModule({ - declarations: [ HomeComponent ] - }) - .compileComponents(); - })); - - beforeEach(() => { - fixture = TestBed.createComponent(HomeComponent); - component = fixture.componentInstance; - fixture.detectChanges(); - }); - - it('should create', () => { - expect(component).toBeTruthy(); - }); -}); diff --git a/example-apps/PDD/frontend-src/src/app/home/home.component.ts b/example-apps/PDD/frontend-src/src/app/home/home.component.ts deleted file mode 100644 index 3094dc2..0000000 --- a/example-apps/PDD/frontend-src/src/app/home/home.component.ts +++ /dev/null @@ -1,23 +0,0 @@ -import { Component, OnInit } from '@angular/core'; -import { ToolbarService } from './../toolbar/toolbar.service'; - -@Component({ - selector: 'app-home', - templateUrl: './home.component.html', - styleUrls: ['./home.component.scss'] -}) -export class HomeComponent implements OnInit { - // images = [944, 1011, 984].map((n) => `https://picsum.photos/id/${n}/900/500`); - images = ["./../../assets/images/iotlatest1.png","./../../assets/images/iotlatest1.png","./../../assets/images/iotlatest1.png"]; - public currentUser; - - constructor(public toolbarService: ToolbarService) { - this.currentUser = localStorage.getItem('currentUser')? JSON.parse(localStorage.getItem('currentUser')) : ''; - } - - ngOnInit() { - // this.toolbarService.show(); - this.toolbarService.show(); - } - -} diff --git a/example-apps/PDD/frontend-src/src/app/menu-item/menu-item.component.html b/example-apps/PDD/frontend-src/src/app/menu-item/menu-item.component.html deleted file mode 100644 index e4ed020..0000000 --- a/example-apps/PDD/frontend-src/src/app/menu-item/menu-item.component.html +++ /dev/null @@ -1,23 +0,0 @@ - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/app/menu-item/menu-item.component.scss b/example-apps/PDD/frontend-src/src/app/menu-item/menu-item.component.scss deleted file mode 100644 index 04ff354..0000000 --- a/example-apps/PDD/frontend-src/src/app/menu-item/menu-item.component.scss +++ /dev/null @@ -1,12 +0,0 @@ -.mat-menu-item { - background-color: rgb(34, 33, 33); - color:white; - border: 0px; - font-weight: bold; -} - -.mat-menu-item:hover { - background-color: black; - color: turquoise; -} - diff --git a/example-apps/PDD/frontend-src/src/app/menu-item/menu-item.component.spec.ts b/example-apps/PDD/frontend-src/src/app/menu-item/menu-item.component.spec.ts deleted file mode 100644 index 8434313..0000000 --- a/example-apps/PDD/frontend-src/src/app/menu-item/menu-item.component.spec.ts +++ /dev/null @@ -1,25 +0,0 @@ -import { async, ComponentFixture, TestBed } from '@angular/core/testing'; - -import { MenuItemComponent } from './menu-item.component'; - -describe('MenuItemComponent', () => { - let component: MenuItemComponent; - let fixture: ComponentFixture; - - beforeEach(async(() => { - TestBed.configureTestingModule({ - declarations: [ MenuItemComponent ] - }) - .compileComponents(); - })); - - beforeEach(() => { - fixture = TestBed.createComponent(MenuItemComponent); - component = fixture.componentInstance; - fixture.detectChanges(); - }); - - it('should create', () => { - expect(component).toBeTruthy(); - }); -}); diff --git a/example-apps/PDD/frontend-src/src/app/menu-item/menu-item.component.ts b/example-apps/PDD/frontend-src/src/app/menu-item/menu-item.component.ts deleted file mode 100644 index 313ea9d..0000000 --- a/example-apps/PDD/frontend-src/src/app/menu-item/menu-item.component.ts +++ /dev/null @@ -1,21 +0,0 @@ -import { Component, OnInit, ViewChild, Input } from '@angular/core'; -import {Router} from '@angular/router'; - -@Component({ - selector: 'app-menu-item', - templateUrl: './menu-item.component.html', - styleUrls: ['./menu-item.component.scss'] -}) -export class MenuItemComponent implements OnInit { - - @Input() items: []; - - @ViewChild('childMenu', {static: true}) public childMenu; - - constructor(public router: Router) { - } - - ngOnInit() { - } - -} diff --git a/example-apps/PDD/frontend-src/src/app/pcb/pcb.component.html b/example-apps/PDD/frontend-src/src/app/pcb/pcb.component.html deleted file mode 100644 index d8e9335..0000000 --- a/example-apps/PDD/frontend-src/src/app/pcb/pcb.component.html +++ /dev/null @@ -1,234 +0,0 @@ -
    -
    - -
    - -
    - - - - - - {{ toast.textOrTpl }} - -
    - -
    - - - - - - Upload PCB images Input - - - Upload - - -
    - Please select an Edge Site - - - - - {{edgesite.viewValue}} - - - -
    -
    - Please select PCB Camera Input - - - - - {{camera.viewValue}} - - - -
    - - -
    - -
    - -
    - -
    - - - -     -     -
    - -
    - - - - - - Upload PCB Trained Model - - - Trained Model Upload - - - -
    - Please select Defect Category - - - - - {{defect.viewValue}} - - - -
    - -
    - Please select an Edge Site - - - - - {{edgesite.viewValue}} - - - -
    -
    - -
    - - - -     -     -
    - - - -
    - - - - - - PCB Images Defect Detection - - - Expand Below to detect defects in PCB - - -
    -

    Click Below to Detect Defects in PCB images

    - -
    - Please select an Edge Site - - - - - {{edgesite.viewValue}} - - - -
    - -
    - Please select PCB Camera Input - - - - - {{camera.viewValue}} - - - -
    - - - -
    - -
    -
    - -     -     -
    - - - -
    - - - - - - Results showing PCB defects - - - Expand Below to view defects in PCB - - -
    -

    Click Below to Detect Defects in PCB images

    - -
    - -
    -
    - -     -     -
    - -
    - -
    - -
    \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/app/pcb/pcb.component.scss b/example-apps/PDD/frontend-src/src/app/pcb/pcb.component.scss deleted file mode 100644 index 92731a2..0000000 --- a/example-apps/PDD/frontend-src/src/app/pcb/pcb.component.scss +++ /dev/null @@ -1,51 +0,0 @@ -.maindiv { - // background: linear-gradient(to right, #fdb4c0, #fdac7d); - background: linear-gradient(to right, #5efbac, #6bdbf1); - // padding: 10px; - // background: linear-gradient(to right, #7de4b0, #bbdfe6); - .toolbar { - - } - .toaster { - // width: 100%; - padding: 15px; - margin: auto; - width: 35%; - text-align: center; - } - - .upload-accordion { - padding-left: 15px; - padding-right: 15px; - padding-bottom: 15px; - padding-top: 80px; - } - .model-upload-accordion { - padding-top: 10px; - padding-left: 15px; - padding-right: 15px; - padding-bottom: 25px; - } - .pcb-detect-accordion { - padding-left: 15px; - padding-right: 15px; - padding-bottom: 25px; - } - .pcb-results-accordion { - padding-left: 15px; - padding-right: 15px; - padding-bottom: 25px; - } - -} - -mat-accordion{ - // background: linear-gradient(to right, #7de4b0, #bbdfe6); -} - -mat-expansion-panel{ - // background: linear-gradient(to right, #f6adad, #feffa7); - background: linear-gradient(to right, #50eef3, #50eef3); -} - - \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/app/pcb/pcb.component.spec.ts b/example-apps/PDD/frontend-src/src/app/pcb/pcb.component.spec.ts deleted file mode 100644 index 8b08b1a..0000000 --- a/example-apps/PDD/frontend-src/src/app/pcb/pcb.component.spec.ts +++ /dev/null @@ -1,25 +0,0 @@ -import { async, ComponentFixture, TestBed } from '@angular/core/testing'; - -import { PcbComponent } from './pcb.component'; - -describe('PcbComponent', () => { - let component: PcbComponent; - let fixture: ComponentFixture; - - beforeEach(async(() => { - TestBed.configureTestingModule({ - declarations: [ PcbComponent ] - }) - .compileComponents(); - })); - - beforeEach(() => { - fixture = TestBed.createComponent(PcbComponent); - component = fixture.componentInstance; - fixture.detectChanges(); - }); - - it('should create', () => { - expect(component).toBeTruthy(); - }); -}); diff --git a/example-apps/PDD/frontend-src/src/app/pcb/pcb.component.ts b/example-apps/PDD/frontend-src/src/app/pcb/pcb.component.ts deleted file mode 100644 index 96049b2..0000000 --- a/example-apps/PDD/frontend-src/src/app/pcb/pcb.component.ts +++ /dev/null @@ -1,307 +0,0 @@ -import { Component, OnInit, TemplateRef, ViewChild, AfterViewInit } from '@angular/core'; -import { FormBuilder, FormGroup } from '@angular/forms'; -import { ToolbarService } from './../toolbar/toolbar.service'; -import { ToastService } from './toast.service'; - -import { uploadImageData } from './../datainterface' -import { EaltserviceService } from '../ealtservice.service' - -import { PcbimagesComponent } from './../pcbimages/pcbimages.component' - - -import { DomSanitizer, SafeUrl } from "@angular/platform-browser"; - -export interface Edgesites { - value: string; - viewValue: string; -} - -export interface Defects { - value: string; - viewValue: string; -} - -export interface Cameras { - value: string; - viewValue: string; -} - -@Component({ - selector: 'app-pcb', - templateUrl: './pcb.component.html', - styleUrls: ['./pcb.component.scss'] -}) -export class PcbComponent implements OnInit { - - imageDeployForm: FormGroup; - modelDeployForm: FormGroup; - url; - msg = ""; - - modelBool = false; - imageBool = false; - detectBool = false; - detectPendingBool = false; - selectedEdgeSite: String; - selectedDefect: String; - selectedCamera: String; - - parentData: String; - detectData: String; - - uploadImageData = {} as uploadImageData; - isTemplate(toast) { return toast.textOrTpl instanceof TemplateRef; } - - pcbInfo = {} ; - - edgesites: Edgesites[] = [ - {value: 'beijinglab', viewValue: 'beijinglab'}, - {value: 'shenzhenlab', viewValue: 'shenzhenlab'}, - {value: 'shanghailab', viewValue: 'shanghailab'} - ]; - - defects: Defects[] = [ - {value: 'all', viewValue: 'All Defects'}, - {value: 'missing_hole', viewValue: 'Missing Hole'}, - {value: 'mouse_bite', viewValue: 'Mouse Bite'}, - {value: 'open_circuit', viewValue: 'Open Circuit'}, - {value: 'short', viewValue: 'Short'}, - {value: 'spur', viewValue: 'Spur'}, - {value: 'spurious_copper', viewValue: 'Spurious Copper'} - ]; - - cameras: Cameras[] = [ - {value: 'camera1', viewValue: 'camera1'}, - {value: 'camera2', viewValue: 'camera2'}, - {value: 'camera3', viewValue: 'camera3'} - ]; - - @ViewChild(PcbimagesComponent) pcbchild; - - constructor( - private formBuilder: FormBuilder, - public toolbarService: ToolbarService, - public toastService: ToastService, - private serviceobj: EaltserviceService, - private sanitizer: DomSanitizer - ) { } - - ngOnInit(): void { - - // let pcbimagesObj = new PcbimagesComponent(); - - this.imageDeployForm = this.formBuilder.group( - { - deployzipfile: [''] - } - ); - this.modelDeployForm = this.formBuilder.group( - { - deploymodelfile: [''] - } - ); - - this.selectedCamera = JSON.parse(localStorage.getItem('camera')); - this.selectedEdgeSite = JSON.parse(localStorage.getItem('edgesite')); - - } - - ngOnDestroy(): void { - // localStorage.removeItem('camera') - // localStorage.removeItem('edgesite') - } - - fileProgress(event) { - console.log("Inside fileProgress...") - debugger; - if(event.target.files.length > 0 ){ - const deployfile = event.target.files[0]; - let filee = event.target.files[0]; - this.imageDeployForm.get('deployzipfile').setValue(deployfile); - debugger; - let filecon: string = ''; - let fileReader: FileReader = new FileReader(); - // console.log(YAML.parse(deployfile)); - debugger; - } - - var reader = new FileReader(); - reader.readAsDataURL(event.target.files[0]); - - reader.onload = (_event) => { - this.msg = ""; - this.url = reader.result; - } - console.log("Ends fileProgress...") - } - - modelFileProgress(event) { - console.log("Inside fileProgress...") - debugger; - if(event.target.files.length > 0 ){ - const deployfile = event.target.files[0]; - let filee = event.target.files[0]; - this.modelDeployForm.get('deploymodelfilefile').setValue(deployfile); - debugger; - let filecon: string = ''; - let fileReader: FileReader = new FileReader(); - // console.log(YAML.parse(deployfile)); - debugger; - } - console.log("Ends modelFileProgress...") - } - - onSubmit() { - console.log("Inside onSubmit() ....") - const formData = new FormData(); - - formData.append('deployfile',this.imageDeployForm.get('deployzipfile').value); - debugger; - - this.uploadImageData.uploadFile = this.imageDeployForm.value.deployzipfile; - - console.log("uploadImagedata..."); - console.log(this.uploadImageData); - console.log("formData...."); - console.log(formData); - // formData.append('file', this.fileData); - // this.deployData.deployFile = formData.get('yamlfile') - console.log(this.imageDeployForm.value); - this.imageBool = true; - this.modelBool = false; - this.detectBool = false; - debugger; - this.showSuccess(); - - localStorage.setItem('camera',JSON.stringify(this.selectedCamera)) - localStorage.setItem('edgesite',JSON.stringify(this.selectedEdgeSite)) - - debugger; - - // this.imageBool = false; - this.serviceobj.postDeploymentPackage(formData) - .subscribe(data => { - console.log(data); - this.showSuccess(); - // console.log(data); - } - ,error => console.log(error) - ); - } - - onModelSubmit() { - console.log("Inside onSubmit() ....") - const formData = new FormData(); - - formData.append('deployfile',this.modelDeployForm.get('deploymodelfile').value); - - this.uploadImageData.uploadFile = this.modelDeployForm.value.deploymodelfile; - - console.log(this.uploadImageData); - // formData.append('file', this.fileData); - // this.deployData.deployFile = formData.get('yamlfile') - console.log(this.modelDeployForm.value); - this.modelBool = true; - this.imageBool = false; - this.detectBool = false; - this.showSuccess(); - // this.modelBool = false; - - this.serviceobj.postDeploymentPackage(formData) - .subscribe(data => { - console.log(data); - this.showSuccess(); - // console.log(data); - } - ,error => console.log(error) - ); - } - - onDetect() { - - this.detectBool = false; - this.modelBool = false; - this.imageBool = false; - - this.toastService.show('Defect Detection in PCB images In progress', { classname: 'bg-warning text-dark', delay: 10000 }); - - this.detectData = this.selectedEdgeSite + '/' + this.selectedCamera; - debugger; - - this.serviceobj.pcbDetect(this.detectData) - .subscribe(data => { - console.log(data); - if(data.responce == "success"){ - this.detectBool = true; - this.detectPendingBool = false; - this.showSuccess(); - } - } - ,error => console.log(error) - ); - - this.detectData = ''; - debugger; - } - - - // Success message display - - private async showSuccess() { - - //wait for 3 seconds - await this.delay(3000); - console.log("Inside showSuccess...") - - if(this.imageBool == true){ - this.toastService.show('PCB images from cameras uploaded successfully', { classname: 'bg-success text-light', delay: 9000 }); - } - - if(this.modelBool == true){ - this.toastService.show('PCB Trained model uploaded Successfully', { classname: 'bg-success text-light', delay: 9000 }); - } - - if(this.detectBool == true){ - // this.toastService.show('Defect Detection in PCB images In progress', { classname: 'bg-warning text-dark', delay: 3000 }); - // await this.delay(3000); - this.toastService.show('Defect Detection in PCB images Completed', { classname: 'bg-success text-light', delay: 9000 }); - } - - if(this.detectPendingBool == true ){ - this.toastService.show('Defect Detection in PCB images In progress', { classname: 'bg-warning text-dark', delay: 9000 }); - // await this.delay(9000); - } - - } - - private delay(ms: number) - { - return new Promise(resolve => setTimeout(resolve, ms)); - } - - onEdgeSiteSelection() { - console.log("on Edge Site Selection triggered...."); - console.log(this.selectedEdgeSite); - } - - onDefectSelection() { - debugger; - console.log("on Defect Selection triggered...."); - console.log(this.selectedDefect); - } - - onCameraSelection() { - debugger; - console.log("on Camera Selection triggered...."); - console.log(this.selectedCamera); - } - - onPreview() { - console.log("Inside onPreview.....") - debugger; - this.parentData = this.selectedEdgeSite + '/' + this.selectedCamera; - debugger; - let pcbInputObj = new PcbimagesComponent(this.serviceobj, this.sanitizer); - } - -} diff --git a/example-apps/PDD/frontend-src/src/app/pcb/toast.service.spec.ts b/example-apps/PDD/frontend-src/src/app/pcb/toast.service.spec.ts deleted file mode 100644 index 2dc86dd..0000000 --- a/example-apps/PDD/frontend-src/src/app/pcb/toast.service.spec.ts +++ /dev/null @@ -1,12 +0,0 @@ -import { TestBed } from '@angular/core/testing'; - -import { ToastService } from './toast.service'; - -describe('ToastService', () => { - beforeEach(() => TestBed.configureTestingModule({})); - - it('should be created', () => { - const service: ToastService = TestBed.get(ToastService); - expect(service).toBeTruthy(); - }); -}); diff --git a/example-apps/PDD/frontend-src/src/app/pcb/toast.service.ts b/example-apps/PDD/frontend-src/src/app/pcb/toast.service.ts deleted file mode 100644 index 8cda912..0000000 --- a/example-apps/PDD/frontend-src/src/app/pcb/toast.service.ts +++ /dev/null @@ -1,17 +0,0 @@ -import { Injectable, TemplateRef } from '@angular/core'; - -@Injectable({ - providedIn: 'root' -}) -export class ToastService { - toasts: any[] = []; - - show(textOrTpl: string | TemplateRef, options: any = {}) { - this.toasts.push({ textOrTpl, ...options }); - } - - remove(toast) { - this.toasts = this.toasts.filter(t => t !== toast); - } - constructor() { } -} diff --git a/example-apps/PDD/frontend-src/src/app/pcbimages/pcbimages.component.html b/example-apps/PDD/frontend-src/src/app/pcbimages/pcbimages.component.html deleted file mode 100644 index 1973a04..0000000 --- a/example-apps/PDD/frontend-src/src/app/pcbimages/pcbimages.component.html +++ /dev/null @@ -1,42 +0,0 @@ -
    -
    - -
    - -
    - - -
    - - -
    -
    -
    -     - -
    - -
    - -
    -
    - -     - - -
    - -
    - -
    -
    -     - -     -
    - -
    \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/app/pcbimages/pcbimages.component.scss b/example-apps/PDD/frontend-src/src/app/pcbimages/pcbimages.component.scss deleted file mode 100644 index 9737610..0000000 --- a/example-apps/PDD/frontend-src/src/app/pcbimages/pcbimages.component.scss +++ /dev/null @@ -1,17 +0,0 @@ -.maindiv{ - .toolbar{ - - } - .input-carousel{ - display:block; - color: white; - img { - - width: 54% - } - } -} - -ngb-carousel{ - display:none -} \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/app/pcbimages/pcbimages.component.spec.ts b/example-apps/PDD/frontend-src/src/app/pcbimages/pcbimages.component.spec.ts deleted file mode 100644 index 4c767a4..0000000 --- a/example-apps/PDD/frontend-src/src/app/pcbimages/pcbimages.component.spec.ts +++ /dev/null @@ -1,25 +0,0 @@ -import { async, ComponentFixture, TestBed } from '@angular/core/testing'; - -import { PcbimagesComponent } from './pcbimages.component'; - -describe('PcbimagesComponent', () => { - let component: PcbimagesComponent; - let fixture: ComponentFixture; - - beforeEach(async(() => { - TestBed.configureTestingModule({ - declarations: [ PcbimagesComponent ] - }) - .compileComponents(); - })); - - beforeEach(() => { - fixture = TestBed.createComponent(PcbimagesComponent); - component = fixture.componentInstance; - fixture.detectChanges(); - }); - - it('should create', () => { - expect(component).toBeTruthy(); - }); -}); diff --git a/example-apps/PDD/frontend-src/src/app/pcbimages/pcbimages.component.ts b/example-apps/PDD/frontend-src/src/app/pcbimages/pcbimages.component.ts deleted file mode 100644 index 0e52d2c..0000000 --- a/example-apps/PDD/frontend-src/src/app/pcbimages/pcbimages.component.ts +++ /dev/null @@ -1,73 +0,0 @@ -import { Component, OnInit } from '@angular/core'; - -import { EaltserviceService } from '../ealtservice.service' - -import { DomSanitizer, SafeUrl } from "@angular/platform-browser"; - -@Component({ - selector: 'app-pcbimages', - templateUrl: './pcbimages.component.html', - styleUrls: ['./pcbimages.component.scss'] -}) -export class PcbimagesComponent implements OnInit { - - input_images = ["./../../assets/images/input_images/01_spurious_copper_03.jpg","./../../assets/images/input_images/04_missing_hole_04.jpg","./../../assets/images/input_images/07_spurious_copper_05.jpg","./../../assets/images/input_images/09_missing_hole_04.jpg","./../../assets/images/input_images/09_spurious_copper_09.jpg","./../../assets/images/input_images/11_open_circuit_03.jpg"]; - inputimg = true; - - thumbnail01 : any; - thumbnail02 : any; - thumbnail03 : any; - thumbnail04 : any; - thumbnail05 : any; - thumbnail : any; - data: any; - - cam: any; - edge: any; - - constructor( - private serviceobj: EaltserviceService, - private sanitizer: DomSanitizer - ) { } - - ngOnInit(): void { - // this.inputimg == false; - // this.getImage(this.data); - this.cam = JSON.parse(localStorage.getItem('camera')); - this.edge = JSON.parse(localStorage.getItem('edgesite')); - this.data = this.edge + '/' + this.cam; - - this.getImage(this.data); - } - - getImage(data) { - - //let myCompOneObj = new CompOneComponent(); - debugger; - this.serviceobj.getInputImage(data) - .subscribe( (data:any) => { - - debugger; - - let objectURL = 'data:image/jpeg;base64,' + data.image01; - this.thumbnail = this.sanitizer.bypassSecurityTrustUrl(objectURL); - - // let objectURL01 = 'data:image/jpeg;base64,' + data.image01; - let objectURL02 = 'data:image/jpeg;base64,' + data.image02; - let objectURL03 = 'data:image/jpeg;base64,' + data.image03; - let objectURL04 = 'data:image/jpeg;base64,' + data.image04; - let objectURL05 = 'data:image/jpeg;base64,' + data.image05; - - - // this.thumbnail01 = this.sanitizer.bypassSecurityTrustUrl(objectURL01); - this.thumbnail02 = this.sanitizer.bypassSecurityTrustUrl(objectURL02); - this.thumbnail03 = this.sanitizer.bypassSecurityTrustUrl(objectURL03); - this.thumbnail04 = this.sanitizer.bypassSecurityTrustUrl(objectURL04); - this.thumbnail05 = this.sanitizer.bypassSecurityTrustUrl(objectURL05); - - }, - error => console.log(error)); - - } - -} diff --git a/example-apps/PDD/frontend-src/src/app/pcboutputimages/pcboutputimages.component.html b/example-apps/PDD/frontend-src/src/app/pcboutputimages/pcboutputimages.component.html deleted file mode 100644 index 45a41a6..0000000 --- a/example-apps/PDD/frontend-src/src/app/pcboutputimages/pcboutputimages.component.html +++ /dev/null @@ -1,41 +0,0 @@ -
    -
    - -
    - -
    - - -
    - - -
    -
    -
    -     - -
    - -
    - -
    -
    - -     - - -
    - -
    - -
    -
    -     -     -
    -
    - diff --git a/example-apps/PDD/frontend-src/src/app/pcboutputimages/pcboutputimages.component.scss b/example-apps/PDD/frontend-src/src/app/pcboutputimages/pcboutputimages.component.scss deleted file mode 100644 index 21f069c..0000000 --- a/example-apps/PDD/frontend-src/src/app/pcboutputimages/pcboutputimages.component.scss +++ /dev/null @@ -1,20 +0,0 @@ -.maindiv{ - .toolbar{ - - } - .output-carousel{ - display:block; - color: black; - margin-left: auto; - margin-right: auto; - img { - width: 56%; - margin-left: auto; - margin-right: auto; - } - } -} - -ngb-carousel{ - display:none -} \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/app/pcboutputimages/pcboutputimages.component.spec.ts b/example-apps/PDD/frontend-src/src/app/pcboutputimages/pcboutputimages.component.spec.ts deleted file mode 100644 index 67476f4..0000000 --- a/example-apps/PDD/frontend-src/src/app/pcboutputimages/pcboutputimages.component.spec.ts +++ /dev/null @@ -1,25 +0,0 @@ -import { async, ComponentFixture, TestBed } from '@angular/core/testing'; - -import { PcboutputimagesComponent } from './pcboutputimages.component'; - -describe('PcboutputimagesComponent', () => { - let component: PcboutputimagesComponent; - let fixture: ComponentFixture; - - beforeEach(async(() => { - TestBed.configureTestingModule({ - declarations: [ PcboutputimagesComponent ] - }) - .compileComponents(); - })); - - beforeEach(() => { - fixture = TestBed.createComponent(PcboutputimagesComponent); - component = fixture.componentInstance; - fixture.detectChanges(); - }); - - it('should create', () => { - expect(component).toBeTruthy(); - }); -}); diff --git a/example-apps/PDD/frontend-src/src/app/pcboutputimages/pcboutputimages.component.ts b/example-apps/PDD/frontend-src/src/app/pcboutputimages/pcboutputimages.component.ts deleted file mode 100644 index 843c5d1..0000000 --- a/example-apps/PDD/frontend-src/src/app/pcboutputimages/pcboutputimages.component.ts +++ /dev/null @@ -1,58 +0,0 @@ -import { Component, OnInit } from '@angular/core'; - -import { EaltserviceService } from '../ealtservice.service' - -import { DomSanitizer, SafeUrl } from "@angular/platform-browser"; - -@Component({ - selector: 'app-pcboutputimages', - templateUrl: './pcboutputimages.component.html', - styleUrls: ['./pcboutputimages.component.scss'] -}) -export class PcboutputimagesComponent implements OnInit { - - output_images = ["./../../assets/images/output_images/01_spurious_copper_03.jpg","./../../assets/images/output_images/04_missing_hole_04.jpg","./../../assets/images/output_images/07_spurious_copper_05.jpg","./../../assets/images/output_images/09_missing_hole_04.jpg","./../../assets/images/output_images/09_spurious_copper_09.jpg","./../../assets/images/output_images/11_open_circuit_03.jpg"]; - outputimg = true; - - thumbnail01 : any; - thumbnail02 : any; - thumbnail03 : any; - thumbnail04 : any; - thumbnail05 : any; - - constructor( - private serviceobj: EaltserviceService, - private sanitizer: DomSanitizer - ) { } - - ngOnInit(): void { - this.getImage(); - } - - getImage() { - debugger; - localStorage.removeItem('camera'); - localStorage.removeItem('edgesite'); - this.serviceobj.getOutputImage() - .subscribe( (data:any) => { - - debugger; - - let objectURL01 = 'data:image/jpeg;base64,' + data.image01; - let objectURL02 = 'data:image/jpeg;base64,' + data.image02; - let objectURL03 = 'data:image/jpeg;base64,' + data.image03; - let objectURL04 = 'data:image/jpeg;base64,' + data.image04; - let objectURL05 = 'data:image/jpeg;base64,' + data.image05; - - this.thumbnail01 = this.sanitizer.bypassSecurityTrustUrl(objectURL01); - this.thumbnail02 = this.sanitizer.bypassSecurityTrustUrl(objectURL02); - this.thumbnail03 = this.sanitizer.bypassSecurityTrustUrl(objectURL03); - this.thumbnail04 = this.sanitizer.bypassSecurityTrustUrl(objectURL04); - this.thumbnail05 = this.sanitizer.bypassSecurityTrustUrl(objectURL05); - - }, - error => console.log(error)); - - } - -} diff --git a/example-apps/PDD/frontend-src/src/app/pom.xml b/example-apps/PDD/frontend-src/src/app/pom.xml deleted file mode 100644 index 033e206..0000000 --- a/example-apps/PDD/frontend-src/src/app/pom.xml +++ /dev/null @@ -1,61 +0,0 @@ - -eliot - - - com.github.eirslett - frontend-maven-plugin - 1.6 - - - src/main/frontend - - - - - install-node-and-npm - - install-node-and-npm - - - v12.9.0 - - - - - npm install - - npm - - - install - - - - npm build - - npm - - - run buildProduction - - generate-resources - - - - - maven-war-plugin - 3.2.2 - - false - - - - - src/main/frontend/dist/frontend - - - - - - - \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/app/server.ts b/example-apps/PDD/frontend-src/src/app/server.ts deleted file mode 100644 index 22ec8bb..0000000 --- a/example-apps/PDD/frontend-src/src/app/server.ts +++ /dev/null @@ -1,4 +0,0 @@ -interface Server { - temperature:number; - status: String; -} diff --git a/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.component.html b/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.component.html deleted file mode 100644 index ee21651..0000000 --- a/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.component.html +++ /dev/null @@ -1,55 +0,0 @@ -
    -
    - - - - - -
    - -
    - -
    - - - - - - - - - - - - - - - - - - -
    -     -     -
    - - -
    \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.component.scss b/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.component.scss deleted file mode 100644 index 85cf9d5..0000000 --- a/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.component.scss +++ /dev/null @@ -1,39 +0,0 @@ -.main { - .menu { - - .akraino-icon { - padding-right:20px; - } - - .menu-toolbar { - .ealt-menu { - font-size: 14px; - font-weight: bold; - } - .home { - margin-left: 07px; - } - } - - .home { - font-size: 25px; - font-weight: bold; - // padding-left: 20px; - } - - .other-button { - font-weight: bold; - } - - .power-button { - position: absolute; - right: 0px; - margin-right: 5px; - } - } -} -button:hover { - background-color: black; - color: turquoise; - font-weight: bold; -} \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.component.spec.ts b/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.component.spec.ts deleted file mode 100644 index 784fc64..0000000 --- a/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.component.spec.ts +++ /dev/null @@ -1,30 +0,0 @@ -import { async, ComponentFixture, TestBed } from '@angular/core/testing'; - -import { ToolbarComponent } from './toolbar.component'; - -import { Component } from '@angular/core'; - -@Component({selector: 'app-toolbar', template: ''}) -class ToolbarStubComponent{} - -describe('ToolbarComponent', () => { - let component: ToolbarComponent; - let fixture: ComponentFixture; - - beforeEach(async(() => { - TestBed.configureTestingModule({ - declarations: [ ToolbarComponent, ToolbarStubComponent ] - }) - .compileComponents(); - })); - - beforeEach(() => { - fixture = TestBed.createComponent(ToolbarComponent); - component = fixture.componentInstance; - fixture.detectChanges(); - }); - - it('should create', () => { - expect(component).toBeTruthy(); - }); -}); diff --git a/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.component.ts b/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.component.ts deleted file mode 100644 index 6423f16..0000000 --- a/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.component.ts +++ /dev/null @@ -1,44 +0,0 @@ -import { Component, OnInit } from '@angular/core'; -import { ToolbarService } from './toolbar.service'; - -@Component({ - selector: 'app-toolbar', - templateUrl: './toolbar.component.html', - styleUrls: ['./toolbar.component.scss'] -}) -export class ToolbarComponent implements OnInit { - toolbarmenu = []; - menuicon: string; - constructor(public toolbarService: ToolbarService) { } - - ngOnInit() { - this.menuicon = "menu"; - this.init(); - } - - init(){ - this.toolbarmenu = [ - { - displayName: 'EALT-EDGE', - route: '/home' - }, - // { - // displayName: 'PCB USECASE', - // }, - { - displayName: 'USECASES', - children: [ - { - displayName: 'PCB Defect Detection', - route: '/pcb' - } - ] - }, - { - displayName: 'Akraino EALT Edge Wiki', - route: '/akrainowiki' - }, - ] - } - -} diff --git a/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.service.spec.ts b/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.service.spec.ts deleted file mode 100644 index cae2a91..0000000 --- a/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.service.spec.ts +++ /dev/null @@ -1,12 +0,0 @@ -import { TestBed } from '@angular/core/testing'; - -import { ToolbarService } from './toolbar.service'; - -describe('ToolbarService', () => { - beforeEach(() => TestBed.configureTestingModule({})); - - it('should be created', () => { - const service: ToolbarService = TestBed.get(ToolbarService); - expect(service).toBeTruthy(); - }); -}); diff --git a/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.service.ts b/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.service.ts deleted file mode 100644 index efa922d..0000000 --- a/example-apps/PDD/frontend-src/src/app/toolbar/toolbar.service.ts +++ /dev/null @@ -1,35 +0,0 @@ -import { Injectable } from '@angular/core'; -// import { Observable } from 'rxjs/Observable'; -import { Observable,throwError } from 'rxjs' -import { of } from 'rxjs'; -// import {} from './../../assets/images/akraino-icon-1.png' -// import 'rxjs/add/Observable/of'; -// import 'of; - -@Injectable({ - providedIn: 'root' -}) -export class ToolbarService { - - visible: Observable; - vis: boolean; - - constructor() { } - - hide() { - this.visible = of(false); - console.log("inside hide method"); - console.log(this.visible); - } - - show() { - this.visible = of(true); - console.log(this.visible); - // console.log(this.visible.value); - // this.vis = this.visible.value; - } - - toggle() { - this.visible =of(!this.visible); - } -} diff --git a/example-apps/PDD/frontend-src/src/assets/.gitkeep b/example-apps/PDD/frontend-src/src/assets/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/example-apps/PDD/frontend-src/src/assets/data/nodes.json b/example-apps/PDD/frontend-src/src/assets/data/nodes.json deleted file mode 100644 index 7963cee..0000000 --- a/example-apps/PDD/frontend-src/src/assets/data/nodes.json +++ /dev/null @@ -1,76 +0,0 @@ -{ - "eliotNodes":[ - { - "nodeName":"eliot01", - "nodeStatus":"Ready", - "nodeRole": "Master", - "age": "10d", - "version": "v1.15.0", - "internalIp": "10.10.0.206", - "externalIp": "none", - "osImage": "Centos Linux 7 core", - "kernel": "3.10.0", - "containerRuntime": "runtime01" - }, - { - "nodeName":"eliot02", - "nodeStatus":"Not Ready", - "nodeRole": "Worker", - "age": "9d", - "version": "v1.15.0", - "internalIp": "10.10.0.209", - "externalIp": "none", - "osImage": "Centos Linux 7 core", - "kernel": "3.10.0", - "containerRuntime": "runtime02" - }, - { - "nodeName":"eliot03", - "nodeStatus":"Not Ready", - "nodeRole": "Worker", - "age": "8d", - "version": "v1.15.0", - "internalIp": "10.10.0.210", - "externalIp": "none", - "osImage": "Centos Linux 7 core", - "kernel": "3.10.0", - "containerRuntime": "runtime03" - }, - { - "nodeName":"eliot03", - "nodeStatus":"Not Ready", - "nodeRole": "Worker", - "age": "8d", - "version": "v1.15.0", - "internalIp": "10.10.0.210", - "externalIp": "none", - "osImage": "Centos Linux 7 core", - "kernel": "3.10.0", - "containerRuntime": "runtime03" - }, - { - "nodeName":"eliot03", - "nodeStatus":"Not Ready", - "nodeRole": "Worker", - "age": "8d", - "version": "v1.15.0", - "internalIp": "10.10.0.210", - "externalIp": "none", - "osImage": "Centos Linux 7 core", - "kernel": "3.10.0", - "containerRuntime": "runtime03" - }, - { - "nodeName":"eliot03", - "nodeStatus":"Not Ready", - "nodeRole": "Worker", - "age": "8d", - "version": "v1.15.0", - "internalIp": "10.10.0.210", - "externalIp": "none", - "osImage": "Centos Linux 7 core", - "kernel": "3.10.0", - "containerRuntime": "runtime03" - } - ] -} diff --git a/example-apps/PDD/frontend-src/src/assets/images/akraino-icon-5.png 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b/example-apps/PDD/frontend-src/src/assets/images/output_images/09_spurious_copper_09.jpg deleted file mode 100644 index f2bc642..0000000 Binary files a/example-apps/PDD/frontend-src/src/assets/images/output_images/09_spurious_copper_09.jpg and /dev/null differ diff --git a/example-apps/PDD/frontend-src/src/assets/images/output_images/11_open_circuit_03.jpg b/example-apps/PDD/frontend-src/src/assets/images/output_images/11_open_circuit_03.jpg deleted file mode 100644 index 04e9d22..0000000 Binary files a/example-apps/PDD/frontend-src/src/assets/images/output_images/11_open_circuit_03.jpg and /dev/null differ diff --git a/example-apps/PDD/frontend-src/src/assets/md/README.md b/example-apps/PDD/frontend-src/src/assets/md/README.md deleted file mode 100644 index 9804b63..0000000 --- a/example-apps/PDD/frontend-src/src/assets/md/README.md +++ /dev/null @@ -1,106 +0,0 @@ -# Edge LightWeight IoT - -[eliot-gerrit-link](https://gerrit.akraino.org/r/gitweb?p=eliot.git;a=tree) - -ELIOT is "Enterprise Edge Lightweight and IOT" project under Akraino approved blueprint family and part of Akraino Edge Stack, which intends to provide an platform for a fully integrated edge network infrastructure and running edge computing applications, on lightweight Edge Nodes. ELIOT targets on making the edge node a lightweight software stack which can be deployed on edge nodes which have limited hardware capacity by leveraging lightweight OS, a container running environment and container orchestration applications. - -In addition, ELIOT stack focuses to have an infrastructure for edge computing which will enable high performance, high availability, security and reduce latency. - -IoT Gateway Blueprint will provide an infrastructure platform for an edge node to provide features of an IoT Gateway. This is achieved by leveraging the upstream opensource application like Dockers, Kubernetes , Prometheus , cAdvisor, KubeEdge and EdgeX. Companies having applications to be deployed on edge nodes related to IoT application , can use the IoT Gateway platform to have a functional end to end to ecosystem of IoT. - -## Advantages - -#### Where on the Edge - -IoT gateway - -Smart cities -Smart homes -Connected vehicles -Connected farming, agriculture -Logistics -Industrial, IIoT - -#### Overall Architecture - -ELIOT IoT Gateway Blueprint Architecture consists of an ELIOT Manager and multiple ELIOT Nodes (edge nodes). The scale of ELIOT (edge) Nodes can range from 1 single node to 10, 100, 1K or more. For development environment we have tested with one ELIOT Manager Node and two ELIOT Worker Nodes. - -ELIOT Manager is the central controller which manages the orchestration, life cycle, management and networking of edge nodes (IoT Gateway). It can be installed on a bare metal server or cloud VM with Ubuntu 16.04 /18.04 or Cent OS 7.5 version (currently supported OS in IoT Gateway Blueprint). - -The node ELIOT Manager is to facilitate auto deploy, high availability, orchestration, self-healing of the IoT Gateway Nodes. - -ELIOT Nodes - IoT Gateway are Edge compute processing nodes which will provide the software stack for deploying IoT applications. - -The IoT Gateway can function as a Kubernetes worker node or a KubeEdge - Edge node when used as Kubernetes or KubeEdge respectively. - -As the edge nodes can have restricted hardware resources of CPU , Memory etc, ELIOT uses ELIOT Minimal OS for the Edge nodes which is basically tailored version of Cent OS. ELIOT Minimal OS is a lightweight OS suitable for ELIOT Edge nodes. - -ELIOT blueprint will use lightweight OS, Lightweight Kubelet -- KubeEdge , lightweight container runtime interface -- like containerd. By this we can leverage the core features of OS, K8S, CRI and make the edge node lightweight and efficient edge computing node by executing edge applications on node and reducing the interaction between Central Controller (Cloud Node) and the Edge Node devices. - -Many cloud native monitoring applications can be used to collect container matrix and show them in a graphical manner, like cadvisor , prometheus. - -## Platform Architecture - -#### ELIOT Manager - -#### ELIOT Portal - -ELIOT Portal is new feature added on the ELIOT Blueprint which is currently applicable for both IoT Gateway and uCPE Blueprint. ELIOT Portal main objective is to have a dashboard for operations and management of ELIOT Cluster. It consists of ELIOT UI and ELIOT API Gateway. - -In Release 2.0 the portal is giving the following operations:- - -Display ELIOT Nodes -Display ELIOT Services -Display ELIOT PODS. -Other options like Application Deployments, are currently in design phase, which will be developed for future releases. - -#### ELIOT CLI: - -ELIOT CLI (Command Line Interface) is a new feature added in ELIOT Blueprint which is currently applicable only for IoT Gateway Blueprint. It is providing a command line interface option to setup the ELIOT Cluster. Currently this feature is supported only for Ubuntu OS. - -The ELIOT CLI application currently provides the following operations: - -- ELIOT Cluster Setup. -- ELIOT Cluster Cleanup. -- ELIOT Cluster Reset. - -#### Node Monitoring: - -Node Monitoring main task is to collect the container performance metrics from various IoT Gateway Nodes and display on a dashboard in graphical representation. - -#### Network Controller & Node Resource Manager: - -The core part of the ELIOT Cluster which controls the Node health, high availability , overlay networking , cluster management. To manage the same Kubernetes and KubeEdge are being used for Node Resource Management with Calico for Container Networking. - -## Software Platform Architecture - -#### APIs - -Not Applicable - -#### Hardware and Software Management - - -Currently in IoT Gateway Blueprint virtual machines are being used in public cloud. Currently no dedicated bare metal are being used. - -Software Management:- - -ELIOT gerrit - https://gerrit.akraino.org/r/#/admin/projects/eliot - -The core part of the ELIOT Cluster which controls the Node health, high availability , overlay networking , cluster management. To manage the same Kubernetes and KubeEdge are being used for Node Resource Management with Calico for Container Networking. - - -## Documentation - -The detailed documentation for ELIOT and its modules can be found at [https://wiki.akraino.org/display/AK/ELIOT%3A+Edge+Lightweight+and+IoT+Blueprint+Family](ELIOT wiki). - - -## Contributing - -If you're interested in being a contributor and want to get involved in -developing the ELIOT code, please see [CONTRIBUTING](CONTRIBUTING.md) for -details on submitting patches and the contribution workflow. - -## License - -ELIOT is under the Apache 2.0 license. See the [LICENSE](LICENSE) file for details. \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/assets/md/readme.md b/example-apps/PDD/frontend-src/src/assets/md/readme.md deleted file mode 100644 index e69de29..0000000 diff --git a/example-apps/PDD/frontend-src/src/environments/environment.prod.ts b/example-apps/PDD/frontend-src/src/environments/environment.prod.ts deleted file mode 100644 index 3612073..0000000 --- a/example-apps/PDD/frontend-src/src/environments/environment.prod.ts +++ /dev/null @@ -1,3 +0,0 @@ -export const environment = { - production: true -}; diff --git a/example-apps/PDD/frontend-src/src/environments/environment.ts b/example-apps/PDD/frontend-src/src/environments/environment.ts deleted file mode 100644 index 7b4f817..0000000 --- a/example-apps/PDD/frontend-src/src/environments/environment.ts +++ /dev/null @@ -1,16 +0,0 @@ -// This file can be replaced during build by using the `fileReplacements` array. -// `ng build --prod` replaces `environment.ts` with `environment.prod.ts`. -// The list of file replacements can be found in `angular.json`. - -export const environment = { - production: false -}; - -/* - * For easier debugging in development mode, you can import the following file - * to ignore zone related error stack frames such as `zone.run`, `zoneDelegate.invokeTask`. - * - * This import should be commented out in production mode because it will have a negative impact - * on performance if an error is thrown. - */ -// import 'zone.js/dist/zone-error'; // Included with Angular CLI. diff --git a/example-apps/PDD/frontend-src/src/favicon.ico b/example-apps/PDD/frontend-src/src/favicon.ico deleted file mode 100644 index 8081c7c..0000000 Binary files a/example-apps/PDD/frontend-src/src/favicon.ico and /dev/null differ diff --git a/example-apps/PDD/frontend-src/src/index.html b/example-apps/PDD/frontend-src/src/index.html deleted file mode 100644 index c80dbd2..0000000 --- a/example-apps/PDD/frontend-src/src/index.html +++ /dev/null @@ -1,14 +0,0 @@ - - - - - EALT-EDGE - - - - - - - - - diff --git a/example-apps/PDD/frontend-src/src/main.ts b/example-apps/PDD/frontend-src/src/main.ts deleted file mode 100644 index c7b673c..0000000 --- a/example-apps/PDD/frontend-src/src/main.ts +++ /dev/null @@ -1,12 +0,0 @@ -import { enableProdMode } from '@angular/core'; -import { platformBrowserDynamic } from '@angular/platform-browser-dynamic'; - -import { AppModule } from './app/app.module'; -import { environment } from './environments/environment'; - -if (environment.production) { - enableProdMode(); -} - -platformBrowserDynamic().bootstrapModule(AppModule) - .catch(err => console.error(err)); diff --git a/example-apps/PDD/frontend-src/src/polyfills.ts b/example-apps/PDD/frontend-src/src/polyfills.ts deleted file mode 100644 index 7c9e495..0000000 --- a/example-apps/PDD/frontend-src/src/polyfills.ts +++ /dev/null @@ -1,67 +0,0 @@ -/*************************************************************************************************** - * Load `$localize` onto the global scope - used if i18n tags appear in Angular templates. - */ -import '@angular/localize/init'; -/** - * This file includes polyfills needed by Angular and is loaded before the app. - * You can add your own extra polyfills to this file. - * - * This file is divided into 2 sections: - * 1. Browser polyfills. These are applied before loading ZoneJS and are sorted by browsers. - * 2. Application imports. Files imported after ZoneJS that should be loaded before your main - * file. - * - * The current setup is for so-called "evergreen" browsers; the last versions of browsers that - * automatically update themselves. This includes Safari >= 10, Chrome >= 55 (including Opera), - * Edge >= 13 on the desktop, and iOS 10 and Chrome on mobile. - * - * Learn more in https://angular.io/guide/browser-support - */ - -/*************************************************************************************************** - * BROWSER POLYFILLS - */ - -/** IE10 and IE11 requires the following for NgClass support on SVG elements */ -// import 'classlist.js'; // Run `npm install --save classlist.js`. - -/** - * Web Animations `@angular/platform-browser/animations` - * Only required if AnimationBuilder is used within the application and using IE/Edge or Safari. - * Standard animation support in Angular DOES NOT require any polyfills (as of Angular 6.0). - */ -// import 'web-animations-js'; // Run `npm install --save web-animations-js`. - -/** - * By default, zone.js will patch all possible macroTask and DomEvents - * user can disable parts of macroTask/DomEvents patch by setting following flags - * because those flags need to be set before `zone.js` being loaded, and webpack - * will put import in the top of bundle, so user need to create a separate file - * in this directory (for example: zone-flags.ts), and put the following flags - * into that file, and then add the following code before importing zone.js. - * import './zone-flags.ts'; - * - * The flags allowed in zone-flags.ts are listed here. - * - * The following flags will work for all browsers. - * - * (window as any).__Zone_disable_requestAnimationFrame = true; // disable patch requestAnimationFrame - * (window as any).__Zone_disable_on_property = true; // disable patch onProperty such as onclick - * (window as any).__zone_symbol__UNPATCHED_EVENTS = ['scroll', 'mousemove']; // disable patch specified eventNames - * - * in IE/Edge developer tools, the addEventListener will also be wrapped by zone.js - * with the following flag, it will bypass `zone.js` patch for IE/Edge - * - * (window as any).__Zone_enable_cross_context_check = true; - * - */ - -/*************************************************************************************************** - * Zone JS is required by default for Angular itself. - */ -import 'zone.js/dist/zone'; // Included with Angular CLI. - - -/*************************************************************************************************** - * APPLICATION IMPORTS - */ diff --git a/example-apps/PDD/frontend-src/src/styles.scss b/example-apps/PDD/frontend-src/src/styles.scss deleted file mode 100644 index 557dd26..0000000 --- a/example-apps/PDD/frontend-src/src/styles.scss +++ /dev/null @@ -1,24 +0,0 @@ -/* You can add global styles to this file, and also import other style files */ -// @import "~@angular/material/prebuilt-themes/indigo-pink.css"; -@import url("https://fonts.googleapis.com/icon?family=Material+Icons"); -@import "~bootstrap/dist/css/bootstrap.css"; - -@import url("https://use.fontawesome.com/releases/v5.7.0/css/all.css"); - -@import './styles/material-theme.scss'; -// @import "~@angular/material/prebuilt-themes/pink-bluegrey.css"; -// @import "~@angular/material/prebuilt-themes/deeppurple-amber.css"; - -body { - margin: 0 auto; -} - -.carousel-inner { - // max-height: calc(100vh - 305px); - img { - // max-height: calc(100vh - 135px); - } -} -// @import "~@angular/material/prebuilt-themes/deeppurple-amber.css"; -// @import "~@angular/material/prebuilt-themes/purple-green.css"; -// @import "~@angular/material/prebuilt-themes/pink-bluegrey.css"; diff --git a/example-apps/PDD/frontend-src/src/styles/material-theme.scss b/example-apps/PDD/frontend-src/src/styles/material-theme.scss deleted file mode 100644 index 3a7dce0..0000000 --- a/example-apps/PDD/frontend-src/src/styles/material-theme.scss +++ /dev/null @@ -1,10 +0,0 @@ -@import '~@angular/material/theming'; - -@include mat-core(); - -$primary: mat-palette($mat-grey, 900); -$accent: mat-palette($mat-pink, A200, A100, A400); -$warn: mat-palette($mat-red); -$theme: mat-light-theme($primary, $accent, $warn); - -@include angular-material-theme($theme); \ No newline at end of file diff --git a/example-apps/PDD/frontend-src/src/test.ts b/example-apps/PDD/frontend-src/src/test.ts deleted file mode 100644 index 1631789..0000000 --- a/example-apps/PDD/frontend-src/src/test.ts +++ /dev/null @@ -1,20 +0,0 @@ -// This file is required by karma.conf.js and loads recursively all the .spec and framework files - -import 'zone.js/dist/zone-testing'; -import { getTestBed } from '@angular/core/testing'; -import { - BrowserDynamicTestingModule, - platformBrowserDynamicTesting -} from '@angular/platform-browser-dynamic/testing'; - -declare const require: any; - -// First, initialize the Angular testing environment. -getTestBed().initTestEnvironment( - BrowserDynamicTestingModule, - platformBrowserDynamicTesting() -); -// Then we find all the tests. -const context = require.context('./', true, /\.spec\.ts$/); -// And load the modules. -context.keys().map(context); diff --git a/example-apps/PDD/frontend-src/tsconfig.app.json b/example-apps/PDD/frontend-src/tsconfig.app.json deleted file mode 100644 index 565a11a..0000000 --- a/example-apps/PDD/frontend-src/tsconfig.app.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "extends": "./tsconfig.json", - "compilerOptions": { - "outDir": "./out-tsc/app", - "types": [] - }, - "files": [ - "src/main.ts", - "src/polyfills.ts" - ], - "include": [ - "src/**/*.ts" - ], - "exclude": [ - "src/test.ts", - "src/**/*.spec.ts" - ] -} diff --git a/example-apps/PDD/frontend-src/tsconfig.json b/example-apps/PDD/frontend-src/tsconfig.json deleted file mode 100644 index 30956ae..0000000 --- a/example-apps/PDD/frontend-src/tsconfig.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "compileOnSave": false, - "compilerOptions": { - "baseUrl": "./", - "outDir": "./dist/out-tsc", - "sourceMap": true, - "declaration": false, - "downlevelIteration": true, - "experimentalDecorators": true, - "module": "esnext", - "moduleResolution": "node", - "importHelpers": true, - "target": "es2015", - "typeRoots": [ - "node_modules/@types" - ], - "lib": [ - "es2018", - "dom" - ] - }, - "angularCompilerOptions": { - "fullTemplateTypeCheck": true, - "strictInjectionParameters": true - } -} diff --git a/example-apps/PDD/frontend-src/tsconfig.spec.json b/example-apps/PDD/frontend-src/tsconfig.spec.json deleted file mode 100644 index 6400fde..0000000 --- a/example-apps/PDD/frontend-src/tsconfig.spec.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "extends": "./tsconfig.json", - "compilerOptions": { - "outDir": "./out-tsc/spec", - "types": [ - "jasmine", - "node" - ] - }, - "files": [ - "src/test.ts", - "src/polyfills.ts" - ], - "include": [ - "src/**/*.spec.ts", - "src/**/*.d.ts" - ] -} diff --git a/example-apps/PDD/frontend-src/tslint.json b/example-apps/PDD/frontend-src/tslint.json deleted file mode 100644 index f0adae9..0000000 --- a/example-apps/PDD/frontend-src/tslint.json +++ /dev/null @@ -1,92 +0,0 @@ -{ - "extends": "tslint:recommended", - "rules": { - "array-type": false, - "arrow-parens": false, - "deprecation": { - "severity": "warning" - }, - "component-class-suffix": true, - "contextual-lifecycle": true, - "directive-class-suffix": true, - "directive-selector": [ - true, - "attribute", - "app", - "camelCase" - ], - "component-selector": [ - true, - "element", - "app", - "kebab-case" - ], - "import-blacklist": [ - true, - "rxjs/Rx" - ], - "interface-name": false, - "max-classes-per-file": false, - "max-line-length": [ - true, - 140 - ], - "member-access": false, - "member-ordering": [ - true, - { - "order": [ - "static-field", - "instance-field", - "static-method", - "instance-method" - ] - } - ], - "no-consecutive-blank-lines": false, - "no-console": [ - true, - "debug", - "info", - "time", - "timeEnd", - "trace" - ], - "no-empty": false, - "no-inferrable-types": [ - true, - "ignore-params" - ], - "no-non-null-assertion": true, - "no-redundant-jsdoc": true, - "no-switch-case-fall-through": true, - "no-use-before-declare": true, - "no-var-requires": false, - "object-literal-key-quotes": [ - true, - "as-needed" - ], - "object-literal-sort-keys": false, - "ordered-imports": false, - "quotemark": [ - true, - "single" - ], - "trailing-comma": false, - "no-conflicting-lifecycle": true, - "no-host-metadata-property": true, - "no-input-rename": true, - "no-inputs-metadata-property": true, - "no-output-native": true, - "no-output-on-prefix": true, - "no-output-rename": true, - "no-outputs-metadata-property": true, - "template-banana-in-box": true, - "template-no-negated-async": true, - "use-lifecycle-interface": true, - "use-pipe-transform-interface": true - }, - "rulesDirectory": [ - "codelyzer" - ] -} \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/01_missing_hole_01.jpg b/example-apps/PDD/pcb-defect-detection/01_missing_hole_01.jpg deleted file mode 100755 index 2fa7d73..0000000 Binary files a/example-apps/PDD/pcb-defect-detection/01_missing_hole_01.jpg and /dev/null differ diff --git a/example-apps/PDD/pcb-defect-detection/04_mouse_bite_10.jpg b/example-apps/PDD/pcb-defect-detection/04_mouse_bite_10.jpg deleted file mode 100755 index 9671331..0000000 Binary files a/example-apps/PDD/pcb-defect-detection/04_mouse_bite_10.jpg and /dev/null differ diff --git a/example-apps/PDD/pcb-defect-detection/Dockerfile b/example-apps/PDD/pcb-defect-detection/Dockerfile deleted file mode 100644 index 09c2ad1..0000000 --- a/example-apps/PDD/pcb-defect-detection/Dockerfile +++ /dev/null @@ -1,145 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# Prepare stage for multistage image build -## START OF STAGE0 ## -# FROM python:3.6-slim-stretch -# FROM python:3.9 -# FROM python:3.9-slim-buster -# # CREATE APP USER ## - -FROM python:2.7 - -# Set umask -RUN sed -i "s|umask 022|umask 027|g" /etc/profile - -# Create the home directory for the new app user. -RUN mkdir -p /usr/app -RUN mkdir -p /usr/app/bin -RUN mkdir -p /usr/app/data -RUN mkdir -p /usr/app/help_utils -RUN mkdir -p /usr/app/libs -RUN mkdir -p /usr/app/output -RUN mkdir -p /usr/app/results -RUN mkdir -p /usr/app/tools - -RUN mkdir -p /usr/app/input_image -RUN mkdir -p /usr/app/output_image -RUN mkdir -p /usr/app/input_image/beijinglab -RUN mkdir -p /usr/app/input_image/beijinglab/camera1 -RUN mkdir -p /usr/app/input_image/beijinglab/camera2 -RUN mkdir -p /usr/app/input_image/beijinglab/camera3 -RUN mkdir -p /usr/app/input_image/shenzhenlab -RUN mkdir -p /usr/app/input_image/shenzhenlab/camera1 -RUN mkdir -p /usr/app/input_image/shenzhenlab/camera2 -RUN mkdir -p /usr/app/input_image/shenzhenlab/camera3 -RUN mkdir -p /usr/app/input_image/shanghailab -RUN mkdir -p /usr/app/input_image/shanghailab/camera1 -RUN mkdir -p /usr/app/input_image/shanghailab/camera2 -RUN mkdir -p /usr/app/input_image/shanghailab/camera3 -#output image path -RUN mkdir -p /usr/app/output_image/beijinglab -RUN mkdir -p /usr/app/output_image/beijinglab/camera1 -RUN mkdir -p /usr/app/output_image/beijinglab/camera2 -RUN mkdir -p /usr/app/output_image/beijinglab/camera3 -RUN mkdir -p /usr/app/output_image/shenzhenlab -RUN mkdir -p /usr/app/output_image/shenzhenlab/camera1 -RUN mkdir -p /usr/app/output_image/shenzhenlab/camera2 -RUN mkdir -p /usr/app/output_image/shenzhenlab/camera3 -RUN mkdir -p /usr/app/output_image/shanghailab -RUN mkdir -p /usr/app/output_image/shanghailab/camera1 -RUN mkdir -p /usr/app/output_image/shanghailab/camera2 -RUN mkdir -p /usr/app/output_image/shanghailab/camera3 - -# Set the home directory to our app user's home. -ENV APP_HOME=/usr/app -ENV UID=166 -ENV GID=166 -ENV USER_NAME=eguser -ENV GROUP_NAME=eggroup -ENV ENV="/etc/profile" -ENV PYTHONUNBUFFERED=0 - -# Create an app user so our program doesn't run as root. -RUN apt-get -y update &&\ - groupadd -r -g $GID $GROUP_NAME &&\ - useradd -r -u $UID -g $GID -d $APP_HOME -s /sbin/nologin -c "Docker image user" $USER_NAME - -RUN apt-get install -y --fix-missing \ - build-essential \ - cmake \ - gfortran \ - git \ - wget \ - curl \ - graphicsmagick \ - libgraphicsmagick1-dev \ - libavcodec-dev \ - libavformat-dev \ - libgtk2.0-dev \ - libjpeg-dev \ - liblapack-dev \ - libswscale-dev \ - pkg-config \ - software-properties-common \ - zip \ - imagemagick \ - && apt-get clean && rm -rf /tmp/* /var/tmp/* - -# Set the working directory. -WORKDIR $APP_HOME - -# Copy the application & scripts -COPY requirements.txt $APP_HOME/ -# Install requirements -RUN pip install -r requirements.txt - -COPY data $APP_HOME/data/ -COPY help_utils $APP_HOME/help_utils/ -COPY libs $APP_HOME/libs/ -COPY output $APP_HOME/output/ -COPY tools $APP_HOME/tools/ -COPY input_image/1 $APP_HOME/input_image/beijinglab/camera1 -COPY input_image/2 $APP_HOME/input_image/shenzhenlab/camera1 -COPY input_image/3 $APP_HOME/input_image/shanghailab/camera1 -COPY input_image/4 $APP_HOME/input_image/beijinglab/camera2 -COPY input_image/5 $APP_HOME/input_image/beijinglab/camera3 -COPY input_image/6 $APP_HOME/input_image/shenzhenlab/camera2 -COPY input_image/7 $APP_HOME/input_image/shenzhenlab/camera3 -COPY input_image/8 $APP_HOME/input_image/shanghailab/camera2 -COPY input_image/9 $APP_HOME/input_image/shanghailab/camera3 - -COPY config.py run.py $APP_HOME/ - -COPY configs/start.sh $APP_HOME/bin - -RUN chmod 750 $APP_HOME &&\ - chmod -R 550 $APP_HOME/bin &&\ - mkdir -p -m 750 $APP_HOME/log &&\ - mkdir -p -m 700 $APP_HOME/ssl &&\ - chown -R $USER_NAME:$GROUP_NAME $APP_HOME - -# Exposed port -EXPOSE 9991 - -# Change to the app user. -# USER $USER_NAME - -# Install requirements -#RUN pip install -r requirements.txt - -# Execute script & application -ENTRYPOINT ["sh", "./bin/start.sh"] diff --git a/example-apps/PDD/pcb-defect-detection/README.md b/example-apps/PDD/pcb-defect-detection/README.md deleted file mode 100755 index a29e1ef..0000000 --- a/example-apps/PDD/pcb-defect-detection/README.md +++ /dev/null @@ -1,117 +0,0 @@ -# PCB-defect-detection -## Note -This project code is forked from https://github.com/DetectionTeamUCAS/FPN_Tensorflow. I have only made minor changes on this wonderful and clear project. Thanks for their perfect code. I can learn and apply it to a new problem. The Chinese blog is: https://blog.csdn.net/dlh_sycamore/article/details/87886198 -## PCB defect dataset -The Open Lab on Human Robot Interaction of Peking University has released the PCB defect dataset. If you need to do experiments on this dataset, please contact the dataset author. - -You can download at http://robotics.pkusz.edu.cn/resources/dataset/. or https://www.dropbox.com/s/32kolsaa45z2mpj/PCB_DATASET.zip?dl=0 - - -6 types of defects are made by photoshop, a graphics editor published by Adobe Systems. The defects defined in the dataset are: missing hole, mouse bite, open circuit, short, spur, and spurious copper. -For example: - -![1](a(missinghole).png) -### Dataset Update -The paper of this project will be update. -However, the defect images of raw dataset are high-resolution. -With the respect of such small dataset, data augmentation techniques are adopted before data training. The images are then cropped -into 600 × 600 sub-images, forming our training set and testing set with 9920 and 2508 images, respectively. - -### Some instructions: - -1、This augmented dataset is privately owned, if you want to use it in your paper, please contact me. - -2、If you have questions, please open an issue, I will reply as soon as possible. If you don't have some difficult problem about this project, maybe you **don't need** to send me an email and add wechat, just open an issue. - -3、Of course, if you have other research questions like dataset and defect detection, welcome to send me an email. - -4、If you use our code, please access this paper and cite: -Ding, Runwei, et al. "TDD-Net: A Tiny Defect Detection Network for Printed Circuit Boards." CAAI Transactions on Intelligence Technology (https://ieeexplore.ieee.org/document/8757220)(2019). - -5、Chinese Blog: https://blog.csdn.net/dlh_sycamore/article/details/87886198 - -## Download Model -Please download [resnet50_v1](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)、[resnet101_v1](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) pre-trained models on Imagenet, put it to $PATH_ROOT/data/pretrained_weights. -## My Development Environment -1、python2.7 (anaconda recommend) -2、CUDA Version 8.0.44 , CUDNN=5.1.10 -3、[opencv(cv2)](https://pypi.org/project/opencv-python/) -4、[tfplot](https://github.com/wookayin/tensorflow-plot) -5、tensorflow == 1.121 - -## Compile -``` -cd $PATH_ROOT/libs/box_utils/cython_utils -python setup.py build_ext --inplace -``` - -## Demo(available) - -Then download the corresponding weights: - -For Baiduyun: [weights](https://pan.baidu.com/s/1rvHjihG1fL499SqU28Nang). code：shac - -For Google driver: [weights](https://drive.google.com/drive/folders/1oEHrEslnM5DBKB-610j-gO-x9-e90VwR?usp=sharing) - - -And then put the trained weights into -``` -TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights') -``` -It depends on your project path. Please read '$PATH_ROOT/libs/configs/cfgs.py'. - -``` -cd $PATH_ROOT/tools -python inference.py --data_dir='/PATH/TO/THE/TO/BE/DETECTED/IMAGES/' - --save_dir='/PATH/TO/SAVE/RESULTS/' - --GPU='0' -``` -After running this code, you will get the detected image in your 'save_dir' path. - -Why didn't you get the correct output image when run the inference.py? - -**Note!!! If you didn't load the correct checkpoint but the pretrain model, you need to change the checkpoint file (FPN_Res101_0117_OHEM/checkpoint), because this file contains the wrong path, and you need to change it with your file path!!!** - -## Train -If you want to train your own data, please follow this project: https://github.com/DetectionTeamUCAS/FPN_Tensorflow - -1、Select a configuration file in the folder ($PATH_ROOT/libs/configs/) and copy its contents into cfgs.py. Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/cfgs.py - -2、Generate train and test floder - -``` -cd $PATH_ROOT/data/io/ -python divide_data.py -``` -You should check the image_path and xml_path in the 'divide_data.py' - -2、Make tfrecord - -(1)Modify parameters (such as VOC_dir, xml_dir, image_dir, dataset, etc.) in $PATH_ROOT/data/io/convert_data_to_tfrecord.py -``` -cd $PATH_ROOT/data/io/ -python convert_data_to_tfrecord.py -``` - -3、Train -``` -cd $PATH_ROOT/tools -python train.py -``` - -## Eval -``` -cd $PATH_ROOT/tools -python eval.py --eval_imgs='/PATH/TO/THE/TO/BE/EVALED/IMAGES/' - --annotation_dir='/PATH/TO/TEST/ANNOTATION/' - --GPU='0' -``` -After running this code, you will get the precision, recall and AP of per defect type. - -## Some results -[the more results](https://github.com/Ixiaohuihuihui/PCB-defect-detection/tree/master/tools/inference_results) -![1](01_missing_hole_01.jpg) -![2](04_mouse_bite_10.jpg) - -## The Precision and Recall curve (PR) -![3](TDD_results.jpg) diff --git a/example-apps/PDD/pcb-defect-detection/TDD_results.jpg b/example-apps/PDD/pcb-defect-detection/TDD_results.jpg deleted file mode 100755 index 77de1fd..0000000 Binary files a/example-apps/PDD/pcb-defect-detection/TDD_results.jpg and /dev/null differ diff --git a/example-apps/PDD/pcb-defect-detection/a(missinghole).png b/example-apps/PDD/pcb-defect-detection/a(missinghole).png deleted file mode 100755 index b38fed5..0000000 Binary files a/example-apps/PDD/pcb-defect-detection/a(missinghole).png and /dev/null differ diff --git a/example-apps/PDD/pcb-defect-detection/config.py b/example-apps/PDD/pcb-defect-detection/config.py deleted file mode 100644 index 6a177f3..0000000 --- a/example-apps/PDD/pcb-defect-detection/config.py +++ /dev/null @@ -1,36 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -import os - -# [Server Configurations] -server_port = 9991 -# server_address = os.environ.get('LISTEN_IP') -server_address = "0.0.0.0" - -# [SSL Configurations] -ssl_enabled = False -ssl_protocol = "TLSv1.2" -ssl_ciphers = ["TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256", - "TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256", - "TLS_ECDHE_RSA_WITH_AES_-p 127.0.0.1:80:8080/tcp256_GCM_SHA384", - "TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384"] -ssl_certfilepath = "/usr/app/ssl/server_tls.crt" -ssl_keyfilepath = "/usr/app/ssl/server_tls.key" -ssl_cacertpath = "/usr/app/ssl/ca.crt" -ssl_server_name = os.environ.get('SERVER_NAME', "ealtedge") - - diff --git a/example-apps/PDD/pcb-defect-detection/configs/start.sh b/example-apps/PDD/pcb-defect-detection/configs/start.sh deleted file mode 100644 index 2513297..0000000 --- a/example-apps/PDD/pcb-defect-detection/configs/start.sh +++ /dev/null @@ -1,47 +0,0 @@ -#!/bin/bash -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Validates if ip is valid -validate_ip() -{ - ip_var="$1" - # checks if variable is unset - if [ -z "$ip_var" ] ; then - echo "ip is not set" - return 1 - fi - - if ! echo "$ip_var" | grep -qE '^(([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])\.)' ; then - return 1 - fi - return 0 -} - -validate_name() { - hostname="$1" - len="${#hostname}" - if [ "${len}" -gt "64" ]; then - return 1 - fi - if ! echo "$hostname" | grep -qE '^[a-zA-Z0-9]*$|^[a-zA-Z0-9][a-zA-Z0-9_\-]*[a-zA-Z0-9]$'; then - return 1 - fi - return 0 -} - -echo "Running pcb defect detection App" -umask 0027 -cd /usr/app || exit -python -u run.py diff --git a/example-apps/PDD/pcb-defect-detection/data/__init__.py b/example-apps/PDD/pcb-defect-detection/data/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/data/io/__init__.py b/example-apps/PDD/pcb-defect-detection/data/io/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/data/io/convert_data_to_tfrecord.py b/example-apps/PDD/pcb-defect-detection/data/io/convert_data_to_tfrecord.py deleted file mode 100755 index 17bb8d6..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/io/convert_data_to_tfrecord.py +++ /dev/null @@ -1,123 +0,0 @@ -# -*- coding: utf-8 -*- -from __future__ import division, print_function, absolute_import -import sys -sys.path.append('../../') -import xml.etree.cElementTree as ET -import numpy as np -import tensorflow as tf -import glob -import cv2 -from libs.label_name_dict.label_dict import * -from help_utils.tools import * - -#tf.app.flags.DEFINE_string('VOC_dir', '/home/root1/My_Work/Akraino/MEC_BP/Use_cases/PCB_Demo/Tiny-Defect-Detection-for-PCB/data/pcb_train/PCB_DATASET/', 'Voc dir') -tf.app.flags.DEFINE_string('VOC_dir', '/home/root1/My_Work/Akraino/MEC_BP/Use_cases/PCB_Demo/Tiny-Defect-Detection-for-PCB/data/pcb_test/PCB_DATASET/', 'Voc dir') -tf.app.flags.DEFINE_string('xml_dir', 'Annotations/Missing_hole', 'xml dir') -tf.app.flags.DEFINE_string('image_dir', 'images/Missing_hole', 'image dir') -tf.app.flags.DEFINE_string('save_name', 'test', 'save name') -tf.app.flags.DEFINE_string('save_dir', '../tfrecord/', 'save name') -tf.app.flags.DEFINE_string('img_format', '.jpg', 'format of image') -tf.app.flags.DEFINE_string('dataset', 'pcb', 'dataset') -FLAGS = tf.app.flags.FLAGS - - -def _int64_feature(value): - return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) - - -def _bytes_feature(value): - return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) - - -def read_xml_gtbox_and_label(xml_path): - """ - :param xml_path: the path of voc xml - :return: a list contains gtboxes and labels, shape is [num_of_gtboxes, 9], - and has [x1, y1, x2, y2, x3, y3, x4, y4, label] in a per row - """ - - tree = ET.parse(xml_path) - root = tree.getroot() - img_width = None - img_height = None - box_list = [] - for child_of_root in root: - # if child_of_root.tag == 'filename': - # assert child_of_root.text == xml_path.split('/')[-1].split('.')[0] \ - # + FLAGS.img_format, 'xml_name and img_name cannot match' - - if child_of_root.tag == 'size': - for child_item in child_of_root: - if child_item.tag == 'width': - img_width = int(child_item.text) - if child_item.tag == 'height': - img_height = int(child_item.text) - - if child_of_root.tag == 'object': - label = None - for child_item in child_of_root: - if child_item.tag == 'name': - label = NAME_LABEL_MAP[child_item.text] - if child_item.tag == 'bndbox': - tmp_box = [] - for node in child_item: - tmp_box.append(int(node.text)) - assert label is not None, 'label is none, error' - tmp_box.append(label) - box_list.append(tmp_box) - - gtbox_label = np.array(box_list, dtype=np.int32) - - return img_height, img_width, gtbox_label - - -def convert_pascal_to_tfrecord(): - xml_path = FLAGS.VOC_dir + FLAGS.xml_dir - image_path = FLAGS.VOC_dir + FLAGS.image_dir - save_path = FLAGS.save_dir + FLAGS.dataset + '_' + FLAGS.save_name + '.tfrecord' - mkdir(FLAGS.save_dir) - - # writer_options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB) - # writer = tf.python_io.TFRecordWriter(path=save_path, options=writer_options) - writer = tf.python_io.TFRecordWriter(path=save_path) - for count, xml in enumerate(glob.glob(xml_path + '/*.xml')): - # to avoid path error in different development platform - xml = xml.replace('\\', '/') - - img_name = xml.split('/')[-1].split('.')[0] + FLAGS.img_format - img_path = image_path + '/' + img_name - - if not os.path.exists(img_path): - print('{} is not exist!'.format(img_path)) - continue - - img_height, img_width, gtbox_label = read_xml_gtbox_and_label(xml) - - # img = np.array(Image.open(img_path)) - img = cv2.imread(img_path)[:, :, ::-1] - - feature = tf.train.Features(feature={ - # do not need encode() in linux - # 'img_name': _bytes_feature(img_name.encode()), - 'img_name': _bytes_feature(img_name.encode()), - 'img_height': _int64_feature(img_height), - 'img_width': _int64_feature(img_width), - 'img': _bytes_feature(img.tostring()), - 'gtboxes_and_label': _bytes_feature(gtbox_label.tostring()), - 'num_objects': _int64_feature(gtbox_label.shape[0]) - }) - - example = tf.train.Example(features=feature) - - writer.write(example.SerializeToString()) - - view_bar('Conversion progress', count + 1, len(glob.glob(xml_path + '/*.xml'))) - - print('\nConversion is complete!') - - -if __name__ == '__main__': - # xml_path = '../data/dataset/VOCdevkit/VOC2007/Annotations/000005.xml' - # read_xml_gtbox_and_label(xml_path) - - convert_pascal_to_tfrecord() diff --git a/example-apps/PDD/pcb-defect-detection/data/io/divide_data.py b/example-apps/PDD/pcb-defect-detection/data/io/divide_data.py deleted file mode 100755 index 64821e5..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/io/divide_data.py +++ /dev/null @@ -1,61 +0,0 @@ -# -*- coding: utf-8 -*- -from __future__ import absolute_import, division, print_function - -import math -import os -import random -import shutil -import sys - -sys.path.append('../../') - -from libs.configs import cfgs - - -def mkdir(path): - if not os.path.exists(path): - os.makedirs(path) - - -divide_rate = 0.8 - -image_path = os.path.join(cfgs.ROOT_PATH, '{}/JPEGImages'.format(cfgs.DATASET_NAME)) -xml_path = os.path.join(cfgs.ROOT_PATH, '{}/Annotations'.format(cfgs.DATASET_NAME)) - -image_list = os.listdir(image_path) - -image_name = [n.split('.')[0] for n in image_list] - -random.shuffle(image_name) - -train_image = image_name[:int(math.ceil(len(image_name)) * divide_rate)] -test_image = image_name[int(math.ceil(len(image_name)) * divide_rate):] - -image_output_train = os.path.join( - cfgs.ROOT_PATH, '{}_train/JPEGImages'.format(cfgs.DATASET_NAME)) -mkdir(image_output_train) -image_output_test = os.path.join( - cfgs.ROOT_PATH, '{}_test/JPEGImages'.format(cfgs.DATASET_NAME)) -mkdir(image_output_test) - -xml_train = os.path.join(cfgs.ROOT_PATH, '{}_train/Annotations'.format(cfgs.DATASET_NAME)) -mkdir(xml_train) -xml_test = os.path.join(cfgs.ROOT_PATH, '{}_test/Annotations'.format(cfgs.DATASET_NAME)) -mkdir(xml_test) - - -count = 0 -for i in train_image: - shutil.copy(os.path.join(image_path, i + '.jpg'), image_output_train) - if os.path.exists(os.path.join(xml_path, i + '.xml')): - shutil.copy(os.path.join(xml_path, i + '.xml'), xml_train) - if count % 1000 == 0: - print("process step {}".format(count)) - count += 1 - -for i in test_image: - shutil.copy(os.path.join(image_path, i + '.jpg'), image_output_test) - shutil.copy(os.path.join(xml_path, i + '.xml'), xml_test) - if count % 1000 == 0: - print("process step {}".format(count)) - count += 1 diff --git a/example-apps/PDD/pcb-defect-detection/data/io/image_preprocess.py b/example-apps/PDD/pcb-defect-detection/data/io/image_preprocess.py deleted file mode 100755 index 7d85395..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/io/image_preprocess.py +++ /dev/null @@ -1,80 +0,0 @@ -# -*- coding: utf-8 -*- - -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import tensorflow as tf - -import numpy as np - - -def max_length_limitation(length, length_limitation): - return tf.cond(tf.less(length, length_limitation), - true_fn=lambda: length, - false_fn=lambda: length_limitation) - -def short_side_resize(img_tensor, gtboxes_and_label, target_shortside_len, length_limitation=1200): - ''' - - :param img_tensor:[h, w, c], gtboxes_and_label:[-1, 5]. gtboxes: [xmin, ymin, xmax, ymax] - :param target_shortside_len: - :param length_limitation: set max length to avoid OUT OF MEMORY - :return: - ''' - img_h, img_w = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] - new_h, new_w = tf.cond(tf.less(img_h, img_w), - true_fn=lambda: (target_shortside_len, - max_length_limitation(target_shortside_len * img_w // img_h, length_limitation)), - false_fn=lambda: (max_length_limitation(target_shortside_len * img_h // img_w, length_limitation), - target_shortside_len)) - - img_tensor = tf.expand_dims(img_tensor, axis=0) - img_tensor = tf.image.resize_bilinear(img_tensor, [new_h, new_w]) - - xmin, ymin, xmax, ymax, label = tf.unstack(gtboxes_and_label, axis=1) - - new_xmin, new_ymin = xmin * new_w // img_w, ymin * new_h // img_h - new_xmax, new_ymax = xmax * new_w // img_w, ymax * new_h // img_h - img_tensor = tf.squeeze(img_tensor, axis=0) # ensure image tensor rank is 3 - - return img_tensor, tf.transpose(tf.stack([new_xmin, new_ymin, new_xmax, new_ymax, label], axis=0)) - - -def short_side_resize_for_inference_data(img_tensor, target_shortside_len, length_limitation=1200, is_resize=True): - if is_resize: - img_h, img_w = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] - - new_h, new_w = tf.cond(tf.less(img_h, img_w), - true_fn=lambda: (target_shortside_len, - max_length_limitation(target_shortside_len * img_w // img_h, length_limitation)), - false_fn=lambda: (max_length_limitation(target_shortside_len * img_h // img_w, length_limitation), - target_shortside_len)) - - img_tensor = tf.expand_dims(img_tensor, axis=0) - img_tensor = tf.image.resize_bilinear(img_tensor, [new_h, new_w]) - - img_tensor = tf.squeeze(img_tensor, axis=0) # ensure image tensor rank is 3 - return img_tensor - -def flip_left_to_right(img_tensor, gtboxes_and_label): - - h, w = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] - - img_tensor = tf.image.flip_left_right(img_tensor) - - xmin, ymin, xmax, ymax, label = tf.unstack(gtboxes_and_label, axis=1) - new_xmax = w - xmin - new_xmin = w - xmax - - return img_tensor, tf.transpose(tf.stack([new_xmin, ymin, new_xmax, ymax, label], axis=0)) - -def random_flip_left_right(img_tensor, gtboxes_and_label): - img_tensor, gtboxes_and_label= tf.cond(tf.less(tf.random_uniform(shape=[], minval=0, maxval=1), 0.5), - lambda: flip_left_to_right(img_tensor, gtboxes_and_label), - lambda: (img_tensor, gtboxes_and_label)) - - return img_tensor, gtboxes_and_label - - - diff --git a/example-apps/PDD/pcb-defect-detection/data/io/read_tfrecord.py b/example-apps/PDD/pcb-defect-detection/data/io/read_tfrecord.py deleted file mode 100755 index 8f1605f..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/io/read_tfrecord.py +++ /dev/null @@ -1,99 +0,0 @@ -# -*- coding: utf-8 -*- - -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import numpy as np -import tensorflow as tf -import os -from data.io import image_preprocess -from libs.configs import cfgs - -def read_single_example_and_decode(filename_queue): - - # tfrecord_options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB) - - # reader = tf.TFRecordReader(options=tfrecord_options) - reader = tf.TFRecordReader() - _, serialized_example = reader.read(filename_queue) - - features = tf.parse_single_example( - serialized=serialized_example, - features={ - 'img_name': tf.FixedLenFeature([], tf.string), - 'img_height': tf.FixedLenFeature([], tf.int64), - 'img_width': tf.FixedLenFeature([], tf.int64), - 'img': tf.FixedLenFeature([], tf.string), - 'gtboxes_and_label': tf.FixedLenFeature([], tf.string), - 'num_objects': tf.FixedLenFeature([], tf.int64) - } - ) - img_name = features['img_name'] - img_height = tf.cast(features['img_height'], tf.int32) - img_width = tf.cast(features['img_width'], tf.int32) - img = tf.decode_raw(features['img'], tf.uint8) - - img = tf.reshape(img, shape=[img_height, img_width, 3]) - - gtboxes_and_label = tf.decode_raw(features['gtboxes_and_label'], tf.int32) - gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 5]) - - num_objects = tf.cast(features['num_objects'], tf.int32) - return img_name, img, gtboxes_and_label, num_objects - - -def read_and_prepocess_single_img(filename_queue, shortside_len, is_training): - - img_name, img, gtboxes_and_label, num_objects = read_single_example_and_decode(filename_queue) - - img = tf.cast(img, tf.float32) - - if is_training: - img, gtboxes_and_label = image_preprocess.short_side_resize(img_tensor=img, gtboxes_and_label=gtboxes_and_label, - target_shortside_len=shortside_len, - length_limitation=cfgs.IMG_MAX_LENGTH) - img, gtboxes_and_label = image_preprocess.random_flip_left_right(img_tensor=img, - gtboxes_and_label=gtboxes_and_label) - - else: - img, gtboxes_and_label = image_preprocess.short_side_resize(img_tensor=img, gtboxes_and_label=gtboxes_and_label, - target_shortside_len=shortside_len, - length_limitation=cfgs.IMG_MAX_LENGTH) - img = img - tf.constant([[cfgs.PIXEL_MEAN]]) # sub pixel mean at last - return img_name, img, gtboxes_and_label, num_objects - - -def next_batch(dataset_name, batch_size, shortside_len, is_training): - ''' - :return: - img_name_batch: shape(1, 1) - img_batch: shape:(1, new_imgH, new_imgW, C) - gtboxes_and_label_batch: shape(1, Num_Of_objects, 5] .each row is [x1, y1, x2, y2, label] - ''' - assert batch_size == 1, "we only support batch_size is 1.We may support large batch_size in the future" - - if dataset_name not in ['ship', 'spacenet', 'pascal', 'coco','pcb']: - raise ValueError('dataSet name must be in pascal, coco spacenet and ship') - - if is_training: - pattern = os.path.join('../data/tfrecord', dataset_name + '_train*') - else: - pattern = os.path.join('../data/tfrecord', dataset_name + '_test*') - - print('tfrecord path is -->', os.path.abspath(pattern)) - - filename_tensorlist = tf.train.match_filenames_once(pattern) - - filename_queue = tf.train.string_input_producer(filename_tensorlist) - - img_name, img, gtboxes_and_label, num_obs = read_and_prepocess_single_img(filename_queue, shortside_len, - is_training=is_training) - img_name_batch, img_batch, gtboxes_and_label_batch, num_obs_batch = \ - tf.train.batch( - [img_name, img, gtboxes_and_label, num_obs], - batch_size=batch_size, - capacity=1, - num_threads=1, - dynamic_pad=True) - return img_name_batch, img_batch, gtboxes_and_label_batch, num_obs_batch diff --git a/example-apps/PDD/pcb-defect-detection/data/io/test.ttf b/example-apps/PDD/pcb-defect-detection/data/io/test.ttf deleted file mode 100755 index 4c22885..0000000 Binary files a/example-apps/PDD/pcb-defect-detection/data/io/test.ttf and /dev/null differ diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/Makefile b/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/Makefile deleted file mode 100755 index ab5e3d2..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/Makefile +++ /dev/null @@ -1,9 +0,0 @@ -all: - # install pycocotools locally - python setup.py build_ext --inplace - rm -rf build - -install: - # install pycocotools to the Python site-packages - python setup.py build_ext install - rm -rf build \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/__init__.py b/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocoDemo.ipynb b/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocoDemo.ipynb deleted file mode 100755 index c68f242..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocoDemo.ipynb +++ /dev/null @@ -1,272 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from pycocotools.coco import COCO\n", - "import numpy as np\n", - "import skimage.io as io\n", - "import matplotlib.pyplot as plt\n", - "import pylab\n", - "pylab.rcParams['figure.figsize'] = (8.0, 10.0)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "dataDir='..'\n", - "dataType='val2017'\n", - "annFile='{}/annotations/instances_{}.json'.format(dataDir,dataType)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading annotations into memory...\n", - "Done (t=0.81s)\n", - "creating index...\n", - "index created!\n" - ] - } - ], - "source": [ - "# initialize COCO api for instance annotations\n", - "coco=COCO(annFile)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "COCO categories: \n", - "person bicycle car motorcycle airplane bus train truck boat traffic light fire hydrant stop sign parking meter bench bird cat dog horse sheep cow elephant bear zebra giraffe backpack umbrella handbag tie suitcase frisbee skis snowboard sports ball kite baseball bat baseball glove skateboard surfboard tennis racket bottle wine glass cup fork knife spoon bowl banana apple sandwich orange broccoli carrot hot dog pizza donut cake chair couch potted plant bed dining table toilet tv laptop mouse remote keyboard cell phone microwave oven toaster sink refrigerator book clock vase scissors teddy bear hair drier toothbrush\n", - "\n", - "COCO supercategories: \n", - "outdoor food indoor appliance sports person animal vehicle furniture accessory electronic kitchen\n" - ] - } - ], - "source": [ - "# display COCO categories and supercategories\n", - "cats = coco.loadCats(coco.getCatIds())\n", - "nms=[cat['name'] for cat in cats]\n", - "print('COCO categories: \\n{}\\n'.format(' '.join(nms)))\n", - "\n", - "nms = set([cat['supercategory'] for cat in cats])\n", - "print('COCO supercategories: \\n{}'.format(' '.join(nms)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# get all images containing given categories, select one at random\n", - "catIds = coco.getCatIds(catNms=['person','dog','skateboard']);\n", - "imgIds = coco.getImgIds(catIds=catIds );\n", - "imgIds = coco.getImgIds(imgIds = [324158])\n", - "img = coco.loadImgs(imgIds[np.random.randint(0,len(imgIds))])[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Aow8/yiOPP0pbYyO33bif2ekJZtMLvP8Tn+bMG2/wtrvuYnI6Q0NDKwcP3M7h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\npGFMEkXIiBPI5/IwssglJgx9DF0lcIbk8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seZrs+RL45Tm5ymWi0zXqvywx/+OU89+yJ3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\n5Y3rXL92A280pmwYlGyHZ9//LN//7LMcHu6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FqiHOEoSpE/pFmYMMx/juqJCjjQJpub68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A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\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mdfUAaRxiOeolu721Q5ZlbKyv8+yzz5BnGe12G9uq0ERJq+Yh0lh1c0djTEvDkKhYp6yQ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/R9M0qtUqqqpiWdGQqo9EIkSjUR4+fMitW7e4desW+/v7NBstolaMiGHOg2zib8THvv32\n25yfndHrdnj27Clvv32ZbrfLwBlgRaOsrq3hOoGeIp1McV6psHe4R7vdIWpFKRaWKBVW8D0JCYV6\nrU4imUCd73IYDB3ef/8DJFkhm85wWjkNFsy4I/KFAp99/hlmxKRcKvKD73+PZ0+ecOXtK+gRk1gs\nQb/XYToZ4yszsrkss5nHytoKk5mHpMgcnp4wHg/x/TExyyCfThGPGty6eY1es0lpbYlirsDFjU2S\ncZvJeMTeq5eoCmTSGYauQ61So5gvkcvlmE58ZjKkUkm2dnaYTEdoikar2aHT6rF5cQvTshiPXVbX\nlzk9OSSXyzDs93DdIfVGhb3Dw2D82O9TXCnT7LQp5PM4jsPZ4QnZbIrxZMxo4uH7IKsKDx89Zntr\nk/6gSzQWZTqZkEwm6Xa7nJ6eYllRXr16RSqV4vz8HNOK4PlT2p0Oa+trtFttMpkMz54948qVK2iq\nzm/8xjepVOqcnVVwnCmqqqHpGl/72lep1ao8e/acTCaDZVl0mg18z+P09JhUOokzGuJNJzQaDQAa\njSbGHIG/ePGS7Z0t9o8PUDWdXDGPrulMx1P2d1/Rb9TwRw6FVAxNmTLzhlhRA1lTaDbbJJNJnj9/\njizLXH7v21/+Av69v/g/P/KnE/KZAq16l3anzeHBHkvFIrZt8+TpE5qNJsdHR8StKFHDpNtqY0Ut\nUqkUTr9PNpdjY2ODVrvFeDTi/OycnUs7gQpZkhk6A9qtLqoeQZIULm5uIssyN2+9y6e/+oR6rYZp\nRDg9OSaTTvDZZ5/yB3/wB/zyk08oFArk83lmM4lKo8JoPCWZzFCrd9B1k4HjBkhbkzEiJgN3hM8M\nWVFIRKMkU4kQmYriMZ0XTlkORF+i4Eaj0VCEJJCGmDGKwxQI0d5o5DKb+eHBLWhU8f+EwEoInXx/\nxmQ6DUVmovAqiow3P/RFARLCKXH4C+HUbB4yIuha4eddLOwB9TUIGwJBHws0J/zngqJfpNwF0hNj\nACEcE4euruvhtRTIUiC9Xq8XUulCHS1Qp6B/RREdjUYhba2qakj/KoqCZUXpz8VbwTXx8bwpsVic\n8WgYhsQIJGzNmYjFoBXRnI3nTRgEjcdigyWum/gZECrYRRFcFMuJYitU9Yt72sX1WKS5hXpc3DPx\n/HRdD2N1hX5C6A/E3Fw0XIsiOlH8BPsgBG7j8ZhMJhM+Z/HeSKVSDAaDsLESO9RlWaYxX7QSNKMe\nSCqargVaECCiykjSjAubF+j2emQymZDu1zSNoRPsAxdaAfG6VldXGY1GpFIp6vUGnU6Hq1evAYFw\nstVq0et1saIRXu2+pNNuc3J0TCaT5vj4GN/3yGZz+LMZUy9ovkTTXa3VSCRtzEgwejINi0a9QTwa\nw/e8ufhqFIwJeoHwzhk4uMMh2XSGXDZLr9djbX2No6MjDF3F86c4gx5vv/0Wqqpg6Dq+pDDxJjDz\nuXhhA3yP4cChUa0Sj8XZe7lHoVAI7F1GnLPjc6IRG103qFebxGM2Dx/cxxm5NGpN9vcPOT894+T4\niF6/w/HJMTvbO8RiCey5WjoACDGits3QDVIO/emUer1BNBLn5ctXZHN5fvXpp7RbDS5c3OD07Ijx\nnMnz/SmyKvPxx79EMwyePn1CNGFjJ2yYzYhFo8gepHNBCJYzHJFKZRhNxsx8QJoy6AWK+Hq9HmYF\niE2M4/GYUqnEz3/+c4rFIq1Wi3qjST6fp1FvMJ1OaLdb1Go1ZnO9xI9+/ENq9TqGYbK6vko0Ftg1\no9EYve6AWr3G4eEh2ly7IssKZsRiNByRSqWpVmtIMsTsBJOJTyKZZG9vj3y5yPalHY5PTxk5QzRV\no1Gps/9ql067Tz6bRVYUhs6QRrvFk+cvqdabbGxs8Pnnn3PhwgV+67d+Cym+/OUv4Pc//dlH56dn\n4MtsXrzMJ7/8jEHP4dXzVzx69piIbmDoBpVKhfLSEjOR2OUGCCGZTDGdTMnkckwnU9bW1smXCkiy\nzHf/+q8xIhH29g4xrRjNZgvN0NF0jZOTU0zL4tnTJ2xtbtHvdvnaB1+l2+vg+z6d+b7nq9evcXR8\nytNnT9GjCTLZIvtHR1jRONVmHVVXmEl+sAbU95khz4ukhB21UCTwmYWWJm88maPc0ZwS1bGs11Q0\nvPZrCxp4sRAGB+sM3xce59feb3FYS5KEOxxhmpHw8J2Mg8NoPJmgaUFRAglZnqeFya/z1EejEfF4\nPKTGBU29mAQn/gkL0iKVLURuwtokPNTCUiKU2iKdS2wVEzNVgZYFihS+adHECPS3iL4FohSPvVjA\nhMpa2LqE2EogSmHfCVBwMI82Ita8kQoakVQqheMEK21FLrhoRrw5IyKYBhE4AzBa2LUt2AExQhAF\nWsy5hYocgr3ZYpYtCrSwxImceFGYxWhlUZwmy3I44hDXVOwgf62BeD3uaLfbYSMo5o/ifon3pGAw\nZrNZWJjF31nUXYj3rFhlKzzf4n0qmrxOpxM8xnjC1J/hez6KLPH4wT2ajRpLxYBy9Pxgx7awwwXv\nmwDNN5tNHj58SDabxXVdTk5OaLVaSJLE3t4ev/d7v4dpmnz++ef82tc/pFgqgDTj6OgoeG+ZFnY8\n2HDYbDV4+9o10qkkSBK1eg1fgpgVpdFsBo2jMkNRZGQFMuk0R4dHrJRXOD87YffVC5KpBOfn59Sq\nVdKpNKZhcLR/yHiuKRhPJnx+5zY7l7aRpRmNegV3EDA3/synXq8z8SXKy8tk0il0TaXT7qIqGsgS\njVaLTrdPt9Oj1e5g6FHsWIKjoxNSdopGo8XnX3xBoVzEcUesr23QaXcYOgNa7QYPHz/k177+dTx/\nxrPnz9jc2mIwcDg/q1JcKpEr5KlUK7iuS7vdZG1ljfv3HxKL2WQyacrlMkOnz8Dp0+t12Lywyf17\nD/C94PP++Z27vHPtHTY2NjivnLG+voHT71OvNnAHDnYyhqapJJIp2u02njclkUjRaTWp1WqhpmMy\nmaBqBpKkcO/uHayISXmpjCxJpFNpDMOiWqlgRWPkshn29/dZXl6m3W6ztbPFyekxtVqFt65e5eTo\njOWVMnv7r6g36ly+dIlWq41h6CwvL2NETIyIScyKsbu7h+f5rK4GmeitboejoyNGUy9oDqdjdg/3\nidkJYlaU4WBILpPlRz/8EWYkSjSVQo9ESGVSDIcDLl1+i1QmjSKpwQZIWabT7sBsRm7j74GN7IuP\nv/cRkkunW2N39wWZbIZkIsrMn1Is5LEsE0mCdCpNzI5z++4drl6/xngkxF0uBweHyIrK3Tv3GE8m\nNLttTs5Oefr8WRBDOBozcFw+/sXH3Hr/PcauizfzuXvnNtevX2fsDNF1FU1VababFOfChdF4jKRo\neL5PfzAkmSnR6w1QdZ27Dx6QL+aDdCNDxR2OiMVtJlMfVQsOy0zKDuIJpdc2L28yndPNr1HXdBoU\n1UgkmJcKZCOQXHD4S3heMK9eLHRIhKEcAjkPh0NUJYh9FN5lTQuaATNqhapuWX49i1bnxUFQ0cE+\n3dfWMoHOBCIXHzJB2wshk6B5RTEVOeS+74eUsxBSiQIqbE9ASDVnMpng8A6v0esgl2q1Gha1RYGb\nyOAG3hCzLSL3RfGceOzBYBDO5oNNTsb8us6YzKMgBR09maN3sXtalmX0eXMlNp4Jans2m2HPZ/kC\nmQsqWzRCr8chemhdWwxuWVxOIoqnKNiCWRHhEOJxRqNRGEYjmJl+vx8K1UTB/f+zBIprLRoxob9Q\nFCXcIiaaKnF/BfqNxWLhdRSzb9FMCKbBtm06nU54WBuGgTPos7RUxh0F8cBRw8CbjLi0s0N/0Auv\nq2hiVVWl0Why7do1SqUSqqqGdjThXR8Oh8RjMeq1CkdHh8x8j6PDfQaDLjeuX8X3fT54/32ePXtK\nu9Wk1Wrxm7/17cCG6Ax48PAhqmFw7fo18tlcaJPTIwr1epW4FaNeb2DOk7pMUyeXTTEZu+zv7dFu\ntYhFo/gTj5PjIwrZLKPJBN0yUVU58MJPRzDzMQ2DSq1Ko9VAQmFl7QJRK4oiwenxMbpl0R30cUZj\nDCtKdzAgm82TzeXp9SpYpobvTej3+xRLSyiaRnF5ZR7a0mU28zBMg4gZ4eLmRdrdNt1+j1y+gKoZ\neDOfa9euU6nXqNXrzGRQkEilEiQTKUbulG63x2Dg0Ot1qNdqzKQZO1sXMfUo62sbRGMxZkDSThCL\nxihks2xub8NMwoyYKJKMHU8Qt+fskm7Q7fawoia1Wh1FkbFtO7zHM4RuY8ra8jIiXVF8foVltVKt\nEItG6S6ExshKEHvdH/bwvBnJVA5vOqbRrDGZjIPNjTOJTqdLvpAjYkbQjYAFvHfnPjdvvssnn/yK\ngeuwsrKK5/lUqlXS6TSe7/OXf/4fWSmUiEZMti5s8sXtL7j41mV6I5eVtRXSGRtVV5Bkn/FkyNh1\nA/eK53Pv3j0ajTp7e3t89du//+Uv4I/v/OwjaSazt3fIZDKhVCxQLBXIFjKsra6wv7+HJAX5tvVG\nHVVTAQkzalFtNjivVSiWigycIZqucXRyDJLM5tYmrV6HRDrNwHHRDZOvfvABjWYdSQErGiWbzZHP\nBik9ibhFvXpKMhVnqbyEGUtwYWsHSTUYjiboEZP+eMRwPGbqBfM0XTOQJZnxaE5Re1NGwwGJmEU6\nEWfijuh2e5gRnfFojCprGIbOZDLCtCKMxiMg2NRlmhFkKSjSiiIHytQ54pzNPPyZh6ZrwAxZkXGc\nIbqh/X/QjziYFVnDm/qMx1MkZAwjgu97DB0nQN2yjMSMfq9LfH7wiuhTUTxEzrewBAnqXCBDcfCL\noivm9BDMlcWMU6BHIUQTvyvGA8L+JObswjssaHXhSxe0tSjEIkdcUOwQ0KT9fh/LshgMBuFrEQxC\nvV4Pi7woTOK1Bih8vspUkfG9IKkpalmoiszM98LHEa9dzLklIGIYr+lCScIZDNB1FVmWmE58NP21\nrkEg+PQ8dUso+UUTItgDEcAivkSx7HQ6r9mW+RhikZYXtCMQxAXncqElTFDZIYMwL7QAlUolDLQR\nYjMgZDzEfY7H42GDINgYca0F7QyEjYC4vqIJE/8/cGbM8HyPZNzm7PCQdMJmMh5jWBFGkymj0Zhu\nu4szcMgm0+TzBTKZDO1ui9PzMy7tbPPsxXNKK0ts7uxQKBZp1hsYqka33aWQy5HPZojGtLnKvMrE\ndZCZ8WrvJcfnx6SyKVx3hB2Pk8qlufHODfK5HJWzcyRJ4vDwiJE7pXJeZbW8gqHrTFyHk+N9ut06\n6YzNg/v3uLy1zXQ8JqpbvLXzNo1aIxgNzKYk7SQX396k73T55c9+SkRVMTSVsT9k9eIWpfIyXcdh\nfWmFfqfL2dkZfXeI446IRG2mM5lMsYSmBaFVkYiGaVjs7R9gRCIUS3lu3/6cycyn1ulx+OIVmqLw\n//zVf2T70jY9Z0C2WOSLO3f54KsfoukGnX4PMxolErNodTqkUxlOjk8pZjP4kwnHhwc06kEAytCd\nsLJ6kZHnoasq6WSKCQqSZmJFg6Kla3pwnvgz+u029+/cpZgvkEynkCSVVqdLq9nE9zxOjs7A88nn\n0gwGLtl0nqE7xhlPiSXSWDEbDRV/MsIyTeLRGINen6VSiV6/R66QRVEkZkzJZFNUqhWsqEk+V6Tf\nGzDouOSLRbqdHppuIKka+VKJRqOJZZlsXbhIrV5F1RUqlRrxeIKVlQ1ajS53v7jH+oWL7B4esLy2\nzq133mEytxB++N4HxE2LdrtNxx1Qr9cpZjMYtkkhG2M0dsnmMsiKzsgNGqux65CMJ/jxD3/CeaVG\nMV/mK9/+vS9/Ab/7ix985LpuQH3nikynPnu7u/ODyuDhw0eUSiXq9TrNZpOV5VXq9XoghtF0avU6\nQ8fBjBh87cMP6HTapLJpnjx9ylKxzPLSKnYsycrSMh988BX0iEatfsbIHeF7E54/f8pk7LK9s8na\n+irOMPCtHp9XMKMxzs7OGU/HTCZTGq0WmUzgi4xGo29YkYRwKJfLhfPiiG7Mi8UssE1IMpPJGNOM\n4HlB0RKzYUmS6Pf6c4r3daZ0PB4sNJHkALUK5XYymUDX9VD5uziH1nUdTdXfCGwJ6NbgkPZ8L0Sh\nYqYq5pQQFCch6hLrIxdT2gQKE0ir1+uRSCTwPI9utxvSxyJ4RiA2QXULRLyYeCZmuKJAtdvt19a3\nBeQv5vCi6C4qnMXSDdu2iUQiWJZFPB4Pn7/QIIgiLIpcfL43ezHVTqjdA5W0+8aIQ6Bb0ZwIJLpY\nwMR8dzQK7vcMQkZBxJV6nhcWS+FNF6MCUaBFYyXGFWJGLSjtRU2CQOCyLIc74sVzFqyOGDsIZmNx\nk1ir1QoT/cRmtsWYWxH0IlgLTdPCQi2ocdEoiXuqqirdbhd4bRdcZEA0TaM3GKBrKuPRmNHQYdDr\nITGj2WpiJxKsrq7iDgLXg8+M58+fUy6XqFQrPHzwgGazyeHBAbadYGP9Aq9evWI0HPHxzz9muVym\n0+kiyzLlcpn9vWNOj8+5eGGbF89fcXRyzB/+4T8hl83PPfpqKDwdDAakUilu375NPp/n/PyM0WgY\nhMi0muxsb7FSXmF1dZWDvUOmUz+geDNZEqkMjuvyau8VM8VDUgDJ5+TwgId37pFL5clmyjx98ZJs\nLk8sHqPRqJHJZLATCQ72D5A1hU6vy+ryGtPpeL63XuPgYJ9E3GLkupimznTiBYWq10OWZFrNFr/6\n9AuWS3m2tjbRDJ0b79xAkWVajSbLy8usra6i6zp7u7t0Ox18zyMRT+C6Q0zTwul3efr0Cc1mk77j\nsH3pMleuvsPUh0G3Q7lcpNVqcGH9IscHx9TrFaJWsGZXAsrlMn/2Z/8Xf/4X/zeHR8eMRwHb1O0F\nKW6ZTJb9/QNyuTx2KslkMsXHR9V18oUiztBBUWSm0zEJO4mERLVaIx63efz0GaWlIjMCAWc8brOy\nshqMfqw4mUyGer3BZDLFnY5JJtLcuXeXXC6LLEv0e13yuSzNeX65OxqgaQq1aoXy8hK3b3+B70+J\nWBFW1lZZKpcZ9Pq0Ox3sRApn2OfpkyfBGW+atJsttrd3cKdjao0ayUQKVVaZDifouoE3nXFWqaAY\nJvV2j6XVDQ5Pz/ntf/iPv/wF/M//7H/7KJ3NkkplGDgOg4HL4cERz589J5FIoCoq6+sbHB8dk0ln\nuH//fihomUwm6HPRzObFi/zoRz/ENCNMpxNMyySRSOH0XV48fY4di1OrnQEesWiEjfU1ctk0H3zl\nfT784Kv0ej2OT07R9QhGxGQmyVSqNSQlUMh2ez0mc4QkFNZiriuKn0A+wgKlzZFTLBYFJIINYZPA\nAmGZIXX7OjDFmB/C4mA1cZxBIG6JGHMrmYZhRMIFAYJSFXNHcZBPJt4bUaNBpGZAxzP3fgvaWxQE\nIfYS4SBCkDQcDsNZt0BQgkJdnKeKebegeuF1kIxoUjKZDOl0OkRmgpoX1L8oAvF4/I1CI1TxArGL\n0YKwYS2qzAeDQRggItC7KNzi3gnWQFDEQigmHkM8p8UMb0HRCfuWuHciLlVcH9EMiOYCXgsMFUUJ\n58ei2E+n02Dv8rxgimssmAvBOAi/uPi9RVGZQNJi7m2aZqj2FwyHJAU73gV1LoqxsByKXPjXdkUt\nbGCA8HksriBNJBJvKN2FnVCI24QWotvthk3g7u5u2CSZpslk6tHttJEliVTCppjNkkomKC2VkGSZ\n58+fE9GC9/H169eJRCIMBn1OTk+D12FE2N7ZZv/ggHQ6w49+9CPisTg3rl/HGbpIM9jd28OOJ1ha\nWiGfK7G7e0A8leLK21d5+uwJR0dHVKtVABxnOHcejMPRg6Zp88/AjEajQTabQVM0KufnPLj3kEa9\nSdpOkSuW6PYGHJ+cMpNmTJmgWzrX3rmMrEoMm32a1SajoUS7O+EnP/kFM9nn8OA56lzYqUoq2XwO\nZ+jS7QfNsaJqOMMA6JwdH7NcKoDvMxj0SaYyQWF1Av/1xsYGyUSKy9tbJBPBLmx3EAg4VUXh4sZF\nnIGDqmioqo5hBDvDZVlmMh4zHU+Jx2KkkykkGbKZDKqukUyl6Qx6RFSFuB3j6YunmIZFKpni+OiQ\ndqeJ67oslZcwdINWu83W5iZ7e3soisLq6irjyYijkyOu3XgHZzjk4PCQ1dU17GTgKomYFo47JJnJ\ncHh8RMZOs7+/G+xXUFRevHzF+XmF7Z0dIhEDz5uQTmeoVqshSLl37z4A2WyabCHPvbv3KJeXME2T\naNSicn6GLEmsrS3z8OF9SqU8qqrQbrcDC2sihj/zyReLKKqKBNRqTUbumCePHtNs1UkkElhRC90w\nqFXqlEpFKpVzlgpFsskUTx89xnVcZEnm89u3KZVX0cw4L/eP2T045bRS57/6p//dl7+A/7t/88cf\nnZyfs390xEySGQ7HrCyvUCwWSSQSOM6QyWSKoqgsLZXnHukpMgrJVIJeLxCf+ONJsC83amHZUbzp\nhHw+jzzzSKds0mkbz5tSr9dI2AlWl1eIGAb93oCf/+KX+P6MWr2JLGvIqsbAnTIjSNWq1Bt4sxmF\nQrB0fjAYhJanRU+taVmYc1QNICPNKcsBvV6feNxGliXi8dhcREZIAeu6Pp9b6/PtYsEaTvDmhXEy\nfxyd2cxnNvPRtNcBJeLgF4ezJL1eFCIKgqYFKEpRlbDATiaTsOEQCFIUN/G3BHru9/s0Gg3S6TT9\nfj+w16TTeJ6H4zgMhkPsRIKp582T54ImK5VKhfQtEBZz8bpF4RJqYoGU6/V6OJcV9LCY9YuisYic\nxesVqFE0OIv+caHcFoVONDyqqhKPx8N5rUCR4udiNicKtvjZolhOkiS63e4bme3CWiWU1wGj8jq9\nTKB5ca06nU5Y/MWMWETDipGBQMBiz3YQKfraSy7WxgLhc5tOp6FlTzRZgkqHgIlpNpvh51I0R8JB\nIASNi5YwoX1YbCQFGyGukxo2sbGwARDXWMzm47ZNp9tBkSTW1Pry4AAAIABJREFUV1c4Oz7mwf17\nZHNZbNvm7bff5mBvn2KhQL/f5/T0mFwux9n5GaVSgUw2QywWp9ZsoOkGV95+m4HjkEmlGI8nKLKM\nYeg063VUTeP09IR8PkfcjmFZEaKx4P198+ZNjHnQh6podLptVDUQH21tbQWpcYkEnU4XQzcZuS4v\nn7+g3WpzcX2dzc1NDvb2+au//Cvc0YhUMkXEiuA4PU5OD6nVOpzvV9jbO+Tf/fvv8YvP7qLoCqoB\na+USS6US2VSW8cTj6PgESVZIZdJohkG1WsOfyfz4Jz/l5o3rPH78ENOM8OjRYzK5IqYVIxKxaLXb\nGIqKO+xSrZwTi0XpdTvoeuB+mc31OM5wTDQap1KpBmKtVpvj4xNMK8rPPv6YS5d3WFte5uzkmFjM\n4uTkiP6gR8K2UWSVkTfGGTlMnDGV03MkWSJXysMsEBw6wwGZbIbtnS3e+8otkCFuB/u+kaC0tESt\n1iCdyRKP2+TyWfZ2D4Ic+GngQTd0k3/+P/5zyuUSr3Z3iUQsOp0uX//6r2NEDLq9DpubWxweHuJ5\nHu12m2KxyNB1UNQgW3/9wkXuP7hPoVDk1f4ev/71X0PXNFaWl/jZT3/KYODw2Se/wtAtEskUn3zy\nKZpuoqnBNd/bO2SptEKz2eL09Izr129wfHRAsVgkk8vx9Mlz3JFL0o7TqFX46d/9kIk74utf/xp/\n93d/SyoTLKjxZlAoLfPZZ5+ztXWZd2+9x/X3f/3LX8Bn/vCjZrvFeDJhMvWonFV5/OQha6tr/Oxn\nP8WyLB4/foqm6QwG/QB9j4ckEwk63S75YoGoZZHJZlkuL5HNZmnUG9y6dRNNhcm4x1fevcIXX3zC\ng/v3WF29yMlRhb/48+9Sr7V59OgFxdIKzXaf4tIanYFL13Hp9QfIqoo8TwuKGFHAD+k/4SMWoqCY\naeHOxUPC7uJNpnNbUITxeAJIGPMtNePxKFw8IQ7y3txv7LpDJpNgt66g5lVVw/c9fD8I6NRUgxmz\nsBCLw16gaklSwp3VAumIlaQDZ/AGClxcWSkKkfDruq77xoy0VCrR6XRQ1SDH23WGKLKMrmnM5oK4\n+BytD4fB7lsRDiMea9EWJ9TK4jFEYwTBylZhxwJotVphqpwoGqZphsyBoJDFYg1B5YvHXkS2gjYW\nqFg0EYqihMs2Ful7SZJCqlsUQDF/FjSyKK6LSvuh42JGTCbz+x+JmLjuMHzv9Hq98HUIv7VQuYvX\nLYq3eH6iQIr7ZpomqVQqfN6LanBh59M0LbTZCcW72Hrm+6830AFvLCcR/8+yrHD00O/3Q5+usAB6\nnhf64MU9zWQyIc0vRIwijQ4Iv//i2XMu7Wzjjlzc4RDXHfKb3/om49EIfzzFMiJYpknKjrO2ukIy\nlaHXD5ri8soa+VyOVqfDN775Tf7Df/hzioUiF9bWA5pfUUimbAxDRVam+Izp9hqk0jEs06DXbaOo\nWrBz2jR58XKXhB0PdCeej67ppJJJCvkC9+7eRZZUUskknufTbDQoLy+Ty2Uplos8evSIhB3HcQYo\nSFza3qHT7BCNmLi9EYX8BfruhFpnyIff+AallSx/9Ef/hK++/xViZhxnMKJ+XmeKxHQ6wzItapUq\nDx89YXtrm73dPS5d3mEydjmvnKPPm5VGs8WL3X0SqSTtZgNJ9nn44D62ZVOv1Wk0WzQbDR4/ecKv\nPvsV773/Ho8fP+P09JRcLo8kybRaLUzT5MKFCxQKOdxBn3g8OJM0VULTVTKpFNGIwfP9g2DZi6Lx\n4x//mPff/yoDp893/+av+fqvfY3ZbMbJ+Tm5Uh5NV0GWsO0YdiJBOpNhNPFYXd9gPJ0SsSwSqQz9\nTiBCc0cjFFXl7PScGVAurSBL8M1vfotMNks6kyZi6UymEzqdLpXzKqVSMWw2gwZ9Rq1WpVjMcefu\nfW7dusnxyRH4EnY8RbvV5OjwkJ2LOxRyRZbLa/zt3/yYq1dv4fsGD+4/5cqNW+TyJUq5JcyISbfX\nYWNjA8uyeGvnKrFYjEa9yvWrb7G0VKLRbHH06oB/9Lv/gKfPnuNLkM5nyJdKDPo9vPGE87Mj1srL\n5DIpjg/2+ODb/+DLX8D/5F/+Lx9NpxPiVpTqaYXLW9uslJeZTkbcuHEDCOJDFUVibW2N8dhlc/Mi\n5aUy+wd7TCYTlpaWePn8Bd//ux8wcAZcvHiRVrtNu93FjEQ4PTnl9ue3WVpa4emzF5SXV2i225i2\nzdLKCrKhEU3YPH3+lHavh6woqJqMbSfQNJWhM0DVNFK2jaaoTEZjlkolvOkU04gEu73dEdPJBFmS\nmPk+3mQaLvpotZoYRgTDiKAoMo4zwDD0ENEJe49hGIzGI6JRi2gsimmZjMcjfF8UGhVN1eZJRCMm\n40mgXJ+BhMTIHSHNt1y12503VokGNqoA5Y3cEdFojIgRLNKYjCdEjEio2I9FY/R7fTzPx4yY9Lo9\ndN0gakWDx5JkDM1AJvBOepMpI3eEZVok7CSdbjfwmssK7vz7U28aUs7dbjdUTguUJlDyIiX9nyqy\ngZDyFchPKKZFIRGFT6BYgW5FM7K4GKTf74dzehGgs4hyBaIUQjeBjoUQTKB+gYSFGK3T6YTKcllW\n5tSxjiQFxS9iREJGJRqNhop9gYoFNS0aGTECEMVQWOLERjZFUajX6yGyFY2SQMulUikcOfi+Tzab\nJRKJhM9XjHKEAFFcc/E6BDMhfPniegmK3HVdUqlU+HOBtMVzF4tPhCe/1WqFiNzzPMbumLgdp16v\nk8/niBg69UqFyukpvuexsb5Ou1FnPB5zdHSEbkR4/uIFxWKRWzff5U//9N+ws71D5azC25ffwjIi\nPHnyBEPVqNVqLC2VabUaGGbQ9PS6ferVKtvbO9h2kl6nz41rNzg5O6GQz+DN36t7e3uk02l2d3fZ\n3d0lmUxiGBqVs1M67RalUolyeYlOt0fENDGtKP1Bn/LKMhc3L9KoN9jZ2WZvbw9D1YhFY6xtrLG2\ncoGIriL7DroKjx8+Y29/D9dx6Xa6QfLZ55+yt/uKdDLN4cE+/nRKOpUglYojSwGtfX56SrV6hqpr\nOM4AXdWQFDg5OePl7h43btxk6s/Y299jf3+fs/NzCrk8H3z4If1+D/BR57ntI3dIrVblwsYqn3/2\nKd/+1jdwxyPito07nTJDYYrMeArD0QhN1TFVlf3dAzYvXuTo6JBcNsfq6jqypOLPZhhRE9+bBWzc\neEKj2aS0VEaWNcbTKe7IJV8oIssaqjzhxcsXFIsFvFnwubXMCL4/48WzZwF7NB6yu/uK8XhEMpkK\n2MBMhle7e0w9n9F4QiqdRpZV7j94wO7uPt/4jV8nadt0Oj329o6w7QTpdApZmnF6ekomm+GLO/eI\nWBbZQoHzaoWbt96j0W6gSDLSbMp4PMSMB57/WCzGeb3OeOIwHnQZNpt4oxErW1dYW9vkF599yslZ\nheFwxN9+7/tsXthk0Hf43ve+RyqXZgacVWqUSktcef+bX/4C/m//1f/6kaFrXL1yBVWWmI4nPHv2\nFMPQME2L09NTTDPC5uYmn332GZlMmhke1UqV977yHrVaDQBFVbiwcSHY2z0Zc3pWwXVGxKIJbn92\nm1JpifLyKrKi0ul1WdvcJJnJ0u52GU8nnFYrKFqwZEIzNLR5XJ8sS8iygu9Nw9msWIghCpDv+xi6\njhkxGc9tRyIrWpIkbDuOoqhB4TP0+d+UwsjQSCRCtVp9wzYkZqYBWiUUBfX7Trj7OPCPewyHLrMZ\nuO6ISMTE9wNkI1CmoH+DebqKZcXwPJ/BwEHTdDzPx/N8ZFkJXqs/Q1HU+Sx+jKpqwarQ+eMEwgwP\nSZKZjoOYSkWSmSEFIqC5eEs0Le12B3/mvZEuJr4W57YiAnUxXlYo2UUBEqI3YY8TBUzM8cXffj1/\nDpCeEKqJ4qzrOul0Opyni8cVCWTAGyluguIX11JQ9IIuF68rHo+jKEqo0va8oElrtYLd79FoDLFK\nUdj+FtkC3/fDJLR4PB7OlBcpZxFTKn4mBIRiNr0o/hPPTyB2IZwTr0vsBBf3Zjweh3YxoSkQwTCC\nARDXQmQbLNLthUIh9PYDoQ5DoH3BoAgk3mw2KeYLqKpC1LbZ39+llM/Ta7e5tL2NikQ+mwmYBVlB\n1VQKxRLNZpNsoUC9UuNg/4BsNkM2myWXzTIdjYkaJjNm4fM3jUhgF9IM0qkcmqqQSqbZ3zug1e5y\n+/YdcoX0/DMvh7768/Nzrl+/TjKZpFQqc3q8x/HxId/5znc4Ojrkzp17/Off+V1i8QSDocPdh/e4\nsHmBWq3G3v4ezGasLC8Ti9vkswm++9ffxVAtXj5+RNrWqVTOqdZ6KKpMNpPh1cuXGKbJw4cPsW2b\n9969yQ++/3c0m03+m3/2XwOBHc7Q9Pla0QjjyTj4zM/HSR//7BfcfPcraHM/8unpGb/9O7+Noigs\nryyRz2VRZQXTMNh79Yq3Ll1i79Ur3rv1DqoiY0Z0vPGEw+MjpsyoNdrkl5aZoSIrJtlsCiYeZ0cn\nbG1tUiqXGY/G2LEYvZ7D+fk5qWyOeCJGbzBAQmI69VAUmfFoSjqb5eDgACsWZ9DvB03uqEu/351v\nWdSQFJmff/xzNFXj4toGh8f7GIbOL3/5SxzH4dq1q1SrwbpSO55ElhUiEZOoFUNRg+jqWCzO+voa\nT589Jh5P0mx2uHDhIufnpxh6YMHtD/pYiRhvX71CPJkAGcyoxe3bX7C2WiZpx/H8CT4Sjx48otPq\nIkc1KpVT3rt6iYeffgYz+J//5f+Oose5fO0KhVKJw/0jdjYvcXZ6Hoh3JQkzbpDOFvB9ie/+zff5\ngz/8oy9/Ab/3xacfRc049+4+pHJWIx6PMxq5XL16hV6/w2DQI27HODg6YIZPMpngs88+p9vpsr+3\nF0QydgK0WSwUsKMxKrU668srlIpF6vUKFy5e5OjojIPjE2rVJmokQjKVms+ZJDrdDoN+H03XUTQN\nWVKJ6BFMw6Tb64X7lgVdLma1ggYFmHrBbFmkbbmuSzabDb2MwVx2iu8H9KsZMcOC2u12Q9EREM5d\nRVHw/RmaZtBud0LBmudN0DQ9RGdCfS6+FhPYBH2saQEiFAc3EFp8RqNRmDct1MVCaS+QrrBrTccB\nDa4prwNQRvNdz/3BAEl+vftalmVkggPbn8/931iuMkd2YtY6Ho8DpItMt9OlP+iHRXdRrR7YyFxU\nVWEyH1UIEaHwsZumGf5NIfISAq1FIZuYMS9atoSoTszqxfcWmwRRvASSj8fjdDqdN0JrdN2Yz+2N\nULTX7w9I2HawjU1Vic3HLkJpHnrzFx53cQ+3uEdiv3ar1Qppe3i99CQ698cKdqff74eFVFjEBLJf\nTNdbtLOJvydEgqLhECMCMW+3bTuk70XinVhwIp6zuF7tdjtkLRRFwen0GE+mJBI2vV6biTuk22rz\n7vV3MHSNg+NDapUquqahaxq5bIaz8wrPnj6lUCxw892bKLLMJ598gq7rJO0E6xvrPH36FF3Xefbs\nGZ7vkUkH6uR6vc7u3hGaZhC3E5RKJYrFPLIkYccTPHhwl6WVZTTLpNFtY8xdBCNvzPala3QHI7Yv\nX+H5q31evNhleWWNf/2v/jWffvoF6WSWkTNm7E4oFcqosoJtRxn0WmTyGZ48esnz589ptBpM/BmK\natBonGOZJrad5Oy8yru3bmHHYmRSaZ48eUY6FWdjY421C+uomsrQGdFzBswkUCQVwzRxnCE/+8nP\nMCNRtra2WFlfx+32GTpDXrx4ScyKEo+brKwuBwmFowmtZpPNra1QgOnNfKaex8AZsvvsOd1Oj4Sd\nQtJV7EQSXdNRpRmGGpwHL56/4MaNdzg5OUZSFPqOQ6/boVGr887Nm4xnM7zRmH67g2FG8DyfXCHP\ngwcPGA5djDlYaXda+N0Og55Dv+2wWl7l9OiI6lmF7Ytv4QwmNGot7HgCCYliscBSaYmZJzHzZqQz\nGQaDAefn50hyAFgODw+QJIlyeYVup8OFi5t89vkddN2g2+1gRU0imoYdjzMcj8gVisTMKJ12F2/q\nMej1Odg/IBGP0ew0OTk9oVDIIuFRLq1zfnjChfUNHj17wv/xb/89PcdjNB3SazVwnB75Yo4Z02Ah\nT7PJW29dolKrkU1lKS8tE4/b3Pr673z5C/if/sm/+Ojunbu4Q5dYNMaFjQuk0oEiNzD5z60V8Ti3\n3n0XTdNIJpN853e/gz8LEp76/T5bW1usrqzwx3/yJ7zz7jvsXL7EvQf3GU+njKYeaxcu4E48llZW\nWVpe5qxSC95ERoTJxKdUXmY0clGVgIodu4GiM2Ka4TxYFAih7BX2HnFwiQNqkQIWc0KBrDwvKODu\nKEBYJycn2LZNoVAIBVnicBaFd+bPQiQnikckYlKv10MaUhQfUeBEIVvcVy0QlPi5OKTF98XhvDhv\nFd8Xs19d12nUG0FR1fQ3lMaSJDFwHEzLDGfUEDQ3njcNrXDi4Pe8IK/9P037ms1muCOXbC4bzmwX\nkblQiVuWiarqIVJe3N8t1OaLOeLioBIoUtxL4XUXqW2L/nORPy+K/qJgS8xwReMly3KoEh8MBnP/\nvTG/pkG62WuaW2I4dNDn97TdbodFfNFTLTQNgsqfTqdhxrhwC4jxw+LWNaEeF+JBIUITGg2h3m82\nm2HErXg88X4WDUskEgnT7ATjJN4TQmG+KFwTf08o1Hu9XjjiGAwG4bUWq0UlH9LZLO7YRVEkdEXh\n7PiEQa9LPBpD1VQ++/RTjg6PsCyLdCbDD370I5LJZDiS6fV6HB8ccvnSJU7n6vSzs7OQ3YnHg01h\nkYhJs9lA1w1arRa3bt3CMAweP36ErEg8efKEeDzK1PfoDQbkCwXGoxH1Wi1Quh8ekS8WODs/p95o\n4Etw7fp1jGiE9Y0NBoM+sVgM1x3iTz1mvofjDNjc3uTzzz/FccYwkwLvcKGEoipBrKoR5KenUkme\nPX9EoZAnkbB5/PgxN268y/Xr72DbKXZf7LG2soYqKdQqNexEnMHQAUnGTiRJJtPEYjHiiSQx0+Ll\ny5cUi0VevnhOeblELB6n3W4z86Hb75FOp4O91t6U4+MTHGfI3t4+lbMq4/GEwvIyyXQOO5GgWqlh\nx+J0O51gEZMi4898Go0GpWKRoeMEnufRiLevXsWIRjk9OuTu7dtkc1kkH1ZX1zg4PGA2g0uXLgVB\nTJZFVJGpVOuoqk632+Pk9IzpxMc04/TcAa1uB0lRWVvdwDTjRGM2XadLMpNmMhpzcnKCJAWrlweD\nIAei1+vOA5fAiER4+uwlnudjx6P4/pRyocxsJqFbJrphMBpPqJyfMRj0WL+wRUTXMU2LVy/3ufzW\nVbzJDEXWsdM2teo5jx49QlFl1i9s8Tvf+S9RDZm4FQMkCsU8nXaHiTshYhrIioQ088im0jCTyGay\nbFz56pe/gP8P//1/+5EZ0fjOd/4L4lGL8/MTut0OruvM08kiYW61LMu8ePGCr33tazSaTXZ3d2k0\nm0QiEa5fv87HP/85G+vrnJ6cBpvGmk3Kyyt8+vkdMvkikahNpdZA0UyajRaKrCMR7HOWZRWQAwWj\nEmQSK3KQkWtZVijKEVShZVlhlragN4X6WXyJg3hxTWOAwn00LTggY7HYG78rvNKLliwIUoOEihsI\nbV5AaBOTZTmc8wpBkUCci0VdHMRizuo4TkjbitluPB4P1caLanTf91GVoGAMnWHIHIzH4+BwNs1g\nCfb8SwjLVFVh4AzCvysQt0Bri5540ciIQiOU28PhMPQUB+yEEnrJBSIUoizBLIimRxR+MW+G1x54\n0eAAobpeWOFEdKlA9It2KfE7wgomxh7iGhpGZE7tS2FTICJSjXmkrwg9EayHYDlE8RSoWLxuQXEL\nv7xlWWG2ueM4c6tMNMwLEPdGXCPBDNi2HTYxokEQqF8E6ghvuuM4DAYDms1msJd+Mgl1DKLBEjNw\nMcoQvyfoevE+EOtzRWNoGAZu36HWDIrRZDImZducHh3j9PsU8wUUOXj/xW2bWDxOtVrl5js3iVlR\nfvSDH5K0E5ydnvLhhx/ieR6NRpCBfu3aNT755BN2dnZoNJqsr2/QbLaIRCzW1lbpzTPW2+02Dx7c\np1gssLJSZjrzuXrtGrlcnsePH1PI5bl8+TKaqjH1pliWSTJho8gSW1ubVKsVcrkcS+UypqEzm01h\nnrx35eqVudulx9nZMTNf4ZNPPsV1xywvr+DNXTX1epNYLBjPHZ8ckkqlaLe73Lhxg8pZhRvv3kSN\n6FRbDTLJBE8fPWIyGjEYOqRTaSaeR73ZYn11nbPzcz791aesr67SaDQ4PDzkw699SKvd4MWLF8HZ\ngBSCi/39fVbX1zg9PSedzrC2ts71t2/Q7zk0Wm1S2SxWNEq/18PpD9DmDE+1UgFeZwlomsbS0hLu\nyMWKxnj26gWbGxcwVI2T81MS8TgDx6XZeu2aaDabnJ2f8/zhE0DGHU0CF46sMJ54dHsO/szjgw8+\nIJlMkM5mMHQNRZHod7v4nketVkOW5dBlYVkmtm3z/e9/n0Qqwxeff0YukyeVyhCNxVhfW8WfTJmO\nR9iJOF/cvYuqKDhDly8+u40sK7x15TKdVouZ75O0E9y5fZed7R2isRgxO8r5aZVup8f2pbdIZ0vk\nC2Wy2RT7+0dcv3aNqBXFnDuLuu0WL1+94Mrb27Q7HVKpDO1mh8vv/T3YRraxYn+0vXWBH/7g+1y5\ndplyqUAiYaGqEslkkkTCJpVKoszFYdeuX+P/5e7NYizL7/u+z9nvvt/a962r92V6Vg6HQ1IiKVJL\ntDmyLCRB7LcYSAIYRp6CgR8MBAkQIE9OLMWyLBiKZUmWTEsURc6QnIUzPd09vXd1dXXXvt19v2e9\nJw/n/k/fdvIS5CGhGmh0NarqLuece36/3/f3XcbGxtndDSCSiGGQTWfYfv6cne1tLl26RLvR5Mql\ny8TiCSRJYWZunuPjEpKkIqsqt+7eo5AvYOhGeNOtNxv4EkQiBrIP+XyeTDYbFq9kMo5lvcg/Fjdx\nceGKqVtMd0IiJYhB4ibc74tJ+YXBipBpiZs1vHBUCybqYC8t9qSC6TsaBCKgTzGFjiZzCX/z0ekK\nCNPChCmHYJeLHb8o9AI6Fe89n8sFr2v4+kTBFF7Pkiy9NI0FRTAo7LZth17VAuoV77XVaoW704Dh\n3QOksJAlk8mXWNOj5iyiIRDHRhQfMT0LwmC326Xb6ZGIJ3DcgFwlCoqYesUuWBRIgUYIQxeBqIhj\nKIq3mMQlSQrIOyFRLzh3R0dHJJPJ4QoBNO1FIpn43dGUNyC8psR5FiYwwhNArFHgxfV0enoaNkQC\nVRGNpGmaZDKZ8LUL+ZywvI3HEihKAJE7jkOhUAgd3cS1LpoRce5E06HreogOiKZAKB1Eapi4TsU1\nVavViKg6yBKyElx7nWaL06Mjzqys8Nabb1KrVHiyuQkEJMaDgwNUWSWdSrH19Cnf+ua3SCWT5LI5\nusNmVKxczp49y+bmJslkkqXFZf74j/8tc7NzqKpKIpHk8eNHzM/Ps7a2yu3bnxOLRCkU82xv7wRk\nu1iMWzdvEo/FiEWjKJ5L+fSEnWdbjOXzOGafaqlERNP48EcfUCzmmZgYI59Lk8umcWybWq1Co94k\nFotQrTao1apkM3kmJiYolcrc+eIOb7zxZdZWz3JyfEo0FjRFv/RLv8InH3/Kq9ev8eDBfb744jYT\nxTzPNjf53d/7X5mcGieVyqDpEe4/fMQHP/ox3sCnUi7z1ltfIpVIMjs7y6VLlyiVTmm1G7RaTcbH\nJ0gmUmxtbRGLxVg7u85gEBjtZDIZWs0OrmVTHB/j8tWreL7H7u4uZ9fXh46DwbVRLp2gyjKKqoX3\n882tLZKJBO1Oh6OTYzqtFnPzc0xNzxDRAm14u9Oh0WjQaDSIJ5Ps7u6yMDXDg4cPKddqNNotEsk0\nlVqdTz77lCsXLjI1UaRRLxGNyCiKS6l0jOu49Ds9Or0uiUSCVCrF8fExjx9tBLLCUoULFy+jSDJj\nYxN0uj2SiQSHRwekkglarSqqrnB0dEw2W2RqYhrb8lAVDccOMufTyTQP7j+gb/bZ2toim83y8P4j\nrl69zgfvf0QuN879+xucu3CJer3M8uoyz7a2aLfblE9PyWVzKLLP8uoif/PB35BIJqk1mpyclvnS\nN37tZ7+Af/qT777X6bRxXZfr119hb3eHiclJxsfGabWauK7DzPQUM7PTHBzuMzk5wUcff0yn3eEH\n3/8bvvz220GHt3/AlcuX6fR6XLt+nZ39fSRVI5nJ02p3Kdcb9E0LSVHwkMjnsgG85nk4joUeNUgm\nE2QyGQauF6ZaCSmTafbpDt2ggJd2zmKCE0xmUdREAQVGYPTk8Otgv61rBp4XkL5c10NVteGEG0yY\nruMOp8loSLgS05m4eZqm+RIMrGkasqbiS+B63uhAHELpwoBGwMSjCViiQGqaFjqZpdPpcHVg2Tb+\nwEdRFdwRApRmDCVU0cgwqECke8VwXQfHcfH9ARMTE0ET4Utomo5lB1NhNBJDluTAucz38X3Cojsa\nhCEgfkHMEtNxp9MJ0Q7h9CYQDjGdtrvd0I9+4A/CIjsKGwfuTklsKyChWaZFZPiaIkYUzxugyAqe\n6+G6Hul0BkVR6ZtBQ2TokRAdCR430PFHoxFkWaLTaQfTuNkPC7TQqQszm1G3MnEuhCRPTNSO44ZN\nnOt4SEhBIIii4g08crlcWKRFMpvYz4spWaxdut0uiXhyyCLXwutJrInEexF+6mJlJBzjCoVCaIYz\nytAf/RwI+ZplWdTrdTzPo1arEdUMNN2gVCkhyxJ2v4/vuqQTCfB9+r0u2VwQfvGd73wHz/M4LZWZ\nnJjk8uXLPH36lMnpKSrVCpubm7RaLb787lfYePQ4XHXs7u5i2w6XLl0KmtVWk6OjI+bm5hlIPolk\ngkcP77O3v8fS0iLlUgm7byH7sL66xtH+Aa7tEI3qTE4Ctvf+AAAgAElEQVSMM1YssPH4EU82HhOP\nGUxPjDNeLNDrt6mVSzSbNQq5LPfvfIHk+3iOzQAPs+9QLpcxTZN2u0MqlWJqappkMjFkV6c4Otnn\nN37jNzg6OqZSruC4Lh/88AP6rS71Uo3joyPOXrrM+sXzzE3OUalWSWYyXL32Cgtzs8PrUebpkw0s\ny8SyTCqVEpNTE1y7dhXHcZifX+Tk5CQwqInFyRVy7O8fkM/nufHZ5zi2zfziHD2zRzaTBgn6nQ6l\n0inJZILnz59RKZXRDR3HcTktVxj4Mg8ePWBlcZlKrYoz8ChkchweHmK5DmanSyQWpVAsMjUzg+s4\nnDt/kZ2dXWamJ/n2L36HTD7D8ekxk7Mz6IbB5PQkV69cpN1tkc2l6Vo9Go06H3zw44DRLgUmV77v\ns729jSzLVCoV8vk8Fy5cYGxigoiuo+sG9x7c4969uySiMTRN5fHjDZrNNt2+yeHBPrIkMTkxwdHR\nAaWjEseHxxSLYzzdfMLi4iwbGxscHhwSjWqcHJXY2z9iaWmRp0+3+Dd/9K85PT4gkYpxdHxIp9MM\nMtNnZ9jf26V0ekxxrECva9GsN1ldXeP8638L4kT/l//pn743NTnL17/281imw/zsAk+ePOXkpMST\nzSdEDCNIg+n1iRgGPvB8d5ej/aNgMkPi3t27XHvlFQaSxP7BEa6m0LVdupZDs9tl+/CQ4uQUXbOP\n7QYQWNSIoCoSruvQM/ukEkkGBJBoLp9ngI/nD2g0mwGr2nZQZSV83WJKEhprsVMU6VDxeJxGoxEW\nPuEKlkgkQ7vCSOSFRlZM3+JnX7bGVMPpZtRC1Bl4IEuYloURiWBEIiiqijOcCMXO1nad4WQsE4tE\nX2K4jzKpxfQv9uiieIpJVxRIXddxRPa052JEDAbDxoAhu1rI414wxxXi8Ri2ZROkoL2YLPEhmUwR\njydpt1tYlo2mvcgjF6SqUZOWUetXUTB0XSeXzTMYNkSWZeM6LrYdWBp2h2zxWDTOYBA0SwP/RZKY\ngL+TySQS8vB4yCiKHLJc+7YVkJoksF2HAT7uwCMSi2I7LpIs0+kFueueP8AR02ssihGN0Ol2URWF\naCzykoOZsMUVKIPY448a64h8YmFOIyRs1UpteIyMoexwQDKRpF6vhXtmsV4RvycmZtHAKIoCPkPN\nd5x4PEG1WqNvvuyFL86tQB5SqVTIZxjNQRfrC3F+hE2s2NuL9Dnf90nFEtiugztwSaXSHB8cMj8z\ni9M36XW7pDMpZmdnmZ2dpdfr8cUXX2DbDrF4nIePHpHOpNl4uok78JiemWFmbpZbt2/TbjZDx8J4\nPI5u6Ny6fYtms0Gr1eLg4ICxyQnq9Rrf//5f8+rr17l67Rq9fhCnOT0xRbvVJplKBchAq43puXR6\nXdrdDgvLC0xPTzE1M0mn06LdbTM3NU2tWmNqcoxGrUy1VmZpcZF+rx8SuGZmZlhfP0MqleLk5Ji5\nuVk0XaLdbuAzYGV1CcvuUSqdousaR/tlIokU737n22Smp/g7v/M7XLl6nUa9iecOMC2LUrlMrVZn\n5/l2YHQlS8i+j6FrJJJxZmanaLSbdLptVE2l1zGZX5jFZ0AsHsV1bKJGBNd2iUUjFAsF+mYXy7bo\ndju0GnVazRoQoCanR0fouobreviSwvbOAcWJaWZmJvFsh1anzdTMFO1moC8vjo9hdnuomobtOBwd\nH3PpwmVOSmWKxTF8z+a0WqY4VkTXDQYDj7W1NZaX52i0m2RyOdLZAqYD7kBDi8QYn5jAiBnI+Miy\nhGn2iUQNNE1nfGj64w7cIdl5nB9/8AGffvYp165cod/vs/v8CG8gkc2k2Xn+nEhEJaIrVMrHdFoN\nms0qV69doto44ee+/haq5OHYfWLxKK12h0azRiab5MrVizTqVX75l77J4ckh8XiUZrsdDASuz+H+\nLifHR7x67RWcvk0mmULyXC6984s/+wX8h3/5p+8dHR5xcnLCzMwUlm0xMzuDqqtkUgUUWScRT2Kb\nDs1Gm+2dfbyBhGFofOWdr8DAZ+3MGfYOD7nxxR2W1tc5rtY5PC7R7ph4A9C1KI5pEtEN0vE4mmIw\nwKff75LJZkLvbKfvoEdUotEANnWHU1lEwNHDCUZArWInLCY2cbP1fZ/2kL0ubDoFlBikfcXodLoM\nBj6SJBPAxIE8y7YdVDX4YMTjCXyfcOcpiGRiL22MmGI4jhMWTMFWFoVewPn9fh/PH4SsZ9E4COON\nUctQARELEpZhGKE/u+/7JFNJur0eSBKmawfs6eF7B8LXIPTCQfG18bwBg4EfyuBqtTqpVBrLcun1\n+vR6geRJAvxBEPQioFfhkT4aUypuzoLR3u30kSQZTdNJJlN0u0PZnaaFEjtVUYLJ3zIR/vCj0Zmy\npOA4Xsj+Dwh/OrVanWgsjiTJtFptkskUmhYkI3W7PYxIYAsajcaQpACZkVUZPWLgSz72cD/XN/vo\nhoGqBHLCXi8IhxnVYgu/eXHsRFa8KI7CREbXDFx3QLE4husOhioFlXK5QjT6IppRmLGMcisE2UyS\nJOq1xhCe1zGM2HBdEFw73V43RGvEeRA7crGDF0oFQZIc9Y4X70NwEKrVanguAayuharrFItFTk9L\n7O/skctkOD06Jp/N0jW7RFSdWrXGs+fPaHU72J5HIpsGRebmnS84e/4cDx8/RlYVPv7pJ7xy/Tpm\nN8gKf+WVV9jYeEQ6nSCZjLOyssSVS1dJJGNousbRyTEXrlxkY+MRyVQKy3P46KOPsE2LldVVDo6P\n2NnbxXVdmj2LfKGIphusrZ5hf2+PTr1Nr91hfnaeJ48eMfB9ms0Gz7e3mZya5E/+7b9DkjUihjFs\n0hLs7Dyn02mTzabRDY1yuUK5VKPbtXj8eIsL56+gqRG2nu3x6luv8M3vfIP19TM8uHeXjfv3+Zu/\n+itUBXQjDr6E2Tcxe33WV9fIpNIM8IhoGolEnFarydazJxTH82i6TDQeZF53u11mZqbpdNpDOWIK\n0+zhD3wuXrrASblEr90hm00xNT6OpiiMF8dYXV3i+vVr3PniLqoe5cmzXT76+HMsZ8B3vvPzVEtl\nSqenFMfydNptksk4kiYzOTGG47kcnhzhuh4RPYLnDFBVA7yAFf/v/uzPePvNN7l29RqNahXH7FA5\nOmJmaoqtzQ3yqRQHe9tkU0lc02TgOCwsLmLbFu12m+npaXq9/lB6ahGNGZSOTrl96xbnL57jt3/n\n7/Knf/anaKrC1Nw0ekQjGYvx5be+jCKp5NNFImoUp2PxjZ//Jtu7u0xNT7L/bAe7N6Bbd1g6d5ZY\nLMlJucx3fvHbxBJRFueXeP58G0X22dnf5e/9Z/8503OLyIrGRHGKTCqLooBl98nlE0SiEuuv/y0o\n4Han/N7ly5c5f/48qmZQa9Q53D2gXWvi2zb7B/s8e/aMZCbHablCPJGkkMuQTCSpVGv0LYtEJk00\nmQJZQZI1LDcIjBj4Hp1uYKunKAqO6+IOfGRFJZ6I4A8GqKpMNBobGtNHhjCkR7fXIx6LBTfQIWEp\nNpwqBAlLSGYEzDiaTS3ytIFwyhV7XzHNiEZATPPi5jrKOE4OSTu6qmH2TRRVCQttKpN+CSYWxCux\nHxds9tAww7YDNrFlh1C62FWKEIvRBmGUnS5cvoTMSRh2AAyGN3aBQgBhgRGscoFKjJLvRGEKcp0D\nZrDve0NHORtNV0MI3jAMSqVSCJGLXbl4X4lEIpBxNVo0m218fxAeA0VRGAyPSYB4SPStPrFEDPzB\nS/v4oID6Q3RCR9jPBquKgO07Sl4bRU6EvjyIT/Wx7RfOfJ1uB/wXRjSKJA2nBnPY2HjhDl9wFcRq\nRjy+WM2MoiWeG0D0vg+dzougEk3T0A0tNLkRvyu03cH1quF5QdpXELYTQOJBnoA7hLtNQEJRlbAh\nFBwPwS0Q/4pCLs5Hq9WiWq2GxDWhzbesPrIsYVnBtH94cEwmn8NybdLZNDNT0yzMzGB22ywvz2P2\n+kiawptvvMH777+P7wWBPKquMTc3h6Fq3L51i0KxSCwRZ2Z6mnK5THFsjJXVVQ73D9A0HUlVOC2V\nqVVqtFp12u0OB4fHZNN5VpYDOdXAG5BKZxkvjBHRdTKZDMlUitdee42xXIG90yMODw7QFQ3Hshi4\nHp12i7n5Bfb2dml3ugwGHpl0Bsu2aLU6nDt/ntUzq7S6HdK5LKqhsrC8zMUrl8jm8jRaLZ5tPcd2\nPFZXz9I1LW7fvQeqwle//i7vfPMr1Gs17n5xB0WW6bTbJOIJFhYW8WUZx7NRFZluJ7jur71ylQd3\nH6IrEt5w7aZpQcOkG1FSyTTNSot0OkWv36PdbrOzs0NhvIiPQiqTxXZcmrUab731FrpuYFo2luVi\n2y6yr/D48SatVhtDN0glE1x79TV+47f/U+onJ/yL3/+X/P1/8PdpdzoUx4pksmlkfE5PTgDIZ3PU\nqjUmxidxHI+xYoFWo8bh3iGL8yvcu/uA5ZU19nb2adVaPH74AKvXp91oMj42jqFHKFdqpPN54qks\n+9uHzM4tsre3S7VSo9Xq0Wi0KBTT7O+esLa6yuHxIdmxPJMzM0xPzzI+Pk25XGJhfoXPbv6UV199\nlVs377OxscnSygK5QpF7j+9wUt5neXGZk/063b7J7bs32T86pml2OTw9YnFhgadbz5mcnCAdSdDt\n23z6+W2+/O5XiagavUaTx/fuY/f7pHJJxifG8XyX49MjXv3ab/3sF/AbH/7Ne61WC9/3+cH7PyAW\nifLxRz9BBmrVCtvPtrl+/U1KpRp7e4dcvHiZT376IaelCqtnzjDwffaPj3EGPj3Lxh0MUFSDft9E\nkmSymQyqoobQdpDQ5OH7HplUGkmCRqMZkskguMmawwnVsizS6XSoQRXFWxTYUUcp4X4lJm5hSSmK\nJRDegAUbXEzygiU96vEdjUZpt4PuOBaPEYsHE5TQ1vYt8yUWsSBzCfb4aKEBwoahOczQFlMZvAiw\nEGYyjUYj3JeOQqgC8hX7WsH8FK9BkKeSyWRowiImZFFYYrEYiUQifF3iuUzTIplMDB/fA/zw+ArL\nUcH4Frv7UYlbIJWySSZTwIs41mDf7Yf7W1mRcJxg4tZUBVWVCZLigvNm9oV//CC0fRVkQn3YSAgy\nnfieeC5xbIKmzg4n18iIAUw2m0VTVTKZLKqqhXtswYgX50oUS/H+RNEU10omk6HXC+SIQVjOy9eA\nYWjh1C7OgZB+BQiLNER6grAdVdXC6VnouyMRY7g2CWBz0WiI4y2Y+oIVLxAfse+emZkJv/9CyqjS\n6XSJxxOMjY3RrDaYnJoAoNNukYrGefL4EefOrLG7s0Muk+H4+DjgV+gGk1NTKJqKLEkU8nmOjo4w\nDIOpmWnOrq/T7XY5d2adn37yCbvPt+m2O+xs76DIEgtz8ywsLNBotmi1Orz77tfI5/M8e/4cSQ68\n/V3HpNVs0G93ScTjTM9McXJ6yh//0b/hzsPHdHt9srk8mXSGg4MDstksSDK3bt8ikUxQKp1yeHTI\nuQvnmF9YoNlq4nou2zs7XLl6hQcP7zMxOcnx8SmffnaD1ZU13vzSlzh77jLf++sfceWVSzx4dI9f\n/fXf5O2vf4O/+JM/5cGdByTjcbY3nzIxNoYvSxwcHgI++Vyerc1NPNdF1zQa9TqKInF6vM+TjUek\n02lymQz1WpPJ8Ul63R6NVoN2p83t27dRFIWVlRVkSebSlWtossLB3i7NZpP9/f2Ra1KjXq9zenLK\n062nnD9/Edfx6Fs2kqIhySqteo0nG4+ZnBzn+OiIpeVFTCvgEN25eyckhk5OTrG9d0BxbJynW1tU\nTk8Z+D5Tk1MAbD7dZGFxgdOTEr2uRSZfYHZhiZNqiWgywUnpBF3ROD48JJXOEI1G2dzcYHp6msmJ\nGcrlCktLS5h9m1Qizb2Hj4inUmzv7vH48VMGvsz4+CSzCyssLS3Q6/dxHZep6Ql29jZxLY92p0Or\n1aNWa6IMmUS/+AvfQTUktp8/4/y5s9j9Pr1Om163h6wqtJttDo+P+flvfoNyqcTe9nPGx4r4DCgW\ncsRiEcx+oKi6+PbfgjjRv/rzP3pPURT29vZIJpKB9GtiAk3VcEyLQnGMWDxNNJrm8pVrVKs1fMkj\nEkkyPTeLL8s0Wm26lkWj28Ee+PS7FkEClI5IAdO0gGwR5GK7ZFLJ4Q1TCfe6nU4Q5ynkUELCJG6M\n3tCQxTCMkKVbr9eBF2lZYuJsNpvk8/nwpit01+IGOKqxHpVqCQhS7LrFFCxgeVEwTNPEtK2XYHqh\n8xWs4XDaG9p8AhiqFjKBxXQuCrRoJAQrXUyX4n2JXb9oIMTvjdp2ClldvV4Pd6zCPU0Q+oR0TZCj\nxIQfjb5gTXueiyzJw0nNCtcTYtKOx+PIkoLt2KHRTqfdJZvN0RtCvuKxO50OjheEeQSuYf2QCa5r\nGu12K/haDxoW07SG07Ua+suLIm0OXcgE6U94k4u97miYh2HoNJuNsHgBpNPpoFEaPt5ogRZcBMH+\nF3C5OIeC6S6mb9sKGsBgH+2Fk67gZaiqHPqWt9vtkCBXq9UYeD66EeSKB0iRhOMEiJFwvkskAmJV\nwMsQxjT6S1O9eF3CnEf4HUiSFK50qtUqIvVOkNgKhQIAlUqFdDxFt98jlohSq9XQFZUvbt1kvJDn\n577+NUqnJ+iGzsbGBpcuXubg8JDJqSD3oN/v8+DBA959910sJ0AyFhYWUCSZVjMwaFpaXGJubo5Y\nPEapVCKdTFFvNJmZmeH+gwfs7OxxfHTM7NwskWiEaqXMw3v30DWNL3/5yzx/vs3dL+5QrVR58513\neffdr3J8fMTHH35MKpWi3WlzdHyCpmhcvHqZJxtPaLaaNFstxsfHcD0Py7Y5e+ZcyAKv1+t4rk+z\n0WZv74CffvYZr73+FtVai9/+nd/EdPocHB3zz//Z7/H557dZP3OGRqmMDjRbLXb29+laNrlUCtdx\n2Nvb42vvvkMum+H9H/6A1998ndODfdqtJrF4DAmZGzduBL4JLhjxKJqhs7q8wszM7Au7Ys/n6OiQ\nwWAQeiAEcc41+n2LpaVlXMdGURUsyx5aNidQjSiddodnzzbxPBvbsmh3moyNjfHo8SN6poWmKjSb\nLdrtLs12l9/93d9jff0sp6en4A0CJ8moQTqTYWd/j2Qqw+72HoWxabZ398lPjNPs9ShXKwERslLF\n7JjMzi0MCaHB9bf17DlPNp6SSqZwXJN79x8wNTuPEYmzvXPMp59+wd/9rf+SufkVPv3pHer1Fo8f\nPSGTydHtttl88oTr1y9w/dU3qZT73LzxiO3tDVTNY+3sPAfbB6wsrvCtr3+Tzz75lMlCkcnCJK2e\njWO5IT/p2fZzMqk4qVQc33O4cO4M3XaHH/zwh1Srdb7+a//gZ7+A3/zwB+95jsv+zh7RSBQ8D8dy\nSEbjGEYEWVb4/OYXNJpNbn9xB2fgsry2RjyZYvPZcxqtDvFkik6/j6ZF0CMGumowPT1NJBKh3++T\ny2VIpQKHt2I+F2i8FTmEFG3bDidl4XOtqSpGNPCLFgVPRgr136OuYKKYi5upuPmKG/Lo14HhiU8A\n09oYho5p9vE8l0jECPehQuI1ShwyTZNUKsVAggF+WHThhfuWIAmJYqooCtLAx9B0NDXI8ha65kQi\nEWaB12q1sNCOumZFIhGq1SrpdDr0tR5FIcTziucUxigCjheIg+M4pNNpisUi1Wo1tKUFQnhYaLSF\nbjx4zBdQsphufd8nm80iSTK9bg/Hcen3zSHcHg8Jg2Li1XUd3xsgySI5LkKv18WzXZACu9hYLHhu\nTdWH7z9odDKZzEtwNtILH3VRYAV/QPilv7BIldB1LZSACVRGGRIVdc3A9xmiDfzfNngCtXhBaJRf\nyAcHfjhVR6PBe2q1msO9vo8RMcJJWGjChRmOrhtDR7g0/eE1Z5pW2GiJa8CyArRIkl844I2mo4nP\ngPBqEPnwotETnwuBLLVaLRQ58PQPrOhkBraLZmh0egFJst1qM14osLy4iDdwgxhcTSeXzfF4Y4O3\n336bdq9Lp9/jL7/3V/zKL/9yoP1utZiYmkSWZJ5vPaNQKLCyukqjXieeSNDqdrly9QpPNjYo1yrk\nC0UGA5+Tk2NWVpZptZoYeoRCPk+tWqVSrZFIpjAdh2Q6zeXLV3j7nXdRJYlapUI0EhjnCJSi3mhg\nWTaLywtUK+UhWe0s7XaHsbHxoW2oxDtvf5lKucLG4ydYpoUsKdy5c4eToxOKxTwHR7sc7O0xMzXF\nv/rDP2FudozxQpF2s8nC3DyPHj9CUhXefONNlhcXOD09pV6vY9kO0VicbK7A1tZzGs06r73xBjs7\nexSKYwyQiMeTTE7NYCRixKMJSuUKMjLRSBBjenh0GJq/BKoDk0gkijOctHP5PM+ePQ0srGUFxx2w\nuLrKzOws+/sH9Hstzqytcu7cGYq5LDOzc9TrDXxJoljIYts2r7/+BhuPNijkCly8cIFkIs7Zs+c5\n2N/n9PSE5bVV2p0OtXqDpYVl/vy7f8WznW2MWISr167xfGeb2dlFdDXCvYePOLN+Bk2Vuf/gDrdu\n3ebixct8+ukNUqk0kbhBo9Wj1mxxfFLm137113n86AkT49Pk83lqtTr37z3g3p07bGw85mtffYeB\n55NIRqjWOvzk45vkcpOcO7tGfixJtXrImZXzpGJpfvzBh6ytrfEvfvefc+XyVf78L/+an3z4EWNT\n03z4yUeMjRX5jV//VTRVRtcUPv3sUz77/AZm3+G0XOFX/4v/5me/gH/wH/79e9tb24yPTQZWeq0O\nMcPAMi1OSxVKpTJrZ9eQZOhZPVZWV1C0KCflEqqqoQ1JO912YOGpqyrxeJxOp43nucQTEeLxGJ5t\no8gSkgSGEcE0A0MNkdgV5GWbYZGKRCJEDIN+t4eh6ejai9xlMWGJQiEmZHHjFdPZqEOb2JkHE24n\nhNcFbC4ex3Hc0PVKwLJC8yxrKqquhYV91NITCKd9XdeJGREs0yQyfA5hmiF02VNTAVQldtfie6MT\n+SgkKuBSMXECIctd2MeKKVlM1gLeF8QswSAXPtrNZjOMtIzFYrRarXACDwqxhaLo9Hom1WqdZDLN\nYACW5dDt9mm1Agtd4QEekPG6IUtbGNWEdrDDsBnPdcEnJKmJKFfHCYquYM232+3wuIpGqt8NGMrp\nVArPcXEdh1gkiqHrDFwPyYeoEQk08r5POpVEIuAJ9Hu9YfOokIgnw9eoyBqSpKCqOp7no6k6nU6P\nTqeHquqYfYtet8/4+CSKotHvmXS7/bC5EQVy1P40m80CL/TamUwmcJPrmYE8cTili/Pr+1LIFBcN\nbRAEYw7Ja4Ra7lFHtdHrtFQqvYRYCQRn1F9ej0SxHBtkCdfzhsY5gSvY2OQErueB77O7s83c3Cxn\n1s/QbLZotdtYts3E5CSSIqPHojx5uskvfPvbTE1Ph2qAwWDA/MI89+7f52B/nzffeIPHGxucO38e\nSZa58flNzp0/Ty6XxfcHRGMxdEPn0uVLNBo1Eok4ExOTRGMJrr16naOTEzQ9kHp6PpjdPu1Wk+mJ\nMSYnxnj+7CkT42NcvXKZV69fp1ypIA18Lpw/iz8YkM1k2X7+PNibqxqFTAZFgmQiyec3PkdXdRbm\nF0hEYlw8d4Zet86Pfvg++/v7JONxLp1f4uqVs/R7XTrdLtnCBMlslleuX6VerzI5NUnfNLlz9y6u\n64OkkskU+MN/9Ud86zvfQlI0ao0m+cI4V65d49btOwxkSCQyvPra67RbXbKZDDIB4TSTSWHaJpMT\nM/RtmzPr63R7Jo7rMRgETd7N258TTyaIRGMsLK9QrdUJpMA205NjKLKPpimMj42xvbvLpctXiUai\nqMNs8aPDY1LxJBfOXeDJ4w16nQ6KqqLIErbt0O60mZ1b4MzZcwxcKDfKnD13hm67Ta1S4Y1X32B7\new/T8bj/8Alfe/cNLMfC0AxqtQbRaARNU8lkcpRqfbb3DpidnSWbSfDs2QaJhA6Szfr6AhOTOba3\nt3n7S68Ri6mkk3E0TWX7eYm7Dx5xfHqEafd4+PA+//gf/yO+/8PvM1Ai/OD9H/HTmzeRDZ1MLksm\nn6c7cFk5c47182exHQvL6pPLpWnV62TTKfb2D9CjUcYnpihOTPLmN37zZ7+A/9kf/v57S0tLAIyP\nj9Pt9lAVhUw6Q9e0KYwX2d3fY3puhompSZAV9g9OiKcSWLZDp9MdWmoGO7GoEcFyAoeddDoVRPn1\nu0NCUaBHFvCuruthprH4I4qOKGZi16jrOrVaLZyKBEFN3NAEWWlUMwu85KcdmKwE0HYikcBngCRL\nWLYFPmE33+32Xir+lmVhe244BY4GeLxgtkfDm6TneXhDRrMoqMKzW8DNgh0uJj5h5vFi3/tiCh/9\nK1ALEZs5ai8aMqOHO+vA194KmxRx7ETSlUjUEhakgqcgmijPG4RwsWDAC226CD0I9N4mqWHWsDgG\no3afYictUrdSqUzAgo1EsSwTx3HJZnND5UB0GMLyIodcGJWIxk6WZcrlMrquk06nX/JSF8dc7PEd\n13kJYRDHqtPu4DgetVo9jJQdnXxfTMov/O2FWkCsJhiGdaiqijdwkRWZSCSQ4JiWGa43xPXa6XRI\nJJJDvkeCTqcLSMOAlUFYtIFwbRGNBtdGr98Lr71ut0s+nw/tc4XL28TExJCUZ4V+CMKxrWeayIoy\nbB4GQ2vZBNlsBs920KIGjufS7nSIRaNIA4lut8WD+/eJRCOUqxVmpqeJDVcjjudx/dXrJBIJnm1t\nBWsfXSORSHD/7j3Gx8eZnpxClmUODw8xDIPdwwMisSiTExOUyqe0Wy0WFxfQNBWz2yWiG+SyWT76\n5KfMzs/S6fV5/4Mfsba6Qjab59MbnxPXNDY3HrO3u0O320FTZbyBR7vd4uBgn75pYfZ7VKtlWq0W\nsUiUaq3GysoKESPKzPQUR4cHdLsdokaEDz74gGA9KxQAACAASURBVLnZOQ72dslmkiSSMSqlGrNT\ns0xPjeNYfdLJKLdu3SKdLTC/tMzGk02ymSSaonBaKaOoGq+//iYLi8s8e77N2NgEZ89dQNIUbHeA\n50s8e/ac66+/Tr5QQFFVUpksB/sHvPLKFTqNBgPfxbJNJEUmkUjwePNpeB/QNSOUarbbHZaXF5ga\nH6Pb7wVN1fg4lcopc9NjGIpCv9chmYyxt3vIk80tLNNi4PscHezSaXWplKs8ffqMTqfDxx9+hNnv\nk0qmUFWFnZ0dbt3+gqm5WY6PjoPPWUSl1+9xfHBIOpVmZnoGz4d0Lsf5yxeI6WDbDvMLKwHbW9PI\nZLO4nksyk6XVabG8tMAbb7zK4f4eiZjBK69c48aNj8nnsnz3L/6Sn37yIW+8do0H9x/w2ac3mZ8/\nQ7Ve4xvf/gZ3791jbW2N09IBi0vzDLQskUSK+eUzHFeb6Iksdx8/5b//J/+EfC7DvXt3SMSiXDx3\nlng0wvOtLZ483uCdr30NXY+wMozOvfzWt3/2C/if/x9/+N71V18lnUqzu79Hq9Ph+fYOiWQCxw8i\n51KZHJoepVStEY0lqDcbIWksKBwyqqogqUrQnQ8JVbZto6sa3W6PSCSKSNsKA0hGLDgFY3nUJ1xR\nFAqFQmhxKaZDUSwFRCgY50KXK2BbsQ903cFQM+nS6wWTizcICqzYr8ZiUVw3KPq+z7CgBh7iKHK4\nYwTCyVsUOzERjWrEfW8Qwqti2hJ7SyE9E5PxKHtePJZgfreHekaxrxUwrth3jjqQiWMmyH2j++BC\noRAWdNF4FIvF8HHEvk3I1hzHodPpkM1mwyxqkRomlAC27YTQea1WC68JUWRHmxBhGxpA+w66roUs\ncV3XKZfL4XsURUkQs8SxEeYxYjLPZDKhtlrssEXhE17tgdGFAwyG8sEO8VjgECjkemLtIv4/igKJ\n4i8KYighNAxAGmrUX3AFxGpCNJWyLJNIxICAoBWNxtC0gOgmyIMA1Wo1lIUJglo0GqVSKZPNZrEs\nM2wghKWssFgVqW4CQhfnRxwXgGwuh2XZYWMmwlUMI4JjWfgEaXgS4NkOChLHh/ucXT/D2toqnu3Q\n7XT46Mc/IZlIYA/JeBuPH3NyeISh68zNzVEsFCifnDI/O0shn6dRrWH1TSzTJFcsMBgM+OjDD1la\nWiaZSgfGJLE4BwcHoVHN1GRgNNTsNLl69QqxaJR0OkUkFmGiUETTVHx/EDTb/eDznMpmqDXqZDJp\n8D2y2UxwzaeSRKPRgN0fidJp1/nL//DvGS8UWFxeot/vs7K6wvTkBGtn1ymXK+zsHVIoFrhw4Rzd\nbo9nW8/JZPPoegRNNSiOFbh4/gKz87OoisKZ9XUUWaVebzA1Nc3s3CylUolGq0oqlWRlcZFGo87y\n4iJjE2NIEszOz9HtNHAdk3aryoMH97h65SqHR0fcufcAQ5PpddvUShVmp6eQfOh1uiTjMTRFYX9v\nF/wBrjdA1xRKJ/voik8um6HXbXP/wX1kzWBjY5Ner8fS4iKbTx7hD2Br6xkgMTU5zR/8wb8kl8sw\nwGdubo7Z2VnKlQpTszNMTk2ys7dNPpPj5o3P+a/+4T+kUCxSKpeZGC9yfLLP7NQ409PjNOptPvrJ\nTR493uLk5IBMLsPY5CTxhIHtODQaFZYXFjA0nVeuvYrr+gzcAbV6nZOjKtdfucbW1gbTM3M8erBJ\nu91gbXWFXL5AuVInGdFZXpolFjPQlSStVpOxQpG//v77/MX3fkS71+Ov/uK7JKMq28+2eProIal4\nnPXVNba2tvCRaLe7jI1PUCgUsRyb1ctf+X9VwCUxrfx/+ed/+x//qf/5zU/JJBOoWhA3Z8SitFoN\nbGTSqQwDJCzHC2VM0hCygxcTczqXHt4QNDqdXghlRyIRPDvIIZb8oFj5w1xmoSceJZkJ2FgUEQHv\nimZB3JwDhm7kJUnPS5aiQ0a3mNyEdAcGDIZ5t5nMC7KPaQbwZgCXe4yPTw7lYD2cIVQ68H3cEcIY\nkoQ+lI+5rhummuXzeVr1Rvi6xEQsXs/oTn20iWm323iex+TkJNVq9SVp2uh0KEhOYvoTmdKj/xf7\ncEGqymQylEql8PgcHR29xEYX5yCZTFKv10NVgDBXgRekOQHNirCM/f39sOAIktfo62u1WuFjWZYV\nBqiIojy65hD/CkZ6wHcIZHtBc2KH6IMo2qNfi11vEH7SJRqLvLRiEBK6eq1BrdYIiXUC+haSLPF8\nmqYMofRgIu90OmGDaBgaqqaEr0E4ygnGumEY9HsmiWQ8lAGqioauR15CiASULo6FkP31+32yuRSn\np6dDeF0L4fVarUYqlfm/fCYC2D1ojESGgKIoyGrgLtjtDt3nZI1avUIikcBqNIimErhyoH83ZI18\nIkm/3aDdbqLIEsvLy3SaLQxdx+z2yGQyxFOBr3673aZYLAYmLzdv8c477+C6LqWDIw4ODnjnnXc4\nrVbYPtxnZmYmIJ4qMsVcHss0mZub4eTwiMuXL7O3t0e1WsWTHMbGCjTbLXb2DvB9n/Wzq5h1k0w2\nhYyEbZuMT4zx9NkW8WSSWCzG7v4ettVnrJDBc1xarSbZbBbHccnnc2zce0A8YXD5yrngHOoRms02\nxycljg/LfPThDZ48fcq5i2d46/W3+OHfvM+v/PKv8fFPP2JucYFXX3uNfCGD5zkcHx0wszDHxMQE\n21s7JFJJvnhwh4WFedaXF3Bsn3qzSbvVp1AoICPR6bVBkXnw8AnPnj/m13/11/jwgx/RatZQFYOL\nV14lk8/Qqh6Ti0dxXIt6s027Y2L2g6ZS0VVyuSJ922Nyeppn21sszMzSaXWJRFQUXeLug/u8/ubX\naDQ63L9/l/Wzqxwe7mN2e0jAs83Aye2XfuWXWVxZpHRaCbzRGw3mFhaIJxNUKhVc28HsBYoFx/Op\nVqvUq2UuXTzHwtwU3//e95hbWsFxFCKxDLduf8HZC2d4663X+NGPf0gqHiOXSlNrVEllMmQyGcrl\ncrBm7fXpdyx816fRKJFOxNGUwHylb/p89OFnfHHnHv/ov/uvefb8c4qZcTQ1gdJt4foOU3Oz/PGf\n/TmdjsPYzCz58TEmJovUKlVkoHxc4ty5s2xtbQX3h6jC3OISq2fX2d3d5ed+878dNcn8f/zn/xcT\n+D/7n/+H99ZWlzktHdPt9xmbmKbZapPNZUln8nR6XRRFxbKdcAphZN8c3BCDYgYBk1iQpyzLwjED\nCDeXy6FqQVa18GsW+18xLY5GJo7uukWBFIYvIqhBTCzi+wKOFtDwKMlN6LVVVQEkDENHkgL5ksjv\nHoSJYwqqquB5A1RVwXLs4DFjsfDmLXTHwjBGFK5wBTC0VxUMZDGViolXkMuEJAgI4W+A09PTEFYX\nU1oikQgNYMSkJZjyAl4XRCdBdhJ+8KLoj5LyBGQuWPIiCUyw4cUUJ6ZJgXKIya7b7YbucEKHL867\nQEDE+xERr8IrXky4QNgkiGldGJYI1rXvD7AsE11/sWMW10kmkwlT3wSKIBzjTLNPvpAPm6XRAJRe\nt4ckBWiOIEaKcyEKYcB1CIx+xM+kUikGg8GQvOijyAo+ftiMCTJmPJag2+2QSWfo9bqhfC+fyyNJ\nclhYR9cmYpctjFeE3j6bywLgOC6SJOO6gcmNWBuNShYFPyKbzb7U3LaH07aiBCqHXrdPMpUI3rdh\n0Oy0kFUVVVaxun3Mfp9K5ZTLVy6RTibJJlM823zK+YsXMC2LXCHPlYuX2DvY5969e2Sz2eBvLkdx\nfIxILIoqKURjMT7+5BNyxQITU1PU6/WgEWq3WV9fD1An1yWbzYbX6+7uNtdfu4Zh6ERjBrIEnusg\nSz6teodCMR9o7F2HTq/DH/zB7zM+Oc7C4jy7O9ucWVlmMDwmV69eZWVldYjUKBSyGR4/fsBg4FIu\nn/LRRx8xPjbGaanCRx9+wuTkFF96+y1s16bT6vCVd75CrV7j0qVLPHn6hFQmRTafZeC56BGdeqPO\n9NQs9VqT05NjpqcnWZib4e69+wx8MPQoHnBydIjrOAE7vNvBs5UgnMX1yaWzJOIxHM/l1VdfY/9g\nh8vnznHrxmf4A4+xsQLtbg/Xc6k3GkQVjWq5hjOAze3nrJ0/x827dynXa1y8cIlytUY6myWZKpJO\nZ4knEtRrderVKlMTU6yvnuHenbu88fqbSLJEvVonm0mhaAr9fpd0KsnM7DSFQo4nTx7DAObm5kjE\nYyRiCYrFAslEErPXwzT7OL7PyvpZ6s0Gr3/pTWqNKqbdY3v3GefXzwTkv/l56vU6/eFnMB6NMvBt\nyuUSuqISixn0+20GQ/fNdCKFZZqMjecp5NI8fXKfdCLN6fExpbbFQaXBzMoaEzPzfPTZDTpWDy2q\nEU+nsF2P1TNnWF5bIZ5MougaetRgcWGJqdk5DCPK7Mwc8cLCzz6E/hd/9L+/l0zFOS1X0CNR4ukM\nK2trdLp9Hj9+TDwxzBnW9BfMX0MbmlcMhjGBahhm73ke6USSiG5gaHp4wxXOYILVK/bJQfSfGXqD\nV6vVkFGcy+XCoickM0KzHkxAAcQsCEKC+SwIYIJAJWDl4GcGxOMxFFnBdT28IQPZtkaDQ/zhFBns\nnZyRfbYgu8myTL1eJ5tK43sDGrU6iiwTTyaGUZaEdqOiIISTOwFhRfhgRyKRMMdaTIpi0haEsF6v\nR6/XI51O0263wwm1Xq+HARmCpBYUryAVTTjUbW1tAYTFZ5QIKORhQp4njmO9Xg8LiZgOBRrS7XZD\njoJACETYhrgOQmkMBBnjQwh9VGP9H1t7GoZBr9dBUWR8f4AkEbK6NU0deta7YWPRbDbD8ytei+M4\nQ418ioH/QksuGs6gmUuGO3uRaieuG1kGx7FDeFxkngsURKwm1GH+/GDgI3bZmqYjDzXtA3+A2TdB\nIgwY6fdNfD+YvFOpVKgrt207bOh83yedTtNoNHAcG9uycRyXeCwI9PFcj1QyRSRihMdSNK6iebUs\nC88d4A8C1r8qB775EhKST6C/HzZx7WaTdDoTvA9vEERTdoOEtvMXz5FOJPj8J5+wsrSMrKk82dxk\nrFjk+OAwOGe6zvLKCv1+n1qtxv379ymXy5ycnjC/uIBmBPyV4vgYN27cwBgiKDPT0xSyOd5//4cs\nLi7SaDRoNpuBL7llUa6UiUXjTE5MMjE+wVhxjFazxcT0FD4+kUSURCJJPBlnb3uHYr5AtVIiEY+y\ntrJKs9FAUzU0VaNcqXB8fIzd7/OtX/gGqVSCXq9LOp1heXkJXdPJ58cYDCSiiQTr62cweyb37z0g\nX8wyNz/L8ckxmWyaZquB63mUSxW2nm4yOz1Pq9Vh5/k2r1y8QKtRY/uoRCqVwXYdKpUyjx7eZ25u\nFkWRiUXj6EaCdrtBrVZn4PlEozFazQ7lWoPphXkOtp5x++ZNNEXGMKJEYhGmpqY4PDmk1w2IkHsn\nZcanp8iPFckXiiwsLXG0v08iEScaT1AoTBCPJ5BliVa7TqFYoFgosL27R3FsnLGJcWYX5jk6OebZ\n0y3OrJ6hUW/gugNcx6Z0ekoylWJhdjpw8TOiGBEDTdWoVMrEolEW5pdwcWi1AjJyr9NkaWGecumY\nfDbNZCHP6dEJa6trtFotWq0WvW4XyYdEIsbe9jbLC0s4lsXnt26zvXvA3sERtUYLyzLZ298jnozy\n2ac3sB2fo5Myd54cMbmwzOziGRKZMVQ1QiQaxdAj5HMZCoUituNQKlXQIxHOnVsnGouRyWSIxeNE\nYzE6vS65qbWf/QL+4MZP3ut0OyysLKEYMZyBzKONTRzHCZx6hiQgyw52XvGoQW6YhiWm4GB69Ygl\ngqnac4J9XCaTQZIkms1muGcVRVsYqwiIW0zhYnIWN3jBmhbEtlHHLMFgF7szMX2KHbkoPuIxxE3c\n96FUKpNKpYewuhTe9IJJCFx/gB4xsC2LxDBQJJDE5YI832FhVYfGMWIdIGJQReESN33xtSCNCWRA\nTJlC6gSEZLdRBy/x3sTPCWa3LMthlKWYDkURTiaTL+3FM5lMaHRTKpXQNC083hAYuojgkn6/HxbU\n0alb7IvHx8cDiDebDZ3oBGwvDFvENCjOhUAhRIyiQAZGoWddV1+kaQ1cVE3Fdmw8z0XTA/hZpNCN\n7snFRC6aieAaGOC4ThiHKsJDguuljz8kLorjmM1m8TxnuB7xicVj2LYz5CV0GM2EDwplbGhKNAjP\nvzieIvFOQgpe+9DXvt838bxBWHBFA9dut8MoUgFLB8S7xBD+LQzDfBJEozEGA49WsxU2KOI4J5NJ\nVEUjGomGnxXhlR4QJ19wRizTpNvpUCgW2d/fQ5FlErEknuNxcnzE4fFBoPH+8tvEJZVCIc/nd25z\n9uIFnm9tgTdgemaG6elpnmxukslkWFhYoG+anDt/HmSZxaVFur0eumEQjUaD9LKf/wbzc3Ps7e5y\n9+5dLMtkdXWVzc1Nms0m28/32dnZI58d49nT5yRiKfZ2Drhz+y5LS4u0Ox083yMaj1NvNTiztkYu\nk6HTalIpnWBbgbzzyZMnSJLE4eEx1UoNVVP56jvvYtsWpdMSuWyOZDIRIkwzM3N8fuMmjVabne1d\nZEVhenoSxzLZ3nmOP/A4OT0JzqnrUSwWWZibo9Xugi/z8P5dpoo5JHyMzBjRSDSQgLk26+tn0DWJ\nzSebjI2P0Wy1mZufQkLBccDQdZLpIvWWxZ/9xXdZXVrk4cOHjI9Pk0lnaNSbdPsm9+7fY+38eR5v\nPmV3/whN0zi7fgZD0VAGPtGYiun2Ma0ehWKedrvByfEey0vznL9wiZ7ZJxKLokd0yrUKr73xOp1e\nj63Hm6yurjExMYnVN9EUnZPDEyKJOAd721j9LulEEte2qdQryLLE0yebVMplzp09Q71cYWDbpJJx\nPNNExceQVfb2dgja7wH5QpF7d+8SMSLsbe+QTKaxejYP7jzkq+/+HI82tvjz736PpZV1fvzxp/wn\nf+c32Ts8ZHxymkq9S8d0abR7oNv8vd/5LY6Od3HtPtevXqHTbLCytEAqHsMxTUrHJyiSxPzsLHdu\n3qJZq1GvB+Ev0XicnZ1tFtZf/dkv4B/98LvvxeJxYukMe4dHZAsTWGafZDoZErOi0Sj4A3RNJZfL\nYZpmqIEW+tpIJILnuoFcZ8hWHrUoFaYmL6BJJSQdCX2spmnUarWXUq3ELlX8rICeRZ6ykEMJKF3A\nw+I5Rpm9o25YgpXc6QQrAt8PyGuypuF4LpGIHhiBDG+sL8w51HDaM4YxfqJJEJOq0OgKaFsUFEFo\nE8dUwLXiWAijj2KxGBbQ0SIuIFMIpvv/eA9eqVRCEp9YUYjkrFHZmHBwE4xsEZgyOgmLpkMULFEk\n4/E4k5OTIUwtiqZYA4iiL/bCotkQU71gcYvzLcx7XjznkLhm9UPVgOs6xGJRJAkajXb4fkaP66jh\nTiQSpLG1Wk1y+dzwPHfC5iewjq2HELpt2/R6PTqdDuCj6epwD28hyTKyJA93+GrYbMTj8cBEpN3G\nsuxAvaFqmKY1LPoDBgMPSfo/2zvPH0nu/Lx/uqq6ujp3z/R09/Ts5A0zs2l2uSTvmETyAnVBCbZ8\nlgxZFmzDkg0D+hNoAxYgwPALR8C2YEBnCIat4JNE6nRMR3KPXB6XJrlhNs1ODh2mc6qu6qryi+pf\n7azeCX5hr1EPsOAbctk7XVvf9AQ3jEU45alBdeS1P/QS24QLnNiIiAYhmUwCPKZvF9+LG3FrkcmM\ne42teNbcFbuNJCn0en3a7Q79vs7BwaG3tq9Wq56/+2G5hGmYpJNufG8kHOHLGzdpt1q8+urL1IpF\nZiZc85OxiQyNdovx9BjxSJR4Is7Vq1cZy4xTqVT4/ve/TyqdZnV1lT978w12d3ZYXFx0Xb5G5i/j\n6TEiWphSscjc7ByThTy5XI7PPvsMwzC4fPkKa7fXCKkhdnf2mJ6ZYWqqwHBokh7PcFg8BElme3uL\n4dBke2uTZDzOw/UH/JN//FscHh5im0NsHBKJFAcHB8SiCX7+F36Bf/HP/xm3b90hlUrT6XYZGxvn\n4cOHDIdDbty4yccff4IxhHsP7rlkvcGA2ze+IOA4qKrG0DA5MTXFytIyP3zzTe6u3eXzz79kd2+P\ny6urDI0BnW6XielZEiN+SV/v0e93sYdDxsbSSLLM3kEJSYa333mPt95+l2arSTqdo93Xube+zurF\nVR6sP2R+8TTvv3+VcvmIVrvN3v4hU3MznDq9xGT+BHt7O6hKkNXz57l7+zbRuKuj77Y7xCIRolqY\nXq9NPBbj4LDk+pk5DpGIhuPY5LITZMbHyI5nGMuM0+60icdj9Ltd4sk4zV6X+/fWWJiZY2N9HWPQ\n5+DggKPqEY5t0e10yOey6P0eqWScZr2Gaejk8zn2dnc4LB4wMz1DrVpjZ2eLF59/Eb3fxx5a/PSn\n15k5MUtQCXLr1hq2E+CX/sYvI8kKZ86d4+y5Fc6dW3Etmps65y6s4gDPfeUSDx/co9Wok4zH6bdb\n9NpNDNNAdmxu3biBZVlsbmwwlk5RrZTY3nrIqVOLlEtH7O7uEdY05laeefIL+I3rH7/uSBKlSpXB\n0OKo2kANKti2xWB0rwSXlZ1MJj39qXipi3WvmIgMwyCfzz/2/xCrYmEpeVwnLO5+hmHQaDSIx+Nk\ns1nvbiyMUsSNVEywwhBFNABikhPFUEytx+U5ohCKO2+r1fJ01F6WtOy+BMWqE/CIW/B4aIkzmsiE\nTOu4rEcUO1G8/qqdpmhmxDQumhZRUMTWQBT9TqfjGcCItav4mQsZlDA9EcVUFFCxnhVyt+O6ZX2U\nENbr9bwmSJCoxJpdTOqiaPf7fW8dJv7bdrvtnQvEZ6jX649J5MQdWJwRgMcaQPcZcZ+nbrcz+t67\n3pTtFruw9/MTf8ZWq0U6nfYmfpFoFotFPXc58b0AIwJYEm00pTabTQqFAqGQSzB8xFx3UNUQakhl\nOBTF+NFpo9vtks1mHyMZinOPO7EP3WcogKexd3XyPLbyF82aeB7AbQqr1Sqaprle/H/lXGGaJs1m\ng63tLfr9vvf99ft9TMMkFAo/MtFxHI6OjpiZmXFTvUYJYWKjM7QtTp86hRYKMejr3u//wvPPkR1P\n88lHH5EZG6Pb67J3eECtWmVtbY2p/CR37t7l/PnzSLJrhvLp9evous7BwQEBWWJ7a4tPr18nnU57\nzaGhD7BHxMLBYECtXvW2OJlMhv2DHdJjafTBgM3NDRKJCLICe3vbVI5q9HWd3b19isUiwiaXgEOj\n3uDO2i1UVePW2hq67j6v7U6P73z3u7z99lsc7O6yvLzMzMws2YksmqZRqx2xeuki9+7dJxSKYNiQ\nSCQ5f+4sN778nO9++2fpdrucXVlmIjPB1GQBczBga2OD06fP0Ol0XHfCiMbLr77CB1d/ws+8+irR\naJRms0mz1WBhYY7EaEsjIZEam0SW3ZzsjfWHhMMh/s6v/xq1epO1+7fZ3t7n3MULyIqCbQfciOdC\ngSFD8pkJUvEUhclpzl+4CDjoA516vU5IDVGr1jnYK2MOLLrNDpIjY+gGzUqVh/fuU5iYoFVvMNQH\nNCpVjJ47sVvOkK7eIRiUiUY0KpUSd9bvcvH8BTqNNr12l4+uXmXu5DzpsTSmZbKytMJ+uYSqqUiK\nQk93vT3iiQTbO9sszM3x4ME6juNw995dSiU3T+Hw4IBGs04sGkZVZdSgwr//D/8WLaLy1ee+gizZ\nfPThj4nHIuQnxmk1mvzwjTepVCqkk+O8+cZfMjRsfuPXf531B3c5feokhmnSbbfo9nvMzMyysbHB\n7u4u584vAzaZ8Tx7e4fk89MoSoiZ5ctPfgF/40//5PX+wKRaq2OYFp22yw5XpACZTIaxsTFvejie\nBy2KiZiSRbjFcXMUsXIVDGtBcBIvX3F7FEQ28ZKWZfmxF7J44QsPajGViibiOOv6uP5Y3KrFS+94\nTKjIaT5+qw0osndDFcxoUcyEnlrkc9u2DVIAczQxCamUCDqBR7GT4p+iMB/XFwvynVijDwYD7z4u\nLE9DoRDj4+N0Oh1vVS6Y2OLnfJx4JaRZoqE6PgF3u93HDFbE5xAkNXEHF1Od+PkL9rX4GQtug5BM\nuQRB5bHmQPi0i02A2LSIqfk4n+DR9zZa2yvyqEGUvRuzLCvIctD7/sGdTlOp1Gg93feKtHtjdt3/\nJEliZ2fHazZdJUDIs2wVXIVIJDySgpn0+71Rmlr3GMFR4Y033iSZTHrcgsFgQKPR8LYlQj0ArlVq\nWAvT7XW9RnNoDqmPFArCMU08b+LZF37V4lkUtrTHFQeu5j1CLpclEAhQKBSIRsMIC+NQyDV5SafT\nBINulrlobKPRqGf7ure3R2TE/u12OpiGSTKdojA5yfWffkL58IDf+PW/yw//4ofe93p4cMCVZ54m\nkUoSHilBSpUyw+GQfC7Hyy+/zNTUFDt7u8iSRKFQ8P7eFYtFpgoFup0uH3zwAa1Wi5OnTpJKuYz6\ng4MDcrkssqzw3HPPE3DANAfogx5zc3MsLJ7Bsm1KxRKvvvwKrWYTNaSydHqJqekpLHPI+PgEDx6s\nMz09QyYzwccff8zu7i7nz58jFtZYWloiFouzu7tHt9vms88+BSwWFhb55NqnLJ9bZXd3l3t373Lu\n7ApKUKbebKBFIrSaDSKRKOvr664JUEAhmUoQiagMhkOCkRjhWJxwJDg638TodbpYxoBw2CW7Bkd2\nqqdOLzBVKCAj8dprX+PGlzcplWusXlxla/Mhp06dpNXp8MlPr7G4uMBEIct+cZ+xxBiXLz/D//yz\nN9k/OODipUsUS0Vu377FzZu3mJo8wXvvfYAztCkUpnAcuP7Tz3Bsm1w+RyKR5P69e1TKZVqNJsXD\nQ6SQwtrdO0zNznDr9m2KxTLJVJpGp4OExu/9x99jcW4RyxrS6+u89PJL3L55k07LtUk2zSErK2fB\ncrBttzkbGxsDB4rFIq1Wi1xukp39PWamPNwBDwAAGhpJREFUp+l2Opw8vUilWiE3kWFze4PpqSk2\nNx6yv7/DP/iHf592o048GqNeq7G8fJpYNIZEgKlClp/77rd5/vln6PebHB5soioBGvUj1KDG0pkV\nIuEI5sDka197le2tTeqNGkPTRh8Oebixxe3bd3j15/4/MHL5sx/8yevNVhNFCRKORBhLptH7XTKZ\nzGP3ZfHCFKtsUTQEc1oYaojuXbygRQET/tVi/Shu1qKIBINBqtUq+Xzem+LEhC/W1mLyFExmcesV\nhUCQzMTtT6y0xfpeFCRxd3QcNx8c3EKqj4qWV1wtC2NUlIXDlXjZeoQtx2WHC4JfpVLxplzxUhdN\nR6/Xo9FoeBO8KFri84liK2744pcofGKyFylp4hZ8PGlNTMFi86DruldI0+m0VygE8zkUCnkNipgg\n2+22p68W/AOxCRHEwuOsf9FUHDeLEQVefH4xoYrNiJg2RVMlPq8sSyOJYdST2Il1svs9Bz2tutje\niG3GX916xOMxlxB1jKAmmrtms0UgILG7u4thGHS7Xba3tzg6OuLmzZuMjY9RLrtFqTLKenYch+Xl\nFU+P3e12OTw8PNYUqKPbfni0XXILf3TEDXF9CpIjLbjb4GQyGU9dkUqlvCZV/MyFemI4NEgmE4RC\n7s8jkYiNmojASDtujP6cJuGR54JYx4sJXmjoo9Go14gCKGoQyYFysYQ8OpO1mk0+u3aN5559hukT\nU/R7PVeP3tdZWVnBsize/+ADDg8OXJOlRt0LFsnn80xOTvLg/n1OnDjBxQsXqNVqrrlLocCdtTW6\nHdfr4NJTl7l79w737t0jn89TKpVYWlrGGsKXn98mEom625JwGGQFB5mQqhEOazQaddLpFMlUglw2\nS6fT9fg3siyztbPN0pklXnrpJXfrkoxy+8YXqGqI/f19Njc30cIqYNNuNtjdO0CSgqjhONbQ5sL5\nc2ghhVqjRiAgE41EiETiDE2TRCJBuVzm4cMNpIBNrpBn6ewyb771Lt/41rcZ6iP5XkBG13Ua9Rr7\n+7vEozHKlRKpZJyH63dp1uqEtTC2ZfMH//W/8/3f/xP+0W/9JjMzeS5cuIA5HJBKJJmZnyES01g8\nvUh+cpqQFmFvr0xAkvjxB+8jBxzkgMylyxdptlpsb22hSBLvvfcu2ewEIVXjzr07DKwhpUoZ3TBQ\ntTCHxSIr585RKlW5/PSzKCGN7e093n7rHerVFn1jyFe+8gI/futdJNvmmaefJaAEGJgmrUbLdUIM\naZxaWMSxbO6u3SEajjKWSvHh+x9QmD7BzRs3mZ6Zozdwn53aURUtpKFoEeLRGANjwPLSWarVOk9f\nuUIiGqPTaiHJQWKxOPv7+zgBSCXSvPjiy6STEXqdDu1Wk3ajRrdZo1auEQyE+PDqR0RUDWdos7u9\nQ0TTONjf58H6fXKFLAPTpHRU4eHGQ/7mr/3mk1/A//yNH7yOJGE5EIm6U1QwFERWFPqdNtmJDNbQ\nRpFlwprG0DTBcZBk2VvxPh6RGPJunKIQCQ2wmNZcQk+bYFAhEgkjy4pHSAsGZVQ1OJLa9JCkgEfC\nOs50dtfHtmcO4wZiBLziIbyhxZperCbF1Aq49++AhGmYDC0LKRAY3b81b8qMjO7eImBEaGwDgQB9\nXUceNQmDwQBZDRIf3b1EkRHrZEmSSCQS7pQaVOgPdOSA24Q4lo0+GHmJa2GCqsrQsdFCIRKxuDsZ\nDU1v29DqdtysdFl2P//QJBRUIcBj1qXC+MUeuo5kxxsdMU2JLYgg+GUyY9i2Rb/bJxaOMTD7BIMK\n5sAkrGlIssu+dySHiBbx+AWKItFo1Ece3x3vtgt4gR3ieRGrf7fgu+vmQACi0RhDy/B82I+T/8TN\nvtXqMBwO2d3d9VzGxFmh1Wqh6/qIJOfqz3u9LuVy2VM4FItFqkc1ej3dazYE70DTwgSDCvl8nvHx\nMeLxOKlUipMnTxKNugUzEnGLo2vKEiafz6IoMqlUckRoc0NYwCW+KUF38yI2FKoaZGiZOI7L6+gN\nXCKZ8LtXlSAOLkGz2+1SKBRGPAHHu5275whlJJnTR3+TA8Tj7vOFA+FwxGu+XYJdEFUNYttDjo4q\nFIslVFUlm81SKdaQJJtYMoksyYTlIKoWY3Nrk9deeZpyqcqZk2d47933MEyXU5GIx4hFIhiDAS+9\n+DNsbe+ghtxGvlatkkykvImzXKkwOzvL0BgyHFpkxsaxrCELCwt8cu0a2zu7XLnylMsLCGsUK0dI\nisT+/g6BgEMkFiYej7Hz8CE7B0W0UAhFDnJidhbTGHJ+6Sy7+/toiSg7Dx9y1KyzdGaZ3GSB/VKJ\nvc0tLl04h6qpzBYK9AYD9vcPePmFl5CQ2T88YG5xnmQiyfzMHNdv3OLrr73G4cEhvU4XRXJX6nNz\nC9y5/4DxbB4roPD9P/hvPP3sRb7+jW8yfWIW05aYOblIJpfBHvRxAkH6g757igxA8bBELB7n4KCI\nFg7SbLQ5fWqZUEhja2uHUEghmVR54wd/hN5rcen8ecbHMuRO5NFiUcbGJzh39jwYQz764BoBW+be\n1gaqHeDaZ5+xevEiDpDL5TFth4XTS+yXyrT7Bp2BgRIKsrWzz/bOIVu7B7z27Z/jB2/8iMmpeTp9\nAweF+/c2mD4xz6XLV2i0Ovzwh+9ytLPHz7z0IqlkHEmBkKaQSCSJJuIk0mNIUoBoLO7e+o0ek1OT\nbG9vc3RU48ozV1AVjfPnzlGv1tjd3WV6eppIIobe7bO6epHDwx20sIwalJidmabVbPCXb/2I9959\nl6l8jkqlwvvvv4+hDzB0nU+vf8JPPrzK3MwM0ZDE/u4DZqen+fDqVQ73ikxO5qlWaxjGAMe22d8/\nJDOWI6Bq9HodMmNJLl0+z7mnv/7kF/A//eM/fH1oDpECARLxBKl0gmBQBhy0WIRKrQpyACfg0O13\nsQMOAUUiKAe9m6qY6I7LpFRV9Sw3j7uVGYZBs9kkGHTXyLIiYRg6YU2j3+95rkn9vk4k4jKLxa1V\nkl1jFdseYpoDbGeIqgaxLBNNC3ufRxQqYRwiph9xQxdrVgsHOaiALIEUQB41HWJSN0cSK6GHFmQ6\nYUQA4Iwm/kgkghoK0RrJwUQhFRMn4G0lBn2dcEjz/p2BYbgpZparn+8N3K1Gt91xY/IkCduyIACG\nNfRY4rZlYVsWDg6242BYQ6LxGAFZoqv3UTUNy3FQtRByQPIm7uMnDoBoNIyqBrn/4C663qXZrGMa\nBoahE4vEXW2/bVGuHaFqQYKKzNA0UFWFvt5DkgOIJLFA4JF5jpjaRfMmvgt33e3a64ptiq7rVCpl\n+r0+lcoRtWqNXrfHQDdHZCydTrvrmc+EQiEmJydHCoAI4bBGMhUnHNGYmBgnNiq4hmGSTKZQgyqx\nWJxUKk08nnS9mUcbiePbiHg8gSTJ9LpdZElGUULs7x2gKEFkWRnF5D6y++12O8iKhGka4DijG76E\nPnh0lw6HwzQa7trcM3QJyrTaTUJqkLCmEY/H3AYAx2tC3QncldTZtoVlmxjmgEDAIRzWGBgD71mz\nLAtd10dkN9cOWLgbuva3MSzLpNNtY9sW2YkJ1+NgoBNLR+kN2gS1EKoWotPq8Lv/8l+RSo9TyE0g\nh4JIIYXZUwvopsFBucS9zXUmCnni4RitdhvdHKCEFA6LJRLpNJlcnq7lul999tEnfOP5l9jYuM9B\nZZ+Pv/gpAWQuPbXKnft3Wb14wTt/hEMaoWCQ7a1NxlJjzC8usHLxHDOnTnJYrdBotzAGOrZjEQmH\nufHFlzimSXHvEMMwODo8YmJigqNKhWg0wn5xh0IuQ09vc3hwyMREmodbD5kqTFKtFIlGVaamC2A6\n3Lm9Rr/XpVqt8tn1n3Lx7DLFvR2mp6YZWiYHh/t862e/Rs/o4TDkhRe/yotf/xZf3rjN0LJ478fv\n0u/2iYY0ZibzyKqM3u1gDHSWls64VrvhMJIskc7mKExPc1DaZ3vrAWrQ5sqlc4RDMvPTBZ66/DSp\nVIpbN26ghTUky2bQafP+j37k+iKYBjPzi9x/cJ94LMJ3vvMt3nn3LZ5//jlmZmawTROj3yOsaWQm\nxllaOk1+Zob5+XnmFhaYn5shqDgEJRM1aFMp7TKZzbA4P82JE3kymTS31m7xO7/zuwwDEj98+0co\nkRC5mUlyhRzF8iFyALqNOrnJAtXqEfFElM3NDcChWCpi455rJEXi2rVPyOVz5PN5bt++TfXoiPn5\nOa5evYo+GBCOxGiPtjLxRJzTJ08zc+IEAdtGxmJ+bprGUQUFh3a/x2//9j/l448/oFop0e+1kYNh\nBvqQn3z0Cbfv3WVgWlz/4gv6loUZsJlZmGc6X2Bhbp6IFsLU+5x99ptPfgF/90dvvJ5OJZmZPkFQ\nkcFxsK0h0XCEVrftTkMjdre73huO3NhcUlCj0Rit+IZeARdMYLGuO+6K5iU5OTYOtseENkY3ItN0\ntdpuPrLiEagikQhKMICiuCYryWRixBy3MQ2TweCRg9bxQqUoCtFwxJuEul036tI8lkAlJmbx+YDH\n/KTFDVvccsXnsh0Hc8SE1zSNgWkg8Si2VPiGey/t0e8hGN3iFCHLMp2+6+NtjjTO4K42o1oYOSC5\n1pWphHvOkFxWsyLLxKIxTMMgqLppZ5Zjs7e3RwC8abTdbtPrundYsR4X7HT33DHEtl32daEwSTDo\naoZlSabTbHNwcIiNw607a3S7HarVirdq9xjc3Q6KrNBqten1elQqFa9YdzodTzvebDYBV9udTCYJ\nSKCFXUaspoXIZXPkcjnC4TDpdBpZlkgkkt75QJD8ROEdDg2Pi2FZBoYxQJLcLU693hhp61NIkpuU\n5k6yiqdmADxOgWEYHlsdAt55RRD9hCQyGo16xDNJChCLuUlqWljDGrrFWYTSCPMYcX8XnvAuJ0Jn\nODSxRjf2fr+HsFwVLnXDoYkaCjIwBp5+X5A4x8bGRmEkAY/0CRCNRLEs29taBAIi1W1IZiJDNptl\nODTpdXtEomEkJUCr1SQYDKH3dSqlIs1Wl+9975eZSMXo9d1m4MMPP6TT7dDtdZmbm2dyskC/0UYN\nhfjiy89ptVpMz0wzP7+IoqpIaohCNsudmzc5KpXITmYpVUq89q1vYTsOjUaD9QcPWFpeRu+7EbNv\nv/02zzzzDPu7ezz99BWarQbNTpvcZJ7Z+Vleev4l1m7eYu32Gg4OL371q7SbLba2tphbXCQoSXz+\n5eecPn0KWZGYyGcxej3GM2MszC9SLpdJJFNIODC0GA5NdvZ22dk8AEdCDarUW20WT57kYG+f3b1d\n1m7dxxxYHBwc8rWvfZ3/9J//CxcvXuHy5Wc5dWaZ3//+71Mul1lZWmJhYZ5Ws0FmPIMkuZsQYVQk\nTk87Ozt0R0qQeq1GuXzIwtwsd9ZuY1kWp06eZntvlwfr6yyvrDAYDNhYv8/Ww3VUKQBKgHa/T7Pb\no96oEQ4FWVo6TeWoQqvV4I/+8H+QiEaRJZmABM1mg1w+x/LyMvV6g4mJDP1+l0q5zFOXLvDOO29z\n4sQUL7zwHO12m2Qyjt7v0ajVqFZr7O7tk4hHuH3nJrF4lKeevky71SIZjaHKMrl8ju3tLR7cf0B2\nYoJa/YgzZ06zvLyCZY22LpkJarUa0WjUIyxaluXG16phNje3WVhYYGHxJPVGE3U0KCViUZKpBKbR\np9/r0et1SGXH+cEf/zEn52fp9ztcvHCO4VAiHE1QLpaYmZ7GcmyOGnWufOWr3Ly1RjgaY7owxYP1\n+1hDky++/Jyv/9Lfe/IL+Kcfffh6YnS/Fi9Ecf+UZRnLMHBGOm/Htul1euAEHjPHEIVS5BALdinw\n2Jqy1WqhKArpdJp2u4UbBuFGezrOowAMWZIJhTRvbe7+HmIFrGBZNqrqrqhxhAuba1Ai1rbivgog\nS25utbhFO5K7arcEGSvwyJ9cGGzIsuy5RgHe/Vvc3nVdR1YU4iPdbq/Xo9lukYjFvUbiuMuWYP2K\naUl4WAOeVau411sjslmn3QbLBsdBCSoc1Wveur5cciUhvW6PZrOJw8jApOeG0QQVBWMwYGI8g2UO\nvQxvcVIQDUsulyMQcIli4Uho9L0EiISjRMJRFEklEo2RSCUYy2RIJV2HJPccYuEarAhSoUy328NN\nGHP/vLFYzJPNidure98Nj7TPmqfPdpu+FpIUwLItDGNALBalO0rMCoVU+n3dI36592/Jy/i2LHPE\nRXDd9dSgIApaHlNfVVUajYYnWRTTruA+HFccuN+VQzabw7YdJCngPdPC2z4YVDCMwSNOB87oOeoS\nDIqm95HaQPBEYpEosiShyAr6YIDt2KNn3vKeNfd0ZKIEFWQ5gKaFcBzXHdCV1UleRO5xFrfe111D\nmRHJU1VVms06QdVteur1+mg7ZRBUg9TrTbBtwuEo7UaTdDzO3NwcjXqdF56+RL/bIzueod1oYg8t\nLl1c5czJU1jmkOz4BOVSmYmJcX7le3+Le3fv8ODhBptb27z31tvkJjK88uILVKuudvjUqdP0dR3D\nGvKTn3zEd77zHQb6gJnpaTY3N5mZmSEcDhOPxWk0Gnz5xZe0ux02Nzaolo8w9T6OabK1sYEaUpmb\nmaV0WKRUKXPj3h1S0TALi/P0Ol2mpqfITxaIaiG6/T4P7j/g5KnTKKqKIklEQiEUSaLd65GIj7N6\n6RKOrPDUM88yHNoYus7q6kVCSpBsboK//avfwwE+unadE1OzJBLj/Ot/8++whybRsEY+P8npM6eY\nnp6mr/cJOAFsyyVgloplLMtBDYZot7vEY1EatRoRLUS76WaGB4NBrn92g6WVsyyvnCWVSmOPuC22\nNSQ7MUY+O8Gdu3cZm5ggnZ2kuLtPPpehkMuzemmVmdlpls6cwhjoPP/V57h9+yanT58hGo3gOAFO\nnTyFZVnMT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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# load and display image\n", - "# I = io.imread('%s/images/%s/%s'%(dataDir,dataType,img['file_name']))\n", - "# use url to load image\n", - "I = io.imread(img['coco_url'])\n", - "plt.axis('off')\n", - "plt.imshow(I)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Aow8/yiOPP0pbYyO33bif2ekJZtMLvP8Tn+bMG2/wtrvuYnI6Q0NDKwcP3M7h\nIyepTzWybccOrpw/wfCFk8wvpCmUNVfGZrlx/07mZifp7u1hsVikvXcNSvnMz88zl8mQrKsn2dDA\n66dPs/eGA6Ak0rbJLmRYXMwyPjVNS3MTylfMzczQmnT491/4Lf7i63/NwVtuw3IirO1oeutD6Lly\n6X5YWTk6jkOpVArg3xKxWIy6ujqmp6crD5FSCqHCpVvVc2s9rdrfS5Xz1QLqZ82mXS6cwuVlUoiK\ngrCCZWcq8H5L5RI+GtuxgqSsqudqenO1IvO1bzTDsjhprVGy3PhYfmvLlbi0RMVjj8fjFQ9eSF1J\nfKs1nqqevqIKkV997aADVykB45nLJUZIKGY1esl63r9PaWhdvbnqmF6dHRxCruFxYnl/KjwxvQk/\nRZC8VIHRqc4ZIQz8LLTC0kECmTZxb60M7mBSEhRCKCwJQvho7SGkjw69WW0yrwUyWPZmlLgUIliu\ntpRqld6S+XkNBV5JNBOGD5ZlvGkJ4Vo6VIBkLLHH9NXjVuX71XNMcK3n6up5sRIvl8PxWutq6CD0\nuJcZg5XvBu83qIUOjNnA7CH8lAZ1C+etr02iHAIsaVW8cMdxzMoNz0NKSTweZ3Z6Bs91iUdjaCCf\nzxONRikWixU0KxKJUC6XKRaLAFy+fIlYLEYkEkFKiet6jIyOkp6eY3xyAq9YRPkeMzOzbNi4mZ7u\nXi4ODGBZklQqST6bxfNK2FozOz1FXX0ds3OzKFVmeGiY8clZOnv7OHrkdSanp+no6mBtXz/Z3Cw/\neOBbrO/rZX5uhg9+6IO4WpJOz9Pa2srw8BB1dXV0dXUwMztLZiFLfbyBhro6Rq4MUy4WGblyiVMn\nTpCIJ2ioq2d6LoMlJa0tjbiuoqm9h5bWNizH48ixVxm/PIwlHdas62ddfz+xiEMmPcvsbJoNGzaR\nW8zT17eOxcUs8wuLdHR109nRQa5QYGp6hq1br6MuVc9rJ07yjne9m/aWRu5+29uZnZlF+5odO7Zx\n6IUXiMfi3HTnbYyOXGHo4gWU64G22LJ5C9lsDuV5TE1Nc8cdd/LqkVe49eCN3LD3Oh74zrcZuDhM\n27qtxByLjtZmxibGGRi8wMc+/mGidUlGR0coa82ezZs4/PzTeOUSR0+c4s577iXuSF5+8UVGRq+Q\nd13qW5uYmJhkcSHP/htvJJFKMZueJ190Wdu3npLrIW2bqYkJtl23g6Lr0hCLsJDNUSossn9zP9/+\n3g/5L78IAhC4AAAgAElEQVT7fzG3sICFoKOl7q2vwAuue/+1PNuwYIqBPExRhTDePD09jWVZxONx\nhJRBIYiVBNrV3nfNVa7av/zz76OVPIpQ8dm2BUGsreSWcSIRAFzPo+pt1RYvWVlYVbLUll13pWS7\nirDV176HMDs3PD+TyeA4TpC5XL1u6KGExknV+1625M3sRAhp4pYrxuvlEoShosBVeH/V/i+/n9rv\nYc5C7e/QqKjtc+2yLpOTcG1lU+v5+soP8gauVvZCCCyhgpCMCnShuRMdJIVJESTE4YHQCOWb3I4A\nHdImrmDCArLmXpDVBKwl91pVnrLGQjX3yopUNeK0KWgUrF00HmngtSJRynj5mioPw7aXz+sVw1OV\n61nUohnh8bXIybV5b06rxR6kEBXvO/Rywz6FT4IAk8wYKvxgp6ygGaE8UBAUKdIIc2DQVa11xYNO\nJBIsLi5y6dIlmpub8T2PYqFIMpEgkUwyMzODV3Zpa29nbm6O5ubmilORzWapr69nZmaaeDxOKpkM\nlnIq4tEETU0tPPLjR2htSFEo5EjUpejpW0fZM3H19o4Ortu6i2R9PVpDenyCo0eO4fmK9p5umhqS\njFwa4/kXXuLmW+9gemKapqYm7nrb7ZRKLi8f+in4BWanJ9m7ayeRqE2iroH6hhZisRgDAwNs2bKF\n+bk5orEodfUN9HT1sLa3i5b6JHffehPjly7z5OOPs//6/aTn57hu63Yunj+HJTy2bN3B+PQCFwcH\naWqO89STT7Jp/Xqu27EXO5akUCozMzNNW0MzxVKR1tY2pqZnaG5qRinF3uv3s5jNceH8BeYWMuzZ\nuwfbdnBdl5a2NhazCwycO8/b73knPV1rSCaS/OjBh7h+334mJsb43o9+QCGXZX5mmh3bd7Jjxy7a\ne3rpX7+B3rVruTQ2RckvozX89InH2L6pn/PnzhBL1PGpX/lVRi4Nc+HCWXrWruHpZ59mw8atLJYK\n3LBvHy8fPc7bb7uFJx78Po7QXH/gRj76mX/Gxr517N25i9OnT9O7ro9UUzO7d95ALJpidGycQrGE\nbUfILGRJJBPYkRjSspBA0XXxhaS8mAEpWL9+LemBCxStKL3rN3D+wkWaU010tP8voMBDD7yWagVW\nKARqK53FYjESiQRzc3MUCgWTBS4FylNYlqnSVH3Ml9LPqsCvPhaqWbtVDzQkS8hK5nQodJQyUT0Z\nVEArBQVVTJZqGJ+uJjAtz+4WQhh8U4hAAC+N29by6qrfK3jgVY9dLQlBhJ6FlBaWXY0/1wrgaruh\nErnG+mZlMuA1S5WfL5YaIYIqDIqoGgRvJuhrkZOlBlPtPQZLnqxqHFUEMHdtomHYiyXsq/FAl19X\nWsK0qU1Wty1NcpoZ6yDxCx0sO/MrBWWM4tABX6zA6zZeY8inyk28CXS+kkL/+xS4MmYTCLPGW2sf\n7WuToSAslFZXebihF1w7xa/lWVdGI8zq5+rjV+pX7fWWjDdB4R1ZjWvbUhoDfdk1lxdLqmpxc5BZ\nWRJCO+Y/HYRGwjYikQilUskUaNGaZDJJKpUik8mY4iuZBVpbW3EiDolEgrNnziCkIJFI0NDQgO/7\nOBEbz/eQlqSzs5OBgQHaW9sQQlAolBHCxpYW+/ft58XnnqKnt4tkYwNNbR1MpedYyGcZm5jAtjTp\n+VmSdUneOHmMD33oPo6/fpRsYYH52Syd7e3s2rGDucw0p147QX1dPR/9yMd54rHHKecWaGuqY2F2\njp07dnLp8mU6OtaCtMksZLBtm127dlFfX0c0EjHPuiMYHx3itSMv09PRygvPPkNfXx95v8Rrr5/i\nPe9+Fx0dbfzVn/8ZGzZsYV3fZjy3zJmTr1L2POrrmkimmlEIEql6tO/x5E+eIF/K07euH8e2icZi\nWLbN7OwsM+k54vE4Fy6ew7EtSuUiU9NTbNq8nhdfep7Gpk6y2RLSjtDe1s6+G/aTyWRp62jjlz/z\nz7hu4ya62zr4gz/4Mjfefhv1Lc3Mlwsslors3LWf3rVruOXmgzz9xBMUs/Ok6uL84MEf87b3vo9U\nvIHRkTFuvvUuHDvO/FyBzq41pFJNNLW2Mz99hUJ6ghef+Sk79h3gpjveyfPPPsv77n0fP3nscQ69\nepibbn07jlVHMtnMwsIsExMTtLW2s7a3F7fkVpIno7EYI6PjNDQ1MXzmNGv61rChby1/97Wvcuu7\n7yMST5JK1DEydImtW/v+UQr85yKJTQWJNLWbRla+u54RQwgL11MoLcgsZPF8TUdnNxrJxPgUSiki\n8RiFcglYGY69lqe//HMl7+vq85YW79CiVnGrAKIWNYpaIKVNLlfA8zzj8QaK3FwnrAAlTDlSLatl\nSQM48c3g5ZX7uDL0WvXQZMWDTSaTlMtlSkUX3zNLz7QSuGUftFyy7GsJfF5TKGQ5/yr7hNlvMu+v\nPsYkWRl+Gp7WZGgv+Y/Kp9a168z96nlowiVS4X4ZeGCVREVNsOkKlKy0xtfe0izvCsN0pW/hmAsh\nliAKEhEkolUr8RneCgQ2UkQQIlDgYYxdVMMoSxTZmxQWWjGGfA2SVBMPfa1QCvPpgzYZgyvO98o1\nKrbFzz7vlverFhWpNQZr25Q15y6H18Owy3Kem/MVlmObzarhmzZhjlqeSoTJ8FfaVAxCUy6XiEYj\npmZCuYxQimQsRkdHBz1d3SilGB4eNsvGAiNiZmYGKSULixlTGClACnO5HKVimcLCAqdOHKWcy5HP\nLjI8OIRSisx8mr27drKwmEZamsnxUcbHx6mrbySaSDCXW2R8dpbZbA4iDucHB7j9tlso5rI8+uPv\nc/OBvbz9rtsZOHuOt991M011Nk899hCZmTF6OpsYHrzAO+65G9s2qOTY+AiWgM72Djo7u1lYWGBq\naorxkSskow5zU+McefkQtx28Hr+Ypbu9jVQixa//21/nM5/5LCePHKM52cBHP/wxvvrVP0ZJl5Kb\nI1XXwLq1/axds476xkbK5TLJaITerm7aOtrZtHEL/f39FMsuhUKBgYEBnGiE5uZGmluaWJjPsHXr\nZqanJnj6qScpFrLs2rmV1o5ORiamGboyxhsXBmhq7eSXfuV/49VjR/k/fu3fom3J3gMHeOe77uWL\nv/vfeOKRh8hMjdHb0YL2PaYnxzl37hzvfe/7+f73v082m6Wzp5vmxiTd3d0oIRGW5LrtW4Aia3qa\nWFyYoZhdZHRshlRLG2NTM7z44os89dMnGJ/JMD23iMamo6OL3q71OHaCpqYmuju7GL8yQnZ+Dq9Q\noj4RZ+DMGSZHR5FSkEjESCUS7Nu3h5b6FLpU4qfPPcudd+wHz6VcyHLy1LGf+Zm6Fv1ceODZUnlJ\nJ4xg1hXPKFQYtYpDCGOVe66HZdnUNzQghFnakM/liCcSgUA262zDdcMmlUnU+Ao1ZU9roLrwulfH\nd32qkVsVaFXTTlhkZDn8rZRR5J5nYOhIxAjyYrFUWZImhBUcu9QjNMK9Ni4ertcOyoiaIF8lmSr8\nHsYTYanAr8afJYKwKI3E801deccx617z+YLxLhyH0EtVKizIgVH8YTFSHcQflYHDK+u3QxtBKDzt\no7UMHKEKfmnQicCLkssEd3gfJuNbVorRCHNTECwjq46NKc5j28YLl1KbJCABytd4ysNXBgXx3DCW\nD7Weo7RE7c/KOnKtfESwHtxSVLK5lVjqRUohTUKVAoFtDFEsNJbJbA+MBq1MvQAfASKs5FWFz0M8\npuLZhhZciP5Ufr852aJaUjZsWYpg7JS/ZLy06ZxBRmqSFy3bXoJkmMqBQaa4lNiOrJQrtmwrSNeo\nPiNm/TVIWa1YFs7HipKu6XP4/9IVGktXWaigwr3SmpJXDvJEQsi9moluB5slLWzLxrIcLCmxTFYf\njrQqyXJSSiYnJir35fserlumuaWZ02dOE3EcNmxYj/Y1c+k07e1tFAtFwlS+02+8QWtzK7OTV1iY\nm+LkieMkEglm07OgfEYuXSRiWQycP8nu7dsp58uook/cirChey1WMkUy0Ughr7n1tjt47LGfcPyV\no2zv30R3bycvHXqKBx98gO6uHlJJzbe++XWu33cAVSxy+vXXaG5qpL6+lYuXRtm0bTtl38Hzykyn\nZ0nW1aO0ZmZqmqiUjAwNUMznOH36JNryWSgVae7sZe2GbRw5cpKN/Vu44ebb2dC/mVOnznLm/Hm+\n9vWvsnHTeq7ftY9kvIE3zp0l2dyIVy6yMD/D6PgVSqqM9jXnzp5n954dZDKzZDJzFAt5tPKZSafJ\nZRe4PDRExIrygQ98hMmJCUYuDXPDgYP096/DiVn0ru3G9VwuDFxkz+7NvOt9H+HIa6/zw4cf5zvf\n/jaf+fBHuXP/Hn78wDcZOnucX//VX+XFp58iPTNDYzzO+971Dl449ALZsqKnu4XHHn+ZX/wX/4ps\nPs/gwBnOnzxOU32E18++QW9XH5NTE/T3dzE2Msz5gSHWbNjMB3/pczS3tXDoledp72oh5jRRX5ei\n6GXIzKV52913UcxlSSaiFHKLPP/C06xb20NmYQ7fLaPyBTJz43Q1NvCl/3I/x88c4Z9+6rN895t/\nxR9+6b9x6tRhPve5z7/1IfTFYun+a1nmsNRzWgk6DuF13/eJx+PYlskqjUajVaETnGbZFr7ysaSD\n0j6msppfWYKyZDmUXuoFhd6U8YqDQp6m9oeBwaUwSukqb8UoOWMcVGONlnQol108v1wRbmEJZxVm\n1epq/fCV6WoofyW6Vpy8qiwlFbWhNbFYrPIShzDBp5bvIlBsYfY4hJnMBMVrQoWkDXR5VdgiTDYy\nNx3yZLl3GRoY4TiEhXL8IHtaElRMk7LShsDUyZYyUlGmlexjZV6IYtUkvYV14c1ckQgrLLRiUQmZ\nCtChskYhZfAik2BcQ35W7kFplFYVT7/6n4/GNf3CRmlT5x1h4wtJiDMIIYJlfVRq9L8ZhagGFXjY\n8NE29QuR2sSOLWQQirFQwmRjazAhDCHMPMaiUuRIWoYnQeUhKYI12iIwCqRJlBOBLRsaV6pmHCtz\nLFxTX1OaNVzHrmv4GM6x2nm63FsPzXAZDM7S2RWsjRcCLU1IDcxKgLLrYTkmeQ1b4vl+xVD0PY/m\n5mbm5+fJ5XIk4wmSsQTTk1PE4gnGxsaJ2g7FQpaZ6RmcIIHNdV3i8ThTUxNcujTEtk39jI2M0NXd\nTVt7DzPpNLatQZV4/uknyGTLdHT3sm3HDiwLpmammZ2bY+D8AJn5OZyIRb5cpLGxiZdffJ4b9+9j\nZHqUDZu3sjAxRUPMZiKdpr19LTfd9XbOXBxkbHQcr1jm+psOEm1swtMO8YY6du/bx8TUFG1dPVh2\nFDeA/K9cvkhmfowdO7eTrG9naHSGe97/T+hc00+xrEA6yESSubyHk6jjvl/4AJeGhnjlxRcZGrrA\n7bfdRl0qyejYFdau6cYt5rHRbOlfx+OP/pjbbr2RiO1wceAc27ZuoSFVTyKa4PixI9h+GXdxnrVr\n13Dm/EU279xDMWKRm8szMzeP0Cbc1dXWwdTkOMWSIFpXx959t/Cxj3+UPXsP8s1v/5A/+6uvMzk9\nwpaNW/j0Jz5MZ2szh48c5dkXn+OlQy+wbk0v02OjdHavxxMWb3vH25kcH6culuCP/vAP+fhHP0lz\nUyeLrsXpw48xfnEQt1jg1Lk3WLN1D2u37qYxEuXws48zNTnMwYPvI+ok6e1ppaGhkWy+iHAcnGiC\nhsZG9u7djZQQi0SZz8yTWZwnm07TlnT42lf+gP17djKXnuOLX/x92tva+Z3/+AU2bbvurQ+hhw/o\n8nXR4X8rrcsO94Xrk8MHXSlFJBIhl8uRTqeXHCeEoFwuA1Aul7FtGwOvGk/Udd2r1lLXrnk1bRnl\nHb65ClhR6NT2H2rXfPuoAHZDKJyIqdpUKBQq9xG+JEUEMdvw/JCWeyb/EB7Xbr7vVwyfcAv/C5MH\nwwSfkBfL72ulMfrZ+vPm65tr2wr75nneVXPB9cuUXTdICqRiJIEM5kaVd6Eysbh6fGr7vfz45f9L\nYV81HssNv9rlUJV5pIMStIEBqIO68Gb+rYT2BAlxfw9fw/i+DBSZuUeJhURJo6CV0PhS4QkfBXj4\n+FrgByVs/cAwDV8UI2QEhENYYMbXqrKJYDOhCDMmphq8DtagrwzvXyskUMujFb3zZYadwmSTh9e7\nql1dU8vAg1LRJL5qJSqZ5o4dwXe9itHgui4R2ywXa29vx3IkYxOjSEdQ31QHwmcxnyHVkCSWTJEt\n5Ik4URqbmpmbzwQllWNks3kjWxxJMpnk9BunWN+31qwVLxZZv2ENmUyGY0de49BzLzE/v0BbazOl\n4iI3HdxPPpuhVMjT2NDEDTccpKt7LT965DGam5uJx+O8774P0NbWwXx6jq1bNjGfnkJJeNs77kYL\nE7abmZlhcmqcGw4cYGxklMvDl2ioS5GIR5EoBi+cp7ujlXjU4e4772J4eJjbbr+T8wODnB8cpLWz\nk66+Ps6du0DfhjXUtzXiSsnv/O7v0dHRxpnTr3Hy+MvkFtP09PSQyxbpXdtPLl/i5Ik32LBhE5FI\njFOnTpGMJ8w6+9wCM+lplC4zMTHGpi0bcaIOu3fvZDGzCK4mmYxTLuTR+MymZxgdvcLQ8DBtbW30\nrd3A4OAgFwYus//Gm/nWd/+Of/FLv0xzcw8PPfwEzS2N7N6zg7/4y7/i8cee4jd+6zd58KEfMZNe\n5OSRl3nfu+5hYmKMo8ePUFaK933wwzz6k2col12S8SjDg+MsLuTJLyyQiji887Zb6G5pIrcwB8pj\n27YtNDSncOKCHz78Qy5cHGBg8CKTE9MMDAwyOjbF2OQMMhJjfHwCz/PIL8xz4/59DJ4/y+zMBPfe\ney/f+Ntv8cnPfIov//FXGJ+euuYz/bPSz4UHvlAo3l+r6GppuaJYSQDIGnjVHKdIJpN4nsfi4iKR\nSIRELF55JaAQAidi4/tuRXmHHkDttZYLzRAqrv4nlm4r9L3W86p4nroGGsXUKY9EIhQLZVzlE41G\naxT30tdlQlW4Vfr397z1YiXhL7ReIvStEGY1GDeu61Zg01Cx27ZdMYSAYH81MzosARvGuc2FjBdu\nliiJyv0v7VdN7HdZnH55OGMlw6G2FgCICs90BSIPPLaKh1eNcIdw/tK4a1CONKiIF4YuLEtiWxbC\nN2GTkHeVDOoAHl/K6LCgr1lGF8A1hi/aDjLzg9fIEuQrvuloLmte6auGvzZJC4JiNlQuW10zLazA\nc6+GlcKIkBZWkP5WrXam0QhRU7c9fAlLEIYwx5nF8IKllduqc5Zl87WKIC2Pf9c+j+FvKaUJX0B1\nYcYylmsVGlVGFpj5IQMDRJMvFLBs2+SsBOe6rovSCsdxmJqaIpFI4ns+xUIRx4ng+j51dfUUC0V8\npSkUi9iOTXt7O/l8nvTsLLZtk0wmWZidwHfLTE9Pk83lKJddXLfE4MUL3HTTPtrb11Jf30JDYyOl\nQoGzZ15nanKCvrU9KG1KM3d299Dd2cXs7BQPPPAAN99yA4vZPBE7QsR2eOHQs+D6FEtFEvE6xgcH\nsbVicXERYTtoBBu2bOTcGyfxfY/2thYcSzIzPsqu7ZuZGr/Erp27+f3f/+/EEg3U1TebTPxYjGxm\njua6FF/4wq/TvaaD4UtD2NJi+MIAcVvjFhZ49pmfsHfvHpxkC93dfVwYGKbsehy44UZypSKbN29l\nZnqak8dfZW4uzbatWxgbH+PSpWHW9a1FWDY9fetZzJdJJWLkcvP4nsfCQoYnnnic/v51dLa3E405\nlFyP5rYeSl6ZgufjC8HU7BQ7d+7kvvs+QLns8uxPHyZXctl43T4WC0XaWutYt7aLZ558jgN7d7Ju\ny1Y6e7oZm5xkXX8fba3tPPPMM9x+251YcYfvf+d7HNi3h7NvHCMVjzKbybBj9z4uvHaEsaGzzM/P\n0NK+ESEt9h+8HoRFW2sHsWSKSDRKJBZFSBsrEiG3MI/2fZpSMdrr43z9q19h29bNdPb08NSLr/Jv\nf+Pfs5DLs2nTZjo7O/7X8cBXVDRYBspkZaseqFjVWuvK0hPfM+/ubWlpYW5ujrHJMRzHvGwCVOX1\nmrXQ3XLjYXn/wFRy8hSVhK6lHqGoeHy1m6mEFcDvuva7Od/3NKWiSyRYYpbPFyre5oqJZ/8AXq6k\nAI1HFb44xKfsebi+X/XEdLU8LFTfuhbeq+d55p3JwbiEXvzyMQxLsi7fVupX0PsKXF0r/EMKhe0S\nPljGk/WpRUt0sFV/h9cxCWJVhVD78pHaflVeSxl64zVJZQpRedtr+FY1T/nmPdErGBkmCi4M9C6C\n4iRhFXUh8E3aG0oEYRkhl+uka5KqdTyDTdV4p1KD0DIo1CaCE2Rl/oUec5jMF7ZXGTv8JWOnfFBe\nsCROS+PtCoJEs4B/OngZkBBLKtitRLXKevkxtQarVTsPggyO2gS75chMFWXSCGEgc9c1eR7FYpH5\n+Tny+Txl38xlK3xRSYDEjY6OIZBkF3MkonFiToz8Yp7RyyPs2rmDufQs58+dJRaNEI9F6etbg1A+\niWgEITXf+953QXl8+EMfZOD8GSbHR7BtSaFgDONYPMLGDX0sZNIUC4vEYzZDQ4Ns2LiOZCrO//jT\nr3Lq5GvccdvtdPd2MXBhEMeJorRgZm6ed77jHubT01y6cJYmRzJ2/ixdDfVMXhkBr0Q8avPqkUN0\nttQRt12GL5zi+OHnwM8zOX6J+roEr586T3axxAfv+xBR6UC5TEL4ZEaGefTvvsF73nEbr738POva\nW0lIja193EKe//1zn0damu8+8Lds374dTwgaWlp42z3vJF6XQjgRZmbniEQiTE1M0NXWhlsqkltc\noKOtnfUbt+AR5dLlCVpb2nn+uScYOHOCNT29tLe38+lPf7oiY9yyTzKRIlcoEquL09DSgIxFyBQy\nyFgEIgk+9c/+Od/74aPsO3grP3zkYV4/fx4nnmDPrt3csHsbt91yE5//3K/wjW/+FXv2bCMVc+jp\nbGFxdoYLQ+cRssiHPv5pbrjjDqLJBJ5yGRsZ4sSrz7O+t51ExOHwS4fZtmkzh196lXOnh5iezTA1\nm0YGYdfpiUl6u9rIZebp7e0hFY9RXMwwNXqJ82dPkYw7fPnLX+bvvvMD2tq7iSUayWRLP+NTfm36\nufDAM/nCkk4shcPC78u83Zrkodo4mfnXCILwPd0NDQ24bpn03BzJVAohJZawUMq/SnjXQqHLYWED\nGVYjjGYTwcqyqisQrrEGCCNsvqrNsF+6KTTSNt5QJBJBKV2B+GsV6XLPu9r3f4jPFiSeiTA+rc0S\nN8LfQM0Ss9rwhOu6aGXqq4cx4OVGTyV2Hdy3ohoL13plIa61Dl4CYt4cF+RoV+KpmvCNW6LyHubK\nuaLGc6MKJ4cx5LDet67xknXgE1feIV7DW0I+BJ6iSb6S2CE6EaybFksKoeggoaq6JKvCDzDxYilw\nMIVdbClAWnhaorXED9oOjQKzBloSus1LVz4vfw4CngthchDCG9NB3Ddo2JTIxZSUJSgtq7WJ6xPy\npIoVVLPtzbhoZQwBAq/cVKqzAo9bIMIExYDfUgcvdBHGaEHXVOjTS9GPSrx6BWOzoty1QY2CJ84Y\nQwQFXAhzMoJ2w6ViQe5EBSHSGsu2kZZFLpdDEyTiOaaOeblcpq6+nmw2S3ZxgeamJlLxKOPjo1gC\n5tKzJBMxLKEolQpEIzZNjfXMTk1Sl4xRKhXw3RL5XIahwYtkMvPs3LWDnp5uDr/yMq7r4nkWQ0ND\noD0ijmRo6Dxr1vTiumU6OntwfQ8pbXZt38H3v/N3LM7PMDl6Cdcr8+nPfpZy2eWlQy/RkIySiEdx\n4lFiiQSF3AL79u6hpbsdbVl0dHWRWZjh4utv4LklNqzfwIXz5zl3doAN6zcyl55jdGKUnp413HLr\nnUxNT1Mu5ynkMjSkktQlktx177uYnkjz4x/8iFQ8xvTECLv37iY9n6G9o42XXz3KbXe/m9fPXmDf\ngRtYzGaIRiXPPP8CPb3dFHI5vEKWcrlIe2srh55/gR27dzM9mybV2Eh7Rwdj4yMkozFuOnAzI+OT\ntLe1MzszS2dHB8PDl8ksZFnXvwnt2PiqDJZNyS3jqSINDY1cPD+I65fJ5rL89Lnn+fznf5nDLz6L\nI8AvlBi9PMjE9ByxhkYGh4eoTyTYvX0LA6+fYmRogGjc5uZbDnDd9uvJ57I8/diPKOcXWdPXx63v\nvI+IE+NHP3yQvOvR2NZDe0cnIxP/H3PvHSXZVZ19/84NlatzTtOTpydqRqMcBqEsoUTONsbYgI3t\n18bhBWxjY4wxtsBkGzDGNgjJCAESApRHmpFmNJoceqYndM7d1V053HDeP869VdWjkQyLb31LZ61a\n3VVd99atc0+fvfezn/3sCeoalMRuTTxOIZuhoTZKPr1INBhgYSHBmYETUMzyzGM/JRY0SCQS5Eo2\nBWmSTGcJhsOcGTjJJRdf9GtF4K8NA54tfrK8e/n121LwSmnSMhTnR09S84yKUKxaV5HTHMfv5OUS\nDocAWFhYACBgBjx2+HnRkqaV5TfVn5ZumH6jMLVpesIcHhlIUA2z+w9NbX7lnsa87LyaR8ByXRXd\nGoaBYRjk84oJbhhGGSGoTiOUDfl5IiiVSNHfnCsfV3Y8BF5XJpXHVDKgmme8hIpgJQhNx3ZcDMPE\ncSWWbSE0z2B4UZvwKM4+Sq5iTsWodxG40n0ZfH7+/VT7r1ve1IV/Mm/Syq0g/c/ya3+rUgHV51Lz\nVHEolkTy1Z+hPsB7j+cA6Lpn6EX5Nd2fYrfiiChHzfGUzhRkrGlevbjfREPoXnpClZgZ3t8FEsup\n/q4ueF3ztCpDJcvXV512kUuuX3jGu3qoNanuua+L7gjNuzeaZ/78G+B7N+CX4VUjIZq/dr1FJLx6\nfp9wpxB0dV8Uwi3KRrYaOnd8Gd5qkR9RSSuVXyvfv0pkruERGhEgPffQR1K8rnPScwQqDoJSZnRd\nWVp7hGYAACAASURBVNaPcFyHeDxGyS4xn5intbWFgKFj2xbFYoFQOEgiMY90imTTCyRmJ6mrjZFJ\nLxAMaDTUx3ni8cdYubyXpoZ6wsEA6VSSdCrF8WNHWbt6NT988EFuvukGJiYmONXfT2N9AwOnB7jk\n0ksYG5mmpaWB02f6aW6pJxIJEwiYWCWXaLyGYCjExMQEHc0tdLe30lQfYeDUCcLRKLlSiVymgFUs\nYFk5ZqZmmJidoX31cvYd2EcwHGJ8doZwTS0bN26kPh5n8ORpLrvkMizbYW4uydq167l4+6UMDo+y\nclUHnR3djIyOYbkOeTtPa1c76WyBmsYmjGAdq1etJRgM8sD999HQ1sT1N9/MTx9/nLe87R20d/RQ\n39zO5VdeycHDh9CEJJdJEYlFSCYSRINBsqkFWpuaOH1qgIu3bWVkbJxYfQ0tbc1MTY0zNzvF8p4+\njuw/gYXi3diOpL2tDcd2OXdukJa2DgLBACXLQhOq3a9dshDAzPQUXd2dXHnN6/nCP32WufEz/OOn\n/oqdTz/N4NAIAwMD1DU0s2HrdtatW8cn/+oTXLZtCxdt6GNocIDOznb6j/YTr2ti5zM7aYmFWZia\nIF8q0bt1B7XxeoqWzcTMHJdedQUXX34Jesigb+16rFIJDZuQqXN43156OlsZHxni9OkBrEKBt7/p\njdz/3e9glwpksln6Nm4m2lBPQ3MTra1N2MUcGzf8eiS214QBX8y+PAJ/eZR5oSHK+S4pJbpuqDyu\nELi++igSTRfYtoKoazwPO5fJUVtbU2lKICW27aLrxgVh0Ao5yY9XlhLJpJTgVCI+f7jyla//QpCy\nH/lqmkYoFCrXkFe/p5qhrVCHV4hsz/ss9RPwc7E+oc7Ps0tXsY2rz13lLPibqYokbI93oJchdISX\nFkB6RuJ8rsDSa6keejn56gug+MZYInSP6SxUKZImVHRaZosvyV8vNdrqwZLnrwTnlgVfdK8ZrfSR\nD9Vcp6LLrVpxaprq7GUYXqmSri0xOrquew1PvGuo1HKBVA6Shupq5veo1/1SNT8q9wy6inlf/qiC\nFUDKivyo9O6ntnT9yaqFIaTAF1ArIxgITxil8ihD5AJA95wDv3e3KN9e12vfCaKMYvgOqytlBTmp\n1tD3yi7OXxMvv59O5YsJlPOuqf/t8n311lzF+VPz7ji2twYkqXQK0zQUm96xWUwkiEbCZNIpauJx\nTMNg8Nw5WlqaCAYCjI+OEjB1CqUC4xNjbNlyEeFQkJ/97FHm5+doa2tlxYrlTE5OsLCwSKlYQtM1\nVq9ZTTgYIB6LEQ4FOTc07MHCDuPjY0QiIVavVsSsaDRGd/cyzp49S3NTI4nEItKyyKeSFHJpauNR\nHvzRj7j0iivYsGETx48ewdSgoamRYydPcfdb3szQmbMUCkXMcJypmTkOvnSQM8eOIXSN6bkZamtr\nOXT4MLfdehvT09OAZGjkLOvWbSIWq6WxqYlgJIDQBIYZIBaLY7sghEMsGsKyHRqamnhx335+47fe\nz/4XDxOJxvjqV7/Ou3/jNwgGAoyMDGIV8/R2d3Bo/wFOHD1OXTzG5PQEpwdO0tHdiRkMowdDlByN\n3p4VtDa3cuzoERJzs+ghg2XLeglFQswnEhw9epRLLr2UdC5PYnaG2lgt0rIIagbClmjSpVjIEo7F\nGDxzhku2b+GJRx+hlC/Qu2IFmWyamclpDh8+TEt7D1u3bWfzpi186m8/xTXXXoWUcPDwYb7//R/R\n3tvD5PQsxeQixfQC49NTmI3d3HL9DTz84x9zdvAs73nfOzl48BB5y6ImHCeTWURIl/bmBnq7u3ji\n8V9w5MgRamui3HH7rezds4tvfePf2Li+j9//yEdobe9AmAGeeHInRiCAdGy2bdv6axlwcaHN9P/v\ncW5yTp5PIIOKCtuFxvmG3Y+Sqv/xK/Du0jynEIJAIEAysYCuaeUmKZZloXn1o6VSCYS75FpcB6T+\nCtfjyqrz+79rZejS/17nG8UL5V/PH4bQKNoWxWKRaDSC41RgfkMYIBWbXpbztDqOozq0ua4PNbrl\naN5xHNAVYWf03DAH9r/EFZdexrKVK1nMZNF0D27UdSzbLsPSAAK9fJ6SVcC2baKRuGKKW6pZi+1K\nXE29LxAIYNslL1/5cqTAH5pbcaQuZIyXDq0qOF/qALnVx2gK4hfSg3uXnNs//5K76F+cdz5PjhVH\n5bGFMsq4Ek33HYpKBy//2jRZubeOVzboeh2xyqiNBNsqF3GVyVkuWrmxR8lVRs/wIWYXLC+3X4ls\nHXxYW6VnHPx+9mo+qtfl0rWmoSOEQ5mMBuW8suY5Xy6g6xXWvc/y99e4lOr6dFA8AVQ1gFSJde+k\nvtZB5f7r6Etc3WpH9WUIkxBITTUIwfEqCYTE0FWE7YhKGZ9/nOv9FLaDNJTTKaXEkBonTp1k3fo+\nivk8MzMzCCFoaGggmUwhpSSTzrGYnGfdqtXMT83guEU0Q1BfX8/xoycxAwE2btzIAw/8gNWrV3Px\nxVs5fuIoATNEJBJjbnaaJx77BatXrmTjxo3s2bMH23KZmp0hl09x6aWX0t7Rw8CpM7S0tHDq5Ak2\nrlvNwLlhwh4xrnvlcgaOHaMuFuHkqWMIM4QwgwRDIZA2+YJDLplg784n2HDRJqyixR233sVCssBN\nt7yBb37n2+TzCeK1cTQTjp84yYYNmylmcrS0NvOJv/ob7rvvh/SfOkV9XTMH9u8jkZglmZpHD4Zo\nbG6ivbWDjevWklpcYF3fGoQWoCZez7M7n+eLn/8cX/zyV3nhxX38y+c+zfhskhcOHmLfrie54vKr\nKWZneOrZ3SzrXsHo8Cmi8UbyOZtIXR1X7biWWG09UgQZH54gFixysv8gbb0bqG9qpKt7GUeOHCEe\njdHd2UlACzA+qsRwcpk8xWKROg+CP33uNHNzc7S31XLuzDmuuPQqTvYf5bOf/Wse+clDREIxLtu0\niX974CfsO3yMa6+9lpqQyUc+/EHmZiaQ4Qg/e+gJUjJDXU03B3c9xejJ3XzrX/+VD3/yc2xcs47h\n02f5vx//GF/5xhcww/U0NC/DymbJ5mcRrmB2KklTY5yGulqkdFi9ZiV/+id/TG1dnBuv28Hc1CQN\ndbUYgSAi3kZjYxPx2hqy6QzbL9nyahHq/zpeExH4Qib3yZcRoM4rJ6serxyVv/z9S41k5e+O4xKP\nxbEdh/n5BLpuEAgEcRxHyQx6rSV93XUFYTtLemUvuabznpX7VFe99Vdxlqrfa3ntP/3Wh1JK6uvr\nValCKYdmKGENV1q4rq3Y9cKPIJWKmdBAGBqOdHA0SbFYoL2lhb//u0/z93/7N9xww/Vs3LKRRDKN\nKQwM3fT0wNVmqwvFvnYR6LpBqVQkYAYxdJPh4WHq6urKxC8FZaouSpZloXlOzPlO1JLn0l2yCS+d\nzqXQsZ+qR0iv5lx6UaSX/j1P5EQZjkqu9ZXWj/+yFD5qUJFV8WuO/XysrglPRlV6vbZFWUgHXC83\nK7y6fqHO6apzVRMJ8bLGtlRGWAoVRbuuq+RaPRRC96RaDU3D0ASGJlSLTeHXtGt+ZtqDwRV5DAzl\nwKApg+1TwKRWMaxaFWoB+A6BxxRQP0VlLsqGUuUqvFpsBapLP/3lkw7UjVIoivf5SFH+U/XdvhCS\nUnZUhM+cl0rX3HN4hJBKrAa/vE1VewipcKBy2keovuymphEOhRgZHaGtuQVNCE71n6SpoVFpnodC\nChrVJL3LesmmM4TDYebmFgiEQkxPzSKESsXpusptLl/ey9mz5+jq6uLkyVOsWN6LVchz9uxpFhMJ\nXClp62ynWCzhOEW2bNzM/HyChYUkyWQKJBw+dITkYoJcKkk8FiWdyyFLFqMjw9h2CcsB2xF8//vf\nY9WqVaxa3Uc2m0fiENF1NqzfwFwyybotF3Hk5ElW960jn03juBoNTY3U1tazoncld9z2Bnbv3oVE\nZ82mS4nVNtHY1MR1r3sdHW1NrFq1nMsu2c6K5cvRjDBP/uJxXnh+D4Pnhjh95jRdXR1MT0/yO7/7\nW3z0o3/EDx+6n29+4+vsPnCYpu4VBGNRNqxcxre/8WWuuPoafvCDB7ntlhuJ19YxPT3PytUbaGxp\nIhQJk03niYUbiYRDSru9roGamhpKliLwdnR2kEwmSczP4boKGVu7Zo0qudUkqjFsic72ZlrbG2nr\n6mZxMcu2rRdxuv8En/n0p/mbv/ssh/bv583vejfDQ4OETZ09u3fxhltuJJVaoLW5jfa2NiaSi4yO\nzTA2eJonf/4wueQiU8lFNm/YRCwcZfcLO7n40ktYSGZZSKawshmeefoJamK1zM3OE4mG6OtbjxnU\neG7nk4QjIaanp+np6mRxcYFiMc/c3Dz9gyNMzs7w4EMPMTM/x3U7rvm1IvDXBAu9LG/pyY9CBc6s\nrvkuM2FfgXHqH+84Do4tcezKa9XsZMdRr+VLRYLBYJmpvrCwgGEYRCIRpCvKx/n14eeP8z8fn+Xr\niirZ0Ze/75d5VJ9f13VKtoXl2MRq4iwsLPDEE09gWRYtTc0qWiuV0ISJJpVcpAZKKtMR6BiKPexF\ncJqmIXQDgPa2FlpbGpifm8KRXiRoGFhSgmFgmkEcqY4Vulm+L4bH2g0EAoTDYT70oQ+hm4ZiZNs2\nrmUjbBfTqyPWdb0q+/ryx6vNreu6nu6pW45i/UizMvdVxCe38rjQWDrXDkI6ngFwPChWLjFY5XWK\nx2qWbpkAp8yhrGp9WRWNu46H4rgeAYsl38mVAtujY7kSLOl6VQ7qu2qugy5dNGxwbe86bQwcDBw0\n1yIgdAxkRfNNyPJz4eWKFUcAKrTJyrVXzwnSly+WOK6GK71SMimoZpu7fprEVQiCoxJKHtqgeA/e\nBCkjq6sr9gl0QuiqTE1cmAh5wXw4PtEShZB4ML7l5dbVwydPVox/MBgkFAphmiamaaLrguamBsKm\nweC5M/R0d7JxQx/Hjh7GdYogHSYnRrGKBVILCZqamlhIJMlm80xPzKILjXQuTX9/P4ahIaVqL7l6\n9Wp2795dbhwSi0Vob2vh4KH9FIt5uru7KVol+vrWcejQAa68/HIE0N7axp133EVrRycd7c3YVpHa\nmhiZbIp169YBsHHzFoaHRtmxYwc3XH8TTz72uPpu0TB79h1gZnyKtpZ2Lt5+KefGRoi3NHLRZZfi\nolHX2EomXUKTQTpbuhg6O8Lunbt565veSiaTo6+vjzWrVnP61ABjY2MUCxZCCxCK1PDhD/0uH/id\nD/PB3/093v62dxGJRPjSl/6FXbufYnh8lDvuegP3/fe3ufO2W3ji0Z8wPz1BY2MjTz/+KFs3baKh\ntoabb7iR+vp6Tpw4yYoVvUQjcY4cPIR0S0TCJsGAwfDQGIlUls7OznKjGDMYRjeDxGriiIBGMp0g\nnU1x8Nghdu55jpJrIw2N9rZOUqkU584MEwxEmZqbI5nN8X//8hOgubzxjhspuTbTU2OsXdPLwKnj\ntDTGmZ8ZJxYwmDg7wHe//a/EYnWs2dDHW9/5bqKxekBw/NCL9B87Tl9fH81NdQyNTBCvrac+HsLO\npnn7G9+qJHZ1SKaThGpifOFrX+DB++/jU5/8JLt37+bqa3aQzRfI5AocPd7PsQN7MR2Ld7zpzVx2\n8cUX3J9+lfGaMOB+frVatOR8YZELGeuXSS26VcdKb3NxFXzpuGA7FUEKiYZVcsgVSuSLFg1NLViO\nZHR8knQuhxkK4UiBrpkYegDHlmV9cl+jvPrhOkqu03WkL8OtoHP//edtSv7v55+nci7Kz21HlvXI\niwWL5qZWXNfl1Il+Xti1B6ckCehhigUXyzYQBLEtMEQA4eo4jkAIEyENTBFEk4oUV7Id2ttbKRXz\nuI6FdGykdJTIh3ApOiXydgFbOBTsAtlilmw2TaGQU3lwp0SxlKeltYm29hZcaaHrgoChK7PkGUHF\nIK7owVdDpP69vBBkXjbI0p838TJjXT2q9eLL8KsrlWGW3k+3YriV8wDV+usarsqtVsyF4oCrJLZa\ncz5zW7gVQ+b62vfemsV3+JSDIlyPPiY9DoUA1z8eWZZk9de964DjSEq2L7Zj4dglXMfCcS1cxwLp\nILARmouGgy4dBA66cKseJfWgiCFKGMLC8J5rnvtQ/r+iokomhARR6UsghQbSwHU8Jrrrve7F/b7x\nrLhjHqwtdA/OrvQ2cL3vr/5Hl1YVVN/7pf8vmockAOc57CVXo+gISi5YUmBJga3YBeSLhSVrzC6W\nyKbSNNU3sDA7x+n+k3S0tWCXCoyNjJBJLSKkQ0dbG47jkEgkWFxMkstkmZ2aJJdeYEVvFx2dLQhN\nspCY45mnH6e5qY66eIyp8TFKhXyZB/HBD34Q2y4RjgSpq6thaGiYhUSCg/sPUMoXMAMGxVKBFSt6\nyeVyxONxdu3aRV9fH4ePHcUIBGhp66BQdGhpaePWW28ll8kzOTHC63ZcRUtzE/l8npm5WV46eID/\nefBBDMPg1KmTNDS3cHboHMlkkos2XcTp/jP86Ic/xrIs0pkF6sIwdOowTz/2CBMTQ0gp6e5dhRap\np2PFJiYnE7zwwm5MU2dZbw/ve9/72LFjB297y9sJBWqZnlrgC5+7l0wyxe++552c3beTrT3NTIyN\ncO7cOT7zmc/wzt94Dw3N7ZQcScGyWbdhFWcGB3jk4R9z4vgR5hKjrFm/kvrGOtLpNOl0lnQ2T2Nj\nM44jSaazhMJR1q5fiWFKhOFw5z130dW7nOlEitODo1i2xoEXd7Nvz06W9fYwOrtIS08f/3zvv/Hk\nY08xOHCCgwdepKenh8nJSXRd52T/UYq5JHfdej2HD+5heWc3I2fOkMpkqGvuIFjbyKpl3Zw9c4ap\nmRkGB8/xZx/7BB1dy2hqbGD9VdsZTsxQ29pCtDZG37o1fOhd72X3ww/TWBujtaWJe++9l/lklkC0\nntGpBLVNbXz0Lz/FjltuJ9jYRNe6ja9uGH+J8ZqA0OcW0y+7iOr816sT2ZYe48cYFzpGbVQAKrLX\ndK/1pRCULIt4TQ26prGQWMS2HYKBkAfBuSB0hKZ74hDCi0oqD19lvRoYVFu1n1eV5ShnCdv+vO9b\nDU/6JsRxlLhL0SohAMd2WL9+A60tzfzJH/8Z97z5zSymUmimEqfIOxYF2yprkDvSwXFt1XjBM1Q4\nNvXxGGcHBnju6afYvu1iLr/qatB1AqZOY20NAdOgJh6lNh6lJh6jrjZGY10N8XiMmpoYDbU1RCIR\n4tEw7Z3txGvizM3OEY/HPHKbpZqI4OJKF90npZ33QF44710pD6vorvsP4ZOnLrgQKtwuDwLBZztr\nnvSnf+uVcZblCFXTwK/C9ulYwtcTx4PjhQua9InXqEImv3bcrdzn6kvw5x2JFMpQllX1pYsmXJAq\nRSM8CNqR/nv8kiyfrFVFxhNe0xZcBYl7vHC/bl11RXM9pnalrEvzctJC+BEuHiHQg+2NyvT6/zOq\n/aifkvDK3KRSEhQeqU3NvfDmpOKoOa70kBNvDsCTmnUQmoahiyUa6ngoCMInpSnipZAeIdCbWAFl\nmV6tyjEsizMhyn3DcVxVISAEsWgUIQT5fJ7k4iLNTc2MT4zS0tLMwkKCUChMT/cyYvE46VQKnCJd\nHc3Y+TSOJslmM0RCQUxD4+jRw7h2ieamRqanJunb0EcmlWZ2dkZBwqUSL770EulUkosu2komkeKF\n518gEAywetUqampjaLpk7ZrVJGbn2Lp1K8IMMjU1RTKdpaWjnRP9p1m5eiXD587w0p69rOlbzaH9\ne2mIR5mbn2fbpZdyZnCYZcuWszif4OCBg/QuX8l3v/dNokGT5oZmNq/fws9//iid3S1cd8MOsukk\nZ8+cwrELdLS3YdkWPctX0tLWg2aGGR86xYH9LxCPh9E0+PGPHqanZwXbt1+G6wgi0SgbNm2itq6J\nXTuf5NDeXdTFAjS0dTI/N8eRE6eJ1TWRyeSob+1i3cZNWI5NT+9yenqWk0qmyOXSlOw8ZjBIIBDF\ndSSaMKjxeEm5fIH6+nqcUoE9L7zA5k2bMAJBSg7kSxZt7W3kc1l0K42ULjfdfjfP7TtKOudy8cZ1\nuNkEQ6cPgxlhZd9GBk4PEQwEaKqNEw+b3HP3PSxfsYKR0Rk6W9vo7elhfGKUI8cOk1uY5/JrbiQQ\nivDg/f/Nqs2XsXpNH9Ojw8ykFghHwmTSObZsWM/q5d38wyf+khU9bVx97bXMJ1Js3HIpP398JyvX\nbGDV2o00NLdx4MQArhZmZGKOodFpLtuy5teC0I1f5+D/r8b5amjVbOFXy4UDr5g3fSVRlurjSqVS\n+bMMwyCVShEMBmlqaiGVSqHIOpUGDIqxfuEpkxK1qUs/f4qXrvUMsl/TU0Woe1k07j13/TxvlROS\nzmYIhSK4toV0XTKpFHU1cV5/8/XUNESQAQdXCsLhMKGg4e1vKpeka0rL3NQVkUoXAiufJ6AJauM1\nGEaA/v5TWPkC44ODmLpgYGEB27bJ5xVp5OzZswhdJ5fPkEnnyOfzZHJ5CoUCxWKR6elpPvrnf8aW\nzVtJLS4SDkcIBEJlln+ZZf4r3MPKKNdwXeD1/12SFUDziFd6maemyGcqVa7j66EL73qErCJCoURY\nPGuhQGOp4F/HM4TKeXTRfK17JJSdumoehuol7gCe2QMk2DZoOqqXiuutvSqGtdDLHdV86Nsvi5JV\njkoVq8D7Hi93Ev1haqhUSZkv4DPtFSfB79YmMMAT7amQTaucFC/PjxS4orJ2NSnK90cTLo5rKxlb\nT3NelXj9cs658JqVqA8UIFzvnqKQCCHQvX4CrieCpGZbrS3DMFRnPdvBdRzS6SztbZ0cOnzAQ/8E\nzc2t2LZLsWhRyJdIZrKEw2Esp8TQ0Dka6zZQVxshK20MXbCwkGD5smUUcml2PbuTDRs2EQqqNNPQ\n6AgNzS2sWL2GfMkiVluHYRjMTM+xbds2rr76au574H/IZFNMjpcoWUUsM8jmzZv5yU9+zC+efZav\nfPErxGIx1m5YzxM/f4b1fauZGBnk5ptvZXx0mJp4iNmZaQ4e7af3xX0ULZeurh56enu54tLLmF1M\n4RTzXHbJNjpaGjk90E9tbS07Xn8lDpKi4xKpidPe1kbIDNAaqGd2YoTJyWn6Nm5Flw5NdVHyuUXu\nv28X191wG1u2bOOnjz6GqbtctHUrNU3NDI9PEa6t5YH//Drf/t59XL3jBo4fPsIb7riH6fkEi5ki\nHd3LKEqX/uP9bFi/hcaGJlKpAv39h+ldsYx4TT25XIFiqURtTQ1OqaREmlyXxWQGQ8Ka1RsYOTtM\nR3cX0gzQ2FCDrruYIUHfhouJ1sTZd/Ag41PT5PMWJ/c9icTirXfdzeGxWfa/uJ/bbr+L/mOHmBgb\npjEe54lndiHMAE//7AHe8pZ38/DAUdb1rcZyJLFgjNV9fcTraolEoqzp68NyJB1tnfzi8Z9z9913\nUGoqMjs5wdzEObpW9fC1f/8yf/wnHydcU0dJ6my/6jrm5xfI2QXODpzjiksv5uzgIGfPDbFyzdpf\nau2/2njNGHBYmpuEV5dVhVff+Ksj+OrX/OeOZaObRrlky7btsuIYQlJbG2diYoKamhpqamJVuXT7\nVT7PU3arev3VzMv5Brz6e1UPTdMwTVNBtK6LoevkchmGB89RH4/x5OOPkk6nSSbTymtNZ8jns8zN\nzah8khlkZmaGUjFPLpfDKRUpZHOkUhmQBr3dvfzwBw/xH//xnzhS5U51oXKhihwmsKRUbRcN5dRE\no3GikTjC0Kmra2BhYYHsYoZ4JKp4Bq5SvPLV5RxH1Qif//3Ph9NfGXE5by2IC8/u+fMohPCiUel1\nxaqCxT1yliYBv64c0DVd5VcdiYtfHleJvqvzthU2uHLcXKfKORPuEiPsrxFwvLSCMuJqnisyoyqC\nFxiGZ+ykg+YT6zxmerlCw48uXeF9H+8zvfy18CJz6UH3VIvpuIocVzayEnVtHvkOF3QpsIWrPkP4\n0rpu2ZaqbvduVQMblTzQXZWn1oUnlqJLHFvz5gz8pm+2j0rIl+8BS1EZiaTSgMYnCy695245veKn\nPTTPAdeEhtQ1MHU0BwJmENtxCEdiDA4Ocv3113Ho0CFquuswjWA5OheaxpoNaxkeHuC555+jraWR\ny3dcz9mzg+TyE5w+e5arr76adDrNmTNnuOOOOwgaOg0NDaTSWV7ct5+tF23mF4/+jJaWJrZv28bX\nv/Qltm3bRqlUIJlcYPv26xgZHiKTyfDCC3t5w+23Mj47g10q0ljfxUJiDs21GD53lr17X+C6HTfx\n+a/+E3fdeQudRYumhlMcP36cD3zw9xgcGSWZWsBF0tzWTdAMo6FzZuAUAwPnWL9xLctWrmDfgYPE\nonWqlruuDgOHbCbJ9PQ4q9Zu4Nj+55kcO0fANGisb0Cp2Tk89fTTXHnVpczPzjI4PESzC2asgTe/\n872cO3OCr3z9XzH37iVihnnXe97DXCbHieOnWEzlWLuhgTXrIoTjcTLFPH0bNxGJhujq6mExmcIu\nFVlYWKCzs51CycZ2HBzHJqiFqK+tR1oFJocGKKUWqWlsIZPNMDw2wtTkKG2NzWSGR4k3dXDNlVeh\nlSQLw5KZU4dIZUsMnTlHuKaVzu5egoEw45PzuFYNlpOnc9kKTp04yLEDK5lLLbB56ybqQiFSiSyT\n01MsX72K1GKSUCREOpVh8PhJdB1+8Ysfs3FdH4889Ag7n36Mr3/lq/zDZz7HQz97nHu/+DWMWD3C\njIFZ4MSJ4/StXsHw0Blamxp5YfcQs5Pn4MPvfPlW9yuM14QBz2QU09M3UtVlYNXG/ZVgcX9cqLTM\n/3n+8YZh4LhLNwC/85btlCiVoKWlicXFRfL5LPGaKIFAgFLRvmC+VgMc16FMNqYSH75aCuCCTsl5\noaqUEl1o2J5yXCAQYGF2hmuuvJKAHsSWOfBEMhpqGnBsG6dUJBaLkcvlWLu2D2HoGIZGQyxKKvAZ\nUQAAIABJREFUpKaRUG+QpuY2ZueT7N27jy1btvKBD7wf2y5RU1NDNBpF0zTC0QidnZ30nx5g2/aL\nKRZyhEIhBCamGcQwgxiGzrFjJ+jq6iKfzaOjK+9ZOJRKNqapl+9t9Xeq3qiXlkZV5bCFXyrlT5Cf\nL39lac7znwvPiAgpy3r1UnjqXsKHXv1SJy/aRhUBuqDIc5rm6QoIz4CJJbl7X+bVdSvkS99o6oYH\nd0tlvP2OdqqkWfOMmZLhVc1C8MhoABVlAeko4p7fftUVeBG/l9LBN1oajlTqaEKzFczuRehKr95z\nPlxVfqhrlTkWKKdAl56xFBJbOkqMR/PIZEuidpXfFsL12qV6LHrPKdE1DQ2JaeiEvNJC5YiqNn66\nlKqpS/X6lxURl4qD5yr7jDdvQsMtl+SppIdrK9EW5XNpnoCOiyY1cFykpuapYJUwNZ1MLkesphY9\nYHL06HG6unoIBEIkk2mKhQKXXXkV+w8fYl3fKqK1NeQGLdL5PLoZYHJ6iqamZvr7+xkdn2TTpk38\n/OeP0du7nHe86x2MTU7xuuuuR6Lx0A9/wOTkJMePHSGZTLNy7UqitVF002BsbIxsJkNLYzNNDXV8\n71v/TtAU3HD96/nrv/w47/vN97Nmw1rGx4bYvWsnzz//PDXxFl5/00388Mc/4bff+1ssTs0zvbBA\n38YNlIBAMMjavjUkFvKEjRg14Rgy6LB8eTcT85NMz84wP5dm8/ptLF++HE3TOH70IIlEgtraGhbm\nZ5mcmGR5bzff+NpXWda7EtMIsri4yKo1q8iXcgyNTRAKKV32mqZm+k8M8IZb7mRweIpzpw6zZcsW\nPveP/8yf/+0nGTw3RnvPchzHYXRsDG16gr6+PjK5NI3NrTzwwAPcdNMN9J84TldXF5MT44QjETAM\ndCFxbJt0Pkl3VytH9jyFic3evftYvW4jw2eG6GhvpaVzJW4yB2YAnBKzoyNMDp5m3YY+3HyW+lPn\nSC/Msfe55+joXEax5OBiIowQgXAdV151Cbt2PsENt9zI+NAApu2QTOdpaGnGxcV2JadO9XP67AjL\n4zX83m//NkcO7+ab//YtQmaMj3zkI/zFX36csYlxbrrjTjZs3c7I1CKxUICCrb730NkTrOldzve+\n+wDFYp725qYL7mG/ynhNGPBsNkuxWKS5ubm8cSt9YlHuD3J+RF3toZ+/4avhlw693EhK6SmI+c+9\numpdaErnGUEgYGLbNs2NjSQWF5mbTVBbW0s4HC47GWXjUwFCy+i5b8ir9Zr963sl1KD8N/Hy121b\n1Vg7lk2hVCRWV893vncfh55/gbe86U2Yug+fR0DXCASCRENh9ux7EdM02LFjB0bAxNUEugm2N0MD\nJ47xxjvfQEdXM29797uwLEA4ZX35SDTMt771bd73/vcxOTWNHoiStwAkspRHygJIjcaWDtJFD7rU\nVE2+gcB2JcViEQ2Bbi5dbksiLQ8GlqKyeav7ZKMtiby8uvpfgn7pooyZJiW61+dal2oj11AGWfNK\n7XBV73NHSqSjK2lXITANE9tSRsfQdIq2hdAMwMK2HTTD9GqeBQHd8FeBKqjyDJOLqkO33VJVQw6Q\nlquKv1wNW2oIu4ghlOiIFBpGIETJKuA4TlnURxiqkYcj/Nyyp/5mariWi+uoaNNL/Hg14UoARyKx\npevl3HVVauU6XvMZiW4EQTOwPeUyXQhsy0ZzNaSjeofbThFNM5S+vOvn4ZVfpAsDIWx0zfsfcyz0\nQIBAwEDYLoIimtCwdeUImFLDsSx0oVN0LEVM0DQ0YXrXra7dRSJcC2lLAnoIiY5mGBTsAgWrREDX\n0UwDGxehOeimwCqVkJpQEL3n3GhAPpMmZAbIZNLk80WsYpHlXcsYHh5i5cqVPPHEE6xZtYbR0REE\ngtpoA4tzaaySZPXqDaQSSZ568mmCoQDScWisr6dUKKALjRuvex0H9uwmGg2RWEhR19BCa3sX199w\nK7ufepxdTz5BY0MrN91+M6Zpsm7jJh579Kc899TTJBMzSFfQ2taMYWjcftMtLMwu8tCPHuSK6ctp\nbm3he9/9Hy699Gp+8/2/RTafYnx4hKP9J1ixbh17HrifibFRujrbGTh1jhPHB7jimmuprY2zrKeT\nvc/v5Kmnn+Ut7/0ANXVd3HDzGkI6zM7NY5XynB44zsTIIOvWraPoCFpa2qhraoFIPQ2dK+hsb6d3\nZTfJdJa2nl7WrXLZf+AQ69YbyPwMTz/9MHWRCLGgjl0osmbTFnY+/xKZxBxPPvkTfvfDv48QknUr\nVjCbmCG1MEsykWBuYoqI6zI/PMzAieM0NjcRLBQIhkIszi3gaiYNpkVybJQXpqYoZC1yRWjvWUZb\nd6fSmtAEo1OzOGaAxcUMp5PDdNRHSeSSRGI11NbW0r2qlSd3H+Kbf/pJBs+cIPnUDHe8+cP8/h98\nhA9sWY9jtBBuTXPoyFGsbJbule0MzU5y6mg/UT1AU2MN4XCUxrpGamrC/OSRh3BtydjIJDfddBOn\nzpyjo6udpw8cpe/KJr713/dx151vgmKSyZMHmRk8yu1vuI1HH/4pwZDJirUbWb5i1f++if0v4zUh\n5HJyeEKGQiEKhQKTk5N0d3er6EarkFH8emwAgVL/8uuzzxdxgOpcXWVIKcsCIBpVx+BQ3TCl/Fle\ntOOXkxmGQSGXI5fLEYvFCIVC6tq843wo3o8cHMd5mZZ59bWAUl5bYtwvEKw7jqMU2vDKlTSNUqGo\nPidkqlwiFRlWx3IpFApEorXYrkt7awP3/uM/8Rd/8VHmFlIUikU0aVMXq2FqeJh77rqdpqYGHv3F\nEzhCI5PP0d7Syr4X93Pffffx+c/fy+zioiedaiGrYF4/Vwug+dVTKDY1VX2sXdvBclSaotpw+6Iw\n5YJ5rULS8uFvHxYFVORaBa1WR57lxiRiqSiIrybmrwsNCBrK2BmaYlQjXI8V7uDaSoBGCB2rpCJm\nTdNwShaGEcDSBKamecbQS/U4DtK1qzQDlsLn5eu1lfPnINAjUYqOyndnckWyqSxWyUXoJpFIhFDA\noqWpDqw0mmZg27bXUc9rOCMMbKuoSv0cR+nn6yZmQDlKpmn6sioVTXuhqhwsx8Y1gpRKJeYXEpRK\nFlPT8zg2GEbAY9v7gkY68ViM2to4sViMSDAEuoZP7guY0lP0s7GKNvlCiYLtEAqF6WhqIBoAo5Ag\nEgpStGxKju01bvH+T4Wg4NX+apqmShA9lEULmKDpuMJgeGiKsdFpMukCRjBEIBjEdtX9AnBkJfct\nNEk4GCIeV053Op1UWuexCOFAgKbGRoLhAMVshunhEQZPn+b06VNcce2V7HzuWUYmR2lq7ODii6+g\npbmdXc89w7bNa9n5iwfp27SRsZFhtm7dSiqVIhxVSJddLCndd9dmYmqGS664CpcA2XwBUwgefOB7\nfOQP/w/f+ObXmJyc5I8+8geMDo+QXJhnxcplPPfsbgJBhcgkFrIEg2p/OXfmDPNTk0Rr67jrrW+j\na8UKzgycIhYK8tNHHqaULzA1NcXa9eu57PLLmZlPkMsXWb1mHftfeJbt2y/m1Kl+SrbDlVdfQ1fP\nKlLFEg8+9ENuvvZa4kGNgy++xMlTZ7nk6msJx6LMLSQYP9vPxi0XEYs1spjJUVcfpaWpgVRikYEz\np8h6yOltt93G4cOHefThR3j/+34LzdDZ9exz/Oxnv2BkcoZndj3P1NwCqWRa3VfXoqGxDilVFYRh\nwcz4JAu5LFddcy0vvriHhvo6FlNZ0hYENJdAOEQ+k6WtpZ29+/Yxk5jntjfcxkD/CRpq4uRtjaa2\nTsxAgGPHD9DUVINTKLF+3QYMJ8+epx7j4Ud+hqYZ3P3GW/nWf/03n7v32+w9dphHH3yQP/rTP6W9\nNsZ/fPVehs4NkMoo5/D3PvYZamobuP+bX+Ham1/PyeMn6X9pL60dy+levgJNN7n9TW/FsYq8+403\ncc97/4jO5Svoam1l/95n2bfradav6uX48aNsvuQStlx+Dcl0ilWrV9DT081tr9vxy5FAXmG8JiJw\nv+e0YRj09PQwMjJCY2MjsXgEy7LKTT18QyalU8lXw8uMNyij7Of7XPyQTZSJVA4VTgxejak6ToJW\nMb6WZSEF6IYOmiAUChGJREin00SjUS/S8EqHPOUx3+kIBAIUrRKmF81XfWD5OvxWGFDFnD5vmKZZ\n3pgsxyYgTAKhINJxlaRgwFB5xXKEaxE2Aoq97brMzSzSWFvPxz76MT7zmU+z4Los5goeBwA0YTAz\nM0cul0ELBGmIBpkYGeT7//Xv/ONnP0s+kwS7hKnrCM3wyqZE2Xj5BsoR5+VYqUpj6BqGMMp5cV8R\n7vy0iVbNAZQS6SvJXSANUn4/IIRbIZvhEyJB1RxXzonrIoSDU7I9qVMdRypnwxSqwYgeMClZDoah\nEYrFkBLyuSIiGCBbKFGQRTLJFEZArYVAIEAoEkCTkmIpjyKtqbVkeNGhguYN1Z9bSoqFEomZBDOz\n8yTTKVw7iONILEdRyTRTIyAKtDQ30BgziMfjRKNRhKm01X2dAikFJak65Ek9iBYIIjUNyyri4JIq\nlFTnOFdScmzyhSLZQlF13jJCpNNpLMvBNIMgoqCDLQWm4aEeQRPbdsikc2TSOXR9riyhq+mossGg\nhvS6RxUKBYRhYFuKM5JoqKW1Ic7y9kYsXWA7Lq40lUMqQNN1hA66NAgaBqapUyqqLk3Fkk06X2Qx\nmWZuocTE2DyGESRa04ztOli2iy01Al6vAMe2MAzfebYpWJCZSWJZc2iaSpEVig6ZVJqW5jTdPW2c\nOXmK8aEhamMh2np6OHr0OB1tncxOTzE7MYGzpcTQ8CCW41K0JfX1bezd/Tw33HA9u559jg0bN9HV\nvYxkMk0iMYd0bQ7ufYG6ujrSiwu4eghHavRt3gxmiFQ6y6qVa9i+dRtPP/kMnZ3tpLIZJqdn2XDR\nZgq5LNOT49x+5/Wc7B/g/vu+y+z0NG3N9azf2MfpgZNcduUVjI0MYwSDjI6N84d/+Ifc//3vk8lk\nkK5LR2sbw+NjjI6OcuLEcd7z3vfyre98hze+8Y0MDw9z5OhxwjWNvPWeNzFw9BBPH3qJxuY2br7r\nHlau24imw2JintmxM1xx2XYWUkVCCynmE1OcmZ/CLhTo6+khkZhj1ZrVzM9M8+3//C/e/s53M5nK\nEixaLO/sZvTsabrWbODIyZM0d3aSsYt01HfiWEVOnzxHS2sj6XQS3dZ5afd+QnUBXnrpJVqbGzDX\nrKC+oZXZoQnaensoOpK5fBIjFOPKq1/H8f5jTE5MoOs6PT09PP/8Ls4OHCUQCLB562Y6u7vQjZCn\nqpfFtm3ChqYqB6TDJRdfzPT8PH0bt/GDb/87lu3w9FPPsePa13P69Clamts5fPIIJdvissuvZvfP\nf8LPfng/0VCYYmqGdCTC9q13Io0QJ08OoAuHqGly9VWXsPmiLXzxX77Ag9/7LmtWr2B6Psmd97yb\njp5eBoYnmJufoTZSx9TwBLe9bsfLN/xfYbxmDLj/U0pJR0cHExMTGKbmSYc6XlQcKEceluOWGeGV\nCLZyTumzhlERu1OFu5aNgc8OrpKSRGqYutocfVarrutohopGItEIuJLWWIyJiQnq6uqIxKKK0e7V\ne2uGWY7CjEAQx3UR+nmGjQq73a8ZPj/3XfVlKFkqeg0EPClUKdE1HaFB0bZwNcWkdy27XFccDpiU\nHBuha3zoQ+/noQd/zIc/8nvce++9GCGTXCZHY2Mj9TX1TM+Mk03n6F3VTmZxgS/c+3k+9rGPIYSg\nkMsRDAbw258KqfqHlxnafnTpSlyhCE0VtMQzzLrAkRqRUJhisYhdUiIwru2ocwkUQUu6Xr666j7q\nFXTE/4Pf6KYiW+uTsapIT9LrSS3cMrtcw1UiKMLw6tRddEOVfwlNxyCE5ZoEghrpbI6x2RmEZjA5\nPUs2q5rLOEAxX1CpAt0kFArQ0lhPbTxMNBYBIBxU5L2C41DIFCkWiywupLFLDrbtkkqlyNoODpKA\nGcKQBhqSUECAoVNySrhSY2x8hlHHRdcF4UiISChILBYjGgsTjUYJBEJIV2BGTIrFIuOJRfJ5VR2Q\ny+XIl+yyBKzjOBQtG0dK1ZrSzaDrGkFNxym5GIbqAqda4FZY7KZuYIbCOI6NdByKxaJCTVDokE8M\nBBBmEGGpkreAIUgl00xPTzOdSBIOCIrFoqeRIHEcG9M0CYVCBIIxdK9EYHFhnkK+RNGysV2XVDZH\nJFxLrKYW15HYrpJFNQM6Ohp20SqvN/AQEW99BoM6ZlBxMObmZpBYtLU3MDc3y7nBAVYtX44ZCzMy\nM01dfQ2HDh3ixte9npZ4A8fH+2mMBuifHiKVTpArFInXN9NpZykWSxQKRZLJNFNTM0SicbLZIi0t\nTRQKBSzLIplMMjY9SDhey6YtF7F67ToSiwuMj49z950f4gv3fp6LLtrMocMHEALWre9jeHiQts4O\njhw9Snt7J+/+jfcyPjLCN775b7zn/e/nK1/+GnPTM3T0LOMt73g7BdthVd9aGhobeeThh7n77rsZ\nOH2adDbHZZdv4diBPYyOjuI4klQqg2FoXHH55axeu4HBU2eJhcKEYzHe9I530NjZw08ffYzerk7W\n9C5jw8ZttLb1cOTYc6TSWU6fOYVVyPKGW24lsZBkfiGDce4sX/zK13nP+97Plg0bKRQthNBorKvh\n37/zn/zBn/8Fxw8f4q4VK6GhnkBAY++BA+C6dHQ20tRcz9n+QVauX0e8LkihUKD/2EGmJ8eZmZsn\nUtvKocMHWLN8NWuX9ZCZnaOmNsbmVSs4NXAcUbKYHBli9bLl9J88RjQaoaOhnZHTUxihMNlSiRXd\nrdxw8w5WLe9kz8497HrqGabzRa43DK655hrqa+IcP3KAn//oEb7z9X/hmoEdHDxwhDXLWvnml/8Z\nO5djw+pVLC6OU9/QTDFboKm5jvGxAbSAiavpJBcT3HTj1bhWhk9/8hMcPHyIP//4X9Ld3YNOANMM\ncezoCZrru9i0cTupdAIhf/12oq8JA67rOqFQiGJRKaO5rsuaNWuYmZ0ilUrR2tpKMKgibsuy0Ewd\nXTMR0u9LrM5THZ1Vmoh4RluDanxa01QJT3n79yNFTSlJBUJBhJQEw0a5eUcwHMXyWeClEk1tbSQS\nCSioWkUNVMTuVcAWHQVp266L8QpkPNV05dUTuq7rEgwGAShaFgEPkQAlsyoMFYFLKRCGSVBopNNp\ncjlFOJOOzdDIJHe/6S4amhv5nd/5Hf7hHz5LR2sbcxOTKjVQKODaDkHN4M/+/nO8+zc/QHN7N1PT\nMwSDYUrlshzVIEPJdepei0lAuOiOQHMFrqZga//7qvnVMQJqww8FTSzLolTMK5a6VOfyy39x5ZI2\noRca1eptPlFNjWqugTffiolWNvYSiasSzViuq7qJmWECZozFZI6DR/txBRhmkJnEIpZUUbHSdRfo\nZoRAJI4t1fzn0kVS6Tlcu0ggYHj6+jWA4nIUrRLFYpFQJEKpUFCMcS2IGQqA16K1ZGlecw4NwzCR\nbglHCyICYYJ6gEIhRzJrsZjJoC1k0DQwdYPG5haFUAlVBrm4uAh4JE1HEgzGELoSU3GkjTAcgrpR\nTrUIj8CHdLzmFQJHyiWYlOM4ZW10TdOIRCJeakilbZaQRXUDhIXmWkhXohs64UicqYWiUsZzJZFA\nECV44xMbs1hWukwqdV2XUDCIYUZBNwiEoqoqwK4IIhl+Hb+UGKbnaLtK4tZyPN16Q6FFQUOtt8aG\nBtKLCwQMk7raWrLJFGNjY9TU1TG3MMfY1CSNDc0cO3qUTevWMzE+yo8f+gF9mzfT0ljP0NA55saG\nWbO8hRMnTqDrOkePHmd8eo6Nm7aQzRUQQicSq2FqcoZsweKq193I8Pg0w8PDyrAXS5w4cYJdu54F\nIBqNkknnmJ9PsOeFvUxNj9PZ3sFFF2/nhef30txYz9ve+U7u+8EDxGvrueeee9i/bz+9q1YzMDBA\noVTkpf37ef2NN/D8s8/xzJNPsmnrVhpaWgkFTUKhMD/50cPEwhEa6xpxgWd3Pk+p6HJoz36yxSw3\n3HI7R070E5mYQrolRgdPs3FlD4VSkYNHjpNKp3nu2We47nVXcfml2zl6pJ+apiaYGGRkaAjhSrZv\nu4y9ew6wrm8T06l59h8+Qn00gqlrfOnef+TO225BFnOMTEzT27sM09BIJ5MMnjtNKFpHR2cntTVR\nFhcTlGyX+fl5mlo6mJ2aBGmTWpxm+NxpLrpoG0ODJwmFTeJxk+mpRXS9nqHhswRNwfU7ruX4yUHq\nm9spuZLlLS3kckkEBW64+SYGTw6y54XnmC/ZPP/CbqYX0rS0NtPTXEd3VxtPP/c8LirwsRxwnSK5\n5Dxf//IXaFzWSe7UWS7bfDEd3W20dfaSWphhQ98avv/A/YxPzfCpv/tbrrvmBj7x8U+SzdvkLY14\nPE6hVGTr9i2k8kVy+QwEBS/u2wd84FX3/v9tvCaEXJLZwiddVzXXsG27bOTiNTE0TfNqsiXhcNjb\n7BwsR3rtLwVeL0yq5UGE0JUREQKhaWrDFnrlvULghWWqhMc7jzpWw3GV6IsSoFDHFC0LqWnkSyWE\nplOyHYLhCJbrMpdI4AqBHghQclxKloUZCFIslQgEg0oApur6KtdL+fyv9NB0A4mqYTXNQLnFp9B0\nNN1QbF/htcB0JH4r1Wg4gus4iowlBJlUmi2b1rNs+Qr+5q/+miuvuJyamhhf+cqXmJyY5P/84R/z\nrW/9Jxu3bOG663YwOjlFJB6lYJUwdKNcYldmCHvCGz57W/NYexqapw/u9/VWEZljWSo6FwJT1zF1\nHde2PWa4VtYT99tRakIovZAqI65+98uEKH92tRFXt11TUbZeKZPyiNhY6EjdxEZDaEE0M8rMfJaB\nc2OcGBii4AgyuRIFG2x00AMII4gUOmg6jvX/uHvTKMnSs77z977vXWLPjNwrt9qz9q2X6urqvbV0\nt5CEZTACIyFLSJqxYZDH/oDNAR9xfAwzNh4zwwweZmETCARIlhCSutV7d/VaXdW1V3XtuUfuGXvc\n9Z0P743MrBYw+PAFfM+JU5FLZUbEvRnP8/yf/2Ic/cI4MlO+lrhuCsdyEoKXIAyh2fJpeCExCqEc\nI5USFiibWCu80LxWSll4gFA2USwIYuMpHkSaKBJEYYwWYDsOyrLRQqIslyDSLCytsrxaYXm1SqPl\no2wXoWzCGGw3nTjmxQRxZBjawoLYZAHoJJverEGSax6x5tEShut7aYQwBTZBrzbaHrfjbsEQQuMo\nwlLS/H0K0EJhWy5OKkvKSZFOZRBakUpnkY6LkjZaKYTlIBwHaTsgFGFsWOVGM24ZmoSQJDYJptGL\nY+J43eXPtm1DhiQh0sUmE0AISRTGtOp1ZmdK5PMFUhmXm9dvkM3nKRaL1CoVDu7dx1tvvM3FK5fZ\nun0zzVadpaVlJidnGejrx2+sAJqFuQX6BgZAKm7dmmBicpJcJsOtG7doNip4zRYjm7fw+pvv8PCj\nj/G973yXSxfOs3vXLixLcuqdk3z0Yz/EzRs3uHbtGvv27aW7q4tGvQFocvku/DDi/vuP8zu//f9Q\nr9W5du06H/v4x9m2bRu3JybJZLOUSvMcPnKIns4iMtZcvXKFxx9/nEwmy8pimedfeokLF87z8IMP\nsW//fr73zPNMTs0wtnOM8YkJdu7ezfDmLfRv2sTs1ASH9uyitrrImydeYd/dR/GB1ZUVfujJD3L+\nzFssLi5hpXLYtiJlS955/QQPP/IBFleaLFZaXHjvOksLi9h2mmJnD7YIWJyf4datG2zduo3x8Rk6\nOossLC7yxokTbOrqIRSKTVu2cvPadTKZHMWOPMQRYRjR3dtHT1cnS8sLFHt60AJefPlFij0dnDt3\nFjeTYXhkK41ag2qtzsTUJMWuIjdv38JrtXjn1CkatSqvvPwiv/4ff53HH3qAanWRs5duMLNQ5oHj\nD9HbnWdx8iZdfZsIY0E2l+fk6ZNk0kby9k8//zkunz7F1dIc+47cx7/6+V9k655DlGsRfYUu5scn\neffdq5y7PsX/9Gu/Qa6jC88PufLedRYWFg36trrE6tIMt2cnuDlxDduVbNsxzCPHjv7y36Z2/p0o\n4EuV2pfbbxYbpTlB6JNJ58hk0tRqNebn5+kbGEAoy5Bz1mjf6zfd/uPewEbX0uy/14E+ucHJS2/4\n/+aINcRmHEm0iMmkro1ftbIsA8lLMwdatk02l2NpeRk/CHBcF8cxOlPHcgxr2giqaUc0tquPVOqO\nzwsh12DpOwlQ7e8xumHbcYxntRJoFGGkEdIyr42OCKIIqWySgGaUFNiOw/zyMrvGdnD0nqP80i/9\nIgf27ePdd0+zsrJKaXaeY0fv5x984mNMTEySSjv4gQ8yRocBCm2iJjHFtc0cX7MwjaM1bXVbzyzE\nhl32HbKgdRKfEMYtSwgD17bDMcyZ2ngu1wv2mhuZIjE52TDxJ5N2HBtzlaR7MsiBZaPsDKHQxCiW\nluvcuDnDlWvjlOshfgRaWEhlEWiQliFRtffNYWiczSBCSYGbtpFS4yjjJqaUyVR3HcdQLhKpU6gj\niAVSmAIfRKBsG0tKoiBCqAgdBthSIoU29wUmqCPZDTspG6lMII1ZHwksy0EpC6Vs2jnd7SYxjo3Z\nadvuFZE0U1oTx1Fis6pBhybKVGqIQywpiOJ1e+M2qbNNAtV3+DO0Gydz/hwpidvRstI0z5aQECYu\nc2hsKfC8JkJCrCNiYoLAMw51lkBZiUJAaGMTq8wKIYh8zNM25yDSkeE+CGnWR22injYNY6w1SpkQ\nIoThNDiWJJ9LUyrNEkQBhY4CczOziBjm5+bo6+ll584xtIJSaZbOzg7qtSaFQhcz09Mc2DnEwtIy\n4xPjHD16lL7+QdxUGtdxuXnzJlrH7N65gyvvXaVca7CyWiWMNfcfO8bkxDh7du3iypXv3pWUAAAg\nAElEQVRLnD1/lrsOH+GN19+gt68bhaDVbBIFAWO7d3Pm/EWCIKQ0V+LlV15h2/AWgjDCCwI+9VM/\nxc/9j/+cwwcOMjM1zZM/9CRXL13mxMsvMz9bIp3JUm80cWyXr33964zt2M4XvvDT/MEf/hF79h3m\nqY9+nEsXztFRLLJSq5Hv7GJhbp7S5DiXz5wi8lt89at/yPHHP0Kuo5uJ21N05TKUpqfQuHT0DNHy\njdrg+e99m5/41Kc5f+kKfUODPP6hD9BohPQNDJHNFjh2ZA8pJXn+2e/TaLS47+gDzJZKpFNpBnq6\n2dRdxM5lmZibo16p0d/XR6NeI5tyGBwc5ty5C9xzZD+XL1xnYX4VqW36e3vpLHQQR5BOFXjhhRMM\nDo0SBpJ0toNWy2NqZppiVzfDg1sY2tTH/r37OPHKq3zw4Qd46aVnCHTEJz/133Pw8BEKBZcb59/h\nzKX3+NwXf5aFuQVeef0VOtMW2bTL09/6BhkpCYoD/Mtf+hVqTQjJs7hSpStj89xf/BlXr9/kZ37h\nV9g2dgBhxewc28nW7VuoV+vMzc3SkU2xsjTL+OQUH/7QhxgaGcEL4YG7D/43UMDLtS+LdjAxpmOO\n4iAhg5k3i46ODpRlMTk5gZvKrO1AIYHupCm2QiVGmMroZLXQ7fdv1n8HdxbvtYxhQCSWkDLxaJbm\nZ7fVKO2CtC5RW3+MxWInYRhQr9cQUuCmDEtWSzMztqFMoVQSChEbOdH7fkf747XPSdN86PZ0Kkzu\nkxYQR3EivTL7YqXWWfNSSWyhINJo3UIlu8FSuUZvTxePPPQQX//jr1JeXMR2XT7wwQ/zU5/5SW6O\nT2C7KcJECqdDsKQNGCQjijVxtN5wGVb5Ommw3e2ItddYI0ScTNZmb24lMishwLYMYBv6IY6dQUhh\nioSOsJICb/bjG3atyXO1lAad+JhLiKMIZSlCHSNFiJTguCm0shCWQxDGlEpzTM2VuXW7xMT0CuWG\nBrtApGxiaSGVMjpxKZNLwiAEOmnCFD6OMNppSYRr26TdFFFgHm+7wLUtPIWQKKGSa9BMqUoYgqFB\nfzCMXEHbOB5LKlxlm0ZFRSipzXQbRsn5NPpYZacgilBEiBiEsI1kTMcIHeJIhSVMEW1nkQkBQsYo\nqQjR5lpMri8hTaa2EiCVRRTFSKmwpCSOAoQESyhzLqVBD1qtJm5KEYRNQh1juRZhFKBiYxvcCnxS\nsYelQlrNesL1iNGxj9AhYdBCSYWUlpHvRaZZFAnLX9opQhEZmaeOkLa5ntASHUks18ZOdvJeEKIs\n28hEhUDoCC00vu+bVD8nRb6QRwrBytIcvV3d5DNZY7OKxHIVVsZh974jfOOb3+LQoSOsrFS4/777\ncFVEde42EQ5RGBNGMYNDw1iWTb3eIJPJ0N3TzeVz53jowQeYLS0xsn03J0+do1GvUl5aoHdwkNLs\nHOO3JxjbtYdr164SeD5Lyyt4zRU2bRrA81oU+4Y4fORuRgaHOHPyJNt2bOWffOHz/Otf/CXuuucu\nzrxzkqDVore7C93wSbkOCMHV69eZninx2KOP4wVNXnj5Jb70P/wcb7x2ip7eAcb27OLq9StMTtxm\naPMIXQODTM4ssDQzS0YHFFMW5989A8plz11Hee6lE1gqIufEnH/3JF19ffQOjlIqlVieK1Gaucbx\nxx6iVK5Q92Bxvk5pchK/ETI7MUOjMc3VGxeYnp3mnZNnuOfe42zfOUYqlaK6skBleZHlZotSucrm\n4UGmJ8aJgoCl5WUsJUHGhNjMr9Y5euw4ncVOWr5Hd38Pr7z+Gn2DW9i7/25y+Rxj+w5Q7Omh2azR\n01Vg3+6dbBroo+VFrC7VOTi2jdrSJA8cP4YfOrz4ymvc9+ADbOnr5sUXvsvkZInunhEmZma5cuU8\ncwuz7Nq5k0989IeZGJ/lS7/8q1iFblabATMr84xt6SRjh/ze7/wWe/eOMvbgRzh75goH7jrAjZtT\nnHnnLI6G/Tu3k0+7BD7sPXA/QWQxObNI2i3ywD27/v4X8PnV2pfNm1jiRiWgvbuWUgGaRqNBOp0m\nnc6ysLhEZ7FrDW43Ui691oWzwRjCHO0fqjfcj9/39fXjrzNeEdgIYYGWyd5WGhgVhe+FpFNZHDtF\nuVyj2fDI5zoI/MhYTCY/u73rE8oU8fauduPvf7/8qP2I2pyt9tRrJnjWYO32a2LiKE3Bj+MQL2ji\nuC6ZTIa049L0W2SzGT70wQ9w6uwZbk9Ns2X7dh566DjVMABbEYqYIIqwbIswipK9cfJ6JxP3uiSM\nBLtOdsy6fTOwu8bok4UQkBCfZGL+IUSEY7uIZDKL4xDLlmvTLEQgI9ARytY4ro3t2CjbBrIoK03L\ng2otQEuXCAvbzoCdo+5pyo2I2bkak7MrjE8vM7NQNpGEGiwnjWUZfkEYG+fxOEnxWvMX0KaotZEA\nIdQanBwLCPyAKNKECZwbxcafLIrjpDCutXvmPAkjj9NrzY1hc0uZyCLbu3tpnn+YNEs6Wk8SUwly\nE+vYNDBx3G5/iXWSD5ZcA3dK7pIVhFRmPbTWiBpdt4hBaIMsGchdIaW5ZpWtEoJbWz2BKeJSoaRl\nin9knrCMwJEKpQR+5BO0aiitDBE1sWYV0qbZbGFZKdMIxxFI1lLdRLJmCqIQISxkDJYS6CjGkhYg\nsZRFq1Uzr3sUk06lCAOfKGiiI28NUjdPO8YR4DerKBHjey0WSnPMzZXo6+1nx45tzJVmmZqYQMc+\nxWKaudIUHZ1FWs2YYkc3y3OTXL36Hnv2jLG4tEBpfoGOYid+GFJr1bBdm2Z5kVMn38QLm3QUO9iy\nbYTZyRtM37yCoz2GhgYIg5B9e/fgN6o0KitsHeylo1CgWq2BsHCyeUPgDVtcvniOJ554gsFNQ0xO\nT/Nbv/V/cejAPuZmZ5mZmqbWqGDbFh1dZvo8c/4ClgVBHPLumXPcc/fdjN+ews3k0Ugy2TTl1Xly\njkMUGPe6wGsw0NNJqTTHtckp7nv0cRotQeAH5DJ5yitVcvkutLDp6CgidYuRoU2srrZ47/YMm7ft\nolyuMjzYx96DB9k+NkIrqoBw2Lb9ALmOXpTj8uqLzzA0PECrUWO+NEM2bZEp5ClXy1TLDerVOkJr\nbGWTyWXZtm0Hr77xFvv2H6Qzn6eyssyB3buZmLxNNpdnx+5d9A70M7dUodxo0Wh5TE5NMjg4zOLK\nCpeuXKW3t8hqtcam0RGefukF9h05zIk3TnDx4hmOHLmXZsPjD7/y2+w5fJTtu/axuFDi2tVzZPw6\n+/ftxUqnePrN17jrwSfJWB2kAhfhr7B78yZ+9Vd/mVPvnueJpz7MoXsfxl9d5vqlW1jaYWjzDvpG\nt+AWOwkE5Ivd+IHP/OICW7ZtxQ9aPHDPnr9VAf87oQO/OLm49iDavlPt4qXaECxRsotTBEGEFpLF\nxUVs22ZwcJBms0kQhetGGX+LYyPR7P3//mWmMG1yD0KYqWiDhrzWbOCFAYVCJynbwfM8hBC4tkMU\nB8ZdawN8/9f9Prmh0Lf/jdBrVqsb9eZKKVzLptIo06xWuHnzJt/+9rf5/f/8Gzz14INIy8Q5nj59\nhhjNffc/gGsrSqUSOpeht7cXopj77j3Kz/7Tf8ZKtUYs1j3qhWA9rlPECZlQIlX73P1galzbXsQ0\nFzphHSdJXsRYwhSwUJv9F0iksrHsPEIoqvUGN8enqHuGoez5PlIqHCeFki6eFyCUItRGIx/65t90\nOptAqwZWNY+nLU0jicFcf52N29dfbeOrsc0OXmqQEVIZpzwdbjClSZ5nG1EBQ7LSWmMncLSUECT7\n2zCI185jW8GwxgdRKsn8itdY4kizI1e2nbzhmcIWo4mE0agjBVZkZJciWTO00RKtDRojIlOOhVDE\nKEIEaEUkDEoQR0aahTSNoIG9jemRFKADH0dZaC2IQtBxiJNyDDqhwQ89qvVVelwLGfrUvRbNKEK5\nLl4Y0dnZiWW7SIx/fxybtUqzWTfnSFpI20GjsIVYI9MZNYREaoGyPaRUTE7PMrp5mHp5hYWZ2/R3\nd1Itl/FRpHMFGi0f1xKkHCNjW15YJBaSdCZLJpPhypWrrMyVkAq2btnO1s0j/MZv/h88/NgHqdRb\njA4NcPXsc8xNTVDs6mZ0yw5eefV1PvDER7l2/SaTM9P09vfQqQMGOjsJEbx66h3233WErlyeqWvX\nESIglCkilWZmboFPfvLHeP3l55ibuk1fXx+2k2Zo8zZqXsihg3t57ZVnmZm4Rf+mEYaGN5MrdNGo\nNvjm17/Kgw/cz7e+9V947PEH2bxlO/Pzy2zdsp3Kapmnn/kuqUKGq1evcv+xh3n8Ax8DlaJSq/PW\n26+wc9sQjlRs2TbGufMXefDYfbz9+mtcv36dnfsPEmMTWjYyjikUO9EIfK/FammKrnya7kInN27d\nZr5S5+BdR+jp7UdoycjQELcnZ7h46TyplMvuvfsozUyzY+sQceRz+rUXePONk/zEJ/8xS0tLOOkU\n06sBH/nYj9JoVpgan8BSgnw2w7mzpwijiB/+hz/Cs88+w87Nm4mDFi89/xwH77qbLTt2slSt0PJD\n+rv7yGU70FpTyKVpNipcvHiBKIoodPUwOjBAT1cHjcYKExO3eO77T1MsFpmZq3DgoY9y7rk/4buv\nvMFv/e4fsHVTD//Lf/wPvPj9p/nZn/4cJ0+/i8wX+eEf/++oVhrEAmo3T+P5dSYmJrh9a4r7H/wA\n71y9RiGbws2mqNSqPPjQcdA+jtTcdeguzl+4SUdnL5cuXcJ1jQfDf/6NX/2rp8W/wfF3goXenk6h\nTTTSa0QmJZWBRZWdaLMFyrYJoojh0RHm5+e5efsW3d3dpFKptYKx8fjBiXqj9OgHj/d//x0sWx2/\n72PzmKUUhHGE49iJXt00EZlsGjuwqK2uEKZS5PN5oiDEazWwLON2tdHTuS27WicBa9qBKgmCm9w1\nd+w2uSyOkmYASgsLTExMUKnU6ezMk7IFw/2b+Nxnfordw3302jZvvP46C4vLdOezlKs1SuPjBEHA\nJz7xCcbnZ4ljYwbTLJfRYWQgyqR4g2GTx+0JNZHhmQIRJcXuTpY4GE6DECqxhRV4QYQQGsuyQUaE\nGNjZcR0sW+JHprCV5peolBuslhtomcaPBVrauIUskR8QxMmqQSkiJFguMTHZQj+tVotK3Tcwsi2T\nIBBBqNfJd+3Xfm3iloqNioW2Qcza9xqvUDM1hhE6cUNTwqBFOiHZ0TbxiXVifPJ+pEWtydsSy/g7\nrr31vX77pxoUQEgzUbbtVjXGBS0kJI4jhEzkXVqt+RkAJhKXCCnbBkKYKR5l+BRCEwujz3Yshyj0\ncC1FqENE8lYhNLjKJgp84sAn8GtY6TS2nSOIfISIaQUNYjSu7SBsibQt4mZAGDYoZFME1TIijrF1\nRFDXxLZLOlNEooh1hO95ZBwHPwqRSqCFyQDQUYSWxtVOCo0kIo4jAr+B7/vMlSZo1VcZ6O1Ehi1q\nS7OkXZtcKsVUaYpIC9yOAq3Qo+X7OJag3mwQWBYL9TrdxU6ieoXlpQXeu3yRbCrDIw9/kMtXrvLE\nD32Yt0+ewMnmqXse3baFk3IpFDrpyHdy7Ogx9lYqnHr3NIOjIyxMzXB7epLunl5Ks/NkRtO8d2Oc\nnp4iQrQYn73C4OYdIBWNwPiJr9bqjGwdYGZhmeHhEYYHh8iksgwNjWC7aXp7e5mYmGFpcYVapcro\n6DCZTIryyirLuSW8VsjK4hKTN42s6uWTb/ITP/GT3L41ybtn3uHo/Q9we/yaSSxbWaVWr3D1xiQD\nAwOcOHGCt996i499/ONMzy0REXHo3oOUyyuM377FyOAm8rkMK0FAOt2FZad59+x5vvwr/47vPfM0\nRw4d4NSZC1y9fp1NA0M88tCjlMtlTr59glazQqMyzabeHnaN7eXZp5/n5Fuv09vbi2w69A4fYrHa\nQntGOdPf18Ps7CzDIyP09nUzOzXJUG8vzz7zXXyvzv0PHEdaDp1dXWzfuZPFxUVeev5Z/GbArVum\nFszOl1hdXSabzXJ7cooPPPoYXVkXETW4564jPHjsQd46eYY3T53m8R/7PD/88Y/yzMsnWCpNMDbc\nzcUL71Es5Nk+tovvPPscn/7kp+gs5Bko5rh85SyFtM3FqXkuX36PX/43v8Lho4/w6rnzBEFAvV6n\n3qhx6+ZVRvuKNCvLfOdP/pxMvocXnn2B0A84et89VCsrP1B7/muPvxsQeqVhHkRC+BGsJ5IZFrj5\nbKwNLKmTCbPleeRzObLZLMvLy2te5m0zlf+/46+Dyt//9fff18lus/2xlAbOazu2tYl0AoHruLi2\nTeD7VJZXyeUyuI6N73lYsh22AesQ//tv5mvtffvGWxD4NJsNHMvi+rVrvPb6CXLZDB2FPMMjW9i2\nbRs7tm4jn83SXeygI5dj374DvPPuaR585CH+19/837l48TyrK6tYrs1XvvY1zp4/wyOPPsy//oV/\nxeHDh2h5Lfw4Qisj24mjkEibN0/NhjxpzdrHG9cSGydx0zQZ+DbWAiltlOUQSUUQSYJYEuOwUq4z\nM7vI5Owi41Mlqo0WsXCwUlkiYVjgcRSDsEy5jCVhrAEjb5LCwnJcbMdt53IQxsYbQEtQlmPIitGd\nwRkiWRO0oW1zjt+HyGgTqyERSNuY+0ghiBOPdMl6E9oOhJEJUQ9EEiCSvC4JerM+Hd+J8JivtR8X\nINb9C6LIT/LFSZqFtTJvCGNS3mFxsDF1bK0RlOtmR6GOzc+XAh0GePUKrmW4BmEc4VoWoe8RNGpk\nUxZ+vUyrsUIuncYPIlLpHLHwUY5Fo1FFSrBshYg14WoFohAhJbatDGogFHEYEwUBQRAQRUaR4Lda\nZFwLHfpYUhJ4TUQco+MQO8kwsKQgajXxm3WazRV0HNHf2021vMzEzWu4MiblKLx6FWVZ2I6N32zQ\n3dmBm3JJZbLowHgRpNw0q+UySggKuTSFQo50yubMmXfZuWOMd8+eZte+nQwNb0KELsvLi1h2mtnS\nHI2GaUrDIKRWrXHx/AXclE1HVw8DI6PML61w6vQ7HH/gOG4qxZl3T3H/3YdYWVpianqKwU3D7N+/\nl+XleSbHb7Nj115S2QLXrl8nn00zNXmb6akp0pkMUayp1Rr09/YztKmfZ5552qgTlCKKYGzHLkIv\ngCigNDPLvoOHKM0tsG3bNgqdBVZXy6RTKRYXFkil8jx4/3Eyrkshl+Wb3/wmW3eOsVJv0DM4yu4D\n+7lxfYqFxWXy2RxbBwcJWzWUkHR39XL56gUuX7/O2M59dHR0MNDfx+btW7FSDulslonJCZaWFnjo\n0QcIwxb1WpnV8hLTt6d46OEHmZmbwnYl+w8cYHT3PlQqxWsvPMf1G9dYXVlhcNMmNm8e4ca1a5x8\n+yRH7zpAs1nm8pXzdPf0cOjIUd555yxf/7P/wmsn3uDgwQNMjk+wb98+ytUqu/bsYcvWbRw//gBp\nV1H1PIRSnD93jnq9gRCK3/nd38fK5bl28waf/eQ/4Nmnn0ZHIe9dusirr73OUx98mM5igZdOvM4/\n+snPYllZhvs6eOiefXz769/ga3/+Hb74Mz/HwXuO8fwbJxke3UIu3UV//zCjm/dw8Mhx+gdGGR7Z\ngpvOcvd991IsFimXV2hUyhzct5fjDx//+78Dn6s2vkzyvrku/RJrBJ9Ya7ODS6bA9vTX3vnGWtPR\n0UGjXqdWq2Hb9prRxF+7z/4bfu2vug/rE5UQBsKUCYms/TlLCyI/QIiYXDaH4zosLS0QxxH5bJYw\n8s3PiU3ghNjwe9rTWbtYR3GU7PrMpBXHhgmdzaRBaAq5LLt2j7F1y2a6u7tw3BSh79NstKhUlqlU\nKly/Oc7o2B4aQcSV27f46Mc/xtLSAiffepNiTw+f/sxn+MOv/REHjxxm09AQ5arZr4VhRBD4SB2v\nEdiEeaBrr0G8FjoS3/H6mJsAJRBKEYQRKEkqlUEjWS1XuDq1xExphanZJRaWa5QWyqxWffxY4qaz\nxEIRJFB7GIWI2BDG4tAUSm2CIxHEJL44NOpl0BFCmyjLOI7WMtN1onmWyjibbTw2RtpunNI3Hpay\nTSOgFOl0CmJtfMPb123SaLYHeS1NvKaU6+c1TkJSDHFTJ0x3TZtJfQcqwBod0DxGAXHkowMjaQxj\ns3yKkkbJOJ1plDaoCGzkVKw3LWvKAMFa+KlC06qv0igvUF9dpFDIgY5wJIStOs3VeeorC8g4IGMb\nnoqSKVqtmFZQJ4x8XGnh1+pEno9SYMuYKAyJtZGYhX6ArSz8RoDvG96ATB6bLUkQpdBoR6LQFHCB\nQeJic54jv4XfrICKUTKm1WjQ3VUkm3aYvH0Lv14j7aaRto2TSjM/V6KQsgFYWlomnXJZWS0zODRC\nd7Gb69evs2fXThqNOumUYveOHVy/eplLVy7Q29NDIdeJVzU8h5SbZXWlwtDQMKdPnmR4dJgoDhke\nHqDajPFwsNJZhoaG8Lwmly9fYnB4FJuQ++46zJX33mPfgSNcv3Gd82dOU8xlqKysEsUxY7v30qjX\n6OrK887bb9HX28PM3DzDQ8P09Q0QhSGtRo0bt24Z6+mhTWgEmXSORq1GypY06jXefvcsh++6h5WV\nVXp6+ylXyuQyecZ27SKTK7B1yyitRoO/+M630DrmrqPH2L77AMceeoQrV2+RzmTo6+1FEVJZXcSW\nmt1jO5kYv80LL77Al/75v+TkuQvs27uXdDpNpVqls9hF10A/gddCEvP222+wd+9ORgY3MTs5yfZN\no8wtLnDgniP89u/9Lvv37eXatRsoR9NV6ObDH/4wA/0DTE5MoKOYVrPOzq07uHTuFIMDPYR+k4nb\nk3z7O89Safj0Dg7z6Ic+SFdXN4VcHt9rJQqdmLm5OQSasW3b+dCHP8SWrdvBcrl45RpnTp/BazUp\ndBTwvSqPPvIYCsV7ly5w8u03kBI++Y9+hD/82p9w6K57+chTP4rjZpgev8x/+tV/w+XrN9m6/27+\n2b/4eRbKdSphgAibpFNpbt0epx60UG6KuaUFgjiku6+b6bkSo6NbePSRx0i5Lo1GnYceffDvfwEv\ntSfw5NhoArK2C1dqzTRCJIU9QiMthdAGMszn8ziOw9LSEvl8/r9qwv6rvv6DcHqcWHSasAWSece8\n0Zv7UgqTY6VjZMLo1STSHSUpFApEUUC9UaOzWEAk5CTDszHkozgyHs9RGBq4lg2TvmWMOMy+VBMH\nAaHvAxpLSerVKl5ipZmAtKSzLplUiuFtO9GZPFu37uA3fvP/5DOf/hRXr1zinTdfR1kWn/nsP+H5\nV17g0UcfIZvLmGIdBihhEAahk0ZDaEQSkNEeAtf38/H7GOptNMVokaUyVpye5zO/sMjk1CTLniLE\nIopso5PGAmkjRIo4FkYLLyGIPBzbQaGI/RgtYzQhkhghYqLIx7EdhI7JWJYJ4ooTS1NpIYTESPvF\n2hpCx++PrzRIwfvJhGvFTirA7OuDyCfSxodcJo2nknKdqChFErkJtkzkbtqsgto7Ea31HZ747dVO\n+zVUyWvdLuyhid1CBz6KGCvlEIQgLAVaE0a+abqiEIWVTNvrU78QCTwvNSTnRgqj3beITZMW1HFl\nTGVlgWw2Q+gHSDSOJbGFT+A1ULGPrQS2mybSLlqn0FYAkSZnOziRNrwGS5sAnVYTyxZoHRAGHsQS\n103jplIEYYhUEt/zyGWyeI2GkcCFgbn2o5hYm3CcKE5S1aKQ0G/iBXWklLi2je+1kLFmbPtWmvUq\nN8ZvE8aCldVVHEvRWF3Gdh000PA8spksStk0600spbAsje+1qJVrxJFPd3cHUeAzOTXNwkKZyIe+\n7l6KnT20Wk2y6RSFjhw7xrZx8dJZOjrzLFUtdu8/TLGrj8DzOLRvP6dOnWJ2bg6XCNexmZlfZqFc\n5Z6j9/He5UuUpm/jSsH+AwexU2mEiFlemuf61as4bgoErK6W2b59JyvLy/T1dDM1PcWtm5Mcv/8e\ntu8co+V7NCpVapUVuru7OHv1GraT5uzZc/QPDJLP5ZlI9OMrlTKzpVl+9/d+mxs3r/LEE08xMDzK\n3gOHefW1t1mYW2Lnrh1sGRlgZXGWTMpmYX6WMPQ49c6bEFp8+rNf4PKtGwwNDRB4PtVKDddOs1yu\nkLFSNMsNAn+V5cUZRBSwZ/sYq3MrXB8f5+EPf5DS/CK/8//+AXt2b2N25jZh6CC0pK+vn9HhEZqN\nurHmjTWvvvJ9smmX5YV5wkAztusQxb5hNm3ZyuzKAi9//zniwCeXTZPNpKlXy2zbPMq+PWPcuDbN\n4uwEpdlZCp09jG7eznxpjma1Smchz87tgziZIoVcB+XlBSrlFfbs3skTjz/F7331q/z4pz6HZRew\nXJdf//e/yMrUe7i9Q/z8v/33rNR8pLLoGejjT//o/2ZwcBMDW4dohB61Zo3eniKtWoWOfAdOugOl\n4NatW+TSxgzp6H13//0v4LOr1bUH0Z4Q1u+b4y/zwRZam5tYn2AcxyaXy1IqTSMluK6DyfBeh6TN\nRBuhJGYU486b+ZpACtZUtW2pkoGDk5+XsH8lEqWhrS8X2sQ8KiHXHqMlBTKOkXGIin0Krk1GaWpL\n82QthYg8lI6wpMYWGtuCjOuQTbtk0y620FgxpJRCxTFRqwVBQOwHRLYw0XuJf7W0jPWrEq6xB1U2\nAosoFvieR7NSZkt/Ny+/8Axjm4dQIuaZP/9zitkMH/+HH2V1fo4DB/aipJnJLMtFWhGO8HA1YFm0\nIoEXkzDwY6SIEJGHLQ2hS0rLSKowTY/jWETYRNKi1gwZn1xgenaFSkMT6BRoZSZUYbzl2zC1+dlG\nz2xLhdIC3/eJI41yjbQtDOLkOSqkBlslwVaYpkoijQd3cgVIbabNOEqarLWccAMha22KmNSm+VJJ\nc2Zgc+NSpkRspFYadBAl06uBtYUAZSUs+jA05KuExGekZW043JRVAys7hrQmYvWVcZ0AACAASURB\nVPzAM34l0sgOQ20RRgbK1xaEaCJtijXeKo6bXtvbR6FPypYQh8jYaL7bUH0b3dIadKyQOkZJE7up\nEUbBhkTEAjsOKM8vknUigvosrpUk9cWe4TLImHwuRcv3QaWx0jZCBTQ9H8dN43shrm1RrS4iZEBa\nCJaWazSbVQodOTQSXwd4UQPbNWYhuYyLLTWuI9FxgNesk3IUodcgl7KpemXi2KeQdZGRh1dZgsgj\nn0pTXlykqzNLs1FB65BGs0Ghq4u0VJQmb1CZn6Qjl6Nch1wuRzHnsjo3hysM2kDcxLYjzp8/Qxh5\n2JamUVtmZaHEfffey8uvvMLUxC2OHR4i3z/CzFKFHXuPUPcFlUbA1I0bbBnZwtbd93Ht8vdZGr8K\nrTmiuILtpNi9ZztTt25gKZer713CUoJWo0YYBZRrNTp6BpiYL3HxynuUyyvsG9uFV2syMz3N2M6d\n9PcN49VbTN68TbNWRsrQhJ4sz5uGTGiajTp+FDO6bRfVukd5tcTxY/cyNLKZcrVJR0cR4eRwC72U\nKxXeefZ7zE+P8/nPfRbLzaKcLK+/eZKuzgKPP3iM777wEsXeATLpPNJK0b95C8JJ8d1nn+P4Iw8g\nVchiaYFdu7dwfWKcjs4RsvkinZ1dfPXrfwpKM9Q7jJvKI5RNd1eRdE8nW/fv5xvf/HM+8eQTzN68\nwo/+yKdpVELmbl9k+uolLpx5laa3wPziLDembnL5ymVyqSxPfuRJJqem2bVrN1u3bOXgwYMszM3i\nKovhjgLZjEUxm6HQYdGiyuLsBJfeu04tWuXwsaMI1+W1109z933HeOTJx7hw4xZ79mznyqm3yXUV\nuXrlHB/7+EfwYkkqnWFzb5E/ffolnvqxT9Pfkad0+UXeeONNRrfexY984RfIF4ZZLq8a7/iFRUYG\n+3j+hZcodnazZdsOYqmQSpBNKRqVJRqNZXKZDFHgUauskkulOfzfgg68VK59+a9ief91R3tvaO7f\nCXOn01lWV8tUqzUymRyumyIIDLPZMHITK0kkFiopvBKZ2IMKLTEKqPXPK2EZ1vEGyFW2DVbaE9XG\nI9boBG4OfR+ZTOxSQBQGyS5T0mg2yGazpLJppFAoux3eEhKGAUFggiL82LhqaQGOrbBtC2nbCNs2\n8GOsTbiHTFYQcYjBKWIQMZLQFEOh6cznWVlZ5sK5cxw/dpQ//spXkELwYz/+SW7cuM2+AwcNHJu4\nhQkdYUuJpSSxNGEXtiWIfY84DIi1JJ0r4mtFvRmxWm1RbUYIlcKPBHOLZRaWyswvrbK4XKVcaeCH\nECcuXG0jl43Trly7b17XdviJ5djEkSbwfWzLaICVMteCkmJNemgKZFtzTwKyrxMAzaHXmsaNv1sl\nedJrHtvizrNryIzrTWZ7tQECnTidRWEMycRvKTMRi+QyibUmSlZDG+Fz27JJpVyzHgoCE/cZxohY\noxQmolNZiaYb/GYZ202jpY2yLAK/hRICN5Uy5EplkB9LCSJtdsjG9z0hiibSMrVhbaA0xGGLRmMV\nS3roMCSKpLERthWWdE0MqFKEXoAUxiffshSB3yLjukhiQq+JVBovaNGRzeIFMSvlBVKuwvM9Mtkc\nUgpaXsM0FFIm1r8Rge+vpeuZ86pYqVTIZHKEYUDk+cSRTyblEEUhKytLpFImKyEIQ7xWi1azTtp1\nsG2barXG1NQ05VqDtGOTz2WIoph6o0o+l8VWklq5jNKa965cZvPQELVqBa/VolZvgu2QSqU5e+oN\nOvpG6O7rZ2z3Dq5fu8yWLcNM3DhPEDRYbdTx6xV2btnBzPQs6XyWRiOk0WiyUJpjenqC0dFhojgm\n39nBwcNHuD0xQU93FwLN3r376Cl2sbK8zPLyAvV6jWP3H+XShctcv36Nzs4OMuksJ157lUJnJ/Pz\nC4yN7eDatWtkMhk6C0X8VkCpVOLGzffYtmMXo5u3MTw6SndXNyvlCk9+5CmymTRvn3iWpz7yEbZt\n3cFb77xLOtfJ5q3bGBgYYGFxjvseeIhcLk3se4zfvE6jtswbr73M5K1rbOrv5tL5c+waO0A6k8Jr\n+nQV+1hdWuL2rQsI3aKrM8vwpj7Gb9/i3LunWFpa4MW/+Au2bhmlkE0zffMG+/fu5q3TZxHSwmuG\ntGK4fPkqaddlZbXC8WP3kU7nCIOQzkIHURhiOTZWLs0bp9+hd2SImcVFhCvJFfs5e30KT9tEVY9i\nsYeRLTvo695CvRoxfnOa++6/lzdffxW/2WTnjm28deJFZNhCOmkcBTu376BebfD4w4/yzT/7Bjdm\nS3z+i18krFf4Vz//JY49/Cj3Hn+Y0d2HmZqeIZW2kTLGkYoD+3czNLyZGzemqNabZDMFLAQqjrGU\npFxeJZ/LkHIsVhcXqFUrHH/o/r//BXyuUv9ye4r+mx7vh7c3vsm3yVJdXV0opVhYWMD3fQqFgoEg\nEyjeUiqBmH/waDcHa5B9ex8Zh2bCTkwxzOci4iiE0DDBgyAgDgLiKEToGCnAtiSuZaOkxLEtHMvo\na1Op1BrsX62bcIkwDPD9YK2YRFEISpBKpbFtA4nGGCa00MLocGNQyRuxJYwtqS0jLAlKxEgipNDJ\nxCXwmx5Dg4N89Q9+n0cffZQ/+spXyDouj3/4QyxXauzdf4CW52NLA0NbSpBWCl975nkJII4IPB/b\ncomFTansMTu/yvR8meXVFitVj6XVBkurTVarLWoNj6YXE0QSpLEnjYVhjlsbiuFaIU0+DgJ/7fNa\nJ85vUmFZFr7vrRVSKSW2ZZuCZdlEUQwJgz8WSWKpANV25AE2kgR/QHe/4XJsf3fbGW4jz6FNQjP3\nDW4Q6dh4gStlzqVmTZkQRZGZtkmiV2NDyGsXciEhDEyDZ1kqWacEpFyHWAck1u6mOYyaZHJ54gjC\nyLioeX6TlJsmimPCoMVael+YNHCWCcExkISRUYikYZIIbMtCyAjPK+M3q+TTOZN0JwXKdmjUW4SB\nh4giFhfmKHQUaXlNdByRy6TwWk0cpXBtC0REy2+AF+OHMXHskc24lEpzdBTyxJisc78ZEoUJSS2K\nWFhYADSB5xNFAdVahUwmi0TTajSwRUzkN2k2VrBdi76eHvzAwO1eyyPl2vi+T7PRYGhwCK0FS8sr\nFIt5Kqsr7N29h0tXLkKscW2bVq2KDgOqKyvUVldRyqIzl6enuws/0ozt2cfk9CQyajGzUmV4dDNS\ngFSSK5cvUpq4SdqxyHb24DfqdBZ6GRwaJowjOjq76ewsIqVgYnKcp558kqef/T6jW7bR3z+A12wi\ngUcff4wLZ8/zhZ/+Iv/p136N6ZkpEDG3bt3AcVL0dPdQqVQozc2RL+RxnRSR1kxPT2ErG6/ls337\nGHNzi9x77z1cuHSObdt2MT07h5tO093TRblSJp3JkM+mePfU63R1d3P54mW6uvvo7O6js9jF2XPv\noqTk0tVr+I061aVFyouzzM1OcO3KeQ7t282unTsgDkm7eZqNKvXyCqXx25w88RKVyixZW1CausnW\nLSO88fKL9HV3USwWqSxVuXnrJj09RWYnJ0FrXjrxMv/4J3+c7TsP43b0sXXrDlxlU6vXqC8vge3S\n2d3Fqy+/Qm+xm1u3bqNSWVoR7N93mPJSlYHRUVZWGzzwoSdQ2MhKwKaBUXq6ByGCXLaT3WN7kKJJ\nT3cHW0e30pkvsFCaoKcjz3vXbhD6Pgf3H+K1197gvqP38uu//r/xM1/6Eh96/GFe/f53+e7zL3Pv\nw4/xyGNPstoKTLaB18BJ1ritZgPbyjCyeYzLF68gohgdxtQbLarlMqOjQ0yN36a/t4vS7AxL8yU+\n+MSH/v4X8NKGAr5G4kn2dndOPXdC6xthdUOAW5feCKEJQx+lBB0deRqNGuXyKo5jJpwoCgy5Ryc6\n2A1QurG/TBi5ov02GxuSVrLfjaKIMAggjJJCarKllRRYUiYTqoXrWDiOjSWEKfLE6CggikxAShia\nTNxsNkcUhlTrNVKuSzabu+M5KiGJw5jQD8yk3S42cYwVC2QUI6MY1YZN4xhXaCxiA/miTTeoMYEj\nGkZGhnn+2WcoFjt48dmnCT2PBx99mEw+z8DwIJ7nY2OmWcsCv9nEcY1+PNQQRgKkDVaa5WqL89cn\nqbdCQi0RykUoBz8U+KFGWDYIG4SFEMb6MxaKONkDW8l6447s9OR6UEre0UQZZdZ6wQ3DcK2gIoxn\nfBiZjLc4Tkxn2kVbCNO0yXaz0L6u9J3XlTZXn0r+z8bCvq7wW8+rXz9XABrHttcUERKw7SSCM5nq\nbdvGdhxMH5YUUSHQUYTQpjlzlFxbA4RBgONIYmLiSCMth0a9gY6axFHiIU+Mpcw1gRSEQQhhgEye\nhyHOaRTtzHKzqoiSv4EoNtenpQRB0EAQ4Dea0PLQwiOKQ/K5Ip7fQhBjK7PyKOTzhMnqQCib1XKF\nzq4u4ijEciTNVh1HWCjlEPoN4sjHtVyUsmnUq4hYE/gRTmK6oqSk1WzSaNQSYqRBO2wlaFSrRIGH\npWJ03CTwa/ie4R+srpRpNJvYlpl2eru7abZa9PT2kUnliOOQpcVpmo06cawZGt7E5MQ4jVoZR0ha\n9SrFYgeuZVOamqQjnyOTzeOk05RrDXSs6SqkyfV0c+nKZTo68/T39lKamaK2ukh/Tx9H73uQWCuy\n2Tzlao2u7k4KHR34fkCzUSedSXHj+g38KGJ2tkQUaUqzM+RSaToKBd45eYp6vcEPfeQplpYXuHz5\nAsPDgxy7936q1QrLyyts376dbTu2MTQ8xM2bt+jt66dvUz9Xr15j9+69bN++C5DcnrnFwMAW3JRL\nuVzGcR0a9TpB4PHic8+Sz6Tp6+kjCkJ6ensRUpLL5xkb28Xg4BDLi8sslmbQzTrTN69x4cJptm8b\n4cHj9zE5MUEhX8RWDinH4uK5d1kuTbB5UxeVRpV9O7Zz6MABGl6LkU1DKASdxSKbh7Zw5PAB3j19\nmrvuvofbk7N4OsbzQvbsO8qthUUOHDjM3NQUjVaNvs4C9UAzMTnFkUMHyLoppqZm2X/gMLlMBt2s\nc/fOrZw9c5YDu8ZYmJmip5Al60pGRoaoVVYRdsDM1C0mJ25QKk1y+uQpvve9Z3n2+99nYvwGt26N\nk3Zs+nr7WV5eZmpqilQqxTunTyOV5vzpt1lemGPZC/kXv/BvqTUjanWfjnyeer1M2nHRgO2kmZ6d\nZ252iUN797A0N8v2rduYW1wiV+ikVl6hUV2lVa+hQ5+OXJajx/92E/jfCSOX0+Oz+g6mt2ZDIf/B\nCXld7iPZqJNZZ+waaLrts93O9W6HLiwvL9NoNNjUP0AcxPi+nzhhJZKaxLd74639e1Xsr01bJtYz\n2kDWWmcMt/+fa1koy4x/JqY0QMfGoMPIzQwxTAhD0CF5g6/VatTrdSzLodCZN3kfsTZWsXrdPMbX\nAQTmTQ61DoOaoIr159N+HXRCi1aRxnIUfq3CFz7/Wa6fOUUhk2Xs4H7+3a/9z/QPjtJoebjKATRO\nxiFoeaTcDPVQMD5fZnahStOLELFGKowJSKxRyjB9W03fwMK2jbIEIpKsOdJLI1vScbuw3Gng025O\nzGsbrUHZcRwT015hCMIwMPeU8ZH3fR8nlbmjsIpYG1/upDFUOiYy+aXm94g7SWxxHJtVRLyxmcQY\npSTXxl/mnNc+p20Tlvb3KSHW4jzb1+kaC12YGNggyURXSUOihERIg3BYrjFOiYMQbUlQDl6oSDk2\nfnkeAWTcFMqxUUqwtLRkiI5CIsIWEZp8Pk/T95FWCjudQSNo+MGa5E0oI+0KPJ8w8HAjj2Kxm9rS\nEimWuHnrPXIdPQinQL5nkNWlZXQcknFTZAodrNYa+IEm11mk6fv09/SzWJohlYJMzmV5chbXySFE\nC6k9pqdN8le+kMXzPGJhzler1aJSNkqSfD5vziMRxa4uZidu0tPVlaxsAhYWZ2n5DVwnRybdgdeI\n6OrtxAs9vFaAa7nUWy0QgmajQUc2TWnyBtPTsyAkHR0FLKXo7+8laHlksinmFxdZWVmht6ObS5ff\nY/fBQ3T0DIDlkstl+A+/8mW++KUv44eakaFB3n77Tc6fP8vu7QMMdHUwMbdMqqOXXTt3sHV0M3/x\nrT8mlXIYHNlNvVXHVg7f/+434P/j7r2CJMnv/L5P+ixf3dXVvqdnusfu7MzOulmswywW5nDA4XBB\nMWRIUUGdQqKkUEgPiqCCkh42Qg8K6U16kDkyeJREho5nBN4RZneBAxZYg/UGO7Z7pr0vb9Jn/lMP\n/6zuWYiiHvRCXE30tKnuyqzMqvz9f7+vSwSaaqCqKtPTs1y6cBHXd3jzzTd58cvPo6oKnj9ge3uT\nYqXM2uoG/Z7D8y9+JXOmkwTXra1N3nr7PW7ceIF+v8/29i5hIJiYmCBKhvzbf+vf43B/F8PK0Wi1\nSZOYsUqJ1177Mf/pf/Zf8M4v3sAyUs4/ch4nCOn2A+bmz2JpBRIlIqcLtMjnz/74H7OwfBqzkGN3\nZ4/65ARhbPLMjW+RL1excxprqzcZdNpovk85X0aoOrESU7B1vnbjBt/73veoVse5dnmB1bufU52Y\nY3HpElv3bvKHf/8fcESR/+Tv/j3ee+stLEWa/ExWStzc3Oc73/waqyt3ufmrT/nGb3+Hf/pnf87v\n/d7vMZ43eO/Nv+Q73/pt3vnofRobO9xa2+RIUbi4fInpiRqVQo5mYx/H8fjyi9/G9UNUI2V2/jRK\nEvDWW2/h79/ms1s3afeGvPSVL/Oj137M5uYmE+NVTM3GCQR/8E+/jx+pNBod5upjrN6/TbFcYWJ6\nliiNcZyAfM6gaJvsbawxOzlJu9On6/osnlniYHudnBoSeQ7zMzW6nSb/1u//nX85k/r/4/avRAe+\n3+u/oijqSeeTFW3toYvnF2+jjifz/FZPLqAy3EB25LJTjvE8T2JjUUgQ+OTzOQxD4+jwCMs2KZeL\njPzXdUNDNVQ0Q5PzUjVFkJCk2YcfSDw6joijGJFFkirHFpXKcYclpTxJxiqPUXXp1KYoMsEpiiJU\nzUDVNCIhRwhJKvCDEHQd07az1CuBYdikiiolWFlKWRRn3u9ZljWqxHlF1oEr6ihzXOFk7iqR4DRJ\n8TyX0wvzjJVLvPPzNzg6aPHVb36Vf+Nv/A26/SGKomNoJpqh4yQxppmn7aSsbO6zedjBizUsM0+S\npCRCyTLCJbGLDI82DR1SgecMMdSR/3c23UCakahKSpI+1OU+PMYmCxRJ5dQjzdzasmeDlvmoS8MS\nTfrNi5QoiuSxyaYjqSJfLyOnP9n0Zos/hS8svk468Ow2WlSoKqqiohnGSTLX6NWoKF/4EGl6fN+I\nPJaITJutahk1Mh1x2Y67eiUjlamadEFLREIcDCCRr6F8zkbVTTwnkPK3yGN3a5u5mdmMgS7QdY00\niVBIsgQ4sO08fhih6LpMKUukrEuVJgvHCzwSgYgSfKeH57kUyxZK5BP6CbqR46h1hGGaxFGIbVh4\nvkN5vIZAA8VE1WRaWM6y6DVbaCpEQkIgQ9cj8Pv0+205UbBtoiSk3WljGAa+71MslPE8D9O0mZyc\nQtN0HMej1+/hD7r4wyFqKi/uURQhRMrm5hbT9SlcN8Bx+8RRkNnNGrS7Xc4uL9NuN4gCH3c4ZHJy\nkpWVFQxdpd/v4bpDVF0l8F1SkUgpkqHhhyG5QolOp08YJaiayt7uNmfOnMfUDbx+D01TaXfa6GnE\nRKXKxNQcZ88vsb+7jtfvowgP3x2iGlUSTcP3HO7d/hW9VpszZxbRDZ3FU4scHTZQVIU3fvYzTi+e\nIkkDbt++zfVnn+HMmTOYVoHd/X1u37lHkirMz01zf+0+ruOSK9jcu3eXa49fw8rZlIolHqyukjN1\nTp87T5qEWDkb2y7RarXYWFnhytXHuPfgAWHgc+78eXZ2DzByeXKFEqcWTmMaBq1uk9mZCZoHm/zk\nL19jfHKSnYMGC4vLCF3Hztc4tXyRQnEcxxkwVsqxfOoUIk3p+BELSxfQdJONnW3urNzh3Nkz3Lq/\nxurdT1lduYVZqHJ/64CNu7d49vqX+Cf/158xMzlJfaLC+HiRwWBIdbzKzv4+h4cHJInAtgqcWlzk\n7bffJIpDitU6tanT/A9//x+Rq9YwiyXOX36Mr3z1uzx29TpPP/Ucu4cHlMp1Ll2+hpWvEANCM9lv\nNnGdIYpu8farf4Kma4zVJrl06SJ/9r3vUy7l6HQc+k7C3/0v/xs+u3UPU7eYmpik0dzDzsn38cHR\nEW7gMjkxSej2qOQNQnfAcNBG0xXcYY80jYiGfTbWVum1j4hjj1bzkOdvfO03f4S+3xu8cmyKghxx\nqhmuOQrqeJhBPvosmzSph47jiCj2SURMmgqi2CVJAlASNB00LSUllmPINMKwFPJ5i36/w9DpkyuY\n2HmTKJGPIdKYMPKJ4oA4CREiIiVBQ15c1ewiq2lZp69KTB2+SMQzDB3dNCBJCKNAWkE+FMUIqvTQ\nJpPHqLJAKFm3rOtyJBnFEXYuh26YxEkiSVW6hiFU0jiRtpaZmF5HQVd0+fMUWTRFIrH7JEZLE4Sq\nk7MtQtehcXTAG6+9ShR6TM/P8e3v/B6uH2KYNoqiESUxQaqwtr7F6k6b1sBFt0ooSHyWWKBoEvs1\nVF3KyoR00UtFQipSbNskjmSutCx8yUkRFwrJQ9LB486ZrJgrSsYil8EaSZKQpPLrMAxkpriqEWdE\nMC2TqcVJfKytT5XM1jSViWojLfRoQw+fszCO5KhdlZGYXyBYpilhkh7HzI4Mhh5eVP16vG0sRni4\nLvHtVHrMa5p2rKU3TI1UZNOSNCvuiYRuCrbkIcR+mMWjqmiahUgSKjkTJUVmlScxqq5hmgad5hGF\nfA5DzYGqE4QCVTVBz5EqOl6QoCvSPS7NXOUUFHRFTlE8Z0CjfYSqBygipj4+RxgLSmMFdne2MA2N\nfM7GcYd4UUCxVCVFpdXYp1odp3lwSMG2yBdyUv+PDKbw/QEH+zvMzc/J95GmEYQhtmGiqhK3npyc\nZm1tA9vOEUUxzWaL+mQdr99jbnqSwaDPoD/KD1fJ53K0Wx1ydg7bNmi2G+i6ieN4jFdquL6D6w3I\n2xZFy8bK2Wi6wt179xBpyuVHH8FxBgwdB6c/QElT2t02Y7UazVYXL4jk4ocUkYQkok++AB999Dat\n1gEXL5+jlNe4e+tzzl66yK3PP2N6coK9rW0MVTA7P8/sqUusbG5zanaKcNgjTUNQwDIsLj1ymVu3\nb1IpVVlYmKfROERR5Xum2eqgKCqrqw84f+ESYZzQ6/X56Rs/QVUU6hM1zp1d5u7dOzxy+SJ+EHL1\nylWODg9IIh89l6NUyLO5vomdL/HMU0/x9i9+ztVrj2MV5PE9f+ESVr6Amcuzcv8BUzOTrN67S9fr\no+uC9dXb0tymVOapZ57jxRdfYhgnlKp1Ou0usReQV0LqJYWt2zcpz87hixQvUVl78IBABESRx/bG\nOorwSdwj9na3iLQSpZllJueWUAsT9Bp7bD1YY25+kp29bWbqs6yurTFWH6damyBfqMpc84rF+upH\n1CdyzMzV+Sd/8qc8/83fZfHMeU6du8DCwjK7K5t0+k1++cvXEZpKFGqMjU2DHtPuDyiNTaEYFns7\na9jFIsW4xeVHrzA1PU8+X+AnP/0ZqpoSRPB3/qP/nK/81nepT0zy4P59fvXpx9RnxiiWbVJFo1Cp\noOkqxVweRfj0W02ODvbottuMVYuUijYiCjjY2WFqcox6fRxLh3srt/nt7/z13/wCftRtvKKmAkOT\nxK5IieSIU5Xd1MO6YlBlxxcrUsKkyAuaJDBZmKaNYdoYRg7dsFEVA0010XUb07AgVUmFItneag47\nVyJJFJrNDnGUYBgWcRxDqmJoeXTdxLbymFaeNFXRcnmZW2xYyGxDnSRVSTMUV9Gks5ii6iQCOt0B\nfhCj6ia6mUczcyRCI1UyrbNukKgWYQyxYuG4gp39Fs32kEZ3yPrWPppmcdTssrN3RKc3pOf6HLa6\nDJyASKiYloJumoCGotnEIsULAhTDRNENFN1CMUzpfW0YCEVBaDp6quIl8OjVa/zP//1/yzCC5268\nyI3f+hYr9/fouSk7Bw2295vs7ffpuD4JMio1iWKJD6dS424YxvHIWwiBSEVmtqOjqNIpSjc1mWaW\nynOnZph4mMW1jgiII3hg1OUmqbxfU7Ix9HFSWyqTw4CHOeLpCIJBjqmTVBzjvbJgyklJksSoiio1\nyKqUZQkhMFWdKI6kbzryQ9U0iSunklkud1gcE8AURabXyQAXkRkRAYrUWCvaKEJWRVW1bHsCVVHR\nMxnXKKrT0DR0Q0e3DSI/QkkFBV1BpBG6lUekBlEqR8uGSDnY2yJnm2BYCFVBFRG6Cv1+n7xlULYN\nep0jgiiiVBrHDxN0S/q9x56PbVsMPBc7l0NNIaeZpFqCZWoMey0sU5rW9Ic98rkCedvi7u17VMpV\nDENBUROGQ49KdYxe+4B285CpqQkC3yUMAmzdoNNv4g4G1OuTtFttTDvHwqlFhkOPfC5Ht98ll8+R\ny5dQdYMgihgMhli2Ta/f59zyaQ73drBMA8s06Ha75PIyVzxOBflCiVanxf7hLsVikThKUBSdvGkR\nRT5J5EEakkYxqgKu52FbBoN+n/29PaYnp2g3m1QrVcbHx/FcFxQVx/WYnpklFYKJ8XFiEfHxR59S\nq9RZmDvFvZW71KsVarU6vaHLg3v30XWdifEi/rDP5uYmpp3HylVZPL1IZ/8IVevT7Q54572PKJZL\n3H+wxqOPPEZ9dpyjowMajX1QBLadpzYxSRQKDLuEXSyTiJhHlpa5cGGZO5/f5GBjh3trq6iaxvVn\nn6HdPKLfbnD98ceIvIBipcyDBw/QTJ2xapU/+IM/xA0THr36GJ1Wg8O9fSzdJAwCGodNSoUc/W6T\nc+cW2dneoX24g+O0OX/+AvlcjZnZM1i2xdZ6g2vXnsSNAzY3V1iYm6I+NfofuQAAIABJREFUVqPf\n69EahJTHpjHzZeamxtAVOLu4SM7UcJwBCxM1pmozXLzyNMXqOJPzp8npMFbS2NpeZdDzOX/+UR5s\nrSAUhWe+9DyVXJmF+dO4fsCPf/oq1595nMGgx9LyBQr5MvXpBdb3GihpjjgISayY5uEeVr7E4uIZ\nrNIEa5t7OE4fVdNBVVFtBUOonJ6fZfPeR5hWjkqpwvf/4nu0OkfMTE5TKJX4/f/4P+T26l00ReHi\nuWWS2Oe1H/0zxss1ioU8tpZKGXGSki9Y7G88oFarcnh0QBKFBI7DmYV5ROgT+R7bWxs89/yzpCQ8\n9cyXf/MLuBf6r0R+hBf4xKnAjQJURSOJR12qIot2khG6VANdN4miGBmikOK6Ad3egEHfJ/ATOj2H\nVqPL4VGDdqdH6McIIbvaJIEkUdg/aNHrDUlTlYlaHd8PcVyPSqWGruVIhEK/7zIYOIRhgufF9Lsu\nrWaXZqODSGQWt6IaKKqBHyU4fkRv4NHpObS7Q46aXdq9IY7rM/RC/DBG5nQY+EnKQavD3lGbo2aP\nw2aHw1aHTs/B8wVxopKmOt1+QKpYxEKn3XVptx2EMPADaDaH9AcDFNVEYNPrBbTaDgeNFj3Hw48E\njh/T7g1JhEKq6oQRCNQsEjElDgP+8H/5n/DjlEp9inOPPs7G9hGOnzIM4myxZKGoZmZKIrtlTdNR\nUE/wa0466BNCIsexoUKkGUtbFuuTKcQXSWkjzsEI944TqbMemZ2ILKhDfZjYlrG+QRbwJMOUYYRR\nj7Zz4namqiqmaWUueFL6pahKlrQmLUUf9tYfLTA03TjJQyebDaUnZjsPE+7gJIXtYXtf+TpWMr+B\njLCnKMdMcJGmpIqKrmiI2ENNBH4QYOULoBjEqVzYmoDT66IZOqaVJ4illWbge4RhSMm2MLSUIHDw\nPKnRTlUFlBgFldj1pQGMoqDpcmpjajpxFFPI5VDTlNAPCf0ITTVYX9ukUikzOzPHyso9JiaqRHGA\nbtukqUbe1IjCENvQGfS7GKqCoUPONOl1OgSug6lpHB0ccurUKfr9Abpu0Gk3yeWLFAqy+A76faan\npxFC0GgckbM09rY3MFSFYj5Pr9vD0HWCKEBPwR04PHLhPJvrD2g3GhTtPKVcCc91gATX6dNpNalP\n1PA8F1LB8tISYRRx7949XN+RDPg4ZnxsDA2wbAuRwtb2LqZuMzU5BWqCbZrYlsHHn3xMfWKcJImJ\nIp8L584TRiFnlpbpdpu0Gk3iOOb0mdNsbOwzXhtj2GuhpAGDfp9er4vrDHjs6mMUc0WZ+aAKhIgw\nLYvp6TlEqhBFMU8++TSGZRF4Hvdu3mbY77F0+jQ3nn8REYZUyxWK+QK5XIF8oUhtYoJPbn3O3t4B\nFy9d4IXnXiAKYz795BOuP32dxYUZhv0+y4sLpCLi8OiAdq9LvV4n9EOiMKLVavPzN37CmTOnKRbH\nKJXraJpFt9tle2eP8VoN0zTIWzoT1ZKUVRXLPPrYs1JLnS9ysLOOmgp0FVbu3GJh4RQWETc/v8W1\np19g97BLHKYYScLMVIVUjXn77Xe4+MgjvPy13+KtN9+jVqvh+j1cd4DvDnnpyy/y7i/fZWVlnYuP\nPMnzL36Nvd02j158BM1zcft99g+bVKuTXHvyeVa3t5icmiEKfdbX7nH9iadQYrBNE3c4gDShpDmS\nVOkG/OiHr5Ivl7BzNgtnlklVna2dvczYKmT5zGleeuFZ/vd/+L9x+tQCV69cYdDtY+gGSeQzVi2R\nxIKxsSqrKytM1mvkbBtV1/E9n/29fYRIyeWKPPbk9d/8Av7WW++8Mje3IDNifYdCZYwkgTgC1wnp\n9x16gwGDoUu/69Dt9un3hvQGQ7q9Pp1uj/7AxXEjHDei23fxvYgoBpHqRHFKf+jR7w0YDDxcL6TV\n6hEECUEg8P2YRqONoqp4XsjGxg6Hh218L6HTGdDtDej3PDod+dlzI8IgoT/06HSHtNp9Dlsd2j2H\nds+h7wS4QYIfpahGDkW38bIFQLc3oNHqctTqctTu0R6GDNwIPxYEiUKq2GhmgVS1CBNFRmPaBcIE\nwgRK1RqqauL6EYlQMXNFkiSh3XVptga0+y5DL0JRTLw4wfESHC/C8WJa3QGdrkNv6NHrO7S7Lr1u\nD893+OM/+sdoukG+PMELL38bJ1JJ1ByoJqg6IpGe46o+InDJwh1HyTELXDalQsqiMvOQEQ4s0pQ4\nSbL7RoCIzHAPMs2vpmnHut+HCV8jNrqSYfsj9necJWmNOu4RkCzECTt8pK+W/AQZKJIk0i0sSWXh\njqJkpKY6kXIpisShHyIoAtlkIOvus457tD+JOAm6GREolWxKcfxcOPnaGGmvkfh8kmTbyP6P0wRN\nVTFUgako+KGPlS/gh5G0FE0ThOuSJtIpTzVtoiTFMg1EErK/t8fcxBi2qTHod+j2BtQmp4lFgqKl\npGFMEkXIiBPI5/IwssglJgx9DF0lcIbk8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seZrs+RL45Tm5ymWi0zXqvywx/+OU89+yJ3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\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UouK5F59ie3ubcZZTZBnN+Xned2KRkytnMFWFG1Q4fkAyjRAV9Pa3SHPJ4kKH+9t3cByL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\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FJDjxn7AgLVLZQuQmrE3CQy0sJUKpLdK5xFYxMVMVaFmgSOGbFk2MQH+L6FsgSvHaiwVM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\nQ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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# load and display instance annotations\n", - "plt.imshow(I); plt.axis('off')\n", - "annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)\n", - "anns = coco.loadAnns(annIds)\n", - "coco.showAnns(anns)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading annotations into memory...\n", - "Done (t=0.58s)\n", - "creating index...\n", - "index created!\n" - ] - } - ], - "source": [ - "# initialize COCO api for person keypoints annotations\n", - "annFile = '{}/annotations/person_keypoints_{}.json'.format(dataDir,dataType)\n", - "coco_kps=COCO(annFile)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Aow8/yiOPP0pbYyO33bif2ekJZtMLvP8Tn+bMG2/wtrvuYnI6Q0NDKwcP3M7h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\npGFMEkXIiBPI5/IwssglJgx9DF0lcIbk8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seZrs+RL45Tm5ymWi0zXqvywx/+OU89+yJ3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7zKweEhpllibW2NPE+xqyU0XSIIIp4+bSMIAppm8MorryCKBS3t8PCQ4XCIZVnU6zW8NKbf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hRBYnoMkR7d7WFvNTLZQ0Zdzv4ro2o7GJ4WooGmRJwInj92HbLmkSU6t5TDVblGVJMJC7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2I4HtDtOgyHJp2LSzbX21TKBbS0wec//zkefvIJXuCDKLC+so4qyzw7fIrnhqRkhbuvv81w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\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1a2TSKZAk6o06ngTxaIxmqxU0joqPosjICmQzGY4Oj1hdXuXs9ITdVy9IpZOcnZ1Rr9XI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\nAI+JiYmJiTmGxAE8JiYmJibmGPK/APaetT/BH9j/AAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# load and display keypoints annotations\n", - "plt.imshow(I); plt.axis('off')\n", - "ax = plt.gca()\n", - "annIds = coco_kps.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)\n", - "anns = coco_kps.loadAnns(annIds)\n", - "coco_kps.showAnns(anns)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading annotations into memory...\n", - "Done (t=0.13s)\n", - "creating index...\n", - "index created!\n" - ] - } - ], - "source": [ - "# initialize COCO api for caption annotations\n", - "annFile = '{}/annotations/captions_{}.json'.format(dataDir,dataType)\n", - "coco_caps=COCO(annFile)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "A man is skate boarding down a path and a dog is running by his side.\n", - "A man on a skateboard with a dog outside. \n", - "A person riding a skate board with a dog following beside.\n", - "This man is riding a skateboard behind a dog.\n", - "A man walking his dog on a quiet country road.\n" - ] - }, - { - "data": { - "image/png": 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Aow8/yiOPP0pbYyO33bif2ekJZtMLvP8Tn+bMG2/wtrvuYnI6Q0NDKwcP3M7h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\npGFMEkXIiBPI5/IwssglJgx9DF0lcIbk8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seZrs+RL45Tm5ymWi0zXqvywx/+OU89+yJ3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\n5Y3rXL92A280pmwYlGyHZ9//LN//7LMcHu6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FqiHOEoSpE/pFmYMMx/juqJCjjQJpub68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A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\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mdfUAaRxiOeolu721Q5ZlbKyv8+yzz5BnGe12G9uq0ERJq+Yh0lh1c0djTEvDkKhYp6yQ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/R9M0qtUqqqpiWdGQqo9EIkSjUR4+fMitW7e4desW+/v7NBstolaMiGHOg2zib8THvv32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\nnTyFZVnMT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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# load and display caption annotations\n", - "annIds = coco_caps.getAnnIds(imgIds=img['id']);\n", - "anns = coco_caps.loadAnns(annIds)\n", - "coco_caps.showAnns(anns)\n", - "plt.imshow(I); plt.axis('off'); plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.13" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocoEvalDemo.ipynb b/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocoEvalDemo.ipynb deleted file mode 100755 index 8b2ff08..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocoEvalDemo.ipynb +++ /dev/null @@ -1,168 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "from pycocotools.coco import COCO\n", - "from pycocotools.cocoeval import COCOeval\n", - "import numpy as np\n", - "import skimage.io as io\n", - "import pylab\n", - "pylab.rcParams['figure.figsize'] = (10.0, 8.0)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running demo for *bbox* results.\n" - ] - } - ], - "source": [ - "annType = ['segm','bbox','keypoints']\n", - "annType = annType[1] #specify type here\n", - "prefix = 'person_keypoints' if annType=='keypoints' else 'instances'\n", - "print 'Running demo for *%s* results.'%(annType)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading annotations into memory...\n", - "Done (t=8.01s)\n", - "creating index...\n", - "index created!\n" - ] - } - ], - "source": [ - "#initialize COCO ground truth api\n", - "dataDir='../'\n", - "dataType='val2014'\n", - "annFile = '%s/annotations/%s_%s.json'%(dataDir,prefix,dataType)\n", - "cocoGt=COCO(annFile)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading and preparing results... \n", - "DONE (t=0.05s)\n", - "creating index...\n", - "index created!\n" - ] - } - ], - "source": [ - "#initialize COCO detections api\n", - "resFile='%s/results/%s_%s_fake%s100_results.json'\n", - "resFile = resFile%(dataDir, prefix, dataType, annType)\n", - "cocoDt=cocoGt.loadRes(resFile)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "imgIds=sorted(cocoGt.getImgIds())\n", - "imgIds=imgIds[0:100]\n", - "imgId = imgIds[np.random.randint(100)]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running per image evaluation... \n", - "DONE (t=0.46s).\n", - "Accumulating evaluation results... \n", - "DONE (t=0.38s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.505\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.697\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.573\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.586\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.519\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.501\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.387\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.594\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.595\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.640\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.566\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.564\n" - ] - } - ], - "source": [ - "# running evaluation\n", - "cocoEval = COCOeval(cocoGt,cocoDt,annType)\n", - "cocoEval.params.imgIds = imgIds\n", - "cocoEval.evaluate()\n", - "cocoEval.accumulate()\n", - "cocoEval.summarize()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/__init__.py b/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/__init__.py deleted file mode 100755 index 3f7d85b..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__author__ = 'tylin' diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/_mask.c b/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/_mask.c deleted file mode 100755 index e0fc353..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/_mask.c +++ /dev/null @@ -1,16137 +0,0 @@ -/* Generated by Cython 0.25.2 */ - -#define PY_SSIZE_T_CLEAN -#include "Python.h" -#ifndef Py_PYTHON_H - #error Python headers needed to compile C extensions, please install development version of Python. -#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000) - #error Cython requires Python 2.6+ or Python 3.2+. -#else -#define CYTHON_ABI "0_25_2" -#include -#ifndef offsetof - #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) -#endif -#if !defined(WIN32) && !defined(MS_WINDOWS) - #ifndef __stdcall - #define __stdcall - #endif - #ifndef __cdecl - #define __cdecl - #endif - #ifndef __fastcall - #define __fastcall - #endif -#endif -#ifndef DL_IMPORT - #define DL_IMPORT(t) t -#endif -#ifndef DL_EXPORT - #define DL_EXPORT(t) t -#endif -#ifndef HAVE_LONG_LONG - #if PY_VERSION_HEX >= 0x03030000 || (PY_MAJOR_VERSION == 2 && PY_VERSION_HEX >= 0x02070000) - #define HAVE_LONG_LONG - #endif -#endif -#ifndef PY_LONG_LONG - #define PY_LONG_LONG LONG_LONG -#endif -#ifndef 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- -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value); - -/* CIntFromPy.proto */ -static CYTHON_INLINE siz __Pyx_PyInt_As_siz(PyObject *); - -/* CIntFromPy.proto */ -static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *); - -/* CIntFromPy.proto */ -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); - -/* CIntFromPy.proto */ -static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); - -/* CheckBinaryVersion.proto */ -static int __Pyx_check_binary_version(void); - -/* PyIdentifierFromString.proto */ -#if !defined(__Pyx_PyIdentifier_FromString) -#if PY_MAJOR_VERSION < 3 - #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) -#else - #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) -#endif -#endif - -/* ModuleImport.proto */ -static PyObject *__Pyx_ImportModule(const char *name); - -/* TypeImport.proto */ -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); - -/* InitStrings.proto */ -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); - - -/* Module declarations from 'cpython.buffer' */ - -/* Module declarations from 'libc.string' */ - -/* Module declarations from 'libc.stdio' */ - -/* Module declarations from '__builtin__' */ - -/* Module declarations from 'cpython.type' */ -static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; - -/* Module declarations from 'cpython' */ - -/* Module declarations from 'cpython.object' */ - -/* Module declarations from 'cpython.ref' */ - -/* Module declarations from 'libc.stdlib' */ - -/* Module declarations from 'numpy' */ - -/* Module declarations from 'numpy' */ -static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; -static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; -static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; -static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; -static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; -static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ -static CYTHON_INLINE int __pyx_f_5numpy_import_array(void); /*proto*/ - -/* Module declarations from 'pycocotools._mask' */ -static PyTypeObject *__pyx_ptype_11pycocotools_5_mask_RLEs = 0; -static PyTypeObject *__pyx_ptype_11pycocotools_5_mask_Masks = 0; -static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_uint8_t = { "uint8_t", NULL, sizeof(__pyx_t_5numpy_uint8_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_uint8_t) ? 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-static const char __pyx_k_gt[] = "gt"; -static const char __pyx_k_np[] = "np"; -static const char __pyx_k_a_2[] = "a"; -static const char __pyx_k_all[] = "all"; -static const char __pyx_k_iou[] = "_iou"; -static const char __pyx_k_len[] = "_len"; -static const char __pyx_k_obj[] = "obj"; -static const char __pyx_k_sys[] = "sys"; -static const char __pyx_k_area[] = "area"; -static const char __pyx_k_bb_2[] = "_bb"; -static const char __pyx_k_cnts[] = "cnts"; -static const char __pyx_k_data[] = "data"; -static const char __pyx_k_main[] = "__main__"; -static const char __pyx_k_mask[] = "mask"; -static const char __pyx_k_objs[] = "objs"; -static const char __pyx_k_poly[] = "poly"; -static const char __pyx_k_size[] = "size"; -static const char __pyx_k_test[] = "__test__"; -static const char __pyx_k_utf8[] = "utf8"; -static const char __pyx_k_array[] = "array"; -static const char __pyx_k_bbIou[] = "_bbIou"; -static const char __pyx_k_dtype[] = "dtype"; -static const char __pyx_k_iou_2[] = "iou"; -static const char __pyx_k_isbox[] = "isbox"; -static const char __pyx_k_isrle[] = "isrle"; -static const char __pyx_k_masks[] = "masks"; -static const char __pyx_k_merge[] = "merge"; -static const char __pyx_k_numpy[] = "numpy"; -static const char __pyx_k_order[] = "order"; -static const char __pyx_k_pyobj[] = "pyobj"; -static const char __pyx_k_range[] = "range"; -static const char __pyx_k_shape[] = "shape"; -static const char __pyx_k_uint8[] = "uint8"; -static const char __pyx_k_zeros[] = "zeros"; -static const char __pyx_k_astype[] = "astype"; -static const char __pyx_k_author[] = "__author__"; -static const char __pyx_k_counts[] = "counts"; -static const char __pyx_k_decode[] = "decode"; -static const char __pyx_k_double[] = "double"; -static const char __pyx_k_encode[] = "encode"; -static const char __pyx_k_frBbox[] = "frBbox"; -static const char __pyx_k_frPoly[] = "frPoly"; -static const char __pyx_k_import[] = "__import__"; -static const char __pyx_k_iouFun[] = "_iouFun"; -static const char __pyx_k_rleIou[] = "_rleIou"; -static const char __pyx_k_toBbox[] = "toBbox"; -static const char __pyx_k_ucRles[] = "ucRles"; -static const char __pyx_k_uint32[] = "uint32"; -static const char __pyx_k_iscrowd[] = "iscrowd"; -static const char __pyx_k_np_poly[] = "np_poly"; -static const char __pyx_k_preproc[] = "_preproc"; -static const char __pyx_k_reshape[] = "reshape"; -static const char __pyx_k_rleObjs[] = "rleObjs"; -static const char __pyx_k_tsungyi[] = "tsungyi"; -static const char __pyx_k_c_string[] = "c_string"; -static const char __pyx_k_frString[] = "_frString"; -static const char __pyx_k_toString[] = "_toString"; -static const char __pyx_k_enumerate[] = "enumerate"; -static const char __pyx_k_intersect[] = "intersect"; -static const char __pyx_k_py_string[] = "py_string"; -static const char __pyx_k_pyiscrowd[] = "pyiscrowd"; -static const char __pyx_k_ValueError[] = "ValueError"; -static const char __pyx_k_ImportError[] = "ImportError"; -static const char __pyx_k_frPyObjects[] = "frPyObjects"; -static const char __pyx_k_RuntimeError[] = "RuntimeError"; -static const char __pyx_k_version_info[] = "version_info"; -static const char __pyx_k_AttributeError[] = "AttributeError"; -static const char __pyx_k_PYTHON_VERSION[] = "PYTHON_VERSION"; -static const char __pyx_k_iou_locals__len[] = "iou.._len"; -static const char __pyx_k_frUncompressedRLE[] = "frUncompressedRLE"; -static const char __pyx_k_iou_locals__bbIou[] = "iou.._bbIou"; -static const char __pyx_k_pycocotools__mask[] = "pycocotools._mask"; -static const char __pyx_k_iou_locals__rleIou[] = "iou.._rleIou"; -static const char __pyx_k_iou_locals__preproc[] = "iou.._preproc"; -static const char __pyx_k_input_data_type_not_allowed[] = "input data type not allowed."; -static const char __pyx_k_input_type_is_not_supported[] = "input type is not supported."; -static const char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; -static const char __pyx_k_Python_version_must_be_2_or_3[] = "Python version must be 2 or 3"; -static const char __pyx_k_home_yjr_PycharmProjects_Faster[] = "/home/yjr/PycharmProjects/Faster-RCNN_TF/data/lib_coco/PythonAPI/pycocotools/_mask.pyx"; 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- Py_XDECREF(tb); - return; -} -#else -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - int is_subclass = PyObject_IsSubclass(instance_class, type); - if (!is_subclass) { - instance_class = NULL; - } else if (unlikely(is_subclass == -1)) { - goto bad; - } else { - type = instance_class; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } -#if PY_VERSION_HEX >= 0x03030000 - if (cause) { -#else - if (cause && cause != Py_None) { -#endif - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { -#if CYTHON_COMPILING_IN_PYPY - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); - Py_INCREF(tb); - PyErr_Restore(tmp_type, tmp_value, tb); - Py_XDECREF(tmp_tb); -#else - PyThreadState *tstate = PyThreadState_GET(); - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } -#endif - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -/* ExtTypeTest */ - static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(PyObject_TypeCheck(obj, type))) - return 1; - PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", - Py_TYPE(obj)->tp_name, type->tp_name); - return 0; -} - -/* ArgTypeTest */ - static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { - PyErr_Format(PyExc_TypeError, - "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", - name, type->tp_name, Py_TYPE(obj)->tp_name); -} -static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, - const char *name, int exact) -{ - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (none_allowed && obj == Py_None) return 1; - else if (exact) { - if (likely(Py_TYPE(obj) == type)) return 1; - #if PY_MAJOR_VERSION == 2 - else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; - #endif - } - else { - if (likely(PyObject_TypeCheck(obj, type))) return 1; - } - __Pyx_RaiseArgumentTypeInvalid(name, obj, type); - return 0; -} - -/* PyIntBinop */ - #if !CYTHON_COMPILING_IN_PYPY -static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) { - #if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(op1))) { - const long b = intval; - long x; - long a = PyInt_AS_LONG(op1); - x = (long)((unsigned long)a + b); - if (likely((x^a) >= 0 || (x^b) >= 0)) - return PyInt_FromLong(x); - return PyLong_Type.tp_as_number->nb_add(op1, op2); - } - #endif - #if CYTHON_USE_PYLONG_INTERNALS - if (likely(PyLong_CheckExact(op1))) { - const long b = intval; - long a, x; -#ifdef HAVE_LONG_LONG - const PY_LONG_LONG llb = intval; - PY_LONG_LONG lla, llx; -#endif - const digit* digits = ((PyLongObject*)op1)->ob_digit; - const Py_ssize_t size = Py_SIZE(op1); - if (likely(__Pyx_sst_abs(size) <= 1)) { - a = likely(size) ? digits[0] : 0; - if (size == -1) a = -a; - } else { - switch (size) { - case -2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case 2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case -3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case 3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case -4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case 4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - default: return PyLong_Type.tp_as_number->nb_add(op1, op2); - } - } - x = a + b; - return PyLong_FromLong(x); -#ifdef HAVE_LONG_LONG - long_long: - llx = lla + llb; - return PyLong_FromLongLong(llx); -#endif - - - } - #endif - if (PyFloat_CheckExact(op1)) { - const long b = intval; - double a = PyFloat_AS_DOUBLE(op1); - double result; - PyFPE_START_PROTECT("add", return NULL) - result = ((double)a) + (double)b; - PyFPE_END_PROTECT(result) - return PyFloat_FromDouble(result); - } - return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); -} -#endif - -/* PyIntBinop */ - #if !CYTHON_COMPILING_IN_PYPY -static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) { - if (op1 == op2) { - Py_RETURN_TRUE; - } - #if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(op1))) { - const long b = intval; - long a = PyInt_AS_LONG(op1); - if (a == b) { - Py_RETURN_TRUE; - } else { - Py_RETURN_FALSE; - } - } - #endif - #if CYTHON_USE_PYLONG_INTERNALS - if (likely(PyLong_CheckExact(op1))) { - const long b = intval; - long a; - const digit* digits = ((PyLongObject*)op1)->ob_digit; - const Py_ssize_t size = Py_SIZE(op1); - if (likely(__Pyx_sst_abs(size) <= 1)) { - a = likely(size) ? digits[0] : 0; - if (size == -1) a = -a; - } else { - switch (size) { - case -2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; - } - case 2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; - } - case -3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; - } - case 3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; - } - case -4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; - } - case 4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; - } - #if PyLong_SHIFT < 30 && PyLong_SHIFT != 15 - default: return PyLong_Type.tp_richcompare(op1, op2, Py_EQ); - #else - default: Py_RETURN_FALSE; - #endif - } - } - if (a == b) { - Py_RETURN_TRUE; - } else { - Py_RETURN_FALSE; - } - } - #endif - if (PyFloat_CheckExact(op1)) { - const long b = intval; - double a = PyFloat_AS_DOUBLE(op1); - if ((double)a == (double)b) { - Py_RETURN_TRUE; - } else { - Py_RETURN_FALSE; - } - } - return PyObject_RichCompare(op1, op2, Py_EQ); -} -#endif - -/* GetModuleGlobalName */ - static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { - PyObject *result; -#if !CYTHON_AVOID_BORROWED_REFS - result = PyDict_GetItem(__pyx_d, name); - if (likely(result)) { - Py_INCREF(result); - } else { -#else - result = PyObject_GetItem(__pyx_d, name); - if (!result) { - PyErr_Clear(); -#endif - result = __Pyx_GetBuiltinName(name); - } - return result; -} - -/* PyCFunctionFastCall */ - #if CYTHON_FAST_PYCCALL -static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { - PyCFunctionObject *func = (PyCFunctionObject*)func_obj; - PyCFunction meth = PyCFunction_GET_FUNCTION(func); - PyObject *self = PyCFunction_GET_SELF(func); - assert(PyCFunction_Check(func)); - assert(METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST))); - assert(nargs >= 0); - assert(nargs == 0 || args != NULL); - /* _PyCFunction_FastCallDict() must not be called with an exception set, - because it may clear it (directly or indirectly) and so the - caller loses its exception */ - assert(!PyErr_Occurred()); - return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs, NULL); -} -#endif // CYTHON_FAST_PYCCALL - -/* PyFunctionFastCall */ - #if CYTHON_FAST_PYCALL -#include "frameobject.h" -static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, - PyObject *globals) { - PyFrameObject *f; - PyThreadState *tstate = PyThreadState_GET(); - PyObject **fastlocals; - Py_ssize_t i; - PyObject *result; - assert(globals != NULL); - /* XXX Perhaps we should create a specialized - PyFrame_New() that doesn't take locals, but does - take builtins without sanity checking them. - */ - assert(tstate != NULL); - f = PyFrame_New(tstate, co, globals, NULL); - if (f == NULL) { - return NULL; - } - fastlocals = f->f_localsplus; - for (i = 0; i < na; i++) { - Py_INCREF(*args); - fastlocals[i] = *args++; - } - result = PyEval_EvalFrameEx(f,0); - ++tstate->recursion_depth; - Py_DECREF(f); - --tstate->recursion_depth; - return result; -} -#if 1 || PY_VERSION_HEX < 0x030600B1 -static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) { - PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); - PyObject *globals = PyFunction_GET_GLOBALS(func); - PyObject *argdefs = PyFunction_GET_DEFAULTS(func); - PyObject *closure; -#if PY_MAJOR_VERSION >= 3 - PyObject *kwdefs; -#endif - PyObject *kwtuple, **k; - PyObject **d; - Py_ssize_t nd; - Py_ssize_t nk; - PyObject *result; - assert(kwargs == NULL || PyDict_Check(kwargs)); - nk = kwargs ? PyDict_Size(kwargs) : 0; - if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { - return NULL; - } - if ( -#if PY_MAJOR_VERSION >= 3 - co->co_kwonlyargcount == 0 && -#endif - likely(kwargs == NULL || nk == 0) && - co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { - if (argdefs == NULL && co->co_argcount == nargs) { - result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); - goto done; - } - else if (nargs == 0 && argdefs != NULL - && co->co_argcount == Py_SIZE(argdefs)) { - /* function called with no arguments, but all parameters have - a default value: use default values as arguments .*/ - args = &PyTuple_GET_ITEM(argdefs, 0); - result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); - goto done; - } - } - if (kwargs != NULL) { - Py_ssize_t pos, i; - kwtuple = PyTuple_New(2 * nk); - if (kwtuple == NULL) { - result = NULL; - goto done; - } - k = &PyTuple_GET_ITEM(kwtuple, 0); - pos = i = 0; - while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { - Py_INCREF(k[i]); - Py_INCREF(k[i+1]); - i += 2; - } - nk = i / 2; - } - else { - kwtuple = NULL; - k = NULL; - } - closure = PyFunction_GET_CLOSURE(func); -#if PY_MAJOR_VERSION >= 3 - kwdefs = PyFunction_GET_KW_DEFAULTS(func); -#endif - if (argdefs != NULL) { - d = &PyTuple_GET_ITEM(argdefs, 0); - nd = Py_SIZE(argdefs); - } - else { - d = NULL; - nd = 0; - } -#if PY_MAJOR_VERSION >= 3 - result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, - args, nargs, - k, (int)nk, - d, (int)nd, kwdefs, closure); -#else - result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, - args, nargs, - k, (int)nk, - d, (int)nd, closure); -#endif - Py_XDECREF(kwtuple); -done: - Py_LeaveRecursiveCall(); - return result; -} -#endif // CPython < 3.6 -#endif // CYTHON_FAST_PYCALL - -/* PyObjectCallMethO */ - #if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { - PyObject *self, *result; - PyCFunction cfunc; - cfunc = PyCFunction_GET_FUNCTION(func); - self = PyCFunction_GET_SELF(func); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = cfunc(self, arg); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyObjectCallOneArg */ - #if CYTHON_COMPILING_IN_CPYTHON -static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_New(1); - if (unlikely(!args)) return NULL; - Py_INCREF(arg); - PyTuple_SET_ITEM(args, 0, arg); - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { -#if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCall(func, &arg, 1); - } -#endif -#ifdef __Pyx_CyFunction_USED - if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { -#else - if (likely(PyCFunction_Check(func))) { -#endif - if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { - return __Pyx_PyObject_CallMethO(func, arg); -#if CYTHON_FAST_PYCCALL - } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { - return __Pyx_PyCFunction_FastCall(func, &arg, 1); -#endif - } - } - return __Pyx__PyObject_CallOneArg(func, arg); -} -#else -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_Pack(1, arg); - if (unlikely(!args)) return NULL; - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -#endif - -/* GetItemInt */ - static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { - PyObject *r; - if (!j) return NULL; - r = PyObject_GetItem(o, j); - Py_DECREF(j); - return r; -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o); - if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) { - PyObject *r = PyList_GET_ITEM(o, i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); - if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) { - PyObject *r = PyList_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } - else if (PyTuple_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } else { - PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; - if (likely(m && m->sq_item)) { - if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { - Py_ssize_t l = m->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (!PyErr_ExceptionMatches(PyExc_OverflowError)) - return NULL; - PyErr_Clear(); - } - } - return m->sq_item(o, i); - } - } -#else - if (is_list || PySequence_Check(o)) { - return PySequence_GetItem(o, i); - } -#endif - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -} - -/* BufferFormatCheck */ - static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { - unsigned int n = 1; - return *(unsigned char*)(&n) != 0; -} -static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, - __Pyx_BufFmt_StackElem* stack, - __Pyx_TypeInfo* type) { - stack[0].field = &ctx->root; - stack[0].parent_offset = 0; - ctx->root.type = type; - ctx->root.name = "buffer dtype"; - ctx->root.offset = 0; - ctx->head = stack; - ctx->head->field = &ctx->root; - ctx->fmt_offset = 0; - ctx->head->parent_offset = 0; - ctx->new_packmode = '@'; - ctx->enc_packmode = '@'; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->is_complex = 0; - ctx->is_valid_array = 0; - ctx->struct_alignment = 0; - while (type->typegroup == 'S') { - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = 0; - type = type->fields->type; - } -} -static int __Pyx_BufFmt_ParseNumber(const char** ts) { - int count; - const char* t = *ts; - if (*t < '0' || *t > '9') { - return -1; - } else { - count = *t++ - '0'; - while (*t >= '0' && *t < '9') { - count *= 10; - count += *t++ - '0'; - } - } - *ts = t; - return count; -} -static int __Pyx_BufFmt_ExpectNumber(const char **ts) { - int number = __Pyx_BufFmt_ParseNumber(ts); - if (number == -1) - PyErr_Format(PyExc_ValueError,\ - "Does not understand character buffer dtype format string ('%c')", **ts); - return number; -} -static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { - PyErr_Format(PyExc_ValueError, - "Unexpected format string character: '%c'", ch); -} -static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { - switch (ch) { - case 'c': return "'char'"; - case 'b': return "'signed char'"; - case 'B': return "'unsigned char'"; - case 'h': return "'short'"; - case 'H': return "'unsigned short'"; - case 'i': return "'int'"; - case 'I': return "'unsigned int'"; - case 'l': return "'long'"; - case 'L': return "'unsigned long'"; - case 'q': return "'long long'"; - case 'Q': return "'unsigned long long'"; - case 'f': return (is_complex ? "'complex float'" : "'float'"); - case 'd': return (is_complex ? "'complex double'" : "'double'"); - case 'g': return (is_complex ? "'complex long double'" : "'long double'"); - case 'T': return "a struct"; - case 'O': return "Python object"; - case 'P': return "a pointer"; - case 's': case 'p': return "a string"; - case 0: return "end"; - default: return "unparseable format string"; - } -} -static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return 2; - case 'i': case 'I': case 'l': case 'L': return 4; - case 'q': case 'Q': return 8; - case 'f': return (is_complex ? 8 : 4); - case 'd': return (is_complex ? 16 : 8); - case 'g': { - PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); - return 0; - } - case 'O': case 'P': return sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { - switch (ch) { - case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(short); - case 'i': case 'I': return sizeof(int); - case 'l': case 'L': return sizeof(long); - #ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(PY_LONG_LONG); - #endif - case 'f': return sizeof(float) * (is_complex ? 2 : 1); - case 'd': return sizeof(double) * (is_complex ? 2 : 1); - case 'g': return sizeof(long double) * (is_complex ? 2 : 1); - case 'O': case 'P': return sizeof(void*); - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -typedef struct { char c; short x; } __Pyx_st_short; -typedef struct { char c; int x; } __Pyx_st_int; -typedef struct { char c; long x; } __Pyx_st_long; -typedef struct { char c; float x; } __Pyx_st_float; -typedef struct { char c; double x; } __Pyx_st_double; -typedef struct { char c; long double x; } __Pyx_st_longdouble; -typedef struct { char c; void *x; } __Pyx_st_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_st_float) - sizeof(float); - case 'd': return sizeof(__Pyx_st_double) - sizeof(double); - case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -/* These are for computing the padding at the end of the struct to align - on the first member of the struct. This will probably the same as above, - but we don't have any guarantees. - */ -typedef struct { short x; char c; } __Pyx_pad_short; -typedef struct { int x; char c; } __Pyx_pad_int; -typedef struct { long x; char c; } __Pyx_pad_long; -typedef struct { float x; char c; } __Pyx_pad_float; -typedef struct { double x; char c; } __Pyx_pad_double; -typedef struct { long double x; char c; } __Pyx_pad_longdouble; -typedef struct { void *x; char c; } __Pyx_pad_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); - case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); - case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { - switch (ch) { - case 'c': - return 'H'; - case 'b': case 'h': case 'i': - case 'l': case 'q': case 's': case 'p': - return 'I'; - case 'B': case 'H': case 'I': case 'L': case 'Q': - return 'U'; - case 'f': case 'd': case 'g': - return (is_complex ? 'C' : 'R'); - case 'O': - return 'O'; - case 'P': - return 'P'; - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { - if (ctx->head == NULL || ctx->head->field == &ctx->root) { - const char* expected; - const char* quote; - if (ctx->head == NULL) { - expected = "end"; - quote = ""; - } else { - expected = ctx->head->field->type->name; - quote = "'"; - } - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected %s%s%s but got %s", - quote, expected, quote, - __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); - } else { - __Pyx_StructField* field = ctx->head->field; - __Pyx_StructField* parent = (ctx->head - 1)->field; - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", - field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), - parent->type->name, field->name); - } -} -static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { - char group; - size_t size, offset, arraysize = 1; - if (ctx->enc_type == 0) return 0; - if (ctx->head->field->type->arraysize[0]) { - int i, ndim = 0; - if (ctx->enc_type == 's' || ctx->enc_type == 'p') { - ctx->is_valid_array = ctx->head->field->type->ndim == 1; - ndim = 1; - if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { - PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %zu", - ctx->head->field->type->arraysize[0], ctx->enc_count); - return -1; - } - } - if (!ctx->is_valid_array) { - PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", - ctx->head->field->type->ndim, ndim); - return -1; - } - for (i = 0; i < ctx->head->field->type->ndim; i++) { - arraysize *= ctx->head->field->type->arraysize[i]; - } - ctx->is_valid_array = 0; - ctx->enc_count = 1; - } - group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); - do { - __Pyx_StructField* field = ctx->head->field; - __Pyx_TypeInfo* type = field->type; - if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { - size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); - } else { - size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); - } - if (ctx->enc_packmode == '@') { - size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); - size_t align_mod_offset; - if (align_at == 0) return -1; - align_mod_offset = ctx->fmt_offset % align_at; - if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; - if (ctx->struct_alignment == 0) - ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, - ctx->is_complex); - } - if (type->size != size || type->typegroup != group) { - if (type->typegroup == 'C' && type->fields != NULL) { - size_t parent_offset = ctx->head->parent_offset + field->offset; - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = parent_offset; - continue; - } - if ((type->typegroup == 'H' || group == 'H') && type->size == size) { - } else { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - } - offset = ctx->head->parent_offset + field->offset; - if (ctx->fmt_offset != offset) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", - (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); - return -1; - } - ctx->fmt_offset += size; - if (arraysize) - ctx->fmt_offset += (arraysize - 1) * size; - --ctx->enc_count; - while (1) { - if (field == &ctx->root) { - ctx->head = NULL; - if (ctx->enc_count != 0) { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - break; - } - ctx->head->field = ++field; - if (field->type == NULL) { - --ctx->head; - field = ctx->head->field; - continue; - } else if (field->type->typegroup == 'S') { - size_t parent_offset = ctx->head->parent_offset + field->offset; - if (field->type->fields->type == NULL) continue; - field = field->type->fields; - ++ctx->head; - ctx->head->field = field; - ctx->head->parent_offset = parent_offset; - break; - } else { - break; - } - } - } while (ctx->enc_count); - ctx->enc_type = 0; - ctx->is_complex = 0; - return 0; -} -static CYTHON_INLINE PyObject * -__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) -{ - const char *ts = *tsp; - int i = 0, number; - int ndim = ctx->head->field->type->ndim; -; - ++ts; - if (ctx->new_count != 1) { - PyErr_SetString(PyExc_ValueError, - "Cannot handle repeated arrays in format string"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - while (*ts && *ts != ')') { - switch (*ts) { - case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; - default: break; - } - number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) - return PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %d", - ctx->head->field->type->arraysize[i], number); - if (*ts != ',' && *ts != ')') - return PyErr_Format(PyExc_ValueError, - "Expected a comma in format string, got '%c'", *ts); - if (*ts == ',') ts++; - i++; - } - if (i != ndim) - return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", - ctx->head->field->type->ndim, i); - if (!*ts) { - PyErr_SetString(PyExc_ValueError, - "Unexpected end of format string, expected ')'"); - return NULL; - } - ctx->is_valid_array = 1; - ctx->new_count = 1; - *tsp = ++ts; - return Py_None; -} -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { - int got_Z = 0; - while (1) { - switch(*ts) { - case 0: - if (ctx->enc_type != 0 && ctx->head == NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - if (ctx->head != NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - return ts; - case ' ': - case '\r': - case '\n': - ++ts; - break; - case '<': - if (!__Pyx_IsLittleEndian()) { - PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '>': - case '!': - if (__Pyx_IsLittleEndian()) { - PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '=': - case '@': - case '^': - ctx->new_packmode = *ts++; - break; - case 'T': - { - const char* ts_after_sub; - size_t i, struct_count = ctx->new_count; - size_t struct_alignment = ctx->struct_alignment; - ctx->new_count = 1; - ++ts; - if (*ts != '{') { - PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - ctx->enc_count = 0; - ctx->struct_alignment = 0; - ++ts; - ts_after_sub = ts; - for (i = 0; i != struct_count; ++i) { - ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); - if (!ts_after_sub) return NULL; - } - ts = ts_after_sub; - if (struct_alignment) ctx->struct_alignment = struct_alignment; - } - break; - case '}': - { - size_t alignment = ctx->struct_alignment; - ++ts; - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - if (alignment && ctx->fmt_offset % alignment) { - ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); - } - } - return ts; - case 'x': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->fmt_offset += ctx->new_count; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->enc_packmode = ctx->new_packmode; - ++ts; - break; - case 'Z': - got_Z = 1; - ++ts; - if (*ts != 'f' && *ts != 'd' && *ts != 'g') { - __Pyx_BufFmt_RaiseUnexpectedChar('Z'); - return NULL; - } - case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': - case 'l': case 'L': case 'q': case 'Q': - case 'f': case 'd': case 'g': - case 'O': case 'p': - if (ctx->enc_type == *ts && got_Z == ctx->is_complex && - ctx->enc_packmode == ctx->new_packmode) { - ctx->enc_count += ctx->new_count; - ctx->new_count = 1; - got_Z = 0; - ++ts; - break; - } - case 's': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_count = ctx->new_count; - ctx->enc_packmode = ctx->new_packmode; - ctx->enc_type = *ts; - ctx->is_complex = got_Z; - ++ts; - ctx->new_count = 1; - got_Z = 0; - break; - case ':': - ++ts; - while(*ts != ':') ++ts; - ++ts; - break; - case '(': - if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; - break; - default: - { - int number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - ctx->new_count = (size_t)number; - } - } - } -} -static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { - buf->buf = NULL; - buf->obj = NULL; - buf->strides = __Pyx_zeros; - buf->shape = __Pyx_zeros; - buf->suboffsets = __Pyx_minusones; -} -static CYTHON_INLINE int __Pyx_GetBufferAndValidate( - Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, - int nd, int cast, __Pyx_BufFmt_StackElem* stack) -{ - if (obj == Py_None || obj == NULL) { - __Pyx_ZeroBuffer(buf); - return 0; - } - buf->buf = NULL; - if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; - if (buf->ndim != nd) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - nd, buf->ndim); - goto fail; - } - if (!cast) { - __Pyx_BufFmt_Context ctx; - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; - } - if ((unsigned)buf->itemsize != dtype->size) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", - buf->itemsize, (buf->itemsize > 1) ? "s" : "", - dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); - goto fail; - } - if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; - return 0; -fail:; - __Pyx_ZeroBuffer(buf); - return -1; -} -static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { - if (info->buf == NULL) return; - if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; - __Pyx_ReleaseBuffer(info); -} - -/* FetchCommonType */ - static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { - PyObject* fake_module; - PyTypeObject* cached_type = NULL; - fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI); - if (!fake_module) return NULL; - Py_INCREF(fake_module); - cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name); - if (cached_type) { - if (!PyType_Check((PyObject*)cached_type)) { - PyErr_Format(PyExc_TypeError, - "Shared Cython type %.200s is not a type object", - type->tp_name); - goto bad; - } - if (cached_type->tp_basicsize != type->tp_basicsize) { - PyErr_Format(PyExc_TypeError, - "Shared Cython type %.200s has the wrong size, try recompiling", - type->tp_name); - goto bad; - } - } else { - if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; - PyErr_Clear(); - if (PyType_Ready(type) < 0) goto bad; - if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0) - goto bad; - Py_INCREF(type); - cached_type = type; - } -done: - Py_DECREF(fake_module); - return cached_type; -bad: - Py_XDECREF(cached_type); - cached_type = NULL; - goto done; -} - -/* CythonFunction */ - static PyObject * -__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure) -{ - if (unlikely(op->func_doc == NULL)) { - if (op->func.m_ml->ml_doc) { -#if PY_MAJOR_VERSION >= 3 - op->func_doc = PyUnicode_FromString(op->func.m_ml->ml_doc); -#else - op->func_doc = PyString_FromString(op->func.m_ml->ml_doc); -#endif - if (unlikely(op->func_doc == NULL)) - return NULL; - } else { - Py_INCREF(Py_None); - return Py_None; - } - } - Py_INCREF(op->func_doc); - return op->func_doc; -} -static int -__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value) -{ - PyObject *tmp = op->func_doc; - if (value == NULL) { - value = Py_None; - } - Py_INCREF(value); - op->func_doc = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op) -{ - if (unlikely(op->func_name == NULL)) { -#if PY_MAJOR_VERSION >= 3 - op->func_name = PyUnicode_InternFromString(op->func.m_ml->ml_name); -#else - op->func_name = PyString_InternFromString(op->func.m_ml->ml_name); -#endif - if (unlikely(op->func_name == NULL)) - return NULL; - } - Py_INCREF(op->func_name); - return op->func_name; -} -static int -__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value) -{ - PyObject *tmp; -#if PY_MAJOR_VERSION >= 3 - if (unlikely(value == NULL || !PyUnicode_Check(value))) { -#else - if (unlikely(value == NULL || !PyString_Check(value))) { -#endif - PyErr_SetString(PyExc_TypeError, - "__name__ must be set to a string object"); - return -1; - } - tmp = op->func_name; - Py_INCREF(value); - op->func_name = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op) -{ - Py_INCREF(op->func_qualname); - return op->func_qualname; -} -static int -__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value) -{ - PyObject *tmp; -#if PY_MAJOR_VERSION >= 3 - if (unlikely(value == NULL || !PyUnicode_Check(value))) { -#else - if (unlikely(value == NULL || !PyString_Check(value))) { -#endif - PyErr_SetString(PyExc_TypeError, - "__qualname__ must be set to a string object"); - return -1; - } - tmp = op->func_qualname; - Py_INCREF(value); - op->func_qualname = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_self(__pyx_CyFunctionObject *m, CYTHON_UNUSED void *closure) -{ - PyObject *self; - self = m->func_closure; - if (self == NULL) - self = Py_None; - Py_INCREF(self); - return self; -} -static PyObject * -__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op) -{ - if (unlikely(op->func_dict == NULL)) { - op->func_dict = PyDict_New(); - if (unlikely(op->func_dict == NULL)) - return NULL; - } - Py_INCREF(op->func_dict); - return op->func_dict; -} -static int -__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value) -{ - PyObject *tmp; 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-static PyMemberDef __pyx_CyFunction_members[] = { - {(char *) "__module__", T_OBJECT, offsetof(__pyx_CyFunctionObject, func.m_module), PY_WRITE_RESTRICTED, 0}, - {0, 0, 0, 0, 0} -}; -static PyObject * -__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, CYTHON_UNUSED PyObject *args) -{ -#if PY_MAJOR_VERSION >= 3 - return PyUnicode_FromString(m->func.m_ml->ml_name); -#else - return PyString_FromString(m->func.m_ml->ml_name); -#endif -} -static PyMethodDef __pyx_CyFunction_methods[] = { - {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0}, - {0, 0, 0, 0} -}; -#if PY_VERSION_HEX < 0x030500A0 -#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist) -#else -#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func.m_weakreflist) -#endif -static PyObject *__Pyx_CyFunction_New(PyTypeObject *type, PyMethodDef *ml, int flags, PyObject* qualname, - PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { - __pyx_CyFunctionObject *op = PyObject_GC_New(__pyx_CyFunctionObject, type); 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- Py_CLEAR(m->func_code); - Py_CLEAR(m->func_classobj); - Py_CLEAR(m->defaults_tuple); - Py_CLEAR(m->defaults_kwdict); - Py_CLEAR(m->func_annotations); - if (m->defaults) { - PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); - int i; - for (i = 0; i < m->defaults_pyobjects; i++) - Py_XDECREF(pydefaults[i]); - PyObject_Free(m->defaults); - m->defaults = NULL; - } - return 0; -} -static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m) -{ - PyObject_GC_UnTrack(m); - if (__Pyx_CyFunction_weakreflist(m) != NULL) - PyObject_ClearWeakRefs((PyObject *) m); - __Pyx_CyFunction_clear(m); - PyObject_GC_Del(m); -} -static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg) -{ - Py_VISIT(m->func_closure); - Py_VISIT(m->func.m_module); - Py_VISIT(m->func_dict); - Py_VISIT(m->func_name); - Py_VISIT(m->func_qualname); - Py_VISIT(m->func_doc); - Py_VISIT(m->func_globals); - Py_VISIT(m->func_code); - Py_VISIT(m->func_classobj); - Py_VISIT(m->defaults_tuple); - Py_VISIT(m->defaults_kwdict); - if (m->defaults) { - PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); - int i; - for (i = 0; i < m->defaults_pyobjects; i++) - Py_VISIT(pydefaults[i]); - } - return 0; -} -static PyObject *__Pyx_CyFunction_descr_get(PyObject *func, PyObject *obj, PyObject *type) -{ - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - if (m->flags & __Pyx_CYFUNCTION_STATICMETHOD) { - Py_INCREF(func); - return func; - } - if (m->flags & __Pyx_CYFUNCTION_CLASSMETHOD) { - if (type == NULL) - type = (PyObject *)(Py_TYPE(obj)); - return __Pyx_PyMethod_New(func, type, (PyObject *)(Py_TYPE(type))); - } - if (obj == Py_None) - obj = NULL; - return __Pyx_PyMethod_New(func, obj, type); -} -static PyObject* -__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) -{ -#if PY_MAJOR_VERSION >= 3 - return PyUnicode_FromFormat("", - op->func_qualname, (void *)op); -#else - return PyString_FromFormat("", - PyString_AsString(op->func_qualname), (void *)op); -#endif -} -static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) { - PyCFunctionObject* f = (PyCFunctionObject*)func; - PyCFunction meth = f->m_ml->ml_meth; - Py_ssize_t size; - switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) { - case METH_VARARGS: - if (likely(kw == NULL || PyDict_Size(kw) == 0)) - return (*meth)(self, arg); - break; - case METH_VARARGS | METH_KEYWORDS: - return (*(PyCFunctionWithKeywords)meth)(self, arg, kw); - case METH_NOARGS: - if (likely(kw == NULL || PyDict_Size(kw) == 0)) { - size = PyTuple_GET_SIZE(arg); - if (likely(size == 0)) - return (*meth)(self, NULL); - PyErr_Format(PyExc_TypeError, - "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", - f->m_ml->ml_name, size); - return NULL; - } - break; - case METH_O: - if (likely(kw == NULL || PyDict_Size(kw) == 0)) { - size = PyTuple_GET_SIZE(arg); - if (likely(size == 1)) { - PyObject *result, *arg0 = PySequence_ITEM(arg, 0); - if (unlikely(!arg0)) return NULL; - result = (*meth)(self, arg0); - Py_DECREF(arg0); - return result; - } - PyErr_Format(PyExc_TypeError, - "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", - f->m_ml->ml_name, size); - return NULL; - } - break; - default: - PyErr_SetString(PyExc_SystemError, "Bad call flags in " - "__Pyx_CyFunction_Call. METH_OLDARGS is no " - "longer supported!"); - return NULL; - } - PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments", - f->m_ml->ml_name); - return NULL; -} -static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) { - return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw); -} -static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) { - PyObject *result; - __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; - if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { - Py_ssize_t argc; - PyObject *new_args; - PyObject *self; - argc = PyTuple_GET_SIZE(args); - new_args = PyTuple_GetSlice(args, 1, argc); - if (unlikely(!new_args)) - return NULL; - self = PyTuple_GetItem(args, 0); - if (unlikely(!self)) { - Py_DECREF(new_args); - return NULL; - } - result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw); - Py_DECREF(new_args); - } else { - result = __Pyx_CyFunction_Call(func, args, kw); - } - return result; -} -static PyTypeObject __pyx_CyFunctionType_type = { - PyVarObject_HEAD_INIT(0, 0) - "cython_function_or_method", - sizeof(__pyx_CyFunctionObject), - 0, - (destructor) __Pyx_CyFunction_dealloc, - 0, - 0, - 0, -#if PY_MAJOR_VERSION < 3 - 0, -#else - 0, -#endif - (reprfunc) __Pyx_CyFunction_repr, - 0, - 0, - 0, - 0, - __Pyx_CyFunction_CallAsMethod, - 0, - 0, - 0, - 0, - Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC, - 0, - (traverseproc) __Pyx_CyFunction_traverse, - (inquiry) __Pyx_CyFunction_clear, - 0, -#if PY_VERSION_HEX < 0x030500A0 - offsetof(__pyx_CyFunctionObject, func_weakreflist), -#else - offsetof(PyCFunctionObject, m_weakreflist), -#endif - 0, - 0, - __pyx_CyFunction_methods, - __pyx_CyFunction_members, - __pyx_CyFunction_getsets, - 0, - 0, - __Pyx_CyFunction_descr_get, - 0, - offsetof(__pyx_CyFunctionObject, func_dict), - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, -#if PY_VERSION_HEX >= 0x030400a1 - 0, -#endif -}; -static int __pyx_CyFunction_init(void) { - __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type); - if (__pyx_CyFunctionType == NULL) { - return -1; - } - return 0; -} -static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->defaults = PyObject_Malloc(size); - if (!m->defaults) - return PyErr_NoMemory(); - memset(m->defaults, 0, size); - m->defaults_pyobjects = pyobjects; - return m->defaults; -} -static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->defaults_tuple = tuple; - Py_INCREF(tuple); -} -static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->defaults_kwdict = dict; - Py_INCREF(dict); -} -static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->func_annotations = dict; - Py_INCREF(dict); -} - -/* BufferFallbackError */ - static void __Pyx_RaiseBufferFallbackError(void) { - PyErr_SetString(PyExc_ValueError, - "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); -} - -/* None */ - static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { - Py_ssize_t q = a / b; - Py_ssize_t r = a - q*b; - q -= ((r != 0) & ((r ^ b) < 0)); - return q; -} - -/* BufferIndexError */ - static void __Pyx_RaiseBufferIndexError(int axis) { - PyErr_Format(PyExc_IndexError, - "Out of bounds on buffer access (axis %d)", axis); -} - -/* RaiseTooManyValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -/* RaiseNeedMoreValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", - index, (index == 1) ? "" : "s"); -} - -/* RaiseNoneIterError */ - static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { - PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); -} - -/* SaveResetException */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); -} -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -#endif - -/* PyErrExceptionMatches */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) { - PyObject *exc_type = tstate->curexc_type; - if (exc_type == err) return 1; - if (unlikely(!exc_type)) return 0; - return PyErr_GivenExceptionMatches(exc_type, err); -} -#endif - -/* GetException */ - #if CYTHON_FAST_THREAD_STATE -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { -#endif - PyObject *local_type, *local_value, *local_tb; -#if CYTHON_FAST_THREAD_STATE - PyObject *tmp_type, *tmp_value, *tmp_tb; - local_type = tstate->curexc_type; - local_value = tstate->curexc_value; - local_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(&local_type, &local_value, &local_tb); -#endif - PyErr_NormalizeException(&local_type, &local_value, &local_tb); -#if CYTHON_FAST_THREAD_STATE - if (unlikely(tstate->curexc_type)) -#else - if (unlikely(PyErr_Occurred())) -#endif - goto bad; - #if PY_MAJOR_VERSION >= 3 - if (local_tb) { - if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) - goto bad; - } - #endif - Py_XINCREF(local_tb); - Py_XINCREF(local_type); - Py_XINCREF(local_value); - *type = local_type; - *value = local_value; - *tb = local_tb; -#if CYTHON_FAST_THREAD_STATE - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = local_type; - tstate->exc_value = local_value; - tstate->exc_traceback = local_tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(local_type, local_value, local_tb); -#endif - return 0; -bad: - *type = 0; - *value = 0; - *tb = 0; - Py_XDECREF(local_type); - Py_XDECREF(local_value); - Py_XDECREF(local_tb); - return -1; -} - -/* Import */ - static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *empty_list = 0; - PyObject *module = 0; - PyObject *global_dict = 0; - PyObject *empty_dict = 0; - PyObject *list; - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); - if (!py_import) - goto bad; - #endif - if (from_list) - list = from_list; - else { - empty_list = PyList_New(0); - if (!empty_list) - goto bad; - list = empty_list; - } - global_dict = PyModule_GetDict(__pyx_m); - if (!global_dict) - goto bad; - empty_dict = PyDict_New(); - if (!empty_dict) - goto bad; - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if (strchr(__Pyx_MODULE_NAME, '.')) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(1); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, 1); - #endif - if (!module) { - if (!PyErr_ExceptionMatches(PyExc_ImportError)) - goto bad; - PyErr_Clear(); - } - } - level = 0; - } - #endif - if (!module) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(level); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, level); - #endif - } - } -bad: - #if PY_VERSION_HEX < 0x03030000 - Py_XDECREF(py_import); - #endif - Py_XDECREF(empty_list); - Py_XDECREF(empty_dict); - return module; -} - -/* CodeObjectCache */ - static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { - int start = 0, mid = 0, end = count - 1; - if (end >= 0 && code_line > entries[end].code_line) { - return count; - } - while (start < end) { - mid = start + (end - start) / 2; - if (code_line < entries[mid].code_line) { - end = mid; - } else if (code_line > entries[mid].code_line) { - start = mid + 1; - } else { - return mid; - } - } - if (code_line <= entries[mid].code_line) { - return mid; - } else { - return mid + 1; - } -} -static PyCodeObject *__pyx_find_code_object(int code_line) { - PyCodeObject* code_object; - int pos; - if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { - return NULL; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { - return NULL; - } - code_object = __pyx_code_cache.entries[pos].code_object; - Py_INCREF(code_object); - return code_object; -} -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { - int pos, i; - __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; - if (unlikely(!code_line)) { - return; - } - if (unlikely(!entries)) { - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); - if (likely(entries)) { - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = 64; - __pyx_code_cache.count = 1; - entries[0].code_line = code_line; - entries[0].code_object = code_object; - Py_INCREF(code_object); - } - return; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { - PyCodeObject* tmp = entries[pos].code_object; - entries[pos].code_object = code_object; - Py_DECREF(tmp); - return; - } - if (__pyx_code_cache.count == __pyx_code_cache.max_count) { - int new_max = __pyx_code_cache.max_count + 64; - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} - -/* AddTraceback */ - #include "compile.h" -#include "frameobject.h" -#include "traceback.h" -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_srcfile = 0; - PyObject *py_funcname = 0; - #if PY_MAJOR_VERSION < 3 - py_srcfile = PyString_FromString(filename); - #else - py_srcfile = PyUnicode_FromString(filename); - #endif - if (!py_srcfile) goto bad; - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - #else - py_funcname = PyUnicode_FromString(funcname); - #endif - } - if (!py_funcname) goto bad; - py_code = __Pyx_PyCode_New( - 0, - 0, - 0, - 0, - 0, - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, - __pyx_empty_bytes /*PyObject *lnotab*/ - ); - Py_DECREF(py_srcfile); - Py_DECREF(py_funcname); - return py_code; -bad: - Py_XDECREF(py_srcfile); - Py_XDECREF(py_funcname); - return NULL; -} -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyFrameObject *py_frame = 0; - py_code = __pyx_find_code_object(c_line ? c_line : py_line); - if (!py_code) { - py_code = __Pyx_CreateCodeObjectForTraceback( - funcname, c_line, py_line, filename); - if (!py_code) goto bad; - __pyx_insert_code_object(c_line ? c_line : py_line, py_code); - } - py_frame = PyFrame_New( - PyThreadState_GET(), /*PyThreadState *tstate,*/ - py_code, /*PyCodeObject *code,*/ - __pyx_d, /*PyObject *globals,*/ - 0 /*PyObject *locals*/ - ); - if (!py_frame) goto bad; - __Pyx_PyFrame_SetLineNumber(py_frame, py_line); - PyTraceBack_Here(py_frame); -bad: - Py_XDECREF(py_code); - Py_XDECREF(py_frame); -} - -#if PY_MAJOR_VERSION < 3 -static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { - if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); - if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); - PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); - return -1; -} -static void __Pyx_ReleaseBuffer(Py_buffer *view) { - PyObject *obj = view->obj; - if (!obj) return; - if (PyObject_CheckBuffer(obj)) { - PyBuffer_Release(view); - return; - } - if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } - Py_DECREF(obj); - view->obj = NULL; -} -#endif - - - /* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(long) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(long), - little, !is_unsigned); - } -} - -/* CIntFromPyVerify */ - #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) -#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) -#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ - {\ - func_type value = func_value;\ - if (sizeof(target_type) < sizeof(func_type)) {\ - if (unlikely(value != (func_type) (target_type) value)) {\ - func_type zero = 0;\ - if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ - return (target_type) -1;\ - if (is_unsigned && unlikely(value < zero))\ - goto raise_neg_overflow;\ - else\ - goto raise_overflow;\ - }\ - }\ - return (target_type) value;\ - } - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_siz(siz value) { - const siz neg_one = (siz) -1, const_zero = (siz) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(siz) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(siz) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(siz) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(siz) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(siz) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(siz), - little, !is_unsigned); - } -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value) { - const Py_intptr_t neg_one = (Py_intptr_t) -1, const_zero = (Py_intptr_t) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(Py_intptr_t) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(Py_intptr_t) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(Py_intptr_t) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(Py_intptr_t) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(Py_intptr_t), - little, !is_unsigned); - } -} - -/* Declarations */ - #if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return ::std::complex< float >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return x + y*(__pyx_t_float_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - __pyx_t_float_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -/* Arithmetic */ - #if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - #if 1 - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - if (b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); - } else if (fabsf(b.real) >= fabsf(b.imag)) { - if (b.real == 0 && b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); - } else { - float r = b.imag / b.real; - float s = 1.0 / (b.real + b.imag * r); - return __pyx_t_float_complex_from_parts( - (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); - } - } else { - float r = b.real / b.imag; - float s = 1.0 / (b.imag + b.real * r); - return __pyx_t_float_complex_from_parts( - (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); - } - } - #else - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - if (b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); - } else { - float denom = b.real * b.real + b.imag * b.imag; - return __pyx_t_float_complex_from_parts( - (a.real * b.real + a.imag * b.imag) / denom, - (a.imag * b.real - a.real * b.imag) / denom); - } - } - #endif - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrtf(z.real*z.real + z.imag*z.imag); - #else - return hypotf(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - float r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - float denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(a, a); - case 3: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(z, a); - case 4: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } else if (b.imag == 0) { - z.real = powf(a.real, b.real); - z.imag = 0; - return z; - } else if (a.real > 0) { - r = a.real; - theta = 0; - } else { - r = -a.real; - theta = atan2f(0, -1); - } - } else { - r = __Pyx_c_abs_float(a); - theta = atan2f(a.imag, a.real); - } - lnr = logf(r); - z_r = expf(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cosf(z_theta); - z.imag = z_r * sinf(z_theta); - return z; - } - #endif -#endif - -/* Declarations */ - #if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return ::std::complex< double >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return x + y*(__pyx_t_double_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - __pyx_t_double_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -/* Arithmetic */ - #if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - #if 1 - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - if (b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); - } else if (fabs(b.real) >= fabs(b.imag)) { - if (b.real == 0 && b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); - } else { - double r = b.imag / b.real; - double s = 1.0 / (b.real + b.imag * r); - return __pyx_t_double_complex_from_parts( - (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); - } - } else { - double r = b.real / b.imag; - double s = 1.0 / (b.imag + b.real * r); - return __pyx_t_double_complex_from_parts( - (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); - } - } - #else - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - if (b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); - } else { - double denom = b.real * b.real + b.imag * b.imag; - return __pyx_t_double_complex_from_parts( - (a.real * b.real + a.imag * b.imag) / denom, - (a.imag * b.real - a.real * b.imag) / denom); - } - } - #endif - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrt(z.real*z.real + z.imag*z.imag); - #else - return hypot(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - double r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - double denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(a, a); - case 3: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, a); - case 4: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } else if (b.imag == 0) { - z.real = pow(a.real, b.real); - z.imag = 0; - return z; - } else if (a.real > 0) { - r = a.real; - theta = 0; - } else { - r = -a.real; - theta = atan2(0, -1); - } - } else { - r = __Pyx_c_abs_double(a); - theta = atan2(a.imag, a.real); - } - lnr = log(r); - z_r = exp(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cos(z_theta); - z.imag = z_r * sin(z_theta); - return z; - } - #endif -#endif - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(int) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(int) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(int), - little, !is_unsigned); - } -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { - const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(enum NPY_TYPES) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(enum NPY_TYPES) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE siz __Pyx_PyInt_As_siz(PyObject *x) { - const siz neg_one = (siz) -1, const_zero = (siz) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(siz) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(siz, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (siz) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (siz) 0; - case 1: __PYX_VERIFY_RETURN_INT(siz, digit, digits[0]) - case 2: - if (8 * sizeof(siz) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(siz) >= 2 * PyLong_SHIFT) { - return (siz) (((((siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(siz) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(siz) >= 3 * PyLong_SHIFT) { - return (siz) (((((((siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(siz) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(siz) >= 4 * PyLong_SHIFT) { - return (siz) (((((((((siz)digits[3]) << PyLong_SHIFT) | (siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (siz) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(siz) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(siz, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(siz) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(siz, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (siz) 0; - case -1: __PYX_VERIFY_RETURN_INT(siz, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(siz, digit, +digits[0]) - case -2: - if (8 * sizeof(siz) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(siz, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(siz) - 1 > 2 * PyLong_SHIFT) { - return (siz) (((siz)-1)*(((((siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(siz) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(siz) - 1 > 2 * PyLong_SHIFT) { - return (siz) ((((((siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(siz) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(siz, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(siz) - 1 > 3 * PyLong_SHIFT) { - return (siz) (((siz)-1)*(((((((siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(siz) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(siz) - 1 > 3 * PyLong_SHIFT) { - return (siz) ((((((((siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(siz) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(siz, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(siz) - 1 > 4 * PyLong_SHIFT) { - return (siz) (((siz)-1)*(((((((((siz)digits[3]) << PyLong_SHIFT) | (siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(siz) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(siz) - 1 > 4 * PyLong_SHIFT) { - return (siz) ((((((((((siz)digits[3]) << PyLong_SHIFT) | (siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]))); - } - } - break; - } -#endif - if (sizeof(siz) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(siz, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(siz) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(siz, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - siz val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (siz) -1; - } - } else { - siz val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (siz) -1; - val = __Pyx_PyInt_As_siz(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to siz"); - return (siz) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to siz"); - return (siz) -1; -} - -/* CIntFromPy */ - static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *x) { - const size_t neg_one = (size_t) -1, const_zero = (size_t) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(size_t) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(size_t, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (size_t) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (size_t) 0; - case 1: __PYX_VERIFY_RETURN_INT(size_t, digit, digits[0]) - case 2: - if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) >= 2 * PyLong_SHIFT) { - return (size_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) >= 3 * PyLong_SHIFT) { - return (size_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) >= 4 * PyLong_SHIFT) { - return (size_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (size_t) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(size_t) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(size_t) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (size_t) 0; - case -1: __PYX_VERIFY_RETURN_INT(size_t, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(size_t, digit, +digits[0]) - case -2: - if (8 * sizeof(size_t) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) { - return (size_t) (((size_t)-1)*(((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) { - return (size_t) ((((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) { - return (size_t) (((size_t)-1)*(((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) { - return (size_t) ((((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) { - return (size_t) (((size_t)-1)*(((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) { - return (size_t) ((((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); - } - } - break; - } -#endif - if (sizeof(size_t) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(size_t, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(size_t) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(size_t, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - size_t val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (size_t) -1; - } - } else { - size_t val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (size_t) -1; - val = __Pyx_PyInt_As_size_t(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to size_t"); - return (size_t) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to size_t"); - return (size_t) -1; -} - -/* CIntFromPy */ - static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(int) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { - return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { - return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { - return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(int) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) - case -2: - if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - } -#endif - if (sizeof(int) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - int val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to int"); - return (int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; -} - -/* CIntFromPy */ - static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(long) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { - return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { - return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { - return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(long) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) - case -2: - if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - } -#endif - if (sizeof(long) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - long val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (long) -1; - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (long) -1; - val = __Pyx_PyInt_As_long(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to long"); - return (long) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; -} - -/* CheckBinaryVersion */ - static int __Pyx_check_binary_version(void) { - char ctversion[4], rtversion[4]; - PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); - if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { - char message[200]; - PyOS_snprintf(message, sizeof(message), - "compiletime version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - return PyErr_WarnEx(NULL, message, 1); - } - return 0; -} - -/* ModuleImport */ - #ifndef __PYX_HAVE_RT_ImportModule -#define __PYX_HAVE_RT_ImportModule -static PyObject *__Pyx_ImportModule(const char *name) { - PyObject *py_name = 0; - PyObject *py_module = 0; - py_name = __Pyx_PyIdentifier_FromString(name); - if (!py_name) - goto bad; - py_module = PyImport_Import(py_name); - Py_DECREF(py_name); - return py_module; -bad: - Py_XDECREF(py_name); - return 0; -} -#endif - -/* TypeImport */ - #ifndef __PYX_HAVE_RT_ImportType -#define __PYX_HAVE_RT_ImportType -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, - size_t size, int strict) -{ - PyObject *py_module = 0; - PyObject *result = 0; - PyObject *py_name = 0; - char warning[200]; - Py_ssize_t basicsize; -#ifdef Py_LIMITED_API - PyObject *py_basicsize; -#endif - py_module = __Pyx_ImportModule(module_name); - if (!py_module) - goto bad; - py_name = __Pyx_PyIdentifier_FromString(class_name); - if (!py_name) - goto bad; - result = PyObject_GetAttr(py_module, py_name); - Py_DECREF(py_name); - py_name = 0; - Py_DECREF(py_module); - py_module = 0; - if (!result) - goto bad; - if (!PyType_Check(result)) { - PyErr_Format(PyExc_TypeError, - "%.200s.%.200s is not a type object", - module_name, class_name); - goto bad; - } -#ifndef Py_LIMITED_API - basicsize = ((PyTypeObject *)result)->tp_basicsize; -#else - py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); - if (!py_basicsize) - goto bad; - basicsize = PyLong_AsSsize_t(py_basicsize); - Py_DECREF(py_basicsize); - py_basicsize = 0; - if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) - goto bad; -#endif - if (!strict && (size_t)basicsize > size) { - PyOS_snprintf(warning, sizeof(warning), - "%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd", - module_name, class_name, basicsize, size); - if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; - } - else if ((size_t)basicsize != size) { - PyErr_Format(PyExc_ValueError, - "%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd", - module_name, class_name, basicsize, size); - goto bad; - } - return (PyTypeObject *)result; -bad: - Py_XDECREF(py_module); - Py_XDECREF(result); - return NULL; -} -#endif - -/* InitStrings */ - static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION < 3 - if (t->is_unicode) { - *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); - } else if (t->intern) { - *t->p = PyString_InternFromString(t->s); - } else { - *t->p = PyString_FromStringAndSize(t->s, t->n - 1); - } - #else - if (t->is_unicode | t->is_str) { - if (t->intern) { - *t->p = PyUnicode_InternFromString(t->s); - } else if (t->encoding) { - *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); - } else { - *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); - } - } else { - *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); - } - #endif - if (!*t->p) - return -1; - ++t; - } - return 0; -} - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { - return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); -} -static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { - Py_ssize_t ignore; - return __Pyx_PyObject_AsStringAndSize(o, &ignore); -} -static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { -#if CYTHON_COMPILING_IN_CPYTHON && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) - if ( -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - __Pyx_sys_getdefaultencoding_not_ascii && -#endif - PyUnicode_Check(o)) { -#if PY_VERSION_HEX < 0x03030000 - char* defenc_c; - PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); - if (!defenc) return NULL; - defenc_c = PyBytes_AS_STRING(defenc); -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - { - char* end = defenc_c + PyBytes_GET_SIZE(defenc); - char* c; - for (c = defenc_c; c < end; c++) { - if ((unsigned char) (*c) >= 128) { - PyUnicode_AsASCIIString(o); - return NULL; - } - } - } -#endif - *length = PyBytes_GET_SIZE(defenc); - return defenc_c; -#else - if (__Pyx_PyUnicode_READY(o) == -1) return NULL; -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - if (PyUnicode_IS_ASCII(o)) { - *length = PyUnicode_GET_LENGTH(o); - return PyUnicode_AsUTF8(o); - } else { - PyUnicode_AsASCIIString(o); - return NULL; - } -#else - return PyUnicode_AsUTF8AndSize(o, length); -#endif -#endif - } else -#endif -#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) - if (PyByteArray_Check(o)) { - *length = PyByteArray_GET_SIZE(o); - return PyByteArray_AS_STRING(o); - } else -#endif - { - char* result; - int r = PyBytes_AsStringAndSize(o, &result, length); - if (unlikely(r < 0)) { - return NULL; - } else { - return result; - } - } -} -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { - int is_true = x == Py_True; - if (is_true | (x == Py_False) | (x == Py_None)) return is_true; - else return PyObject_IsTrue(x); -} -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { -#if CYTHON_USE_TYPE_SLOTS - PyNumberMethods *m; -#endif - const char *name = NULL; - PyObject *res = NULL; -#if PY_MAJOR_VERSION < 3 - if (PyInt_Check(x) || PyLong_Check(x)) -#else - if (PyLong_Check(x)) -#endif - return __Pyx_NewRef(x); -#if CYTHON_USE_TYPE_SLOTS - m = Py_TYPE(x)->tp_as_number; - #if PY_MAJOR_VERSION < 3 - if (m && m->nb_int) { - name = "int"; - res = PyNumber_Int(x); - } - else if (m && m->nb_long) { - name = "long"; - res = PyNumber_Long(x); - } - #else - if (m && m->nb_int) { - name = "int"; - res = PyNumber_Long(x); - } - #endif -#else - res = PyNumber_Int(x); -#endif - if (res) { -#if PY_MAJOR_VERSION < 3 - if (!PyInt_Check(res) && !PyLong_Check(res)) { -#else - if (!PyLong_Check(res)) { -#endif - PyErr_Format(PyExc_TypeError, - "__%.4s__ returned non-%.4s (type %.200s)", - name, name, Py_TYPE(res)->tp_name); - Py_DECREF(res); - return NULL; - } - } - else if (!PyErr_Occurred()) { - PyErr_SetString(PyExc_TypeError, - "an integer is required"); - } - return res; -} -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { - Py_ssize_t ival; - PyObject *x; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(b))) { - if (sizeof(Py_ssize_t) >= sizeof(long)) - return PyInt_AS_LONG(b); - else - return PyInt_AsSsize_t(x); - } -#endif - if (likely(PyLong_CheckExact(b))) { - #if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)b)->ob_digit; - const Py_ssize_t size = Py_SIZE(b); - if (likely(__Pyx_sst_abs(size) <= 1)) { - ival = likely(size) ? digits[0] : 0; - if (size == -1) ival = -ival; - return ival; - } else { - switch (size) { - case 2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - } - } - #endif - return PyLong_AsSsize_t(b); - } - x = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { - return PyInt_FromSize_t(ival); -} - - -#endif /* Py_PYTHON_H */ diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/_mask.pyx b/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/_mask.pyx deleted file mode 100755 index d065837..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/_mask.pyx +++ /dev/null @@ -1,308 +0,0 @@ -# distutils: language = c -# distutils: sources = ../common/maskApi.c - -#************************************************************************** -# Microsoft COCO Toolbox. version 2.0 -# Data, paper, and tutorials available at: http://mscoco.org/ -# Code written by Piotr Dollar and Tsung-Yi Lin, 2015. -# Licensed under the Simplified BSD License [see coco/license.txt] -#************************************************************************** - -__author__ = 'tsungyi' - -import sys -PYTHON_VERSION = sys.version_info[0] - -# import both Python-level and C-level symbols of Numpy -# the API uses Numpy to interface C and Python -import numpy as np -cimport numpy as np -from libc.stdlib cimport malloc, free - -# intialized Numpy. must do. -np.import_array() - -# import numpy C function -# we use PyArray_ENABLEFLAGS to make Numpy ndarray responsible to memoery management -cdef extern from "numpy/arrayobject.h": - void PyArray_ENABLEFLAGS(np.ndarray arr, int flags) - -# Declare the prototype of the C functions in MaskApi.h -cdef extern from "maskApi.h": - ctypedef unsigned int uint - ctypedef unsigned long siz - ctypedef unsigned char byte - ctypedef double* BB - ctypedef struct RLE: - siz h, - siz w, - siz m, - uint* cnts, - void rlesInit( RLE **R, siz n ) - void rleEncode( RLE *R, const byte *M, siz h, siz w, siz n ) - void rleDecode( const RLE *R, byte *mask, siz n ) - void rleMerge( const RLE *R, RLE *M, siz n, int intersect ) - void rleArea( const RLE *R, siz n, uint *a ) - void rleIou( RLE *dt, RLE *gt, siz m, siz n, byte *iscrowd, double *o ) - void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o ) - void rleToBbox( const RLE *R, BB bb, siz n ) - void rleFrBbox( RLE *R, const BB bb, siz h, siz w, siz n ) - void rleFrPoly( RLE *R, const double *xy, siz k, siz h, siz w ) - char* rleToString( const RLE *R ) - void rleFrString( RLE *R, char *s, siz h, siz w ) - -# python class to wrap RLE array in C -# the class handles the memory allocation and deallocation -cdef class RLEs: - cdef RLE *_R - cdef siz _n - - def __cinit__(self, siz n =0): - rlesInit(&self._R, n) - self._n = n - - # free the RLE array here - def __dealloc__(self): - if self._R is not NULL: - for i in range(self._n): - free(self._R[i].cnts) - free(self._R) - def __getattr__(self, key): - if key == 'n': - return self._n - raise AttributeError(key) - -# python class to wrap Mask array in C -# the class handles the memory allocation and deallocation -cdef class Masks: - cdef byte *_mask - cdef siz _h - cdef siz _w - cdef siz _n - - def __cinit__(self, h, w, n): - self._mask = malloc(h*w*n* sizeof(byte)) - self._h = h - self._w = w - self._n = n - # def __dealloc__(self): - # the memory management of _mask has been passed to np.ndarray - # it doesn't need to be freed here - - # called when passing into np.array() and return an np.ndarray in column-major order - def __array__(self): - cdef np.npy_intp shape[1] - shape[0] = self._h*self._w*self._n - # Create a 1D array, and reshape it to fortran/Matlab column-major array - ndarray = np.PyArray_SimpleNewFromData(1, shape, np.NPY_UINT8, self._mask).reshape((self._h, self._w, self._n), order='F') - # The _mask allocated by Masks is now handled by ndarray - PyArray_ENABLEFLAGS(ndarray, np.NPY_OWNDATA) - return ndarray - -# internal conversion from Python RLEs object to compressed RLE format -def _toString(RLEs Rs): - cdef siz n = Rs.n - cdef bytes py_string - cdef char* c_string - objs = [] - for i in range(n): - c_string = rleToString( &Rs._R[i] ) - py_string = c_string - objs.append({ - 'size': [Rs._R[i].h, Rs._R[i].w], - 'counts': py_string - }) - free(c_string) - return objs - -# internal conversion from compressed RLE format to Python RLEs object -def _frString(rleObjs): - cdef siz n = len(rleObjs) - Rs = RLEs(n) - cdef bytes py_string - cdef char* c_string - for i, obj in enumerate(rleObjs): - if PYTHON_VERSION == 2: - py_string = str(obj['counts']).encode('utf8') - elif PYTHON_VERSION == 3: - py_string = str.encode(obj['counts']) if type(obj['counts']) == str else obj['counts'] - else: - raise Exception('Python version must be 2 or 3') - c_string = py_string - rleFrString( &Rs._R[i], c_string, obj['size'][0], obj['size'][1] ) - return Rs - -# encode mask to RLEs objects -# list of RLE string can be generated by RLEs member function -def encode(np.ndarray[np.uint8_t, ndim=3, mode='fortran'] mask): - h, w, n = mask.shape[0], mask.shape[1], mask.shape[2] - cdef RLEs Rs = RLEs(n) - rleEncode(Rs._R,mask.data,h,w,n) - objs = _toString(Rs) - return objs - -# decode mask from compressed list of RLE string or RLEs object -def decode(rleObjs): - cdef RLEs Rs = _frString(rleObjs) - h, w, n = Rs._R[0].h, Rs._R[0].w, Rs._n - masks = Masks(h, w, n) - rleDecode(Rs._R, masks._mask, n); - return np.array(masks) - -def merge(rleObjs, intersect=0): - cdef RLEs Rs = _frString(rleObjs) - cdef RLEs R = RLEs(1) - rleMerge(Rs._R, R._R, Rs._n, intersect) - obj = _toString(R)[0] - return obj - -def area(rleObjs): - cdef RLEs Rs = _frString(rleObjs) - cdef uint* _a = malloc(Rs._n* sizeof(uint)) - rleArea(Rs._R, Rs._n, _a) - cdef np.npy_intp shape[1] - shape[0] = Rs._n - a = np.array((Rs._n, ), dtype=np.uint8) - a = np.PyArray_SimpleNewFromData(1, shape, np.NPY_UINT32, _a) - PyArray_ENABLEFLAGS(a, np.NPY_OWNDATA) - return a - -# iou computation. support function overload (RLEs-RLEs and bbox-bbox). -def iou( dt, gt, pyiscrowd ): - def _preproc(objs): - if len(objs) == 0: - return objs - if type(objs) == np.ndarray: - if len(objs.shape) == 1: - objs = objs.reshape((objs[0], 1)) - # check if it's Nx4 bbox - if not len(objs.shape) == 2 or not objs.shape[1] == 4: - raise Exception('numpy ndarray input is only for *bounding boxes* and should have Nx4 dimension') - objs = objs.astype(np.double) - elif type(objs) == list: - # check if list is in box format and convert it to np.ndarray - isbox = np.all(np.array([(len(obj)==4) and ((type(obj)==list) or (type(obj)==np.ndarray)) for obj in objs])) - isrle = np.all(np.array([type(obj) == dict for obj in objs])) - if isbox: - objs = np.array(objs, dtype=np.double) - if len(objs.shape) == 1: - objs = objs.reshape((1,objs.shape[0])) - elif isrle: - objs = _frString(objs) - else: - raise Exception('list input can be bounding box (Nx4) or RLEs ([RLE])') - else: - raise Exception('unrecognized type. The following type: RLEs (rle), np.ndarray (box), and list (box) are supported.') - return objs - def _rleIou(RLEs dt, RLEs gt, np.ndarray[np.uint8_t, ndim=1] iscrowd, siz m, siz n, np.ndarray[np.double_t, ndim=1] _iou): - rleIou( dt._R, gt._R, m, n, iscrowd.data, _iou.data ) - def _bbIou(np.ndarray[np.double_t, ndim=2] dt, np.ndarray[np.double_t, ndim=2] gt, np.ndarray[np.uint8_t, ndim=1] iscrowd, siz m, siz n, np.ndarray[np.double_t, ndim=1] _iou): - bbIou( dt.data, gt.data, m, n, iscrowd.data, _iou.data ) - def _len(obj): - cdef siz N = 0 - if type(obj) == RLEs: - N = obj.n - elif len(obj)==0: - pass - elif type(obj) == np.ndarray: - N = obj.shape[0] - return N - # convert iscrowd to numpy array - cdef np.ndarray[np.uint8_t, ndim=1] iscrowd = np.array(pyiscrowd, dtype=np.uint8) - # simple type checking - cdef siz m, n - dt = _preproc(dt) - gt = _preproc(gt) - m = _len(dt) - n = _len(gt) - if m == 0 or n == 0: - return [] - if not type(dt) == type(gt): - raise Exception('The dt and gt should have the same data type, either RLEs, list or np.ndarray') - - # define local variables - cdef double* _iou = 0 - cdef np.npy_intp shape[1] - # check type and assign iou function - if type(dt) == RLEs: - _iouFun = _rleIou - elif type(dt) == np.ndarray: - _iouFun = _bbIou - else: - raise Exception('input data type not allowed.') - _iou = malloc(m*n* sizeof(double)) - iou = np.zeros((m*n, ), dtype=np.double) - shape[0] = m*n - iou = np.PyArray_SimpleNewFromData(1, shape, np.NPY_DOUBLE, _iou) - PyArray_ENABLEFLAGS(iou, np.NPY_OWNDATA) - _iouFun(dt, gt, iscrowd, m, n, iou) - return iou.reshape((m,n), order='F') - -def toBbox( rleObjs ): - cdef RLEs Rs = _frString(rleObjs) - cdef siz n = Rs.n - cdef BB _bb = malloc(4*n* sizeof(double)) - rleToBbox( Rs._R, _bb, n ) - cdef np.npy_intp shape[1] - shape[0] = 4*n - bb = np.array((1,4*n), dtype=np.double) - bb = np.PyArray_SimpleNewFromData(1, shape, np.NPY_DOUBLE, _bb).reshape((n, 4)) - PyArray_ENABLEFLAGS(bb, np.NPY_OWNDATA) - return bb - -def frBbox(np.ndarray[np.double_t, ndim=2] bb, siz h, siz w ): - cdef siz n = bb.shape[0] - Rs = RLEs(n) - rleFrBbox( Rs._R, bb.data, h, w, n ) - objs = _toString(Rs) - return objs - -def frPoly( poly, siz h, siz w ): - cdef np.ndarray[np.double_t, ndim=1] np_poly - n = len(poly) - Rs = RLEs(n) - for i, p in enumerate(poly): - np_poly = np.array(p, dtype=np.double, order='F') - rleFrPoly( &Rs._R[i], np_poly.data, int(len(p)/2), h, w ) - objs = _toString(Rs) - return objs - -def frUncompressedRLE(ucRles, siz h, siz w): - cdef np.ndarray[np.uint32_t, ndim=1] cnts - cdef RLE R - cdef uint *data - n = len(ucRles) - objs = [] - for i in range(n): - Rs = RLEs(1) - cnts = np.array(ucRles[i]['counts'], dtype=np.uint32) - # time for malloc can be saved here but it's fine - data = malloc(len(cnts)* sizeof(uint)) - for j in range(len(cnts)): - data[j] = cnts[j] - R = RLE(ucRles[i]['size'][0], ucRles[i]['size'][1], len(cnts), data) - Rs._R[0] = R - objs.append(_toString(Rs)[0]) - return objs - -def frPyObjects(pyobj, h, w): - # encode rle from a list of python objects - if type(pyobj) == np.ndarray: - objs = frBbox(pyobj, h, w) - elif type(pyobj) == list and len(pyobj[0]) == 4: - objs = frBbox(pyobj, h, w) - elif type(pyobj) == list and len(pyobj[0]) > 4: - objs = frPoly(pyobj, h, w) - elif type(pyobj) == list and type(pyobj[0]) == dict \ - and 'counts' in pyobj[0] and 'size' in pyobj[0]: - objs = frUncompressedRLE(pyobj, h, w) - # encode rle from single python object - elif type(pyobj) == list and len(pyobj) == 4: - objs = frBbox([pyobj], h, w)[0] - elif type(pyobj) == list and len(pyobj) > 4: - objs = frPoly([pyobj], h, w)[0] - elif type(pyobj) == dict and 'counts' in pyobj and 'size' in pyobj: - objs = frUncompressedRLE([pyobj], h, w)[0] - else: - raise Exception('input type is not supported.') - return objs diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/coco.py b/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/coco.py deleted file mode 100755 index dc9972b..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/coco.py +++ /dev/null @@ -1,433 +0,0 @@ -__author__ = 'tylin' -__version__ = '2.0' -# Interface for accessing the Microsoft COCO dataset. - -# Microsoft COCO is a large image dataset designed for object detection, -# segmentation, and caption generation. pycocotools is a Python API that -# assists in loading, parsing and visualizing the annotations in COCO. -# Please visit http://mscoco.org/ for more information on COCO, including -# for the data, paper, and tutorials. The exact format of the annotations -# is also described on the COCO website. For example usage of the pycocotools -# please see pycocotools_demo.ipynb. In addition to this API, please download both -# the COCO images and annotations in order to run the demo. - -# An alternative to using the API is to load the annotations directly -# into Python dictionary -# Using the API provides additional utility functions. Note that this API -# supports both *instance* and *caption* annotations. In the case of -# captions not all functions are defined (e.g. categories are undefined). - -# The following API functions are defined: -# COCO - COCO api class that loads COCO annotation file and prepare data structures. -# decodeMask - Decode binary mask M encoded via run-length encoding. -# encodeMask - Encode binary mask M using run-length encoding. -# getAnnIds - Get ann ids that satisfy given filter conditions. -# getCatIds - Get cat ids that satisfy given filter conditions. -# getImgIds - Get img ids that satisfy given filter conditions. -# loadAnns - Load anns with the specified ids. -# loadCats - Load cats with the specified ids. -# loadImgs - Load imgs with the specified ids. -# annToMask - Convert segmentation in an annotation to binary mask. -# showAnns - Display the specified annotations. -# loadRes - Load algorithm results and create API for accessing them. -# download - Download COCO images from mscoco.org server. -# Throughout the API "ann"=annotation, "cat"=category, and "img"=image. -# Help on each functions can be accessed by: "help COCO>function". - -# See also COCO>decodeMask, -# COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds, -# COCO>getImgIds, COCO>loadAnns, COCO>loadCats, -# COCO>loadImgs, COCO>annToMask, COCO>showAnns - -# Microsoft COCO Toolbox. version 2.0 -# Data, paper, and tutorials available at: http://mscoco.org/ -# Code written by Piotr Dollar and Tsung-Yi Lin, 2014. -# Licensed under the Simplified BSD License [see bsd.txt] - -import json -import time -import matplotlib.pyplot as plt -from matplotlib.collections import PatchCollection -from matplotlib.patches import Polygon -import numpy as np -import copy -import itertools -from . import mask as maskUtils -import os -from collections import defaultdict -import sys -PYTHON_VERSION = sys.version_info[0] -if PYTHON_VERSION == 2: - from urllib import urlretrieve -elif PYTHON_VERSION == 3: - from urllib.request import urlretrieve - - -def _isArrayLike(obj): - return hasattr(obj, '__iter__') and hasattr(obj, '__len__') - - -class COCO: - def __init__(self, annotation_file=None): - """ - Constructor of Microsoft COCO helper class for reading and visualizing annotations. - :param annotation_file (str): location of annotation file - :param image_folder (str): location to the folder that hosts images. - :return: - """ - # load dataset - self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict() - self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list) - if not annotation_file == None: - print('loading annotations into memory...') - tic = time.time() - dataset = json.load(open(annotation_file, 'r')) - assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset)) - print('Done (t={:0.2f}s)'.format(time.time()- tic)) - self.dataset = dataset - self.createIndex() - - def createIndex(self): - # create index - print('creating index...') - anns, cats, imgs = {}, {}, {} - imgToAnns,catToImgs = defaultdict(list),defaultdict(list) - if 'annotations' in self.dataset: - for ann in self.dataset['annotations']: - imgToAnns[ann['image_id']].append(ann) - anns[ann['id']] = ann - - if 'images' in self.dataset: - for img in self.dataset['images']: - imgs[img['id']] = img - - if 'categories' in self.dataset: - for cat in self.dataset['categories']: - cats[cat['id']] = cat - - if 'annotations' in self.dataset and 'categories' in self.dataset: - for ann in self.dataset['annotations']: - catToImgs[ann['category_id']].append(ann['image_id']) - - print('index created!') - - # create class members - self.anns = anns - self.imgToAnns = imgToAnns - self.catToImgs = catToImgs - self.imgs = imgs - self.cats = cats - - def info(self): - """ - Print information about the annotation file. - :return: - """ - for key, value in self.dataset['info'].items(): - print('{}: {}'.format(key, value)) - - def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None): - """ - Get ann ids that satisfy given filter conditions. default skips that filter - :param imgIds (int array) : get anns for given imgs - catIds (int array) : get anns for given cats - areaRng (float array) : get anns for given area range (e.g. [0 inf]) - iscrowd (boolean) : get anns for given crowd label (False or True) - :return: ids (int array) : integer array of ann ids - """ - imgIds = imgIds if _isArrayLike(imgIds) else [imgIds] - catIds = catIds if _isArrayLike(catIds) else [catIds] - - if len(imgIds) == len(catIds) == len(areaRng) == 0: - anns = self.dataset['annotations'] - else: - if not len(imgIds) == 0: - lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns] - anns = list(itertools.chain.from_iterable(lists)) - else: - anns = self.dataset['annotations'] - anns = anns if len(catIds) == 0 else [ann for ann in anns if ann['category_id'] in catIds] - anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]] - if not iscrowd == None: - ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd] - else: - ids = [ann['id'] for ann in anns] - return ids - - def getCatIds(self, catNms=[], supNms=[], catIds=[]): - """ - filtering parameters. default skips that filter. - :param catNms (str array) : get cats for given cat names - :param supNms (str array) : get cats for given supercategory names - :param catIds (int array) : get cats for given cat ids - :return: ids (int array) : integer array of cat ids - """ - catNms = catNms if _isArrayLike(catNms) else [catNms] - supNms = supNms if _isArrayLike(supNms) else [supNms] - catIds = catIds if _isArrayLike(catIds) else [catIds] - - if len(catNms) == len(supNms) == len(catIds) == 0: - cats = self.dataset['categories'] - else: - cats = self.dataset['categories'] - cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name'] in catNms] - cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms] - cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id'] in catIds] - ids = [cat['id'] for cat in cats] - return ids - - def getImgIds(self, imgIds=[], catIds=[]): - ''' - Get img ids that satisfy given filter conditions. - :param imgIds (int array) : get imgs for given ids - :param catIds (int array) : get imgs with all given cats - :return: ids (int array) : integer array of img ids - ''' - imgIds = imgIds if _isArrayLike(imgIds) else [imgIds] - catIds = catIds if _isArrayLike(catIds) else [catIds] - - if len(imgIds) == len(catIds) == 0: - ids = self.imgs.keys() - else: - ids = set(imgIds) - for i, catId in enumerate(catIds): - if i == 0 and len(ids) == 0: - ids = set(self.catToImgs[catId]) - else: - ids &= set(self.catToImgs[catId]) - return list(ids) - - def loadAnns(self, ids=[]): - """ - Load anns with the specified ids. - :param ids (int array) : integer ids specifying anns - :return: anns (object array) : loaded ann objects - """ - if _isArrayLike(ids): - return [self.anns[id] for id in ids] - elif type(ids) == int: - return [self.anns[ids]] - - def loadCats(self, ids=[]): - """ - Load cats with the specified ids. - :param ids (int array) : integer ids specifying cats - :return: cats (object array) : loaded cat objects - """ - if _isArrayLike(ids): - return [self.cats[id] for id in ids] - elif type(ids) == int: - return [self.cats[ids]] - - def loadImgs(self, ids=[]): - """ - Load anns with the specified ids. - :param ids (int array) : integer ids specifying img - :return: imgs (object array) : loaded img objects - """ - if _isArrayLike(ids): - return [self.imgs[id] for id in ids] - elif type(ids) == int: - return [self.imgs[ids]] - - def showAnns(self, anns): - """ - Display the specified annotations. - :param anns (array of object): annotations to display - :return: None - """ - if len(anns) == 0: - return 0 - if 'segmentation' in anns[0] or 'keypoints' in anns[0]: - datasetType = 'instances' - elif 'caption' in anns[0]: - datasetType = 'captions' - else: - raise Exception('datasetType not supported') - if datasetType == 'instances': - ax = plt.gca() - ax.set_autoscale_on(False) - polygons = [] - color = [] - for ann in anns: - c = (np.random.random((1, 3))*0.6+0.4).tolist()[0] - if 'segmentation' in ann: - if type(ann['segmentation']) == list: - # polygon - for seg in ann['segmentation']: - poly = np.array(seg).reshape((int(len(seg)/2), 2)) - polygons.append(Polygon(poly)) - color.append(c) - else: - # mask - t = self.imgs[ann['image_id']] - if type(ann['segmentation']['counts']) == list: - rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width']) - else: - rle = [ann['segmentation']] - m = maskUtils.decode(rle) - img = np.ones( (m.shape[0], m.shape[1], 3) ) - if ann['iscrowd'] == 1: - color_mask = np.array([2.0,166.0,101.0])/255 - if ann['iscrowd'] == 0: - color_mask = np.random.random((1, 3)).tolist()[0] - for i in range(3): - img[:,:,i] = color_mask[i] - ax.imshow(np.dstack( (img, m*0.5) )) - if 'keypoints' in ann and type(ann['keypoints']) == list: - # turn skeleton into zero-based index - sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1 - kp = np.array(ann['keypoints']) - x = kp[0::3] - y = kp[1::3] - v = kp[2::3] - for sk in sks: - if np.all(v[sk]>0): - plt.plot(x[sk],y[sk], linewidth=3, color=c) - plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2) - plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2) - p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4) - ax.add_collection(p) - p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2) - ax.add_collection(p) - elif datasetType == 'captions': - for ann in anns: - print(ann['caption']) - - def loadRes(self, resFile): - """ - Load result file and return a result api object. - :param resFile (str) : file name of result file - :return: res (obj) : result api object - """ - res = COCO() - res.dataset['images'] = [img for img in self.dataset['images']] - - print('Loading and preparing results...') - tic = time.time() - if type(resFile) == str or type(resFile) == unicode: - anns = json.load(open(resFile)) - elif type(resFile) == np.ndarray: - anns = self.loadNumpyAnnotations(resFile) - else: - anns = resFile - assert type(anns) == list, 'results in not an array of objects' - annsImgIds = [ann['image_id'] for ann in anns] - assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \ - 'Results do not correspond to current coco set' - if 'caption' in anns[0]: - imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns]) - res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds] - for id, ann in enumerate(anns): - ann['id'] = id+1 - elif 'bbox' in anns[0] and not anns[0]['bbox'] == []: - res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) - for id, ann in enumerate(anns): - bb = ann['bbox'] - x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]] - if not 'segmentation' in ann: - ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]] - ann['area'] = bb[2]*bb[3] - ann['id'] = id+1 - ann['iscrowd'] = 0 - elif 'segmentation' in anns[0]: - res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) - for id, ann in enumerate(anns): - # now only support compressed RLE format as segmentation results - ann['area'] = maskUtils.area(ann['segmentation']) - if not 'bbox' in ann: - ann['bbox'] = maskUtils.toBbox(ann['segmentation']) - ann['id'] = id+1 - ann['iscrowd'] = 0 - elif 'keypoints' in anns[0]: - res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) - for id, ann in enumerate(anns): - s = ann['keypoints'] - x = s[0::3] - y = s[1::3] - x0,x1,y0,y1 = np.min(x), np.max(x), np.min(y), np.max(y) - ann['area'] = (x1-x0)*(y1-y0) - ann['id'] = id + 1 - ann['bbox'] = [x0,y0,x1-x0,y1-y0] - print('DONE (t={:0.2f}s)'.format(time.time()- tic)) - - res.dataset['annotations'] = anns - res.createIndex() - return res - - def download(self, tarDir = None, imgIds = [] ): - ''' - Download COCO images from mscoco.org server. - :param tarDir (str): COCO results directory name - imgIds (list): images to be downloaded - :return: - ''' - if tarDir is None: - print('Please specify target directory') - return -1 - if len(imgIds) == 0: - imgs = self.imgs.values() - else: - imgs = self.loadImgs(imgIds) - N = len(imgs) - if not os.path.exists(tarDir): - os.makedirs(tarDir) - for i, img in enumerate(imgs): - tic = time.time() - fname = os.path.join(tarDir, img['file_name']) - if not os.path.exists(fname): - urlretrieve(img['coco_url'], fname) - print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time()- tic)) - - def loadNumpyAnnotations(self, data): - """ - Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class} - :param data (numpy.ndarray) - :return: annotations (python nested list) - """ - print('Converting ndarray to lists...') - assert(type(data) == np.ndarray) - print(data.shape) - assert(data.shape[1] == 7) - N = data.shape[0] - ann = [] - for i in range(N): - if i % 1000000 == 0: - print('{}/{}'.format(i,N)) - ann += [{ - 'image_id' : int(data[i, 0]), - 'bbox' : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ], - 'score' : data[i, 5], - 'category_id': int(data[i, 6]), - }] - return ann - - def annToRLE(self, ann): - """ - Convert annotation which can be polygons, uncompressed RLE to RLE. - :return: binary mask (numpy 2D array) - """ - t = self.imgs[ann['image_id']] - h, w = t['height'], t['width'] - segm = ann['segmentation'] - if type(segm) == list: - # polygon -- a single object might consist of multiple parts - # we merge all parts into one mask rle code - rles = maskUtils.frPyObjects(segm, h, w) - rle = maskUtils.merge(rles) - elif type(segm['counts']) == list: - # uncompressed RLE - rle = maskUtils.frPyObjects(segm, h, w) - else: - # rle - rle = ann['segmentation'] - return rle - - def annToMask(self, ann): - """ - Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask. - :return: binary mask (numpy 2D array) - """ - rle = self.annToRLE(ann) - m = maskUtils.decode(rle) - return m \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/cocoeval.py b/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/cocoeval.py deleted file mode 100755 index 7a4b4ad..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/cocoeval.py +++ /dev/null @@ -1,534 +0,0 @@ -__author__ = 'tsungyi' - -import numpy as np -import datetime -import time -from collections import defaultdict -from . import mask as maskUtils -import copy - -class COCOeval: - # Interface for evaluating detection on the Microsoft COCO dataset. - # - # The usage for CocoEval is as follows: - # cocoGt=..., cocoDt=... # load dataset and results - # E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object - # E.params.recThrs = ...; # set parameters as desired - # E.evaluate(); # run per image evaluation - # E.accumulate(); # accumulate per image results - # E.summarize(); # display summary metrics of results - # For example usage see evalDemo.m and http://mscoco.org/. - # - # The evaluation parameters are as follows (defaults in brackets): - # imgIds - [all] N img ids to use for evaluation - # catIds - [all] K cat ids to use for evaluation - # iouThrs - [.5:.05:.95] T=10 IoU thresholds for evaluation - # recThrs - [0:.01:1] R=101 recall thresholds for evaluation - # areaRng - [...] A=4 object area ranges for evaluation - # maxDets - [1 10 100] M=3 thresholds on max detections per image - # iouType - ['segm'] set iouType to 'segm', 'bbox' or 'keypoints' - # iouType replaced the now DEPRECATED useSegm parameter. - # useCats - [1] if true use category labels for evaluation - # Note: if useCats=0 category labels are ignored as in proposal scoring. - # Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified. - # - # evaluate(): evaluates detections on every image and every category and - # concats the results into the "evalImgs" with fields: - # dtIds - [1xD] id for each of the D detections (dt) - # gtIds - [1xG] id for each of the G ground truths (gt) - # dtMatches - [TxD] matching gt id at each IoU or 0 - # gtMatches - [TxG] matching dt id at each IoU or 0 - # dtScores - [1xD] confidence of each dt - # gtIgnore - [1xG] ignore flag for each gt - # dtIgnore - [TxD] ignore flag for each dt at each IoU - # - # accumulate(): accumulates the per-image, per-category evaluation - # results in "evalImgs" into the dictionary "eval" with fields: - # params - parameters used for evaluation - # date - date evaluation was performed - # counts - [T,R,K,A,M] parameter dimensions (see above) - # precision - [TxRxKxAxM] precision for every evaluation setting - # recall - [TxKxAxM] max recall for every evaluation setting - # Note: precision and recall==-1 for settings with no gt objects. - # - # See also coco, mask, pycocoDemo, pycocoEvalDemo - # - # Microsoft COCO Toolbox. version 2.0 - # Data, paper, and tutorials available at: http://mscoco.org/ - # Code written by Piotr Dollar and Tsung-Yi Lin, 2015. - # Licensed under the Simplified BSD License [see coco/license.txt] - def __init__(self, cocoGt=None, cocoDt=None, iouType='segm'): - ''' - Initialize CocoEval using coco APIs for gt and dt - :param cocoGt: coco object with ground truth annotations - :param cocoDt: coco object with detection results - :return: None - ''' - if not iouType: - print('iouType not specified. use default iouType segm') - self.cocoGt = cocoGt # ground truth COCO API - self.cocoDt = cocoDt # detections COCO API - self.params = {} # evaluation parameters - self.evalImgs = defaultdict(list) # per-image per-category evaluation results [KxAxI] elements - self.eval = {} # accumulated evaluation results - self._gts = defaultdict(list) # gt for evaluation - self._dts = defaultdict(list) # dt for evaluation - self.params = Params(iouType=iouType) # parameters - self._paramsEval = {} # parameters for evaluation - self.stats = [] # result summarization - self.ious = {} # ious between all gts and dts - if not cocoGt is None: - self.params.imgIds = sorted(cocoGt.getImgIds()) - self.params.catIds = sorted(cocoGt.getCatIds()) - - - def _prepare(self): - ''' - Prepare ._gts and ._dts for evaluation based on params - :return: None - ''' - def _toMask(anns, coco): - # modify ann['segmentation'] by reference - for ann in anns: - rle = coco.annToRLE(ann) - ann['segmentation'] = rle - p = self.params - if p.useCats: - gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) - dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) - else: - gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds)) - dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds)) - - # convert ground truth to mask if iouType == 'segm' - if p.iouType == 'segm': - _toMask(gts, self.cocoGt) - _toMask(dts, self.cocoDt) - # set ignore flag - for gt in gts: - gt['ignore'] = gt['ignore'] if 'ignore' in gt else 0 - gt['ignore'] = 'iscrowd' in gt and gt['iscrowd'] - if p.iouType == 'keypoints': - gt['ignore'] = (gt['num_keypoints'] == 0) or gt['ignore'] - self._gts = defaultdict(list) # gt for evaluation - self._dts = defaultdict(list) # dt for evaluation - for gt in gts: - self._gts[gt['image_id'], gt['category_id']].append(gt) - for dt in dts: - self._dts[dt['image_id'], dt['category_id']].append(dt) - self.evalImgs = defaultdict(list) # per-image per-category evaluation results - self.eval = {} # accumulated evaluation results - - def evaluate(self): - ''' - Run per image evaluation on given images and store results (a list of dict) in self.evalImgs - :return: None - ''' - tic = time.time() - print('Running per image evaluation...') - p = self.params - # add backward compatibility if useSegm is specified in params - if not p.useSegm is None: - p.iouType = 'segm' if p.useSegm == 1 else 'bbox' - print('useSegm (deprecated) is not None. Running {} evaluation'.format(p.iouType)) - print('Evaluate annotation type *{}*'.format(p.iouType)) - p.imgIds = list(np.unique(p.imgIds)) - if p.useCats: - p.catIds = list(np.unique(p.catIds)) - p.maxDets = sorted(p.maxDets) - self.params=p - - self._prepare() - # loop through images, area range, max detection number - catIds = p.catIds if p.useCats else [-1] - - if p.iouType == 'segm' or p.iouType == 'bbox': - computeIoU = self.computeIoU - elif p.iouType == 'keypoints': - computeIoU = self.computeOks - self.ious = {(imgId, catId): computeIoU(imgId, catId) \ - for imgId in p.imgIds - for catId in catIds} - - evaluateImg = self.evaluateImg - maxDet = p.maxDets[-1] - self.evalImgs = [evaluateImg(imgId, catId, areaRng, maxDet) - for catId in catIds - for areaRng in p.areaRng - for imgId in p.imgIds - ] - self._paramsEval = copy.deepcopy(self.params) - toc = time.time() - print('DONE (t={:0.2f}s).'.format(toc-tic)) - - def computeIoU(self, imgId, catId): - p = self.params - if p.useCats: - gt = self._gts[imgId,catId] - dt = self._dts[imgId,catId] - else: - gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]] - dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]] - if len(gt) == 0 and len(dt) ==0: - return [] - inds = np.argsort([-d['score'] for d in dt], kind='mergesort') - dt = [dt[i] for i in inds] - if len(dt) > p.maxDets[-1]: - dt=dt[0:p.maxDets[-1]] - - if p.iouType == 'segm': - g = [g['segmentation'] for g in gt] - d = [d['segmentation'] for d in dt] - elif p.iouType == 'bbox': - g = [g['bbox'] for g in gt] - d = [d['bbox'] for d in dt] - else: - raise Exception('unknown iouType for iou computation') - - # compute iou between each dt and gt region - iscrowd = [int(o['iscrowd']) for o in gt] - ious = maskUtils.iou(d,g,iscrowd) - return ious - - def computeOks(self, imgId, catId): - p = self.params - # dimention here should be Nxm - gts = self._gts[imgId, catId] - dts = self._dts[imgId, catId] - inds = np.argsort([-d['score'] for d in dts], kind='mergesort') - dts = [dts[i] for i in inds] - if len(dts) > p.maxDets[-1]: - dts = dts[0:p.maxDets[-1]] - # if len(gts) == 0 and len(dts) == 0: - if len(gts) == 0 or len(dts) == 0: - return [] - ious = np.zeros((len(dts), len(gts))) - sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62,.62, 1.07, 1.07, .87, .87, .89, .89])/10.0 - vars = (sigmas * 2)**2 - k = len(sigmas) - # compute oks between each detection and ground truth object - for j, gt in enumerate(gts): - # create bounds for ignore regions(double the gt bbox) - g = np.array(gt['keypoints']) - xg = g[0::3]; yg = g[1::3]; vg = g[2::3] - k1 = np.count_nonzero(vg > 0) - bb = gt['bbox'] - x0 = bb[0] - bb[2]; x1 = bb[0] + bb[2] * 2 - y0 = bb[1] - bb[3]; y1 = bb[1] + bb[3] * 2 - for i, dt in enumerate(dts): - d = np.array(dt['keypoints']) - xd = d[0::3]; yd = d[1::3] - if k1>0: - # measure the per-keypoint distance if keypoints visible - dx = xd - xg - dy = yd - yg - else: - # measure minimum distance to keypoints in (x0,y0) & (x1,y1) - z = np.zeros((k)) - dx = np.max((z, x0-xd),axis=0)+np.max((z, xd-x1),axis=0) - dy = np.max((z, y0-yd),axis=0)+np.max((z, yd-y1),axis=0) - e = (dx**2 + dy**2) / vars / (gt['area']+np.spacing(1)) / 2 - if k1 > 0: - e=e[vg > 0] - ious[i, j] = np.sum(np.exp(-e)) / e.shape[0] - return ious - - def evaluateImg(self, imgId, catId, aRng, maxDet): - ''' - perform evaluation for single category and image - :return: dict (single image results) - ''' - p = self.params - if p.useCats: - gt = self._gts[imgId,catId] - dt = self._dts[imgId,catId] - else: - gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]] - dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]] - if len(gt) == 0 and len(dt) ==0: - return None - - for g in gt: - if g['ignore'] or (g['area']aRng[1]): - g['_ignore'] = 1 - else: - g['_ignore'] = 0 - - # sort dt highest score first, sort gt ignore last - gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') - gt = [gt[i] for i in gtind] - dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') - dt = [dt[i] for i in dtind[0:maxDet]] - iscrowd = [int(o['iscrowd']) for o in gt] - # load computed ious - ious = self.ious[imgId, catId][:, gtind] if len(self.ious[imgId, catId]) > 0 else self.ious[imgId, catId] - - T = len(p.iouThrs) - G = len(gt) - D = len(dt) - gtm = np.zeros((T,G)) - dtm = np.zeros((T,D)) - gtIg = np.array([g['_ignore'] for g in gt]) - dtIg = np.zeros((T,D)) - if not len(ious)==0: - for tind, t in enumerate(p.iouThrs): - for dind, d in enumerate(dt): - # information about best match so far (m=-1 -> unmatched) - iou = min([t,1-1e-10]) - m = -1 - for gind, g in enumerate(gt): - # if this gt already matched, and not a crowd, continue - if gtm[tind,gind]>0 and not iscrowd[gind]: - continue - # if dt matched to reg gt, and on ignore gt, stop - if m>-1 and gtIg[m]==0 and gtIg[gind]==1: - break - # continue to next gt unless better match made - if ious[dind,gind] < iou: - continue - # if match successful and best so far, store appropriately - iou=ious[dind,gind] - m=gind - # if match made store id of match for both dt and gt - if m ==-1: - continue - dtIg[tind,dind] = gtIg[m] - dtm[tind,dind] = gt[m]['id'] - gtm[tind,m] = d['id'] - # set unmatched detections outside of area range to ignore - a = np.array([d['area']aRng[1] for d in dt]).reshape((1, len(dt))) - dtIg = np.logical_or(dtIg, np.logical_and(dtm==0, np.repeat(a,T,0))) - # store results for given image and category - return { - 'image_id': imgId, - 'category_id': catId, - 'aRng': aRng, - 'maxDet': maxDet, - 'dtIds': [d['id'] for d in dt], - 'gtIds': [g['id'] for g in gt], - 'dtMatches': dtm, - 'gtMatches': gtm, - 'dtScores': [d['score'] for d in dt], - 'gtIgnore': gtIg, - 'dtIgnore': dtIg, - } - - def accumulate(self, p = None): - ''' - Accumulate per image evaluation results and store the result in self.eval - :param p: input params for evaluation - :return: None - ''' - print('Accumulating evaluation results...') - tic = time.time() - if not self.evalImgs: - print('Please run evaluate() first') - # allows input customized parameters - if p is None: - p = self.params - p.catIds = p.catIds if p.useCats == 1 else [-1] - T = len(p.iouThrs) - R = len(p.recThrs) - K = len(p.catIds) if p.useCats else 1 - A = len(p.areaRng) - M = len(p.maxDets) - precision = -np.ones((T,R,K,A,M)) # -1 for the precision of absent categories - recall = -np.ones((T,K,A,M)) - scores = -np.ones((T,R,K,A,M)) - - # create dictionary for future indexing - _pe = self._paramsEval - catIds = _pe.catIds if _pe.useCats else [-1] - setK = set(catIds) - setA = set(map(tuple, _pe.areaRng)) - setM = set(_pe.maxDets) - setI = set(_pe.imgIds) - # get inds to evaluate - k_list = [n for n, k in enumerate(p.catIds) if k in setK] - m_list = [m for n, m in enumerate(p.maxDets) if m in setM] - a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA] - i_list = [n for n, i in enumerate(p.imgIds) if i in setI] - I0 = len(_pe.imgIds) - A0 = len(_pe.areaRng) - # retrieve E at each category, area range, and max number of detections - for k, k0 in enumerate(k_list): - Nk = k0*A0*I0 - for a, a0 in enumerate(a_list): - Na = a0*I0 - for m, maxDet in enumerate(m_list): - E = [self.evalImgs[Nk + Na + i] for i in i_list] - E = [e for e in E if not e is None] - if len(E) == 0: - continue - dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E]) - - # different sorting method generates slightly different results. - # mergesort is used to be consistent as Matlab implementation. - inds = np.argsort(-dtScores, kind='mergesort') - dtScoresSorted = dtScores[inds] - - dtm = np.concatenate([e['dtMatches'][:,0:maxDet] for e in E], axis=1)[:,inds] - dtIg = np.concatenate([e['dtIgnore'][:,0:maxDet] for e in E], axis=1)[:,inds] - gtIg = np.concatenate([e['gtIgnore'] for e in E]) - npig = np.count_nonzero(gtIg==0 ) - if npig == 0: - continue - tps = np.logical_and( dtm, np.logical_not(dtIg) ) - fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) ) - - tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) - fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) - for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): - tp = np.array(tp) - fp = np.array(fp) - nd = len(tp) - rc = tp / npig - pr = tp / (fp+tp+np.spacing(1)) - q = np.zeros((R,)) - ss = np.zeros((R,)) - - if nd: - recall[t,k,a,m] = rc[-1] - else: - recall[t,k,a,m] = 0 - - # numpy is slow without cython optimization for accessing elements - # use python array gets significant speed improvement - pr = pr.tolist(); q = q.tolist() - - for i in range(nd-1, 0, -1): - if pr[i] > pr[i-1]: - pr[i-1] = pr[i] - - inds = np.searchsorted(rc, p.recThrs, side='left') - try: - for ri, pi in enumerate(inds): - q[ri] = pr[pi] - ss[ri] = dtScoresSorted[pi] - except: - pass - precision[t,:,k,a,m] = np.array(q) - scores[t,:,k,a,m] = np.array(ss) - self.eval = { - 'params': p, - 'counts': [T, R, K, A, M], - 'date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), - 'precision': precision, - 'recall': recall, - 'scores': scores, - } - toc = time.time() - print('DONE (t={:0.2f}s).'.format( toc-tic)) - - def summarize(self): - ''' - Compute and display summary metrics for evaluation results. - Note this functin can *only* be applied on the default parameter setting - ''' - def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ): - p = self.params - iStr = ' {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}' - titleStr = 'Average Precision' if ap == 1 else 'Average Recall' - typeStr = '(AP)' if ap==1 else '(AR)' - iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \ - if iouThr is None else '{:0.2f}'.format(iouThr) - - aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] - mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] - if ap == 1: - # dimension of precision: [TxRxKxAxM] - s = self.eval['precision'] - # IoU - if iouThr is not None: - t = np.where(iouThr == p.iouThrs)[0] - s = s[t] - s = s[:,:,:,aind,mind] - else: - # dimension of recall: [TxKxAxM] - s = self.eval['recall'] - if iouThr is not None: - t = np.where(iouThr == p.iouThrs)[0] - s = s[t] - s = s[:,:,aind,mind] - if len(s[s>-1])==0: - mean_s = -1 - else: - mean_s = np.mean(s[s>-1]) - print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) - return mean_s - def _summarizeDets(): - stats = np.zeros((12,)) - stats[0] = _summarize(1) - stats[1] = _summarize(1, iouThr=.5, maxDets=self.params.maxDets[2]) - stats[2] = _summarize(1, iouThr=.75, maxDets=self.params.maxDets[2]) - stats[3] = _summarize(1, areaRng='small', maxDets=self.params.maxDets[2]) - stats[4] = _summarize(1, areaRng='medium', maxDets=self.params.maxDets[2]) - stats[5] = _summarize(1, areaRng='large', maxDets=self.params.maxDets[2]) - stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) - stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) - stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) - stats[9] = _summarize(0, areaRng='small', maxDets=self.params.maxDets[2]) - stats[10] = _summarize(0, areaRng='medium', maxDets=self.params.maxDets[2]) - stats[11] = _summarize(0, areaRng='large', maxDets=self.params.maxDets[2]) - return stats - def _summarizeKps(): - stats = np.zeros((10,)) - stats[0] = _summarize(1, maxDets=20) - stats[1] = _summarize(1, maxDets=20, iouThr=.5) - stats[2] = _summarize(1, maxDets=20, iouThr=.75) - stats[3] = _summarize(1, maxDets=20, areaRng='medium') - stats[4] = _summarize(1, maxDets=20, areaRng='large') - stats[5] = _summarize(0, maxDets=20) - stats[6] = _summarize(0, maxDets=20, iouThr=.5) - stats[7] = _summarize(0, maxDets=20, iouThr=.75) - stats[8] = _summarize(0, maxDets=20, areaRng='medium') - stats[9] = _summarize(0, maxDets=20, areaRng='large') - return stats - if not self.eval: - raise Exception('Please run accumulate() first') - iouType = self.params.iouType - if iouType == 'segm' or iouType == 'bbox': - summarize = _summarizeDets - elif iouType == 'keypoints': - summarize = _summarizeKps - self.stats = summarize() - - def __str__(self): - self.summarize() - -class Params: - ''' - Params for coco evaluation api - ''' - def setDetParams(self): - self.imgIds = [] - self.catIds = [] - # np.arange causes trouble. the data point on arange is slightly larger than the true value - self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True) - self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True) - self.maxDets = [1, 10, 100] - self.areaRng = [[0 ** 2, 1e5 ** 2], [0 ** 2, 32 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]] - self.areaRngLbl = ['all', 'small', 'medium', 'large'] - self.useCats = 1 - - def setKpParams(self): - self.imgIds = [] - self.catIds = [] - # np.arange causes trouble. the data point on arange is slightly larger than the true value - self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True) - self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True) - self.maxDets = [20] - self.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]] - self.areaRngLbl = ['all', 'medium', 'large'] - self.useCats = 1 - - def __init__(self, iouType='segm'): - if iouType == 'segm' or iouType == 'bbox': - self.setDetParams() - elif iouType == 'keypoints': - self.setKpParams() - else: - raise Exception('iouType not supported') - self.iouType = iouType - # useSegm is deprecated - self.useSegm = None \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/mask.py b/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/mask.py deleted file mode 100755 index 40853ba..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/mask.py +++ /dev/null @@ -1,103 +0,0 @@ -__author__ = 'tsungyi' - -import pycocotools._mask as _mask - -# Interface for manipulating masks stored in RLE format. -# -# RLE is a simple yet efficient format for storing binary masks. RLE -# first divides a vector (or vectorized image) into a series of piecewise -# constant regions and then for each piece simply stores the length of -# that piece. For example, given M=[0 0 1 1 1 0 1] the RLE counts would -# be [2 3 1 1], or for M=[1 1 1 1 1 1 0] the counts would be [0 6 1] -# (note that the odd counts are always the numbers of zeros). Instead of -# storing the counts directly, additional compression is achieved with a -# variable bitrate representation based on a common scheme called LEB128. -# -# Compression is greatest given large piecewise constant regions. -# Specifically, the size of the RLE is proportional to the number of -# *boundaries* in M (or for an image the number of boundaries in the y -# direction). Assuming fairly simple shapes, the RLE representation is -# O(sqrt(n)) where n is number of pixels in the object. Hence space usage -# is substantially lower, especially for large simple objects (large n). -# -# Many common operations on masks can be computed directly using the RLE -# (without need for decoding). This includes computations such as area, -# union, intersection, etc. All of these operations are linear in the -# size of the RLE, in other words they are O(sqrt(n)) where n is the area -# of the object. Computing these operations on the original mask is O(n). -# Thus, using the RLE can result in substantial computational savings. -# -# The following API functions are defined: -# encode - Encode binary masks using RLE. -# decode - Decode binary masks encoded via RLE. -# merge - Compute union or intersection of encoded masks. -# iou - Compute intersection over union between masks. -# area - Compute area of encoded masks. -# toBbox - Get bounding boxes surrounding encoded masks. -# frPyObjects - Convert polygon, bbox, and uncompressed RLE to encoded RLE mask. -# -# Usage: -# Rs = encode( masks ) -# masks = decode( Rs ) -# R = merge( Rs, intersect=false ) -# o = iou( dt, gt, iscrowd ) -# a = area( Rs ) -# bbs = toBbox( Rs ) -# Rs = frPyObjects( [pyObjects], h, w ) -# -# In the API the following formats are used: -# Rs - [dict] Run-length encoding of binary masks -# R - dict Run-length encoding of binary mask -# masks - [hxwxn] Binary mask(s) (must have type np.ndarray(dtype=uint8) in column-major order) -# iscrowd - [nx1] list of np.ndarray. 1 indicates corresponding gt image has crowd region to ignore -# bbs - [nx4] Bounding box(es) stored as [x y w h] -# poly - Polygon stored as [[x1 y1 x2 y2...],[x1 y1 ...],...] (2D list) -# dt,gt - May be either bounding boxes or encoded masks -# Both poly and bbs are 0-indexed (bbox=[0 0 1 1] encloses first pixel). -# -# Finally, a note about the intersection over union (iou) computation. -# The standard iou of a ground truth (gt) and detected (dt) object is -# iou(gt,dt) = area(intersect(gt,dt)) / area(union(gt,dt)) -# For "crowd" regions, we use a modified criteria. If a gt object is -# marked as "iscrowd", we allow a dt to match any subregion of the gt. -# Choosing gt' in the crowd gt that best matches the dt can be done using -# gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing -# iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt) -# For crowd gt regions we use this modified criteria above for the iou. -# -# To compile run "python setup.py build_ext --inplace" -# Please do not contact us for help with compiling. -# -# Microsoft COCO Toolbox. version 2.0 -# Data, paper, and tutorials available at: http://mscoco.org/ -# Code written by Piotr Dollar and Tsung-Yi Lin, 2015. -# Licensed under the Simplified BSD License [see coco/license.txt] - -iou = _mask.iou -merge = _mask.merge -frPyObjects = _mask.frPyObjects - -def encode(bimask): - if len(bimask.shape) == 3: - return _mask.encode(bimask) - elif len(bimask.shape) == 2: - h, w = bimask.shape - return _mask.encode(bimask.reshape((h, w, 1), order='F'))[0] - -def decode(rleObjs): - if type(rleObjs) == list: - return _mask.decode(rleObjs) - else: - return _mask.decode([rleObjs])[:,:,0] - -def area(rleObjs): - if type(rleObjs) == list: - return _mask.area(rleObjs) - else: - return _mask.area([rleObjs])[0] - -def toBbox(rleObjs): - if type(rleObjs) == list: - return _mask.toBbox(rleObjs) - else: - return _mask.toBbox([rleObjs])[0] \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/setup.py b/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/setup.py deleted file mode 100755 index dbf0093..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/setup.py +++ /dev/null @@ -1,27 +0,0 @@ -from setuptools import setup, Extension -import numpy as np - -# To compile and install locally run "python setup.py build_ext --inplace" -# To install library to Python site-packages run "python setup.py build_ext install" - -ext_modules = [ - Extension( - 'pycocotools._mask', - sources=['../common/maskApi.c', 'pycocotools/_mask.pyx'], - include_dirs = [np.get_include(), '../common'], - extra_compile_args=['-Wno-cpp', '-Wno-unused-function', '-std=c99'], - ) -] - -setup( - name='pycocotools', - packages=['pycocotools'], - package_dir = {'pycocotools': 'pycocotools'}, - install_requires=[ - 'setuptools>=18.0', - 'cython>=0.27.3', - 'matplotlib>=2.1.0' - ], - version='2.0', - ext_modules= ext_modules -) diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/__init__.py b/example-apps/PDD/pcb-defect-detection/data/lib_coco/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/common/gason.cpp b/example-apps/PDD/pcb-defect-detection/data/lib_coco/common/gason.cpp deleted file mode 100755 index 0f2c00e..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/common/gason.cpp +++ /dev/null @@ -1,335 +0,0 @@ -// https://github.com/vivkin/gason - pulled January 10, 2016 -#include "gason.h" -#include - -#define JSON_ZONE_SIZE 4096 -#define JSON_STACK_SIZE 32 - -const char *jsonStrError(int err) { - switch (err) { -#define XX(no, str) \ - case JSON_##no: \ - return str; - JSON_ERRNO_MAP(XX) -#undef XX - default: - return "unknown"; - } -} - -void *JsonAllocator::allocate(size_t size) { - size = (size + 7) & ~7; - - if (head && head->used + size <= JSON_ZONE_SIZE) { - char *p = (char *)head + head->used; - head->used += size; - return p; - } - - size_t allocSize = sizeof(Zone) + size; - Zone *zone = (Zone *)malloc(allocSize <= JSON_ZONE_SIZE ? JSON_ZONE_SIZE : allocSize); - if (zone == nullptr) - return nullptr; - zone->used = allocSize; - if (allocSize <= JSON_ZONE_SIZE || head == nullptr) { - zone->next = head; - head = zone; - } else { - zone->next = head->next; - head->next = zone; - } - return (char *)zone + sizeof(Zone); -} - -void JsonAllocator::deallocate() { - while (head) { - Zone *next = head->next; - free(head); - head = next; - } -} - -static inline bool isspace(char c) { - return c == ' ' || (c >= '\t' && c <= '\r'); -} - -static inline bool isdelim(char c) { - return c == ',' || c == ':' || c == ']' || c == '}' || isspace(c) || !c; -} - -static inline bool isdigit(char c) { - return c >= '0' && c <= '9'; -} - -static inline bool isxdigit(char c) { - return (c >= '0' && c <= '9') || ((c & ~' ') >= 'A' && (c & ~' ') <= 'F'); -} - -static inline int char2int(char c) { - if (c <= '9') - return c - '0'; - return (c & ~' ') - 'A' + 10; -} - -static double string2double(char *s, char **endptr) { - char ch = *s; - if (ch == '-') - ++s; - - double result = 0; - while (isdigit(*s)) - result = (result * 10) + (*s++ - '0'); - - if (*s == '.') { - ++s; - - double fraction = 1; - while (isdigit(*s)) { - fraction *= 0.1; - result += (*s++ - '0') * fraction; - } - } - - if (*s == 'e' || *s == 'E') { - ++s; - - double base = 10; - if (*s == '+') - ++s; - else if (*s == '-') { - ++s; - base = 0.1; - } - - unsigned int exponent = 0; - while (isdigit(*s)) - exponent = (exponent * 10) + (*s++ - '0'); - - double power = 1; - for (; exponent; exponent >>= 1, base *= base) - if (exponent & 1) - power *= base; - - result *= power; - } - - *endptr = s; - return ch == '-' ? -result : result; -} - -static inline JsonNode *insertAfter(JsonNode *tail, JsonNode *node) { - if (!tail) - return node->next = node; - node->next = tail->next; - tail->next = node; - return node; -} - -static inline JsonValue listToValue(JsonTag tag, JsonNode *tail) { - if (tail) { - auto head = tail->next; - tail->next = nullptr; - return JsonValue(tag, head); - } - return JsonValue(tag, nullptr); -} - -int jsonParse(char *s, char **endptr, JsonValue *value, JsonAllocator &allocator) { - JsonNode *tails[JSON_STACK_SIZE]; - JsonTag tags[JSON_STACK_SIZE]; - char *keys[JSON_STACK_SIZE]; - JsonValue o; - int pos = -1; - bool separator = true; - JsonNode *node; - *endptr = s; - - while (*s) { - while (isspace(*s)) { - ++s; - if (!*s) break; - } - *endptr = s++; - switch (**endptr) { - case '-': - if (!isdigit(*s) && *s != '.') { - *endptr = s; - return JSON_BAD_NUMBER; - } - case '0': - case '1': - case '2': - case '3': - case '4': - case '5': - case '6': - case '7': - case '8': - case '9': - o = JsonValue(string2double(*endptr, &s)); - if (!isdelim(*s)) { - *endptr = s; - return JSON_BAD_NUMBER; - } - break; - case '"': - o = JsonValue(JSON_STRING, s); - for (char *it = s; *s; ++it, ++s) { - int c = *it = *s; - if (c == '\\') { - c = *++s; - switch (c) { - case '\\': - case '"': - case '/': - *it = c; - break; - case 'b': - *it = '\b'; - break; - case 'f': - *it = '\f'; - break; - case 'n': - *it = '\n'; - break; - case 'r': - *it = '\r'; - break; - case 't': - *it = '\t'; - break; - case 'u': - c = 0; - for (int i = 0; i < 4; ++i) { - if (isxdigit(*++s)) { - c = c * 16 + char2int(*s); - } else { - *endptr = s; - return JSON_BAD_STRING; - } - } - if (c < 0x80) { - *it = c; - } else if (c < 0x800) { - *it++ = 0xC0 | (c >> 6); - *it = 0x80 | (c & 0x3F); - } else { - *it++ = 0xE0 | (c >> 12); - *it++ = 0x80 | ((c >> 6) & 0x3F); - *it = 0x80 | (c & 0x3F); - } - break; - default: - *endptr = s; - return JSON_BAD_STRING; - } - } else if ((unsigned int)c < ' ' || c == '\x7F') { - *endptr = s; - return JSON_BAD_STRING; - } else if (c == '"') { - *it = 0; - ++s; - break; - } - } - if (!isdelim(*s)) { - *endptr = s; - return JSON_BAD_STRING; - } - break; - case 't': - if (!(s[0] == 'r' && s[1] == 'u' && s[2] == 'e' && isdelim(s[3]))) - return JSON_BAD_IDENTIFIER; - o = JsonValue(JSON_TRUE); - s += 3; - break; - case 'f': - if (!(s[0] == 'a' && s[1] == 'l' && s[2] == 's' && s[3] == 'e' && isdelim(s[4]))) - return JSON_BAD_IDENTIFIER; - o = JsonValue(JSON_FALSE); - s += 4; - break; - case 'n': - if (!(s[0] == 'u' && s[1] == 'l' && s[2] == 'l' && isdelim(s[3]))) - return JSON_BAD_IDENTIFIER; - o = JsonValue(JSON_NULL); - s += 3; - break; - case ']': - if (pos == -1) - return JSON_STACK_UNDERFLOW; - if (tags[pos] != JSON_ARRAY) - return JSON_MISMATCH_BRACKET; - o = listToValue(JSON_ARRAY, tails[pos--]); - break; - case '}': - if (pos == -1) - return JSON_STACK_UNDERFLOW; - if (tags[pos] != JSON_OBJECT) - return JSON_MISMATCH_BRACKET; - if (keys[pos] != nullptr) - return JSON_UNEXPECTED_CHARACTER; - o = listToValue(JSON_OBJECT, tails[pos--]); - break; - case '[': - if (++pos == JSON_STACK_SIZE) - return JSON_STACK_OVERFLOW; - tails[pos] = nullptr; - tags[pos] = JSON_ARRAY; - keys[pos] = nullptr; - separator = true; - continue; - case '{': - if (++pos == JSON_STACK_SIZE) - return JSON_STACK_OVERFLOW; - tails[pos] = nullptr; - tags[pos] = JSON_OBJECT; - keys[pos] = nullptr; - separator = true; - continue; - case ':': - if (separator || keys[pos] == nullptr) - return JSON_UNEXPECTED_CHARACTER; - separator = true; - continue; - case ',': - if (separator || keys[pos] != nullptr) - return JSON_UNEXPECTED_CHARACTER; - separator = true; - continue; - case '\0': - continue; - default: - return JSON_UNEXPECTED_CHARACTER; - } - - separator = false; - - if (pos == -1) { - *endptr = s; - *value = o; - return JSON_OK; - } - - if (tags[pos] == JSON_OBJECT) { - if (!keys[pos]) { - if (o.getTag() != JSON_STRING) - return JSON_UNQUOTED_KEY; - keys[pos] = o.toString(); - continue; - } - if ((node = (JsonNode *) allocator.allocate(sizeof(JsonNode))) == nullptr) - return JSON_ALLOCATION_FAILURE; - tails[pos] = insertAfter(tails[pos], node); - tails[pos]->key = keys[pos]; - keys[pos] = nullptr; - } else { - if ((node = (JsonNode *) allocator.allocate(sizeof(JsonNode) - sizeof(char *))) == nullptr) - return JSON_ALLOCATION_FAILURE; - tails[pos] = insertAfter(tails[pos], node); - } - tails[pos]->value = o; - } - return JSON_BREAKING_BAD; -} diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/common/gason.h b/example-apps/PDD/pcb-defect-detection/data/lib_coco/common/gason.h deleted file mode 100755 index 2e728d9..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/common/gason.h +++ /dev/null @@ -1,136 +0,0 @@ -// https://github.com/vivkin/gason - pulled January 10, 2016 -#pragma once - -#include -#include -#include - -enum JsonTag { - JSON_NUMBER = 0, - JSON_STRING, - JSON_ARRAY, - JSON_OBJECT, - JSON_TRUE, - JSON_FALSE, - JSON_NULL = 0xF -}; - -struct JsonNode; - -#define JSON_VALUE_PAYLOAD_MASK 0x00007FFFFFFFFFFFULL -#define JSON_VALUE_NAN_MASK 0x7FF8000000000000ULL -#define JSON_VALUE_TAG_MASK 0xF -#define JSON_VALUE_TAG_SHIFT 47 - -union JsonValue { - uint64_t ival; - double fval; - - JsonValue(double x) - : fval(x) { - } - JsonValue(JsonTag tag = JSON_NULL, void *payload = nullptr) { - assert((uintptr_t)payload <= JSON_VALUE_PAYLOAD_MASK); - ival = JSON_VALUE_NAN_MASK | ((uint64_t)tag << JSON_VALUE_TAG_SHIFT) | (uintptr_t)payload; - } - bool isDouble() const { - return (int64_t)ival <= (int64_t)JSON_VALUE_NAN_MASK; - } - JsonTag getTag() const { - return isDouble() ? JSON_NUMBER : JsonTag((ival >> JSON_VALUE_TAG_SHIFT) & JSON_VALUE_TAG_MASK); - } - uint64_t getPayload() const { - assert(!isDouble()); - return ival & JSON_VALUE_PAYLOAD_MASK; - } - double toNumber() const { - assert(getTag() == JSON_NUMBER); - return fval; - } - char *toString() const { - assert(getTag() == JSON_STRING); - return (char *)getPayload(); - } - JsonNode *toNode() const { - assert(getTag() == JSON_ARRAY || getTag() == JSON_OBJECT); - return (JsonNode *)getPayload(); - } -}; - -struct JsonNode { - JsonValue value; - JsonNode *next; - char *key; -}; - -struct JsonIterator { - JsonNode *p; - - void operator++() { - p = p->next; - } - bool operator!=(const JsonIterator &x) const { - return p != x.p; - } - JsonNode *operator*() const { - return p; - } - JsonNode *operator->() const { - return p; - } -}; - -inline JsonIterator begin(JsonValue o) { - return JsonIterator{o.toNode()}; -} -inline JsonIterator end(JsonValue) { - return JsonIterator{nullptr}; -} - -#define JSON_ERRNO_MAP(XX) \ - XX(OK, "ok") \ - XX(BAD_NUMBER, "bad number") \ - XX(BAD_STRING, "bad string") \ - XX(BAD_IDENTIFIER, "bad identifier") \ - XX(STACK_OVERFLOW, "stack overflow") \ - XX(STACK_UNDERFLOW, "stack underflow") \ - XX(MISMATCH_BRACKET, "mismatch bracket") \ - XX(UNEXPECTED_CHARACTER, "unexpected character") \ - XX(UNQUOTED_KEY, "unquoted key") \ - XX(BREAKING_BAD, "breaking bad") \ - XX(ALLOCATION_FAILURE, "allocation failure") - -enum JsonErrno { -#define XX(no, str) JSON_##no, - JSON_ERRNO_MAP(XX) -#undef XX -}; - -const char *jsonStrError(int err); - -class JsonAllocator { - struct Zone { - Zone *next; - size_t used; - } *head = nullptr; - -public: - JsonAllocator() = default; - JsonAllocator(const JsonAllocator &) = delete; - JsonAllocator &operator=(const JsonAllocator &) = delete; - JsonAllocator(JsonAllocator &&x) : head(x.head) { - x.head = nullptr; - } - JsonAllocator &operator=(JsonAllocator &&x) { - head = x.head; - x.head = nullptr; - return *this; - } - ~JsonAllocator() { - deallocate(); - } - void *allocate(size_t size); - void deallocate(); -}; - -int jsonParse(char *str, char **endptr, JsonValue *value, JsonAllocator &allocator); diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/common/maskApi.c b/example-apps/PDD/pcb-defect-detection/data/lib_coco/common/maskApi.c deleted file mode 100755 index 85e3979..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/common/maskApi.c +++ /dev/null @@ -1,230 +0,0 @@ -/************************************************************************** -* Microsoft COCO Toolbox. version 2.0 -* Data, paper, and tutorials available at: http://mscoco.org/ -* Code written by Piotr Dollar and Tsung-Yi Lin, 2015. -* Licensed under the Simplified BSD License [see coco/license.txt] -**************************************************************************/ -#include "maskApi.h" -#include -#include - -uint umin( uint a, uint b ) { return (ab) ? a : b; } - -void rleInit( RLE *R, siz h, siz w, siz m, uint *cnts ) { - R->h=h; R->w=w; R->m=m; R->cnts=(m==0)?0:malloc(sizeof(uint)*m); - siz j; if(cnts) for(j=0; jcnts[j]=cnts[j]; -} - -void rleFree( RLE *R ) { - free(R->cnts); R->cnts=0; -} - -void rlesInit( RLE **R, siz n ) { - siz i; *R = (RLE*) malloc(sizeof(RLE)*n); - for(i=0; i0 ) { - c=umin(ca,cb); cc+=c; ct=0; - ca-=c; if(!ca && a0) { - crowd=iscrowd!=NULL && iscrowd[g]; - if(dt[d].h!=gt[g].h || dt[d].w!=gt[g].w) { o[g*m+d]=-1; continue; } - siz ka, kb, a, b; uint c, ca, cb, ct, i, u; int va, vb; - ca=dt[d].cnts[0]; ka=dt[d].m; va=vb=0; - cb=gt[g].cnts[0]; kb=gt[g].m; a=b=1; i=u=0; ct=1; - while( ct>0 ) { - c=umin(ca,cb); if(va||vb) { u+=c; if(va&&vb) i+=c; } ct=0; - ca-=c; if(!ca && athr) keep[j]=0; - } - } -} - -void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o ) { - double h, w, i, u, ga, da; siz g, d; int crowd; - for( g=0; gthr) keep[j]=0; - } - } -} - -void rleToBbox( const RLE *R, BB bb, siz n ) { - siz i; for( i=0; id?1:c=dy && xs>xe) || (dxye); - if(flip) { t=xs; xs=xe; xe=t; t=ys; ys=ye; ye=t; } - s = dx>=dy ? (double)(ye-ys)/dx : (double)(xe-xs)/dy; - if(dx>=dy) for( d=0; d<=dx; d++ ) { - t=flip?dx-d:d; u[m]=t+xs; v[m]=(int)(ys+s*t+.5); m++; - } else for( d=0; d<=dy; d++ ) { - t=flip?dy-d:d; v[m]=t+ys; u[m]=(int)(xs+s*t+.5); m++; - } - } - /* get points along y-boundary and downsample */ - free(x); free(y); k=m; m=0; double xd, yd; - x=malloc(sizeof(int)*k); y=malloc(sizeof(int)*k); - for( j=1; jw-1 ) continue; - yd=(double)(v[j]h) yd=h; yd=ceil(yd); - x[m]=(int) xd; y[m]=(int) yd; m++; - } - /* compute rle encoding given y-boundary points */ - k=m; a=malloc(sizeof(uint)*(k+1)); - for( j=0; j0) b[m++]=a[j++]; else { - j++; if(jm, p=0; long x; int more; - char *s=malloc(sizeof(char)*m*6); - for( i=0; icnts[i]; if(i>2) x-=(long) R->cnts[i-2]; more=1; - while( more ) { - char c=x & 0x1f; x >>= 5; more=(c & 0x10) ? x!=-1 : x!=0; - if(more) c |= 0x20; c+=48; s[p++]=c; - } - } - s[p]=0; return s; -} - -void rleFrString( RLE *R, char *s, siz h, siz w ) { - siz m=0, p=0, k; long x; int more; uint *cnts; - while( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0; - while( s[p] ) { - x=0; k=0; more=1; - while( more ) { - char c=s[p]-48; x |= (c & 0x1f) << 5*k; - more = c & 0x20; p++; k++; - if(!more && (c & 0x10)) x |= -1 << 5*k; - } - if(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x; - } - rleInit(R,h,w,m,cnts); free(cnts); -} diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/common/maskApi.h b/example-apps/PDD/pcb-defect-detection/data/lib_coco/common/maskApi.h deleted file mode 100755 index ebc7892..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/common/maskApi.h +++ /dev/null @@ -1,60 +0,0 @@ -/************************************************************************** -* Microsoft COCO Toolbox. version 2.0 -* Data, paper, and tutorials available at: http://mscoco.org/ -* Code written by Piotr Dollar and Tsung-Yi Lin, 2015. -* Licensed under the Simplified BSD License [see coco/license.txt] -**************************************************************************/ -#pragma once - -typedef unsigned int uint; -typedef unsigned long siz; -typedef unsigned char byte; -typedef double* BB; -typedef struct { siz h, w, m; uint *cnts; } RLE; - -/* Initialize/destroy RLE. */ -void rleInit( RLE *R, siz h, siz w, siz m, uint *cnts ); -void rleFree( RLE *R ); - -/* Initialize/destroy RLE array. */ -void rlesInit( RLE **R, siz n ); -void rlesFree( RLE **R, siz n ); - -/* Encode binary masks using RLE. */ -void rleEncode( RLE *R, const byte *mask, siz h, siz w, siz n ); - -/* Decode binary masks encoded via RLE. */ -void rleDecode( const RLE *R, byte *mask, siz n ); - -/* Compute union or intersection of encoded masks. */ -void rleMerge( const RLE *R, RLE *M, siz n, int intersect ); - -/* Compute area of encoded masks. */ -void rleArea( const RLE *R, siz n, uint *a ); - -/* Compute intersection over union between masks. */ -void rleIou( RLE *dt, RLE *gt, siz m, siz n, byte *iscrowd, double *o ); - -/* Compute non-maximum suppression between bounding masks */ -void rleNms( RLE *dt, siz n, uint *keep, double thr ); - -/* Compute intersection over union between bounding boxes. */ -void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o ); - -/* Compute non-maximum suppression between bounding boxes */ -void bbNms( BB dt, siz n, uint *keep, double thr ); - -/* Get bounding boxes surrounding encoded masks. */ -void rleToBbox( const RLE *R, BB bb, siz n ); - -/* Convert bounding boxes to encoded masks. */ -void rleFrBbox( RLE *R, const BB bb, siz h, siz w, siz n ); - -/* Convert polygon to encoded mask. */ -void rleFrPoly( RLE *R, const double *xy, siz k, siz h, siz w ); - -/* Get compressed string representation of encoded mask. */ -char* rleToString( const RLE *R ); - -/* Convert from compressed string representation of encoded mask. */ -void rleFrString( RLE *R, char *s, siz h, siz w ); diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/get_coco_next_batch.py b/example-apps/PDD/pcb-defect-detection/data/lib_coco/get_coco_next_batch.py deleted file mode 100755 index ec56b1a..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/lib_coco/get_coco_next_batch.py +++ /dev/null @@ -1,73 +0,0 @@ -# -*- coding: utf-8 -*- - -from __future__ import absolute_import, print_function, division - -import sys, os -# sys.path.insert(0, os.path.abspath('.')) -sys.path.insert(0, './PythonAPI/') -# sys.path.insert(0, os.path.abspath('data')) -for _ in sys.path: - print (_) -from PythonAPI.pycocotools.coco import COCO -import cv2 -import numpy as np -import os -from libs.label_name_dict import coco_dict - - -annotation_path = '/home/yjr/DataSet/COCO/2017/annotations/instances_train2017.json' -print ("load coco .... it will cost about 17s..") -coco = COCO(annotation_path) - -imgId_list = coco.getImgIds() -imgId_list = np.array(imgId_list) - -total_imgs = len(imgId_list) - -# print (NAME_LABEL_DICT) - - -def next_img(step): - - if step % total_imgs == 0: - np.random.shuffle(imgId_list) - imgid = imgId_list[step % total_imgs] - - imgname = coco.loadImgs(ids=[imgid])[0]['file_name'] - # print (type(imgname), imgname) - img = cv2.imread(os.path.join("/home/yjr/DataSet/COCO/2017/train2017", imgname)) - - annotation = coco.imgToAnns[imgid] - gtbox_and_label_list = [] - for ann in annotation: - box = ann['bbox'] - - box = [box[0], box[1], box[0]+box[2], box[1]+box[3]] # [xmin, ymin, xmax, ymax] - cat_id = ann['category_id'] - cat_name = coco_dict.originID_classes[cat_id] #ID_NAME_DICT[cat_id] - label = coco_dict.NAME_LABEL_MAP[cat_name] - gtbox_and_label_list.append(box + [label]) - gtbox_and_label_list = np.array(gtbox_and_label_list, dtype=np.int32) - # print (img.shape, gtbox_and_label_list.shape) - if gtbox_and_label_list.shape[0] == 0: - return next_img(step+1) - else: - return imgid, img[:, :, ::-1], gtbox_and_label_list - - -if __name__ == '__main__': - - imgid, img, gtbox = next_img(3234) - - print("::") - from libs.box_utils.draw_box_in_img import draw_boxes_with_label_and_scores - - img = draw_boxes_with_label_and_scores(img_array=img, boxes=gtbox[:, :-1], labels=gtbox[:, -1], - scores=np.ones(shape=(len(gtbox), ))) - print ("_----") - - - cv2.imshow("test", img) - cv2.waitKey(0) - - diff --git a/example-apps/PDD/pcb-defect-detection/data/pretrained_weights/README.md b/example-apps/PDD/pcb-defect-detection/data/pretrained_weights/README.md deleted file mode 100755 index f1ae76b..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/pretrained_weights/README.md +++ /dev/null @@ -1 +0,0 @@ -Please download [resnet50_v1](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)、[resnet101_v1](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) pre-trained models on Imagenet, put it to data/pretrained_weights. \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/data/pretrained_weights/mobilenet/README.md b/example-apps/PDD/pcb-defect-detection/data/pretrained_weights/mobilenet/README.md deleted file mode 100755 index 1158885..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/pretrained_weights/mobilenet/README.md +++ /dev/null @@ -1 +0,0 @@ -Please download [mobilenet_v2](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz) pre-trained model on Imagenet, put it to data/pretrained_weights/mobilenet. \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/data/tfrecord/pcb_train.tfrecord_old b/example-apps/PDD/pcb-defect-detection/data/tfrecord/pcb_train.tfrecord_old deleted file mode 120000 index e981168..0000000 --- a/example-apps/PDD/pcb-defect-detection/data/tfrecord/pcb_train.tfrecord_old +++ /dev/null @@ -1 +0,0 @@ -/home/gq123/dailinhui/R-FCN_Tensorflow-dlh/data/tfrecord/pascal_train.tfrecord \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/help_utils/__init__.py b/example-apps/PDD/pcb-defect-detection/help_utils/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/help_utils/tools.py b/example-apps/PDD/pcb-defect-detection/help_utils/tools.py deleted file mode 100755 index 8b696c8..0000000 --- a/example-apps/PDD/pcb-defect-detection/help_utils/tools.py +++ /dev/null @@ -1,19 +0,0 @@ -# -*- coding: utf-8 -*- -from __future__ import division, print_function, absolute_import -import math -import sys -import os - - -def view_bar(message, num, total): - rate = num / total - rate_num = int(rate * 40) - rate_nums = math.ceil(rate * 100) - r = '\r%s:[%s%s]%d%%\t%d/%d' % (message, ">" * rate_num, " " * (40 - rate_num), rate_nums, num, total,) - sys.stdout.write(r) - sys.stdout.flush() - - -def mkdir(path): - if not os.path.exists(path): - os.makedirs(path) \ No newline at end of file diff --git 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a/example-apps/PDD/pcb-defect-detection/libs/__init__.py b/example-apps/PDD/pcb-defect-detection/libs/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/libs/__init__.pyc b/example-apps/PDD/pcb-defect-detection/libs/__init__.pyc deleted file mode 100755 index 83a347d..0000000 Binary files a/example-apps/PDD/pcb-defect-detection/libs/__init__.pyc and /dev/null differ diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/__init__.py b/example-apps/PDD/pcb-defect-detection/libs/box_utils/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/anchor_utils.py b/example-apps/PDD/pcb-defect-detection/libs/box_utils/anchor_utils.py deleted file mode 100755 index d01c54b..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/anchor_utils.py +++ /dev/null @@ -1,92 +0,0 @@ -# -*- coding: utf-8 -*- -from __future__ import absolute_import, print_function, division - -import tensorflow as tf -from libs.configs import cfgs - - -def make_anchors(base_anchor_size, anchor_scales, anchor_ratios, - featuremap_height, featuremap_width, - stride, name='make_anchors'): - ''' - :param base_anchor_size:256 - :param anchor_scales: - :param anchor_ratios: - :param featuremap_height: - :param featuremap_width: - :param stride: - :return: - ''' - with tf.variable_scope(name): - base_anchor = tf.constant([0, 0, base_anchor_size, base_anchor_size], tf.float32) # [x_center, y_center, w, h] - - ws, hs = enum_ratios(enum_scales(base_anchor, anchor_scales), - anchor_ratios) # per locations ws and hs - - # featuremap_height = tf.Print(featuremap_height, - # [featuremap_height, featuremap_width], summarize=10, - # message=name+"_SHAPE***") - - x_centers = tf.range(featuremap_width, dtype=tf.float32) * stride - y_centers = tf.range(featuremap_height, dtype=tf.float32) * stride - - if cfgs.USE_CENTER_OFFSET: - x_centers = x_centers + stride/2. - y_centers = y_centers + stride/2. - - x_centers, y_centers = tf.meshgrid(x_centers, y_centers) - - ws, x_centers = tf.meshgrid(ws, x_centers) - hs, y_centers = tf.meshgrid(hs, y_centers) - - anchor_centers = tf.stack([x_centers, y_centers], 2) - anchor_centers = tf.reshape(anchor_centers, [-1, 2]) - - box_sizes = tf.stack([ws, hs], axis=2) - box_sizes = tf.reshape(box_sizes, [-1, 2]) - # anchors = tf.concat([anchor_centers, box_sizes], axis=1) - anchors = tf.concat([anchor_centers - 0.5*box_sizes, - anchor_centers + 0.5*box_sizes], axis=1) - return anchors - - -def enum_scales(base_anchor, anchor_scales): - - anchor_scales = base_anchor * tf.constant(anchor_scales, dtype=tf.float32, shape=(len(anchor_scales), 1)) - - return anchor_scales - - -def enum_ratios(anchors, anchor_ratios): - ''' - ratio = h /w - :param anchors: - :param anchor_ratios: - :return: - ''' - ws = anchors[:, 2] # for base anchor: w == h - hs = anchors[:, 3] - sqrt_ratios = tf.sqrt(tf.constant(anchor_ratios)) - - ws = tf.reshape(ws / sqrt_ratios[:, tf.newaxis], [-1, 1]) - hs = tf.reshape(hs * sqrt_ratios[:, tf.newaxis], [-1, 1]) - - return hs, ws - - -if __name__ == '__main__': - import os - os.environ["CUDA_VISIBLE_DEVICES"] = '0' - base_anchor_size = 256 - anchor_scales = [1.0] - anchor_ratios = [0.5, 2.0, 1.0] - anchors = make_anchors(base_anchor_size=base_anchor_size, anchor_ratios=anchor_ratios, - anchor_scales=anchor_scales, - featuremap_width=32, - featuremap_height=63, - stride=16) - init = tf.global_variables_initializer() - with tf.Session() as sess: - sess.run(init) - anchor_result = sess.run(anchors) - print (anchor_result.shape) diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/boxes_utils.py b/example-apps/PDD/pcb-defect-detection/libs/box_utils/boxes_utils.py deleted file mode 100755 index 8f4b5a3..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/boxes_utils.py +++ /dev/null @@ -1,115 +0,0 @@ -# -*- coding: utf-8 -*- - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -def ious_calu(boxes_1, boxes_2): - ''' - - :param boxes_1: [N, 4] [xmin, ymin, xmax, ymax] - :param boxes_2: [M, 4] [xmin, ymin. xmax, ymax] - :return: - ''' - boxes_1 = tf.cast(boxes_1, tf.float32) - boxes_2 = tf.cast(boxes_2, tf.float32) - xmin_1, ymin_1, xmax_1, ymax_1 = tf.split(boxes_1, 4, axis=1) # xmin_1 shape is [N, 1].. - xmin_2, ymin_2, xmax_2, ymax_2 = tf.unstack(boxes_2, axis=1) # xmin_2 shape is [M, ].. - - max_xmin = tf.maximum(xmin_1, xmin_2) - min_xmax = tf.minimum(xmax_1, xmax_2) - - max_ymin = tf.maximum(ymin_1, ymin_2) - min_ymax = tf.minimum(ymax_1, ymax_2) - - overlap_h = tf.maximum(0., min_ymax - max_ymin) # avoid h < 0 - overlap_w = tf.maximum(0., min_xmax - max_xmin) - - overlaps = overlap_h * overlap_w - - area_1 = (xmax_1 - xmin_1) * (ymax_1 - ymin_1) # [N, 1] - area_2 = (xmax_2 - xmin_2) * (ymax_2 - ymin_2) # [M, ] - - ious = overlaps / (area_1 + area_2 - overlaps) - - return ious - - -def clip_boxes_to_img_boundaries(decode_boxes, img_shape): - ''' - - :param decode_boxes: - :return: decode boxes, and already clip to boundaries - ''' - - with tf.name_scope('clip_boxes_to_img_boundaries'): - - # xmin, ymin, xmax, ymax = tf.unstack(decode_boxes, axis=1) - xmin = decode_boxes[:, 0] - ymin = decode_boxes[:, 1] - xmax = decode_boxes[:, 2] - ymax = decode_boxes[:, 3] - img_h, img_w = img_shape[1], img_shape[2] - - img_h, img_w = tf.cast(img_h, tf.float32), tf.cast(img_w, tf.float32) - - # xmin = tf.maximum(tf.minimum(xmin, img_w-1.), 0.) - # ymin = tf.maximum(tf.minimum(ymin, img_h-1.), 0.) - - # xmax = tf.maximum(tf.minimum(xmax, img_w-1.), 0.) - # ymax = tf.maximum(tf.minimum(ymax, img_h-1.), 0.) - xmin = tf.maximum(tf.minimum(xmin, img_w), 0.) - ymin = tf.maximum(tf.minimum(ymin, img_h), 0.) - - xmax = tf.maximum(tf.minimum(xmax, img_w), 0.) - ymax = tf.maximum(tf.minimum(ymax, img_h), 0.) - - return tf.transpose(tf.stack([xmin, ymin, xmax, ymax])) - - -def filter_outside_boxes(boxes, img_h, img_w): - ''' - :param anchors:boxes with format [xmin, ymin, xmax, ymax] - :param img_h: height of image - :param img_w: width of image - :return: indices of anchors that inside the image boundary - ''' - - with tf.name_scope('filter_outside_boxes'): - xmin, ymin, xmax, ymax = tf.unstack(boxes, axis=1) - - xmin_index = tf.greater_equal(xmin, 0) - ymin_index = tf.greater_equal(ymin, 0) - xmax_index = tf.less_equal(xmax, tf.cast(img_w, tf.float32)) - ymax_index = tf.less_equal(ymax, tf.cast(img_h, tf.float32)) - - indices = tf.transpose(tf.stack([xmin_index, ymin_index, xmax_index, ymax_index])) - indices = tf.cast(indices, dtype=tf.int32) - indices = tf.reduce_sum(indices, axis=1) - indices = tf.where(tf.equal(indices, 4)) - # indices = tf.equal(indices, 4) - return tf.reshape(indices, [-1]) - - -def padd_boxes_with_zeros(boxes, scores, max_num_of_boxes): - - ''' - num of boxes less than max num of boxes, so it need to pad with zeros[0, 0, 0, 0] - :param boxes: - :param scores: [-1] - :param max_num_of_boxes: - :return: - ''' - - pad_num = tf.cast(max_num_of_boxes, tf.int32) - tf.shape(boxes)[0] - - zero_boxes = tf.zeros(shape=[pad_num, 4], dtype=boxes.dtype) - zero_scores = tf.zeros(shape=[pad_num], dtype=scores.dtype) - - final_boxes = tf.concat([boxes, zero_boxes], axis=0) - - final_scores = tf.concat([scores, zero_scores], axis=0) - - return final_boxes, final_scores \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/boxes_utils_backup.py b/example-apps/PDD/pcb-defect-detection/libs/box_utils/boxes_utils_backup.py deleted file mode 100755 index e9b1885..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/boxes_utils_backup.py +++ /dev/null @@ -1,110 +0,0 @@ -# -*- coding: utf-8 -*- - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -def ious_calu(boxes_1, boxes_2): - ''' - - :param boxes_1: [N, 4] [xmin, ymin, xmax, ymax] - :param boxes_2: [M, 4] [xmin, ymin. xmax, ymax] - :return: - ''' - boxes_1 = tf.cast(boxes_1, tf.float32) - boxes_2 = tf.cast(boxes_2, tf.float32) - xmin_1, ymin_1, xmax_1, ymax_1 = tf.split(boxes_1, 4, axis=1) # xmin_1 shape is [N, 1].. - xmin_2, ymin_2, xmax_2, ymax_2 = tf.unstack(boxes_2, axis=1) # xmin_2 shape is [M, ].. - - max_xmin = tf.maximum(xmin_1, xmin_2) - min_xmax = tf.minimum(xmax_1, xmax_2) - - max_ymin = tf.maximum(ymin_1, ymin_2) - min_ymax = tf.minimum(ymax_1, ymax_2) - - overlap_h = tf.maximum(0., min_ymax - max_ymin) # avoid h < 0 - overlap_w = tf.maximum(0., min_xmax - max_xmin) - - overlaps = overlap_h * overlap_w - - area_1 = (xmax_1 - xmin_1) * (ymax_1 - ymin_1) # [N, 1] - area_2 = (xmax_2 - xmin_2) * (ymax_2 - ymin_2) # [M, ] - - ious = overlaps / (area_1 + area_2 - overlaps) - - return ious - - -def clip_boxes_to_img_boundaries(decode_boxes, img_shape): - ''' - - :param decode_boxes: - :return: decode boxes, and already clip to boundaries - ''' - - with tf.name_scope('clip_boxes_to_img_boundaries'): - - # xmin, ymin, xmax, ymax = tf.unstack(decode_boxes, axis=1) - xmin = decode_boxes[:, 0] - ymin = decode_boxes[:, 1] - xmax = decode_boxes[:, 2] - ymax = decode_boxes[:, 3] - img_h, img_w = img_shape[1], img_shape[2] - - img_h, img_w = tf.cast(img_h, tf.float32), tf.cast(img_w, tf.float32) - - xmin = tf.maximum(tf.minimum(xmin, img_w-1.), 0.) - ymin = tf.maximum(tf.minimum(ymin, img_h-1.), 0.) - - xmax = tf.maximum(tf.minimum(xmax, img_w-1.), 0.) - ymax = tf.maximum(tf.minimum(ymax, img_h-1.), 0.) - - return tf.transpose(tf.stack([xmin, ymin, xmax, ymax])) - - -def filter_outside_boxes(boxes, img_h, img_w): - ''' - :param anchors:boxes with format [xmin, ymin, xmax, ymax] - :param img_h: height of image - :param img_w: width of image - :return: indices of anchors that inside the image boundary - ''' - - with tf.name_scope('filter_outside_boxes'): - xmin, ymin, xmax, ymax = tf.unstack(boxes, axis=1) - - xmin_index = tf.greater_equal(xmin, 0) - ymin_index = tf.greater_equal(ymin, 0) - xmax_index = tf.less_equal(xmax, tf.cast(img_w, tf.float32)) - ymax_index = tf.less_equal(ymax, tf.cast(img_h, tf.float32)) - - indices = tf.transpose(tf.stack([xmin_index, ymin_index, xmax_index, ymax_index])) - indices = tf.cast(indices, dtype=tf.int32) - indices = tf.reduce_sum(indices, axis=1) - indices = tf.where(tf.equal(indices, 4)) - # indices = tf.equal(indices, 4) - return tf.reshape(indices, [-1]) - - -def padd_boxes_with_zeros(boxes, scores, max_num_of_boxes): - - ''' - num of boxes less than max num of boxes, so it need to pad with zeros[0, 0, 0, 0] - :param boxes: - :param scores: [-1] - :param max_num_of_boxes: - :return: - ''' - - pad_num = tf.cast(max_num_of_boxes, tf.int32) - tf.shape(boxes)[0] - - zero_boxes = tf.zeros(shape=[pad_num, 4], dtype=boxes.dtype) - zero_scores = tf.zeros(shape=[pad_num], dtype=scores.dtype) - - final_boxes = tf.concat([boxes, zero_boxes], axis=0) - - final_scores = tf.concat([scores, zero_scores], axis=0) - - return final_boxes, final_scores \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/Makefile b/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/Makefile deleted file mode 100755 index 1e9e686..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/Makefile +++ /dev/null @@ -1,6 +0,0 @@ -all: - python setup.py build_ext --inplace - rm -rf build -clean: - rm -rf */*.pyc - rm -rf */*.so diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/__init__.py b/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/bbox.c b/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/bbox.c deleted file mode 100755 index 747f59d..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/bbox.c +++ /dev/null @@ -1,11128 +0,0 @@ -/* Generated by Cython 0.25.2 */ - -#define PY_SSIZE_T_CLEAN -#include "Python.h" -#ifndef Py_PYTHON_H - #error Python headers needed to compile C extensions, please install development version of Python. -#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000) - #error Cython requires Python 2.6+ or Python 3.2+. -#else -#define CYTHON_ABI "0_25_2" -#include -#ifndef offsetof - #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) -#endif -#if !defined(WIN32) && !defined(MS_WINDOWS) - #ifndef __stdcall - #define __stdcall - #endif - #ifndef __cdecl - #define __cdecl - #endif - #ifndef __fastcall - #define __fastcall - #endif -#endif -#ifndef DL_IMPORT - #define DL_IMPORT(t) t -#endif -#ifndef 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const char *s; const Py_ssize_t n; const char* encoding; - const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; - -#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 -#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 -#define __PYX_DEFAULT_STRING_ENCODING "" -#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString -#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#define __Pyx_uchar_cast(c) ((unsigned char)c) -#define __Pyx_long_cast(x) ((long)x) -#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ - (sizeof(type) < sizeof(Py_ssize_t)) ||\ - (sizeof(type) > sizeof(Py_ssize_t) &&\ - likely(v < (type)PY_SSIZE_T_MAX ||\ - v == (type)PY_SSIZE_T_MAX) &&\ - (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ - v == (type)PY_SSIZE_T_MIN))) ||\ - (sizeof(type) == sizeof(Py_ssize_t) &&\ - (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ - v == (type)PY_SSIZE_T_MAX))) ) -#if defined (__cplusplus) && __cplusplus >= 201103L - #include - #define __Pyx_sst_abs(value) std::abs(value) -#elif SIZEOF_INT >= SIZEOF_SIZE_T - #define __Pyx_sst_abs(value) abs(value) -#elif SIZEOF_LONG >= SIZEOF_SIZE_T - #define __Pyx_sst_abs(value) labs(value) -#elif defined (_MSC_VER) && defined (_M_X64) - #define __Pyx_sst_abs(value) _abs64(value) -#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define __Pyx_sst_abs(value) llabs(value) -#elif defined (__GNUC__) - #define __Pyx_sst_abs(value) __builtin_llabs(value) -#else - #define __Pyx_sst_abs(value) ((value<0) ? -value : value) -#endif -static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); -static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); -#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) -#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) -#define __Pyx_PyBytes_FromString PyBytes_FromString -#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); -#if PY_MAJOR_VERSION < 3 - #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#else - #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize -#endif -#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) -#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) -#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) -#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) -#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) -#if PY_MAJOR_VERSION < 3 -static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) -{ - const Py_UNICODE *u_end = u; - while (*u_end++) ; - return (size_t)(u_end - u - 1); -} -#else -#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen -#endif -#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) -#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode -#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode -#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) -#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) -#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); -#if CYTHON_ASSUME_SAFE_MACROS -#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) -#else -#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) -#endif -#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) -#if PY_MAJOR_VERSION >= 3 -#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) -#else -#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) -#endif -#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII -static int __Pyx_sys_getdefaultencoding_not_ascii; -static int __Pyx_init_sys_getdefaultencoding_params(void) { - PyObject* sys; - PyObject* default_encoding = NULL; - PyObject* ascii_chars_u = NULL; - PyObject* ascii_chars_b = NULL; - const char* default_encoding_c; - sys = PyImport_ImportModule("sys"); - if (!sys) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); - Py_DECREF(sys); - if (!default_encoding) goto bad; - default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - if (strcmp(default_encoding_c, "ascii") == 0) { - __Pyx_sys_getdefaultencoding_not_ascii = 0; - } else { - char ascii_chars[128]; - int c; - for (c = 0; c < 128; c++) { - ascii_chars[c] = c; - } - __Pyx_sys_getdefaultencoding_not_ascii = 1; - ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); - if (!ascii_chars_u) goto bad; - ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); - if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { - PyErr_Format( - PyExc_ValueError, - "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", - default_encoding_c); - goto bad; - } - Py_DECREF(ascii_chars_u); - Py_DECREF(ascii_chars_b); - } - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - Py_XDECREF(ascii_chars_u); 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0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : - #endif - PyUnicode_Compare(**name, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - int cmp = (**argname == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : - #endif - PyUnicode_Compare(**argname, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) goto arg_passed_twice; - argname++; - } - } - } else - goto invalid_keyword_type; - if (kwds2) { - if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; - } else { - goto invalid_keyword; - } - } - return 0; -arg_passed_twice: - __Pyx_RaiseDoubleKeywordsError(function_name, key); - goto bad; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%.200s() keywords must be strings", function_name); - goto bad; -invalid_keyword: - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION < 3 - "%.200s() got an unexpected keyword argument '%.200s'", - function_name, PyString_AsString(key)); - #else - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif -bad: - return -1; -} - -/* ArgTypeTest */ -static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { - PyErr_Format(PyExc_TypeError, - "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", - name, type->tp_name, Py_TYPE(obj)->tp_name); -} -static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, - const char *name, int exact) -{ - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (none_allowed && obj == Py_None) return 1; - else if (exact) { - if (likely(Py_TYPE(obj) == type)) return 1; - #if PY_MAJOR_VERSION == 2 - else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; - #endif - } - else { - if (likely(PyObject_TypeCheck(obj, type))) return 1; - } - __Pyx_RaiseArgumentTypeInvalid(name, obj, type); - return 0; -} - -/* BufferFormatCheck */ -static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { - unsigned int n = 1; - return *(unsigned char*)(&n) != 0; -} -static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, - __Pyx_BufFmt_StackElem* stack, - __Pyx_TypeInfo* type) { - stack[0].field = &ctx->root; - stack[0].parent_offset = 0; - ctx->root.type = type; - ctx->root.name = "buffer dtype"; - ctx->root.offset = 0; - ctx->head = stack; - ctx->head->field = &ctx->root; - ctx->fmt_offset = 0; - ctx->head->parent_offset = 0; - ctx->new_packmode = '@'; - ctx->enc_packmode = '@'; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->is_complex = 0; - ctx->is_valid_array = 0; - ctx->struct_alignment = 0; - while (type->typegroup == 'S') { - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = 0; - type = type->fields->type; - } -} -static int __Pyx_BufFmt_ParseNumber(const char** ts) { - int count; - const char* t = *ts; - if (*t < '0' || *t > '9') { - return -1; - } else { - count = *t++ - '0'; - while (*t >= '0' && *t < '9') { - count *= 10; - count += *t++ - '0'; - } - } - *ts = t; - return count; -} -static int __Pyx_BufFmt_ExpectNumber(const char **ts) { - int number = __Pyx_BufFmt_ParseNumber(ts); - if (number == -1) - PyErr_Format(PyExc_ValueError,\ - "Does not understand character buffer dtype format string ('%c')", **ts); - return number; -} -static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { - PyErr_Format(PyExc_ValueError, - "Unexpected format string character: '%c'", ch); -} -static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { - switch (ch) { - case 'c': return "'char'"; - case 'b': return "'signed char'"; - case 'B': return "'unsigned char'"; - case 'h': return "'short'"; - case 'H': return "'unsigned short'"; - case 'i': return "'int'"; - case 'I': return "'unsigned int'"; - case 'l': return "'long'"; - case 'L': return "'unsigned long'"; - case 'q': return "'long long'"; - case 'Q': return "'unsigned long long'"; - case 'f': return (is_complex ? 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"'complex long double'" : "'long double'"); - case 'T': return "a struct"; - case 'O': return "Python object"; - case 'P': return "a pointer"; - case 's': case 'p': return "a string"; - case 0: return "end"; - default: return "unparseable format string"; - } -} -static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return 2; - case 'i': case 'I': case 'l': case 'L': return 4; - case 'q': case 'Q': return 8; - case 'f': return (is_complex ? 8 : 4); - case 'd': return (is_complex ? 16 : 8); - case 'g': { - PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); - return 0; - } - case 'O': case 'P': return sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { - switch (ch) { - case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(short); - case 'i': case 'I': return sizeof(int); - case 'l': case 'L': return sizeof(long); - #ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(PY_LONG_LONG); - #endif - case 'f': return sizeof(float) * (is_complex ? 2 : 1); - case 'd': return sizeof(double) * (is_complex ? 2 : 1); - case 'g': return sizeof(long double) * (is_complex ? 2 : 1); - case 'O': case 'P': return sizeof(void*); - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -typedef struct { char c; short x; } __Pyx_st_short; -typedef struct { char c; int x; } __Pyx_st_int; -typedef struct { char c; long x; } __Pyx_st_long; -typedef struct { char c; float x; } __Pyx_st_float; -typedef struct { char c; double x; } __Pyx_st_double; -typedef struct { char c; long double x; } __Pyx_st_longdouble; -typedef struct { char c; void *x; } __Pyx_st_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_st_float) - sizeof(float); - case 'd': return sizeof(__Pyx_st_double) - sizeof(double); - case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -/* These are for computing the padding at the end of the struct to align - on the first member of the struct. This will probably the same as above, - but we don't have any guarantees. - */ -typedef struct { short x; char c; } __Pyx_pad_short; -typedef struct { int x; char c; } __Pyx_pad_int; -typedef struct { long x; char c; } __Pyx_pad_long; -typedef struct { float x; char c; } __Pyx_pad_float; -typedef struct { double x; char c; } __Pyx_pad_double; -typedef struct { long double x; char c; } __Pyx_pad_longdouble; -typedef struct { void *x; char c; } __Pyx_pad_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); - case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); - case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { - switch (ch) { - case 'c': - return 'H'; - case 'b': case 'h': case 'i': - case 'l': case 'q': case 's': case 'p': - return 'I'; - case 'B': case 'H': case 'I': case 'L': case 'Q': - return 'U'; - case 'f': case 'd': case 'g': - return (is_complex ? 'C' : 'R'); - case 'O': - return 'O'; - case 'P': - return 'P'; - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { - if (ctx->head == NULL || ctx->head->field == &ctx->root) { - const char* expected; - const char* quote; - if (ctx->head == NULL) { - expected = "end"; - quote = ""; - } else { - expected = ctx->head->field->type->name; - quote = "'"; - } - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected %s%s%s but got %s", - quote, expected, quote, - __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); - } else { - __Pyx_StructField* field = ctx->head->field; - __Pyx_StructField* parent = (ctx->head - 1)->field; - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", - field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), - parent->type->name, field->name); - } -} -static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { - char group; - size_t size, offset, arraysize = 1; - if (ctx->enc_type == 0) return 0; - if (ctx->head->field->type->arraysize[0]) { - int i, ndim = 0; - if (ctx->enc_type == 's' || ctx->enc_type == 'p') { - ctx->is_valid_array = ctx->head->field->type->ndim == 1; - ndim = 1; - if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { - PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %zu", - ctx->head->field->type->arraysize[0], ctx->enc_count); - return -1; - } - } - if (!ctx->is_valid_array) { - PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", - ctx->head->field->type->ndim, ndim); - return -1; - } - for (i = 0; i < ctx->head->field->type->ndim; i++) { - arraysize *= ctx->head->field->type->arraysize[i]; - } - ctx->is_valid_array = 0; - ctx->enc_count = 1; - } - group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); - do { - __Pyx_StructField* field = ctx->head->field; - __Pyx_TypeInfo* type = field->type; - if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { - size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); - } else { - size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); - } - if (ctx->enc_packmode == '@') { - size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); - size_t align_mod_offset; - if (align_at == 0) return -1; - align_mod_offset = ctx->fmt_offset % align_at; - if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; - if (ctx->struct_alignment == 0) - ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, - ctx->is_complex); - } - if (type->size != size || type->typegroup != group) { - if (type->typegroup == 'C' && type->fields != NULL) { - size_t parent_offset = ctx->head->parent_offset + field->offset; - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = parent_offset; - continue; - } - if ((type->typegroup == 'H' || group == 'H') && type->size == size) { - } else { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - } - offset = ctx->head->parent_offset + field->offset; - if (ctx->fmt_offset != offset) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", - (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); - return -1; - } - ctx->fmt_offset += size; - if (arraysize) - ctx->fmt_offset += (arraysize - 1) * size; - --ctx->enc_count; - while (1) { - if (field == &ctx->root) { - ctx->head = NULL; - if (ctx->enc_count != 0) { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - break; - } - ctx->head->field = ++field; - if (field->type == NULL) { - --ctx->head; - field = ctx->head->field; - continue; - } else if (field->type->typegroup == 'S') { - size_t parent_offset = ctx->head->parent_offset + field->offset; - if (field->type->fields->type == NULL) continue; - field = field->type->fields; - ++ctx->head; - ctx->head->field = field; - ctx->head->parent_offset = parent_offset; - break; - } else { - break; - } - } - } while (ctx->enc_count); - ctx->enc_type = 0; - ctx->is_complex = 0; - return 0; -} -static CYTHON_INLINE PyObject * -__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) -{ - const char *ts = *tsp; - int i = 0, number; - int ndim = ctx->head->field->type->ndim; -; - ++ts; - if (ctx->new_count != 1) { - PyErr_SetString(PyExc_ValueError, - "Cannot handle repeated arrays in format string"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - while (*ts && *ts != ')') { - switch (*ts) { - case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; - default: break; - } - number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) - return PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %d", - ctx->head->field->type->arraysize[i], number); - if (*ts != ',' && *ts != ')') - return PyErr_Format(PyExc_ValueError, - "Expected a comma in format string, got '%c'", *ts); - if (*ts == ',') ts++; - i++; - } - if (i != ndim) - return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", - ctx->head->field->type->ndim, i); - if (!*ts) { - PyErr_SetString(PyExc_ValueError, - "Unexpected end of format string, expected ')'"); - return NULL; - } - ctx->is_valid_array = 1; - ctx->new_count = 1; - *tsp = ++ts; - return Py_None; -} -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { - int got_Z = 0; - while (1) { - switch(*ts) { - case 0: - if (ctx->enc_type != 0 && ctx->head == NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - if (ctx->head != NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - return ts; - case ' ': - case '\r': - case '\n': - ++ts; - break; - case '<': - if (!__Pyx_IsLittleEndian()) { - PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '>': - case '!': - if (__Pyx_IsLittleEndian()) { - PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '=': - case '@': - case '^': - ctx->new_packmode = *ts++; - break; - case 'T': - { - const char* ts_after_sub; - size_t i, struct_count = ctx->new_count; - size_t struct_alignment = ctx->struct_alignment; - ctx->new_count = 1; - ++ts; - if (*ts != '{') { - PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - ctx->enc_count = 0; - ctx->struct_alignment = 0; - ++ts; - ts_after_sub = ts; - for (i = 0; i != struct_count; ++i) { - ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); - if (!ts_after_sub) return NULL; - } - ts = ts_after_sub; - if (struct_alignment) ctx->struct_alignment = struct_alignment; - } - break; - case '}': - { - size_t alignment = ctx->struct_alignment; - ++ts; - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - if (alignment && ctx->fmt_offset % alignment) { - ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); - } - } - return ts; - case 'x': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->fmt_offset += ctx->new_count; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->enc_packmode = ctx->new_packmode; - ++ts; - break; - case 'Z': - got_Z = 1; - ++ts; - if (*ts != 'f' && *ts != 'd' && *ts != 'g') { - __Pyx_BufFmt_RaiseUnexpectedChar('Z'); - return NULL; - } - case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': - case 'l': case 'L': case 'q': case 'Q': - case 'f': case 'd': case 'g': - case 'O': case 'p': - if (ctx->enc_type == *ts && got_Z == ctx->is_complex && - ctx->enc_packmode == ctx->new_packmode) { - ctx->enc_count += ctx->new_count; - ctx->new_count = 1; - got_Z = 0; - ++ts; - break; - } - case 's': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_count = ctx->new_count; - ctx->enc_packmode = ctx->new_packmode; - ctx->enc_type = *ts; - ctx->is_complex = got_Z; - ++ts; - ctx->new_count = 1; - got_Z = 0; - break; - case ':': - ++ts; - while(*ts != ':') ++ts; - ++ts; - break; - case '(': - if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; - break; - default: - { - int number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - ctx->new_count = (size_t)number; - } - } - } -} -static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { - buf->buf = NULL; - buf->obj = NULL; - buf->strides = __Pyx_zeros; - buf->shape = __Pyx_zeros; - buf->suboffsets = __Pyx_minusones; -} -static CYTHON_INLINE int __Pyx_GetBufferAndValidate( - Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, - int nd, int cast, __Pyx_BufFmt_StackElem* stack) -{ - if (obj == Py_None || obj == NULL) { - __Pyx_ZeroBuffer(buf); - return 0; - } - buf->buf = NULL; - if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; - if (buf->ndim != nd) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - nd, buf->ndim); - goto fail; - } - if (!cast) { - __Pyx_BufFmt_Context ctx; - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; - } - if ((unsigned)buf->itemsize != dtype->size) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", - buf->itemsize, (buf->itemsize > 1) ? "s" : "", - dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); - goto fail; - } - if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; - return 0; -fail:; - __Pyx_ZeroBuffer(buf); - return -1; -} -static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { - if (info->buf == NULL) return; - if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; - __Pyx_ReleaseBuffer(info); -} - -/* GetModuleGlobalName */ - static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { - PyObject *result; -#if !CYTHON_AVOID_BORROWED_REFS - result = PyDict_GetItem(__pyx_d, name); - if (likely(result)) { - Py_INCREF(result); - } else { -#else - result = PyObject_GetItem(__pyx_d, name); - if (!result) { - PyErr_Clear(); -#endif - result = __Pyx_GetBuiltinName(name); - } - return result; -} - -/* PyObjectCall */ - #if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { - PyObject *result; - ternaryfunc call = func->ob_type->tp_call; - if (unlikely(!call)) - return PyObject_Call(func, arg, kw); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = (*call)(func, arg, kw); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* ExtTypeTest */ - static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(PyObject_TypeCheck(obj, type))) - return 1; - PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", - Py_TYPE(obj)->tp_name, type->tp_name); - return 0; -} - -/* BufferIndexError */ - static void __Pyx_RaiseBufferIndexError(int axis) { - PyErr_Format(PyExc_IndexError, - "Out of bounds on buffer access (axis %d)", axis); -} - -/* PyErrFetchRestore */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - tmp_type = tstate->curexc_type; - tmp_value = tstate->curexc_value; - tmp_tb = tstate->curexc_traceback; - tstate->curexc_type = type; - tstate->curexc_value = value; - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - *type = tstate->curexc_type; - *value = tstate->curexc_value; - *tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -} -#endif - -/* RaiseException */ - #if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, - CYTHON_UNUSED PyObject *cause) { - __Pyx_PyThreadState_declare - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - if (PyType_Check(type)) { -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - } - __Pyx_PyThreadState_assign - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - int is_subclass = PyObject_IsSubclass(instance_class, type); - if (!is_subclass) { - instance_class = NULL; - } else if (unlikely(is_subclass == -1)) { - goto bad; - } else { - type = instance_class; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } -#if PY_VERSION_HEX >= 0x03030000 - if (cause) { -#else - if (cause && cause != Py_None) { -#endif - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { -#if CYTHON_COMPILING_IN_PYPY - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); - Py_INCREF(tb); - PyErr_Restore(tmp_type, tmp_value, tb); - Py_XDECREF(tmp_tb); -#else - PyThreadState *tstate = PyThreadState_GET(); - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } -#endif - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -/* RaiseTooManyValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -/* RaiseNeedMoreValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", - index, (index == 1) ? "" : "s"); -} - -/* RaiseNoneIterError */ - static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { - PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); -} - -/* SaveResetException */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); -} -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -#endif - -/* PyErrExceptionMatches */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) { - PyObject *exc_type = tstate->curexc_type; - if (exc_type == err) return 1; - if (unlikely(!exc_type)) return 0; - return PyErr_GivenExceptionMatches(exc_type, err); -} -#endif - -/* GetException */ - #if CYTHON_FAST_THREAD_STATE -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { -#endif - PyObject *local_type, *local_value, *local_tb; -#if CYTHON_FAST_THREAD_STATE - PyObject *tmp_type, *tmp_value, *tmp_tb; - local_type = tstate->curexc_type; - local_value = tstate->curexc_value; - local_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(&local_type, &local_value, &local_tb); -#endif - PyErr_NormalizeException(&local_type, &local_value, &local_tb); -#if CYTHON_FAST_THREAD_STATE - if (unlikely(tstate->curexc_type)) -#else - if (unlikely(PyErr_Occurred())) -#endif - goto bad; - #if PY_MAJOR_VERSION >= 3 - if (local_tb) { - if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) - goto bad; - } - #endif - Py_XINCREF(local_tb); - Py_XINCREF(local_type); - Py_XINCREF(local_value); - *type = local_type; - *value = local_value; - *tb = local_tb; -#if CYTHON_FAST_THREAD_STATE - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = local_type; - tstate->exc_value = local_value; - tstate->exc_traceback = local_tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(local_type, local_value, local_tb); -#endif - return 0; -bad: - *type = 0; - *value = 0; - *tb = 0; - Py_XDECREF(local_type); - Py_XDECREF(local_value); - Py_XDECREF(local_tb); - return -1; -} - -/* Import */ - static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *empty_list = 0; - PyObject *module = 0; - PyObject *global_dict = 0; - PyObject *empty_dict = 0; - PyObject *list; - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); - if (!py_import) - goto bad; - #endif - if (from_list) - list = from_list; - else { - empty_list = PyList_New(0); - if (!empty_list) - goto bad; - list = empty_list; - } - global_dict = PyModule_GetDict(__pyx_m); - if (!global_dict) - goto bad; - empty_dict = PyDict_New(); - if (!empty_dict) - goto bad; - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if (strchr(__Pyx_MODULE_NAME, '.')) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(1); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, 1); - #endif - if (!module) { - if (!PyErr_ExceptionMatches(PyExc_ImportError)) - goto bad; - PyErr_Clear(); - } - } - level = 0; - } - #endif - if (!module) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(level); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, level); - #endif - } - } -bad: - #if PY_VERSION_HEX < 0x03030000 - Py_XDECREF(py_import); - #endif - Py_XDECREF(empty_list); - Py_XDECREF(empty_dict); - return module; -} - -/* CodeObjectCache */ - static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { - int start = 0, mid = 0, end = count - 1; - if (end >= 0 && code_line > entries[end].code_line) { - return count; - } - while (start < end) { - mid = start + (end - start) / 2; - if (code_line < entries[mid].code_line) { - end = mid; - } else if (code_line > entries[mid].code_line) { - start = mid + 1; - } else { - return mid; - } - } - if (code_line <= entries[mid].code_line) { - return mid; - } else { - return mid + 1; - } -} -static PyCodeObject *__pyx_find_code_object(int code_line) { - PyCodeObject* code_object; - int pos; - if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { - return NULL; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { - return NULL; - } - code_object = __pyx_code_cache.entries[pos].code_object; - Py_INCREF(code_object); - return code_object; -} -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { - int pos, i; - __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; - if (unlikely(!code_line)) { - return; - } - if (unlikely(!entries)) { - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); - if (likely(entries)) { - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = 64; - __pyx_code_cache.count = 1; - entries[0].code_line = code_line; - entries[0].code_object = code_object; - Py_INCREF(code_object); - } - return; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { - PyCodeObject* tmp = entries[pos].code_object; - entries[pos].code_object = code_object; - Py_DECREF(tmp); - return; - } - if (__pyx_code_cache.count == __pyx_code_cache.max_count) { - int new_max = __pyx_code_cache.max_count + 64; - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} - -/* AddTraceback */ - #include "compile.h" -#include "frameobject.h" -#include "traceback.h" -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_srcfile = 0; - PyObject *py_funcname = 0; - #if PY_MAJOR_VERSION < 3 - py_srcfile = PyString_FromString(filename); - #else - py_srcfile = PyUnicode_FromString(filename); - #endif - if (!py_srcfile) goto bad; - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - #else - py_funcname = PyUnicode_FromString(funcname); - #endif - } - if (!py_funcname) goto bad; - py_code = __Pyx_PyCode_New( - 0, - 0, - 0, - 0, - 0, - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, - __pyx_empty_bytes /*PyObject *lnotab*/ - ); - Py_DECREF(py_srcfile); - Py_DECREF(py_funcname); - return py_code; -bad: - Py_XDECREF(py_srcfile); - Py_XDECREF(py_funcname); - return NULL; -} -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyFrameObject *py_frame = 0; - py_code = __pyx_find_code_object(c_line ? c_line : py_line); - if (!py_code) { - py_code = __Pyx_CreateCodeObjectForTraceback( - funcname, c_line, py_line, filename); - if (!py_code) goto bad; - __pyx_insert_code_object(c_line ? c_line : py_line, py_code); - } - py_frame = PyFrame_New( - PyThreadState_GET(), /*PyThreadState *tstate,*/ - py_code, /*PyCodeObject *code,*/ - __pyx_d, /*PyObject *globals,*/ - 0 /*PyObject *locals*/ - ); - if (!py_frame) goto bad; - __Pyx_PyFrame_SetLineNumber(py_frame, py_line); - PyTraceBack_Here(py_frame); -bad: - Py_XDECREF(py_code); - Py_XDECREF(py_frame); -} - -#if PY_MAJOR_VERSION < 3 -static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { - if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); - if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); - PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); - return -1; -} -static void __Pyx_ReleaseBuffer(Py_buffer *view) { - PyObject *obj = view->obj; - if (!obj) return; - if (PyObject_CheckBuffer(obj)) { - PyBuffer_Release(view); - return; - } - if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } - Py_DECREF(obj); - view->obj = NULL; -} -#endif - - - /* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_int(unsigned int value) { - const unsigned int neg_one = (unsigned int) -1, const_zero = (unsigned int) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(unsigned int) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(unsigned int) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(unsigned int) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(unsigned int) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(unsigned int), - little, !is_unsigned); - } -} - -/* CIntFromPyVerify */ - #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) -#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) -#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ - {\ - func_type value = func_value;\ - if (sizeof(target_type) < sizeof(func_type)) {\ - if (unlikely(value != (func_type) (target_type) value)) {\ - func_type zero = 0;\ - if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ - return (target_type) -1;\ - if (is_unsigned && unlikely(value < zero))\ - goto raise_neg_overflow;\ - else\ - goto raise_overflow;\ - }\ - }\ - return (target_type) value;\ - } - -/* Declarations */ - #if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return ::std::complex< float >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return x + y*(__pyx_t_float_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - __pyx_t_float_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -/* Arithmetic */ - #if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - #if 1 - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - if (b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); - } else if (fabsf(b.real) >= fabsf(b.imag)) { - if (b.real == 0 && b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); - } else { - float r = b.imag / b.real; - float s = 1.0 / (b.real + b.imag * r); - return __pyx_t_float_complex_from_parts( - (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); - } - } else { - float r = b.real / b.imag; - float s = 1.0 / (b.imag + b.real * r); - return __pyx_t_float_complex_from_parts( - (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); - } - } - #else - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - if (b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); - } else { - float denom = b.real * b.real + b.imag * b.imag; - return __pyx_t_float_complex_from_parts( - (a.real * b.real + a.imag * b.imag) / denom, - (a.imag * b.real - a.real * b.imag) / denom); - } - } - #endif - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrtf(z.real*z.real + z.imag*z.imag); - #else - return hypotf(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - float r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - float denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(a, a); - case 3: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(z, a); - case 4: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } else if (b.imag == 0) { - z.real = powf(a.real, b.real); - z.imag = 0; - return z; - } else if (a.real > 0) { - r = a.real; - theta = 0; - } else { - r = -a.real; - theta = atan2f(0, -1); - } - } else { - r = __Pyx_c_abs_float(a); - theta = atan2f(a.imag, a.real); - } - lnr = logf(r); - z_r = expf(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cosf(z_theta); - z.imag = z_r * sinf(z_theta); - return z; - } - #endif -#endif - -/* Declarations */ - #if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return ::std::complex< double >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return x + y*(__pyx_t_double_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - __pyx_t_double_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -/* Arithmetic */ - #if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - #if 1 - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - if (b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); - } else if (fabs(b.real) >= fabs(b.imag)) { - if (b.real == 0 && b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); - } else { - double r = b.imag / b.real; - double s = 1.0 / (b.real + b.imag * r); - return __pyx_t_double_complex_from_parts( - (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); - } - } else { - double r = b.real / b.imag; - double s = 1.0 / (b.imag + b.real * r); - return __pyx_t_double_complex_from_parts( - (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); - } - } - #else - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - if (b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); - } else { - double denom = b.real * b.real + b.imag * b.imag; - return __pyx_t_double_complex_from_parts( - (a.real * b.real + a.imag * b.imag) / denom, - (a.imag * b.real - a.real * b.imag) / denom); - } - } - #endif - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrt(z.real*z.real + z.imag*z.imag); - #else - return hypot(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - double r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - double denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(a, a); - case 3: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, a); - case 4: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } else if (b.imag == 0) { - z.real = pow(a.real, b.real); - z.imag = 0; - return z; - } else if (a.real > 0) { - r = a.real; - theta = 0; - } else { - r = -a.real; - theta = atan2(0, -1); - } - } else { - r = __Pyx_c_abs_double(a); - theta = atan2(a.imag, a.real); - } - lnr = log(r); - z_r = exp(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cos(z_theta); - z.imag = z_r * sin(z_theta); - return z; - } - #endif -#endif - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(int) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(int) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(int), - little, !is_unsigned); - } -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { - const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(enum NPY_TYPES) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(enum NPY_TYPES) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE unsigned int __Pyx_PyInt_As_unsigned_int(PyObject *x) { - const unsigned int neg_one = (unsigned int) -1, const_zero = (unsigned int) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(unsigned int) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(unsigned int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (unsigned int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (unsigned int) 0; - case 1: __PYX_VERIFY_RETURN_INT(unsigned int, digit, digits[0]) - case 2: - if (8 * sizeof(unsigned int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned int) >= 2 * PyLong_SHIFT) { - return (unsigned int) (((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(unsigned int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned int) >= 3 * PyLong_SHIFT) { - return (unsigned int) (((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(unsigned int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned int) >= 4 * PyLong_SHIFT) { - return (unsigned int) (((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (unsigned int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(unsigned int) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(unsigned int, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(unsigned int) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(unsigned int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (unsigned int) 0; - case -1: __PYX_VERIFY_RETURN_INT(unsigned int, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(unsigned int, digit, +digits[0]) - case -2: - if (8 * sizeof(unsigned int) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) { - return (unsigned int) (((unsigned int)-1)*(((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(unsigned int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) { - return (unsigned int) ((((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) { - return (unsigned int) (((unsigned int)-1)*(((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(unsigned int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) { - return (unsigned int) ((((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned int) - 1 > 4 * PyLong_SHIFT) { - return (unsigned int) (((unsigned int)-1)*(((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(unsigned int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(unsigned int) - 1 > 4 * PyLong_SHIFT) { - return (unsigned int) ((((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); - } - } - break; - } -#endif - if (sizeof(unsigned int) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(unsigned int, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(unsigned int, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - unsigned int val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (unsigned int) -1; - } - } else { - unsigned int val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (unsigned int) -1; - val = __Pyx_PyInt_As_unsigned_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to unsigned int"); - return (unsigned int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to unsigned int"); - return (unsigned int) -1; -} - -/* CIntFromPy */ - static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(int) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { - return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { - return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { - return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(int) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) - case -2: - if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - } -#endif - if (sizeof(int) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - int val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to int"); - return (int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(long) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(long), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(long) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { - return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { - return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { - return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(long) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) - case -2: - if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - } -#endif - if (sizeof(long) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - long val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (long) -1; - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (long) -1; - val = __Pyx_PyInt_As_long(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to long"); - return (long) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; -} - -/* CheckBinaryVersion */ - static int __Pyx_check_binary_version(void) { - char ctversion[4], rtversion[4]; - PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); - if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { - char message[200]; - PyOS_snprintf(message, sizeof(message), - "compiletime version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - return PyErr_WarnEx(NULL, message, 1); - } - return 0; -} - -/* ModuleImport */ - #ifndef __PYX_HAVE_RT_ImportModule -#define __PYX_HAVE_RT_ImportModule -static PyObject *__Pyx_ImportModule(const char *name) { - PyObject *py_name = 0; - PyObject *py_module = 0; - py_name = __Pyx_PyIdentifier_FromString(name); - if (!py_name) - goto bad; - py_module = PyImport_Import(py_name); - Py_DECREF(py_name); - return py_module; -bad: - Py_XDECREF(py_name); - return 0; -} -#endif - -/* TypeImport */ - #ifndef __PYX_HAVE_RT_ImportType -#define __PYX_HAVE_RT_ImportType -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, - size_t size, int strict) -{ - PyObject *py_module = 0; - PyObject *result = 0; - PyObject *py_name = 0; - char warning[200]; - Py_ssize_t basicsize; -#ifdef Py_LIMITED_API - PyObject *py_basicsize; -#endif - py_module = __Pyx_ImportModule(module_name); - if (!py_module) - goto bad; - py_name = __Pyx_PyIdentifier_FromString(class_name); - if (!py_name) - goto bad; - result = PyObject_GetAttr(py_module, py_name); - Py_DECREF(py_name); - py_name = 0; - Py_DECREF(py_module); - py_module = 0; - if (!result) - goto bad; - if (!PyType_Check(result)) { - PyErr_Format(PyExc_TypeError, - "%.200s.%.200s is not a type object", - module_name, class_name); - goto bad; - } -#ifndef Py_LIMITED_API - basicsize = ((PyTypeObject *)result)->tp_basicsize; -#else - py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); - if (!py_basicsize) - goto bad; - basicsize = PyLong_AsSsize_t(py_basicsize); - Py_DECREF(py_basicsize); - py_basicsize = 0; - if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) - goto bad; -#endif - if (!strict && (size_t)basicsize > size) { - PyOS_snprintf(warning, sizeof(warning), - "%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd", - module_name, class_name, basicsize, size); - if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; - } - else if ((size_t)basicsize != size) { - PyErr_Format(PyExc_ValueError, - "%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd", - module_name, class_name, basicsize, size); - goto bad; - } - return (PyTypeObject *)result; -bad: - Py_XDECREF(py_module); - Py_XDECREF(result); - return NULL; -} -#endif - -/* InitStrings */ - static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION < 3 - if (t->is_unicode) { - *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); - } else if (t->intern) { - *t->p = PyString_InternFromString(t->s); - } else { - *t->p = PyString_FromStringAndSize(t->s, t->n - 1); - } - #else - if (t->is_unicode | t->is_str) { - if (t->intern) { - *t->p = PyUnicode_InternFromString(t->s); - } else if (t->encoding) { - *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); - } else { - *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); - } - } else { - *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); - } - #endif - if (!*t->p) - return -1; - ++t; - } - return 0; -} - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { - return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); -} -static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { - Py_ssize_t ignore; - return __Pyx_PyObject_AsStringAndSize(o, &ignore); -} -static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { -#if CYTHON_COMPILING_IN_CPYTHON && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) - if ( -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - __Pyx_sys_getdefaultencoding_not_ascii && -#endif - PyUnicode_Check(o)) { -#if PY_VERSION_HEX < 0x03030000 - char* defenc_c; - PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); - if (!defenc) return NULL; - defenc_c = PyBytes_AS_STRING(defenc); -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - { - char* end = defenc_c + PyBytes_GET_SIZE(defenc); - char* c; - for (c = defenc_c; c < end; c++) { - if ((unsigned char) (*c) >= 128) { - PyUnicode_AsASCIIString(o); - return NULL; - 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-cimport cython -import numpy as np -cimport numpy as np - -DTYPE = np.float -ctypedef np.float_t DTYPE_t - -def bbox_overlaps_float( - np.ndarray[DTYPE_t, ndim=2] boxes, - np.ndarray[DTYPE_t, ndim=2] query_boxes): - """ - Parameters - ---------- - boxes: (N, 4) ndarray of float - query_boxes: (K, 4) ndarray of float - Returns - ------- - overlaps: (N, K) ndarray of overlap between boxes and query_boxes - """ - cdef unsigned int N = boxes.shape[0] - cdef unsigned int K = query_boxes.shape[0] - cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) - cdef DTYPE_t iw, ih, box_area - cdef DTYPE_t ua - cdef unsigned int k, n - for k in range(K): - box_area = ( - (query_boxes[k, 2] - query_boxes[k, 0]) * - (query_boxes[k, 3] - query_boxes[k, 1]) - ) - for n in range(N): - iw = ( - min(boxes[n, 2], query_boxes[k, 2]) - - max(boxes[n, 0], query_boxes[k, 0]) - ) - if iw > 0: - ih = ( - min(boxes[n, 3], query_boxes[k, 3]) - - max(boxes[n, 1], query_boxes[k, 1]) - ) - if ih > 0: - ua = float( - (boxes[n, 2] - boxes[n, 0]) * - (boxes[n, 3] - boxes[n, 1]) + - box_area - iw * ih - ) - # if query_boxes[k, 4] == -1: - # ua = float((boxes[n, 2] - boxes[n, 0]) - # *(boxes[n, 3] - boxes[n, 1])) - # else: - # ua = float( - # (boxes[n, 2] - boxes[n, 0]) * - # (boxes[n, 3] - boxes[n, 1]) + - # box_area - iw * ih - # ) - overlaps[n, k] = iw * ih / ua - return overlaps - -def bbox_overlaps( - np.ndarray[DTYPE_t, ndim=2] boxes, - np.ndarray[DTYPE_t, ndim=2] query_boxes): - """ - Parameters - ---------- - boxes: (N, 4) ndarray of float - query_boxes: (K, 4) ndarray of float - Returns - ------- - overlaps: (N, K) ndarray of overlap between boxes and query_boxes - """ - cdef unsigned int N = boxes.shape[0] - cdef unsigned int K = query_boxes.shape[0] - cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) - cdef DTYPE_t iw, ih, box_area - cdef DTYPE_t ua - cdef unsigned int k, n - for k in range(K): - box_area = ( - (query_boxes[k, 2] - query_boxes[k, 0] + 1) * - (query_boxes[k, 3] - query_boxes[k, 1] + 1) - ) - for n in range(N): - iw = ( - min(boxes[n, 2], query_boxes[k, 2]) - - max(boxes[n, 0], query_boxes[k, 0]) + 1 - ) - if iw > 0: - ih = ( - min(boxes[n, 3], query_boxes[k, 3]) - - max(boxes[n, 1], query_boxes[k, 1]) + 1 - ) - if ih > 0: - ua = float( - (boxes[n, 2] - boxes[n, 0] + 1) * - (boxes[n, 3] - boxes[n, 1] + 1) + - box_area - iw * ih - ) - overlaps[n, k] = iw * ih / ua - return overlaps - -def bbox_overlaps_self( - np.ndarray[DTYPE_t, ndim=2] boxes, - np.ndarray[DTYPE_t, ndim=2] query_boxes): - """ - Parameters - ---------- - boxes: (N, 4) ndarray of float - query_boxes: (K, 4) ndarray of float - Returns - ------- - overlaps: (N, K) ndarray of overlap between boxes and query_boxes - """ - cdef unsigned int N = boxes.shape[0] - cdef unsigned int K = query_boxes.shape[0] - cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) - cdef DTYPE_t iw, ih, box_area - cdef DTYPE_t ua - cdef unsigned int k, n - for k in range(K): - box_area = ( - (query_boxes[k, 2] - query_boxes[k, 0] + 1) * - (query_boxes[k, 3] - query_boxes[k, 1] + 1) - ) - for n in range(N): - iw = ( - min(boxes[n, 2], query_boxes[k, 2]) - - max(boxes[n, 0], query_boxes[k, 0]) + 1 - ) - if iw > 0: - ih = ( - min(boxes[n, 3], query_boxes[k, 3]) - - max(boxes[n, 1], query_boxes[k, 1]) + 1 - ) - if ih > 0: - ua = float(box_area) - overlaps[n, k] = iw * ih / ua - return overlaps - - -def bbox_overlaps_ignore( - np.ndarray[DTYPE_t, ndim=2] boxes, - np.ndarray[DTYPE_t, ndim=2] query_boxes): - """ - Parameters - ---------- - boxes: (N, 4) ndarray of float - query_boxes: (K, 4) ndarray of float - Returns - ------- - overlaps: (N, K) ndarray of overlap between boxes and query_boxes - """ - cdef unsigned int N = boxes.shape[0] - cdef unsigned int K = query_boxes.shape[0] - cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) - cdef DTYPE_t iw, ih, box_area - cdef DTYPE_t ua - cdef unsigned int k, n - for k in range(K): - box_area = ( - (query_boxes[k, 2] - query_boxes[k, 0] + 1) * - (query_boxes[k, 3] - query_boxes[k, 1] + 1) - ) - for n in range(N): - iw = ( - min(boxes[n, 2], query_boxes[k, 2]) - - max(boxes[n, 0], query_boxes[k, 0]) + 1 - ) - if iw > 0: - ih = ( - min(boxes[n, 3], query_boxes[k, 3]) - - max(boxes[n, 1], query_boxes[k, 1]) + 1 - ) - if ih > 0: - if query_boxes[k, 4] == -1: - ua = float((boxes[n, 2] - boxes[n, 0] + 1) - *(boxes[n, 3] - boxes[n, 1] + 1)) - else: - ua = float( - (boxes[n, 2] - boxes[n, 0] + 1) * - (boxes[n, 3] - boxes[n, 1] + 1) + - box_area - iw * ih - ) - overlaps[n, k] = iw * ih / ua - return overlaps - - -def get_assignment_overlaps( - np.ndarray[DTYPE_t, ndim=2] boxes, - np.ndarray[DTYPE_t, ndim=2] query_boxes, - DTYPE_t FG_THRESH): - """ Used for proposal_target_layer_ignore - Parameters - ---------- - boxes: (N, 4) ndarray of float - query_boxes: (K, 4) ndarray of float - Returns - ------- - overlaps: (N, K) ndarray of overlap between boxes and query_boxes - """ - cdef unsigned int N = boxes.shape[0] - cdef unsigned int K = query_boxes.shape[0] - cdef np.ndarray[long, ndim=1] gt_assignment = np.zeros((N,), dtype=np.int) - cdef np.ndarray[DTYPE_t, ndim=1] max_overlaps = np.zeros((N,), dtype=DTYPE) - cdef DTYPE_t iw, ih, box_area - cdef DTYPE_t ua - cdef unsigned int k, n - for k in range(K): - box_area = ( - (query_boxes[k, 2] - query_boxes[k, 0] + 1) * - (query_boxes[k, 3] - query_boxes[k, 1] + 1) - ) - for n in range(N): - iw = ( - min(boxes[n, 2], query_boxes[k, 2]) - - max(boxes[n, 0], query_boxes[k, 0]) + 1 - ) - if iw > 0: - ih = ( - min(boxes[n, 3], query_boxes[k, 3]) - - max(boxes[n, 1], query_boxes[k, 1]) + 1 - ) - if ih > 0: - if query_boxes[k, 4] == -1: - ua = float((boxes[n, 2] - boxes[n, 0] + 1) - *(boxes[n, 3] - boxes[n, 1] + 1)) - overlap = iw * ih / ua - if overlap > max_overlaps[n]: - if query_boxes[gt_assignment[n], 4] == -1 or max_overlaps[n] < FG_THRESH: - max_overlaps[n] = overlap - gt_assignment[n] = k - else: - ua = float( - (boxes[n, 2] - boxes[n, 0] + 1) * - (boxes[n, 3] - boxes[n, 1] + 1) + - box_area - iw * ih - ) - overlap = iw * ih / ua - if overlap > max_overlaps[n]: - max_overlaps[n] = overlap - gt_assignment[n] = k - #overlaps[n, k] = overlap - return gt_assignment, max_overlaps diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/build/temp.linux-x86_64-2.7/bbox.o b/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/build/temp.linux-x86_64-2.7/bbox.o deleted file mode 100755 index e6dc6a2..0000000 Binary files a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/build/temp.linux-x86_64-2.7/bbox.o and /dev/null differ diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/build/temp.linux-x86_64-2.7/nms.o b/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/build/temp.linux-x86_64-2.7/nms.o deleted file mode 100755 index d381b2e..0000000 Binary files a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/build/temp.linux-x86_64-2.7/nms.o and /dev/null differ diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/cython_bbox.so b/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/cython_bbox.so deleted file mode 100755 index 055b6d7..0000000 Binary files a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/cython_bbox.so and /dev/null differ diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/cython_nms.so b/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/cython_nms.so deleted file mode 100755 index 90e0a20..0000000 Binary files a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/cython_nms.so and /dev/null differ diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/nms.c b/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/nms.c deleted file mode 100755 index 54934ea..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/nms.c +++ /dev/null @@ -1,9561 +0,0 @@ -/* Generated by Cython 0.25.2 */ - 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#define CYTHON_COMPILING_IN_PYSTON 1 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #ifndef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 1 - #endif - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #ifndef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 1 - #endif - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #ifndef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 1 - #endif - #ifndef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 1 - #endif - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 -#else - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_PYSTON 0 - #define CYTHON_COMPILING_IN_CPYTHON 1 - #ifndef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 1 - #endif - #if PY_MAJOR_VERSION < 3 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #elif !defined(CYTHON_USE_ASYNC_SLOTS) - #define CYTHON_USE_ASYNC_SLOTS 1 - #endif - #if PY_VERSION_HEX < 0x02070000 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #elif !defined(CYTHON_USE_PYLONG_INTERNALS) - #define CYTHON_USE_PYLONG_INTERNALS 1 - #endif - #ifndef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 1 - #endif - #ifndef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 1 - #endif - #if PY_VERSION_HEX < 0x030300F0 - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #elif !defined(CYTHON_USE_UNICODE_WRITER) - #define CYTHON_USE_UNICODE_WRITER 1 - #endif - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #ifndef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 1 - #endif - #ifndef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 1 - #endif - #ifndef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 1 - #endif - #ifndef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 1 - #endif -#endif -#if !defined(CYTHON_FAST_PYCCALL) -#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) -#endif -#if CYTHON_USE_PYLONG_INTERNALS - #include "longintrepr.h" - #undef SHIFT - #undef BASE - #undef MASK -#endif -#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) - #define Py_OptimizeFlag 0 -#endif -#define __PYX_BUILD_PY_SSIZE_T "n" -#define CYTHON_FORMAT_SSIZE_T "z" -#if PY_MAJOR_VERSION < 3 - #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" - #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ - PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) - #define __Pyx_DefaultClassType PyClass_Type -#else - #define __Pyx_BUILTIN_MODULE_NAME "builtins" - #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ - PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) - #define __Pyx_DefaultClassType PyType_Type -#endif -#ifndef Py_TPFLAGS_CHECKTYPES - #define Py_TPFLAGS_CHECKTYPES 0 -#endif -#ifndef Py_TPFLAGS_HAVE_INDEX - #define Py_TPFLAGS_HAVE_INDEX 0 -#endif -#ifndef Py_TPFLAGS_HAVE_NEWBUFFER - #define Py_TPFLAGS_HAVE_NEWBUFFER 0 -#endif -#ifndef Py_TPFLAGS_HAVE_FINALIZE - #define Py_TPFLAGS_HAVE_FINALIZE 0 -#endif -#ifndef METH_FASTCALL - #define METH_FASTCALL 0x80 - typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args, - Py_ssize_t nargs, PyObject *kwnames); -#else - #define __Pyx_PyCFunctionFast _PyCFunctionFast -#endif -#if CYTHON_FAST_PYCCALL -#define __Pyx_PyFastCFunction_Check(func)\ - ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST))))) -#else -#define __Pyx_PyFastCFunction_Check(func) 0 -#endif -#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) - #define CYTHON_PEP393_ENABLED 1 - #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ - 0 : _PyUnicode_Ready((PyObject *)(op))) - #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) - #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) - #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) - #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) - #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) - #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) -#else - #define CYTHON_PEP393_ENABLED 0 - #define PyUnicode_1BYTE_KIND 1 - #define PyUnicode_2BYTE_KIND 2 - #define PyUnicode_4BYTE_KIND 4 - #define __Pyx_PyUnicode_READY(op) (0) - #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) - #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) - #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) - #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) - #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) - #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) -#endif -#if CYTHON_COMPILING_IN_PYPY - #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) - #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) -#else - #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) - #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ - PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) - #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) - #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) - #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) - #define PyObject_Malloc(s) PyMem_Malloc(s) - #define PyObject_Free(p) PyMem_Free(p) - #define PyObject_Realloc(p) PyMem_Realloc(p) -#endif -#if CYTHON_COMPILING_IN_PYSTON - #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) - #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) -#else - #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) - #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) -#endif -#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) -#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) -#else - #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) -#endif -#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) - #define PyObject_ASCII(o) PyObject_Repr(o) -#endif -#if PY_MAJOR_VERSION >= 3 - #define PyBaseString_Type PyUnicode_Type - #define PyStringObject PyUnicodeObject - #define PyString_Type PyUnicode_Type - #define PyString_Check PyUnicode_Check - #define PyString_CheckExact PyUnicode_CheckExact -#endif -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) - #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) -#else - #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) - #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) -#endif -#ifndef PySet_CheckExact - #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) -#endif -#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) -#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) -#if PY_MAJOR_VERSION >= 3 - #define PyIntObject PyLongObject - #define PyInt_Type PyLong_Type - #define PyInt_Check(op) PyLong_Check(op) - #define PyInt_CheckExact(op) PyLong_CheckExact(op) - #define PyInt_FromString PyLong_FromString - #define PyInt_FromUnicode PyLong_FromUnicode - #define PyInt_FromLong PyLong_FromLong - #define PyInt_FromSize_t PyLong_FromSize_t - #define PyInt_FromSsize_t PyLong_FromSsize_t - #define PyInt_AsLong PyLong_AsLong - #define PyInt_AS_LONG PyLong_AS_LONG - #define PyInt_AsSsize_t PyLong_AsSsize_t - #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask - #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask - #define PyNumber_Int PyNumber_Long -#endif -#if PY_MAJOR_VERSION >= 3 - #define PyBoolObject PyLongObject -#endif -#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY - #ifndef PyUnicode_InternFromString - #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) - #endif -#endif -#if PY_VERSION_HEX < 0x030200A4 - typedef long Py_hash_t; - #define __Pyx_PyInt_FromHash_t PyInt_FromLong - #define __Pyx_PyInt_AsHash_t PyInt_AsLong -#else - #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t - #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t -#endif -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) -#else - #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) -#endif -#if CYTHON_USE_ASYNC_SLOTS - #if PY_VERSION_HEX >= 0x030500B1 - #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods - #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) - #else - typedef struct { - unaryfunc am_await; - unaryfunc am_aiter; - unaryfunc am_anext; - } __Pyx_PyAsyncMethodsStruct; - #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) - #endif -#else - #define __Pyx_PyType_AsAsync(obj) NULL -#endif -#ifndef CYTHON_RESTRICT - #if defined(__GNUC__) - #define CYTHON_RESTRICT __restrict__ - #elif defined(_MSC_VER) && _MSC_VER >= 1400 - #define CYTHON_RESTRICT __restrict - #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define CYTHON_RESTRICT restrict - #else - #define CYTHON_RESTRICT - #endif -#endif -#ifndef CYTHON_UNUSED -# if defined(__GNUC__) -# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) -# define CYTHON_UNUSED __attribute__ ((__unused__)) -# else -# define CYTHON_UNUSED -# endif -# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) -# define CYTHON_UNUSED __attribute__ ((__unused__)) -# else -# define CYTHON_UNUSED -# endif -#endif -#ifndef CYTHON_MAYBE_UNUSED_VAR -# if defined(__cplusplus) - template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } -# else -# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) -# endif -#endif -#ifndef CYTHON_NCP_UNUSED -# if CYTHON_COMPILING_IN_CPYTHON -# define CYTHON_NCP_UNUSED -# else -# define CYTHON_NCP_UNUSED CYTHON_UNUSED -# endif -#endif -#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) - -#ifndef CYTHON_INLINE - #if defined(__clang__) - #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) - #elif defined(__GNUC__) - #define CYTHON_INLINE __inline__ - #elif defined(_MSC_VER) - #define CYTHON_INLINE __inline - #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define CYTHON_INLINE inline - #else - #define CYTHON_INLINE - #endif -#endif - -#if defined(WIN32) || defined(MS_WINDOWS) - #define _USE_MATH_DEFINES -#endif -#include -#ifdef NAN -#define __PYX_NAN() ((float) NAN) -#else -static CYTHON_INLINE float __PYX_NAN() { - float value; - memset(&value, 0xFF, sizeof(value)); - return value; -} -#endif -#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) -#define __Pyx_truncl trunc -#else -#define __Pyx_truncl truncl -#endif - - -#define __PYX_ERR(f_index, lineno, Ln_error) \ -{ \ - __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ -} - -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) -#else - #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) -#endif - -#ifndef __PYX_EXTERN_C - #ifdef __cplusplus - #define __PYX_EXTERN_C extern "C" - #else - #define __PYX_EXTERN_C extern - #endif -#endif - -#define __PYX_HAVE__cython_nms -#define __PYX_HAVE_API__cython_nms -#include -#include -#include -#include "numpy/arrayobject.h" -#include "numpy/ufuncobject.h" -#ifdef _OPENMP -#include -#endif /* _OPENMP */ - -#ifdef PYREX_WITHOUT_ASSERTIONS -#define CYTHON_WITHOUT_ASSERTIONS -#endif - -typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; - const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; - -#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 -#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 -#define __PYX_DEFAULT_STRING_ENCODING "" -#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString -#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#define __Pyx_uchar_cast(c) ((unsigned char)c) -#define __Pyx_long_cast(x) ((long)x) -#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ - (sizeof(type) < sizeof(Py_ssize_t)) ||\ - (sizeof(type) > sizeof(Py_ssize_t) &&\ - likely(v < (type)PY_SSIZE_T_MAX ||\ - v == (type)PY_SSIZE_T_MAX) &&\ - (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ - v == (type)PY_SSIZE_T_MIN))) ||\ - (sizeof(type) == sizeof(Py_ssize_t) &&\ - (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ - v == (type)PY_SSIZE_T_MAX))) ) -#if defined (__cplusplus) && __cplusplus >= 201103L - #include - #define __Pyx_sst_abs(value) std::abs(value) -#elif SIZEOF_INT >= SIZEOF_SIZE_T - #define __Pyx_sst_abs(value) abs(value) -#elif SIZEOF_LONG >= SIZEOF_SIZE_T - #define __Pyx_sst_abs(value) labs(value) -#elif defined (_MSC_VER) && defined (_M_X64) - #define __Pyx_sst_abs(value) _abs64(value) -#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define __Pyx_sst_abs(value) llabs(value) -#elif defined (__GNUC__) - #define __Pyx_sst_abs(value) __builtin_llabs(value) -#else - #define __Pyx_sst_abs(value) ((value<0) ? -value : value) -#endif -static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); -static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); -#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) -#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) -#define __Pyx_PyBytes_FromString PyBytes_FromString -#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); -#if PY_MAJOR_VERSION < 3 - #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#else - #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize -#endif -#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) -#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) -#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) -#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) -#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) -#if PY_MAJOR_VERSION < 3 -static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) -{ - const Py_UNICODE *u_end = u; - while (*u_end++) ; - return (size_t)(u_end - u - 1); -} -#else -#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen -#endif -#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) -#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode -#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode -#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) -#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) -#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); -#if CYTHON_ASSUME_SAFE_MACROS -#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) -#else -#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) -#endif -#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) -#if PY_MAJOR_VERSION >= 3 -#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) -#else -#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) -#endif -#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII -static int __Pyx_sys_getdefaultencoding_not_ascii; -static int __Pyx_init_sys_getdefaultencoding_params(void) { - PyObject* sys; - PyObject* default_encoding = NULL; - PyObject* ascii_chars_u = NULL; - PyObject* ascii_chars_b = NULL; - const char* default_encoding_c; - sys = PyImport_ImportModule("sys"); - if (!sys) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); - Py_DECREF(sys); - if (!default_encoding) goto bad; - default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - if (strcmp(default_encoding_c, "ascii") == 0) { - __Pyx_sys_getdefaultencoding_not_ascii = 0; - } else { - char ascii_chars[128]; - int c; - for (c = 0; c < 128; c++) { - ascii_chars[c] = c; - } - __Pyx_sys_getdefaultencoding_not_ascii = 1; - ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); - if (!ascii_chars_u) goto bad; - ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); - if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { - PyErr_Format( - PyExc_ValueError, - "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", - default_encoding_c); - goto bad; - } - Py_DECREF(ascii_chars_u); - Py_DECREF(ascii_chars_b); - } - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - Py_XDECREF(ascii_chars_u); 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-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); - -/* AddTraceback.proto */ -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename); - -/* BufferStructDeclare.proto */ -typedef struct { - Py_ssize_t shape, strides, suboffsets; -} __Pyx_Buf_DimInfo; -typedef struct { - size_t refcount; - Py_buffer pybuffer; -} __Pyx_Buffer; -typedef struct { - __Pyx_Buffer *rcbuffer; - char *data; - __Pyx_Buf_DimInfo diminfo[8]; -} __Pyx_LocalBuf_ND; - -#if PY_MAJOR_VERSION < 3 - static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); - static void __Pyx_ReleaseBuffer(Py_buffer *view); -#else - #define __Pyx_GetBuffer PyObject_GetBuffer - #define __Pyx_ReleaseBuffer PyBuffer_Release -#endif - - -/* None.proto */ -static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; -static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); - -/* RealImag.proto */ -#if CYTHON_CCOMPLEX - #ifdef __cplusplus - #define __Pyx_CREAL(z) ((z).real()) - #define __Pyx_CIMAG(z) ((z).imag()) - #else - #define __Pyx_CREAL(z) (__real__(z)) - #define __Pyx_CIMAG(z) (__imag__(z)) - #endif -#else - #define __Pyx_CREAL(z) ((z).real) - #define __Pyx_CIMAG(z) ((z).imag) -#endif -#if defined(__cplusplus) && CYTHON_CCOMPLEX\ - && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) - #define __Pyx_SET_CREAL(z,x) ((z).real(x)) - #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) -#else - #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) - #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) -#endif - -/* Arithmetic.proto */ -#if CYTHON_CCOMPLEX - #define __Pyx_c_eq_float(a, b) ((a)==(b)) - #define __Pyx_c_sum_float(a, b) ((a)+(b)) - #define __Pyx_c_diff_float(a, b) ((a)-(b)) - #define __Pyx_c_prod_float(a, b) ((a)*(b)) - #define __Pyx_c_quot_float(a, b) ((a)/(b)) - #define __Pyx_c_neg_float(a) (-(a)) - #ifdef __cplusplus - #define __Pyx_c_is_zero_float(z) ((z)==(float)0) - #define __Pyx_c_conj_float(z) (::std::conj(z)) - #if 1 - #define __Pyx_c_abs_float(z) (::std::abs(z)) - #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) - #endif - #else - #define __Pyx_c_is_zero_float(z) ((z)==0) - #define __Pyx_c_conj_float(z) (conjf(z)) - #if 1 - #define __Pyx_c_abs_float(z) (cabsf(z)) - #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) - #endif - #endif -#else - static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); - static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); - #if 1 - static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); - #endif -#endif - -/* Arithmetic.proto */ -#if CYTHON_CCOMPLEX - #define __Pyx_c_eq_double(a, b) ((a)==(b)) - #define __Pyx_c_sum_double(a, b) ((a)+(b)) - #define __Pyx_c_diff_double(a, b) ((a)-(b)) - #define __Pyx_c_prod_double(a, b) ((a)*(b)) - #define __Pyx_c_quot_double(a, b) ((a)/(b)) - #define __Pyx_c_neg_double(a) (-(a)) - #ifdef __cplusplus - #define __Pyx_c_is_zero_double(z) ((z)==(double)0) - #define __Pyx_c_conj_double(z) (::std::conj(z)) - #if 1 - #define __Pyx_c_abs_double(z) (::std::abs(z)) - #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) - #endif - #else - #define __Pyx_c_is_zero_double(z) ((z)==0) - #define __Pyx_c_conj_double(z) (conj(z)) - #if 1 - #define __Pyx_c_abs_double(z) (cabs(z)) - #define __Pyx_c_pow_double(a, b) (cpow(a, b)) - #endif - #endif -#else - static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); - static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); - #if 1 - static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); - #endif -#endif - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value); - -/* CIntFromPy.proto */ -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); - -/* CIntFromPy.proto */ -static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); - -/* CheckBinaryVersion.proto */ -static int __Pyx_check_binary_version(void); - -/* PyIdentifierFromString.proto */ -#if !defined(__Pyx_PyIdentifier_FromString) -#if PY_MAJOR_VERSION < 3 - #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) -#else - #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) -#endif -#endif - -/* ModuleImport.proto */ -static PyObject *__Pyx_ImportModule(const char *name); - -/* TypeImport.proto */ -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); - -/* InitStrings.proto */ -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); - - -/* Module declarations from 'cpython.buffer' */ - -/* Module declarations from 'libc.string' */ - -/* Module declarations from 'libc.stdio' */ - -/* Module declarations from '__builtin__' */ - -/* Module declarations from 'cpython.type' */ -static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; - -/* Module declarations from 'cpython' */ - -/* Module declarations from 'cpython.object' */ - -/* Module declarations from 'cpython.ref' */ - -/* Module declarations from 'libc.stdlib' */ - -/* Module declarations from 'numpy' */ - -/* Module declarations from 'numpy' */ -static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; -static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; -static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; -static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; -static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; -static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ - -/* Module declarations from 'cython_nms' */ -static CYTHON_INLINE __pyx_t_5numpy_float32_t __pyx_f_10cython_nms_max(__pyx_t_5numpy_float32_t, __pyx_t_5numpy_float32_t); /*proto*/ -static CYTHON_INLINE __pyx_t_5numpy_float32_t __pyx_f_10cython_nms_min(__pyx_t_5numpy_float32_t, __pyx_t_5numpy_float32_t); /*proto*/ -static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float32_t = { "float32_t", NULL, sizeof(__pyx_t_5numpy_float32_t), { 0 }, 0, 'R', 0, 0 }; -static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_int_t = { "int_t", NULL, sizeof(__pyx_t_5numpy_int_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_int_t) ? 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-static const char __pyx_k_ovr[] = "ovr"; -static const char __pyx_k_xx1[] = "xx1"; -static const char __pyx_k_xx2[] = "xx2"; -static const char __pyx_k_yy1[] = "yy1"; -static const char __pyx_k_yy2[] = "yy2"; -static const char __pyx_k_dets[] = "dets"; -static const char __pyx_k_keep[] = "keep"; -static const char __pyx_k_main[] = "__main__"; -static const char __pyx_k_ovr1[] = "ovr1"; -static const char __pyx_k_ovr2[] = "ovr2"; -static const char __pyx_k_test[] = "__test__"; -static const char __pyx_k_areas[] = "areas"; -static const char __pyx_k_dtype[] = "dtype"; -static const char __pyx_k_iarea[] = "iarea"; -static const char __pyx_k_inter[] = "inter"; -static const char __pyx_k_ndets[] = "ndets"; -static const char __pyx_k_numpy[] = "numpy"; -static const char __pyx_k_order[] = "order"; -static const char __pyx_k_range[] = "range"; -static const char __pyx_k_zeros[] = "zeros"; -static const char __pyx_k_import[] = "__import__"; -static const char __pyx_k_scores[] = "scores"; -static const char __pyx_k_thresh[] = "thresh"; -static const char __pyx_k_argsort[] = "argsort"; -static const char __pyx_k_nms_new[] = "nms_new"; -static const char __pyx_k_ValueError[] = "ValueError"; -static const char __pyx_k_cython_nms[] = "cython_nms"; -static const char __pyx_k_suppressed[] = "suppressed"; -static const char __pyx_k_ImportError[] = "ImportError"; -static const char __pyx_k_RuntimeError[] = "RuntimeError"; -static const char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; -static const char __pyx_k_home_yjr_PycharmProjects_Faster[] = "/home/yjr/PycharmProjects/Faster-RCNN_Tensorflow/libs/box_utils/cython_utils/nms.pyx"; -static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; -static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; -static const char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; -static const char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; 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if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 70, __pyx_L1_error) - __Pyx_GOTREF(__pyx_t_1); - if (PyDict_SetItem(__pyx_d, __pyx_n_s_nms_new, __pyx_t_1) < 0) __PYX_ERR(0, 70, __pyx_L1_error) - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - - /* "nms.pyx":1 - * # -------------------------------------------------------- # <<<<<<<<<<<<<< - * # Fast R-CNN - * # Copyright (c) 2015 Microsoft - */ - __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error) - __Pyx_GOTREF(__pyx_t_1); - if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) __PYX_ERR(0, 1, __pyx_L1_error) - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - - /* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":997 - * raise ImportError("numpy.core.umath failed to import") - * - * cdef inline int import_ufunc() except -1: # <<<<<<<<<<<<<< - * try: - * _import_umath() - */ - - /*--- Wrapped vars code ---*/ - - goto __pyx_L0; - __pyx_L1_error:; - __Pyx_XDECREF(__pyx_t_1); - if (__pyx_m) { - if (__pyx_d) { - __Pyx_AddTraceback("init cython_nms", __pyx_clineno, __pyx_lineno, __pyx_filename); - } - Py_DECREF(__pyx_m); __pyx_m = 0; - } else if (!PyErr_Occurred()) { - PyErr_SetString(PyExc_ImportError, "init cython_nms"); - } - __pyx_L0:; - __Pyx_RefNannyFinishContext(); - #if PY_MAJOR_VERSION < 3 - return; - #else - return __pyx_m; - #endif -} - -/* --- Runtime support code --- */ -/* Refnanny */ -#if CYTHON_REFNANNY -static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { - PyObject *m = NULL, *p = NULL; - void *r = NULL; - m = PyImport_ImportModule((char *)modname); - if (!m) goto end; - p = PyObject_GetAttrString(m, (char *)"RefNannyAPI"); - if (!p) goto end; - r = PyLong_AsVoidPtr(p); -end: - Py_XDECREF(p); - Py_XDECREF(m); - return (__Pyx_RefNannyAPIStruct *)r; -} -#endif - -/* GetBuiltinName */ -static PyObject *__Pyx_GetBuiltinName(PyObject *name) { - PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); - if (unlikely(!result)) { - PyErr_Format(PyExc_NameError, -#if PY_MAJOR_VERSION >= 3 - "name '%U' is not defined", name); 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"" : "s", num_found); -} - -/* RaiseDoubleKeywords */ -static void __Pyx_RaiseDoubleKeywordsError( - const char* func_name, - PyObject* kw_name) -{ - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION >= 3 - "%s() got multiple values for keyword argument '%U'", func_name, kw_name); - #else - "%s() got multiple values for keyword argument '%s'", func_name, - PyString_AsString(kw_name)); - #endif -} - -/* ParseKeywords */ -static int __Pyx_ParseOptionalKeywords( - PyObject *kwds, - PyObject **argnames[], - PyObject *kwds2, - PyObject *values[], - Py_ssize_t num_pos_args, - const char* function_name) -{ - PyObject *key = 0, *value = 0; - Py_ssize_t pos = 0; - PyObject*** name; - PyObject*** first_kw_arg = argnames + num_pos_args; - while (PyDict_Next(kwds, &pos, &key, &value)) { - name = first_kw_arg; - while (*name && (**name != key)) name++; - if (*name) { - values[name-argnames] = value; - continue; - } - name = first_kw_arg; - #if PY_MAJOR_VERSION < 3 - if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { - while (*name) { - if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) - && _PyString_Eq(**name, key)) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - if ((**argname == key) || ( - (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) - && _PyString_Eq(**argname, key))) { - goto arg_passed_twice; - } - argname++; - } - } - } else - #endif - if (likely(PyUnicode_Check(key))) { - while (*name) { - int cmp = (**name == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : - #endif - PyUnicode_Compare(**name, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - int cmp = (**argname == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : - #endif - PyUnicode_Compare(**argname, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) goto arg_passed_twice; - argname++; - } - } - } else - goto invalid_keyword_type; - if (kwds2) { - if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; - } else { - goto invalid_keyword; - } - } - return 0; -arg_passed_twice: - __Pyx_RaiseDoubleKeywordsError(function_name, key); - goto bad; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%.200s() keywords must be strings", function_name); - goto bad; -invalid_keyword: - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION < 3 - "%.200s() got an unexpected keyword argument '%.200s'", - function_name, PyString_AsString(key)); - #else - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif -bad: - return -1; -} - -/* ArgTypeTest */ -static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { - PyErr_Format(PyExc_TypeError, - "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", - name, type->tp_name, Py_TYPE(obj)->tp_name); -} -static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, - const char *name, int exact) -{ - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (none_allowed && obj == Py_None) return 1; - else if (exact) { - if (likely(Py_TYPE(obj) == type)) return 1; - #if PY_MAJOR_VERSION == 2 - else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; - #endif - } - else { - if (likely(PyObject_TypeCheck(obj, type))) return 1; - } - __Pyx_RaiseArgumentTypeInvalid(name, obj, type); - return 0; -} - -/* BufferFormatCheck */ -static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { - unsigned int n = 1; - return *(unsigned char*)(&n) != 0; -} -static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, - __Pyx_BufFmt_StackElem* stack, - __Pyx_TypeInfo* type) { - stack[0].field = &ctx->root; - stack[0].parent_offset = 0; - ctx->root.type = type; - ctx->root.name = "buffer dtype"; - ctx->root.offset = 0; - ctx->head = stack; - ctx->head->field = &ctx->root; - ctx->fmt_offset = 0; - ctx->head->parent_offset = 0; - ctx->new_packmode = '@'; - ctx->enc_packmode = '@'; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->is_complex = 0; - ctx->is_valid_array = 0; - ctx->struct_alignment = 0; - while (type->typegroup == 'S') { - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = 0; - type = type->fields->type; - } -} -static int __Pyx_BufFmt_ParseNumber(const char** ts) { - int count; - const char* t = *ts; - if (*t < '0' || *t > '9') { - return -1; - } else { - count = *t++ - '0'; - while (*t >= '0' && *t < '9') { - count *= 10; - count += *t++ - '0'; - } - } - *ts = t; - return count; -} -static int __Pyx_BufFmt_ExpectNumber(const char **ts) { - int number = __Pyx_BufFmt_ParseNumber(ts); - if (number == -1) - PyErr_Format(PyExc_ValueError,\ - "Does not understand character buffer dtype format string ('%c')", **ts); - return number; -} -static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { - PyErr_Format(PyExc_ValueError, - "Unexpected format string character: '%c'", ch); -} -static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { - switch (ch) { - case 'c': return "'char'"; - case 'b': return "'signed char'"; - case 'B': return "'unsigned char'"; - case 'h': return "'short'"; - case 'H': return "'unsigned short'"; - case 'i': return "'int'"; - case 'I': return "'unsigned int'"; - case 'l': return "'long'"; - case 'L': return "'unsigned long'"; - case 'q': return "'long long'"; - case 'Q': return "'unsigned long long'"; - case 'f': return (is_complex ? 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- } else { - size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); - } - if (ctx->enc_packmode == '@') { - size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); - size_t align_mod_offset; - if (align_at == 0) return -1; - align_mod_offset = ctx->fmt_offset % align_at; - if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; - if (ctx->struct_alignment == 0) - ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, - ctx->is_complex); - } - if (type->size != size || type->typegroup != group) { - if (type->typegroup == 'C' && type->fields != NULL) { - size_t parent_offset = ctx->head->parent_offset + field->offset; - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = parent_offset; - continue; - } - if ((type->typegroup == 'H' || group == 'H') && type->size == size) { - } else { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - } - offset = ctx->head->parent_offset + field->offset; - if (ctx->fmt_offset != offset) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", - (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); - return -1; - } - ctx->fmt_offset += size; - if (arraysize) - ctx->fmt_offset += (arraysize - 1) * size; - --ctx->enc_count; - while (1) { - if (field == &ctx->root) { - ctx->head = NULL; - if (ctx->enc_count != 0) { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - break; - } - ctx->head->field = ++field; - if (field->type == NULL) { - --ctx->head; - field = ctx->head->field; - continue; - } else if (field->type->typegroup == 'S') { - size_t parent_offset = ctx->head->parent_offset + field->offset; - if (field->type->fields->type == NULL) continue; - field = field->type->fields; - ++ctx->head; - ctx->head->field = field; - ctx->head->parent_offset = parent_offset; - break; - } else { - break; - } - } - } while (ctx->enc_count); - ctx->enc_type = 0; - ctx->is_complex = 0; - return 0; -} -static CYTHON_INLINE PyObject * -__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) -{ - const char *ts = *tsp; - int i = 0, number; - int ndim = ctx->head->field->type->ndim; -; - ++ts; - if (ctx->new_count != 1) { - PyErr_SetString(PyExc_ValueError, - "Cannot handle repeated arrays in format string"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - while (*ts && *ts != ')') { - switch (*ts) { - case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; - default: break; - } - number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) - return PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %d", - ctx->head->field->type->arraysize[i], number); - if (*ts != ',' && *ts != ')') - return PyErr_Format(PyExc_ValueError, - "Expected a comma in format string, got '%c'", *ts); - if (*ts == ',') ts++; - i++; - } - if (i != ndim) - return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", - ctx->head->field->type->ndim, i); - if (!*ts) { - PyErr_SetString(PyExc_ValueError, - "Unexpected end of format string, expected ')'"); - return NULL; - } - ctx->is_valid_array = 1; - ctx->new_count = 1; - *tsp = ++ts; - return Py_None; -} -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { - int got_Z = 0; - while (1) { - switch(*ts) { - case 0: - if (ctx->enc_type != 0 && ctx->head == NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - if (ctx->head != NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - return ts; - case ' ': - case '\r': - case '\n': - ++ts; - break; - case '<': - if (!__Pyx_IsLittleEndian()) { - PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '>': - case '!': - if (__Pyx_IsLittleEndian()) { - PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '=': - case '@': - case '^': - ctx->new_packmode = *ts++; - break; - case 'T': - { - const char* ts_after_sub; - size_t i, struct_count = ctx->new_count; - size_t struct_alignment = ctx->struct_alignment; - ctx->new_count = 1; - ++ts; - if (*ts != '{') { - PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - ctx->enc_count = 0; - ctx->struct_alignment = 0; - ++ts; - ts_after_sub = ts; - for (i = 0; i != struct_count; ++i) { - ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); - if (!ts_after_sub) return NULL; - } - ts = ts_after_sub; - if (struct_alignment) ctx->struct_alignment = struct_alignment; - } - break; - case '}': - { - size_t alignment = ctx->struct_alignment; - ++ts; - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - if (alignment && ctx->fmt_offset % alignment) { - ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); - } - } - return ts; - case 'x': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->fmt_offset += ctx->new_count; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->enc_packmode = ctx->new_packmode; - ++ts; - break; - case 'Z': - got_Z = 1; - ++ts; - if (*ts != 'f' && *ts != 'd' && *ts != 'g') { - __Pyx_BufFmt_RaiseUnexpectedChar('Z'); - return NULL; - } - case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': - case 'l': case 'L': case 'q': case 'Q': - case 'f': case 'd': case 'g': - case 'O': case 'p': - if (ctx->enc_type == *ts && got_Z == ctx->is_complex && - ctx->enc_packmode == ctx->new_packmode) { - ctx->enc_count += ctx->new_count; - ctx->new_count = 1; - got_Z = 0; - ++ts; - break; - } - case 's': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_count = ctx->new_count; - ctx->enc_packmode = ctx->new_packmode; - ctx->enc_type = *ts; - ctx->is_complex = got_Z; - ++ts; - ctx->new_count = 1; - got_Z = 0; - break; - case ':': - ++ts; - while(*ts != ':') ++ts; - ++ts; - break; - case '(': - if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; - break; - default: - { - int number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - ctx->new_count = (size_t)number; - } - } - } -} -static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { - buf->buf = NULL; - buf->obj = NULL; - buf->strides = __Pyx_zeros; - buf->shape = __Pyx_zeros; - buf->suboffsets = __Pyx_minusones; -} -static CYTHON_INLINE int __Pyx_GetBufferAndValidate( - Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, - int nd, int cast, __Pyx_BufFmt_StackElem* stack) -{ - if (obj == Py_None || obj == NULL) { - __Pyx_ZeroBuffer(buf); - return 0; - } - buf->buf = NULL; - if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; - if (buf->ndim != nd) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - nd, buf->ndim); - goto fail; - } - if (!cast) { - __Pyx_BufFmt_Context ctx; - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; - } - if ((unsigned)buf->itemsize != dtype->size) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", - buf->itemsize, (buf->itemsize > 1) ? "s" : "", - dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); - goto fail; - } - if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; - return 0; -fail:; - __Pyx_ZeroBuffer(buf); - return -1; -} -static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { - if (info->buf == NULL) return; - if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; - __Pyx_ReleaseBuffer(info); -} - -/* ExtTypeTest */ - static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(PyObject_TypeCheck(obj, type))) - return 1; - PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", - Py_TYPE(obj)->tp_name, type->tp_name); - return 0; -} - -/* PyIntBinop */ - #if !CYTHON_COMPILING_IN_PYPY -static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) { - #if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(op1))) { - const long b = intval; - long x; - long a = PyInt_AS_LONG(op1); - x = (long)((unsigned long)a + b); - if (likely((x^a) >= 0 || (x^b) >= 0)) - return PyInt_FromLong(x); - return PyLong_Type.tp_as_number->nb_add(op1, op2); - } - #endif - #if CYTHON_USE_PYLONG_INTERNALS - if (likely(PyLong_CheckExact(op1))) { - const long b = intval; - long a, x; -#ifdef HAVE_LONG_LONG - const PY_LONG_LONG llb = intval; - PY_LONG_LONG lla, llx; -#endif - const digit* digits = ((PyLongObject*)op1)->ob_digit; - const Py_ssize_t size = Py_SIZE(op1); - if (likely(__Pyx_sst_abs(size) <= 1)) { - a = likely(size) ? digits[0] : 0; - if (size == -1) a = -a; - } else { - switch (size) { - case -2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case 2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case -3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case 3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case -4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - case 4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - default: return PyLong_Type.tp_as_number->nb_add(op1, op2); - } - } - x = a + b; - return PyLong_FromLong(x); -#ifdef HAVE_LONG_LONG - long_long: - llx = lla + llb; - return PyLong_FromLongLong(llx); -#endif - - - } - #endif - if (PyFloat_CheckExact(op1)) { - const long b = intval; - double a = PyFloat_AS_DOUBLE(op1); - double result; - PyFPE_START_PROTECT("add", return NULL) - result = ((double)a) + (double)b; - PyFPE_END_PROTECT(result) - return PyFloat_FromDouble(result); - } - return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); -} -#endif - -/* PyCFunctionFastCall */ - #if CYTHON_FAST_PYCCALL -static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { - PyCFunctionObject *func = (PyCFunctionObject*)func_obj; - PyCFunction meth = PyCFunction_GET_FUNCTION(func); - PyObject *self = PyCFunction_GET_SELF(func); - assert(PyCFunction_Check(func)); - assert(METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST))); - assert(nargs >= 0); - assert(nargs == 0 || args != NULL); - /* _PyCFunction_FastCallDict() must not be called with an exception set, - because it may clear it (directly or indirectly) and so the - caller loses its exception */ - assert(!PyErr_Occurred()); - return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs, NULL); -} -#endif // CYTHON_FAST_PYCCALL - -/* PyFunctionFastCall */ - #if CYTHON_FAST_PYCALL -#include "frameobject.h" -static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, - PyObject *globals) { - PyFrameObject *f; - PyThreadState *tstate = PyThreadState_GET(); - PyObject **fastlocals; - Py_ssize_t i; - PyObject *result; - assert(globals != NULL); - /* XXX Perhaps we should create a specialized - PyFrame_New() that doesn't take locals, but does - take builtins without sanity checking them. - */ - assert(tstate != NULL); - f = PyFrame_New(tstate, co, globals, NULL); - if (f == NULL) { - return NULL; - } - fastlocals = f->f_localsplus; - for (i = 0; i < na; i++) { - Py_INCREF(*args); - fastlocals[i] = *args++; - } - result = PyEval_EvalFrameEx(f,0); - ++tstate->recursion_depth; - Py_DECREF(f); - --tstate->recursion_depth; - return result; -} -#if 1 || PY_VERSION_HEX < 0x030600B1 -static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) { - PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); - PyObject *globals = PyFunction_GET_GLOBALS(func); - PyObject *argdefs = PyFunction_GET_DEFAULTS(func); - PyObject *closure; -#if PY_MAJOR_VERSION >= 3 - PyObject *kwdefs; -#endif - PyObject *kwtuple, **k; - PyObject **d; - Py_ssize_t nd; - Py_ssize_t nk; - PyObject *result; - assert(kwargs == NULL || PyDict_Check(kwargs)); - nk = kwargs ? PyDict_Size(kwargs) : 0; - if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { - return NULL; - } - if ( -#if PY_MAJOR_VERSION >= 3 - co->co_kwonlyargcount == 0 && -#endif - likely(kwargs == NULL || nk == 0) && - co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { - if (argdefs == NULL && co->co_argcount == nargs) { - result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); - goto done; - } - else if (nargs == 0 && argdefs != NULL - && co->co_argcount == Py_SIZE(argdefs)) { - /* function called with no arguments, but all parameters have - a default value: use default values as arguments .*/ - args = &PyTuple_GET_ITEM(argdefs, 0); - result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); - goto done; - } - } - if (kwargs != NULL) { - Py_ssize_t pos, i; - kwtuple = PyTuple_New(2 * nk); - if (kwtuple == NULL) { - result = NULL; - goto done; - } - k = &PyTuple_GET_ITEM(kwtuple, 0); - pos = i = 0; - while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { - Py_INCREF(k[i]); - Py_INCREF(k[i+1]); - i += 2; - } - nk = i / 2; - } - else { - kwtuple = NULL; - k = NULL; - } - closure = PyFunction_GET_CLOSURE(func); -#if PY_MAJOR_VERSION >= 3 - kwdefs = PyFunction_GET_KW_DEFAULTS(func); -#endif - if (argdefs != NULL) { - d = &PyTuple_GET_ITEM(argdefs, 0); - nd = Py_SIZE(argdefs); - } - else { - d = NULL; - nd = 0; - } -#if PY_MAJOR_VERSION >= 3 - result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, - args, nargs, - k, (int)nk, - d, (int)nd, kwdefs, closure); -#else - result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, - args, nargs, - k, (int)nk, - d, (int)nd, closure); -#endif - Py_XDECREF(kwtuple); -done: - Py_LeaveRecursiveCall(); - return result; -} -#endif // CPython < 3.6 -#endif // CYTHON_FAST_PYCALL - -/* PyObjectCall */ - #if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { - PyObject *result; - ternaryfunc call = func->ob_type->tp_call; - if (unlikely(!call)) - return PyObject_Call(func, arg, kw); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = (*call)(func, arg, kw); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyObjectCallMethO */ - #if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { - PyObject *self, *result; - PyCFunction cfunc; - cfunc = PyCFunction_GET_FUNCTION(func); - self = PyCFunction_GET_SELF(func); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = cfunc(self, arg); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyObjectCallOneArg */ - #if CYTHON_COMPILING_IN_CPYTHON -static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_New(1); - if (unlikely(!args)) return NULL; - Py_INCREF(arg); - PyTuple_SET_ITEM(args, 0, arg); - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { -#if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCall(func, &arg, 1); - } -#endif -#ifdef __Pyx_CyFunction_USED - if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { -#else - if (likely(PyCFunction_Check(func))) { -#endif - if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { - return __Pyx_PyObject_CallMethO(func, arg); -#if CYTHON_FAST_PYCCALL - } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { - return __Pyx_PyCFunction_FastCall(func, &arg, 1); -#endif - } - } - return __Pyx__PyObject_CallOneArg(func, arg); -} -#else -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_Pack(1, arg); - if (unlikely(!args)) return NULL; - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -#endif - -/* PyObjectCallNoArg */ - #if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { -#if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCall(func, NULL, 0); - } -#endif -#ifdef __Pyx_CyFunction_USED - if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { -#else - if (likely(PyCFunction_Check(func))) { -#endif - if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { - return __Pyx_PyObject_CallMethO(func, NULL); - } - } - return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL); -} -#endif - -/* GetModuleGlobalName */ - static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { - PyObject *result; -#if !CYTHON_AVOID_BORROWED_REFS - result = PyDict_GetItem(__pyx_d, name); - if (likely(result)) { - Py_INCREF(result); - } else { -#else - result = PyObject_GetItem(__pyx_d, name); - if (!result) { - PyErr_Clear(); -#endif - result = __Pyx_GetBuiltinName(name); - } - return result; -} - -/* BufferIndexError */ - static void __Pyx_RaiseBufferIndexError(int axis) { - PyErr_Format(PyExc_IndexError, - "Out of bounds on buffer access (axis %d)", axis); -} - -/* PyErrFetchRestore */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - tmp_type = tstate->curexc_type; - tmp_value = tstate->curexc_value; - tmp_tb = tstate->curexc_traceback; - tstate->curexc_type = type; - tstate->curexc_value = value; - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - *type = tstate->curexc_type; - *value = tstate->curexc_value; - *tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -} -#endif - -/* RaiseException */ - #if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, - CYTHON_UNUSED PyObject *cause) { - __Pyx_PyThreadState_declare - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - if (PyType_Check(type)) { -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - } - __Pyx_PyThreadState_assign - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - int is_subclass = PyObject_IsSubclass(instance_class, type); - if (!is_subclass) { - instance_class = NULL; - } else if (unlikely(is_subclass == -1)) { - goto bad; - } else { - type = instance_class; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } -#if PY_VERSION_HEX >= 0x03030000 - if (cause) { -#else - if (cause && cause != Py_None) { -#endif - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { -#if CYTHON_COMPILING_IN_PYPY - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); - Py_INCREF(tb); - PyErr_Restore(tmp_type, tmp_value, tb); - Py_XDECREF(tmp_tb); -#else - PyThreadState *tstate = PyThreadState_GET(); - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } -#endif - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -/* RaiseTooManyValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -/* RaiseNeedMoreValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", - index, (index == 1) ? "" : "s"); -} - -/* RaiseNoneIterError */ - static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { - PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); -} - -/* SaveResetException */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); -} -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -#endif - -/* PyErrExceptionMatches */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) { - PyObject *exc_type = tstate->curexc_type; - if (exc_type == err) return 1; - if (unlikely(!exc_type)) return 0; - return PyErr_GivenExceptionMatches(exc_type, err); -} -#endif - -/* GetException */ - #if CYTHON_FAST_THREAD_STATE -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { -#endif - PyObject *local_type, *local_value, *local_tb; -#if CYTHON_FAST_THREAD_STATE - PyObject *tmp_type, *tmp_value, *tmp_tb; - local_type = tstate->curexc_type; - local_value = tstate->curexc_value; - local_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(&local_type, &local_value, &local_tb); -#endif - PyErr_NormalizeException(&local_type, &local_value, &local_tb); -#if CYTHON_FAST_THREAD_STATE - if (unlikely(tstate->curexc_type)) -#else - if (unlikely(PyErr_Occurred())) -#endif - goto bad; - #if PY_MAJOR_VERSION >= 3 - if (local_tb) { - if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) - goto bad; - } - #endif - Py_XINCREF(local_tb); - Py_XINCREF(local_type); - Py_XINCREF(local_value); - *type = local_type; - *value = local_value; - *tb = local_tb; -#if CYTHON_FAST_THREAD_STATE - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = local_type; - tstate->exc_value = local_value; - tstate->exc_traceback = local_tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(local_type, local_value, local_tb); -#endif - return 0; -bad: - *type = 0; - *value = 0; - *tb = 0; - Py_XDECREF(local_type); - Py_XDECREF(local_value); - Py_XDECREF(local_tb); - return -1; -} - -/* Import */ - static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *empty_list = 0; - PyObject *module = 0; - PyObject *global_dict = 0; - PyObject *empty_dict = 0; - PyObject *list; - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); - if (!py_import) - goto bad; - #endif - if (from_list) - list = from_list; - else { - empty_list = PyList_New(0); - if (!empty_list) - goto bad; - list = empty_list; - } - global_dict = PyModule_GetDict(__pyx_m); - if (!global_dict) - goto bad; - empty_dict = PyDict_New(); - if (!empty_dict) - goto bad; - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if (strchr(__Pyx_MODULE_NAME, '.')) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(1); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, 1); - #endif - if (!module) { - if (!PyErr_ExceptionMatches(PyExc_ImportError)) - goto bad; - PyErr_Clear(); - } - } - level = 0; - } - #endif - if (!module) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(level); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, level); - #endif - } - } -bad: - #if PY_VERSION_HEX < 0x03030000 - Py_XDECREF(py_import); - #endif - Py_XDECREF(empty_list); - Py_XDECREF(empty_dict); - return module; -} - -/* CodeObjectCache */ - static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { - int start = 0, mid = 0, end = count - 1; - if (end >= 0 && code_line > entries[end].code_line) { - return count; - } - while (start < end) { - mid = start + (end - start) / 2; - if (code_line < entries[mid].code_line) { - end = mid; - } else if (code_line > entries[mid].code_line) { - start = mid + 1; - } else { - return mid; - } - } - if (code_line <= entries[mid].code_line) { - return mid; - } else { - return mid + 1; - } -} -static PyCodeObject *__pyx_find_code_object(int code_line) { - PyCodeObject* code_object; - int pos; - if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { - return NULL; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { - return NULL; - } - code_object = __pyx_code_cache.entries[pos].code_object; - Py_INCREF(code_object); - return code_object; -} -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { - int pos, i; - __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; - if (unlikely(!code_line)) { - return; - } - if (unlikely(!entries)) { - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); - if (likely(entries)) { - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = 64; - __pyx_code_cache.count = 1; - entries[0].code_line = code_line; - entries[0].code_object = code_object; - Py_INCREF(code_object); - } - return; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { - PyCodeObject* tmp = entries[pos].code_object; - entries[pos].code_object = code_object; - Py_DECREF(tmp); - return; - } - if (__pyx_code_cache.count == __pyx_code_cache.max_count) { - int new_max = __pyx_code_cache.max_count + 64; - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} - -/* AddTraceback */ - #include "compile.h" -#include "frameobject.h" -#include "traceback.h" -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_srcfile = 0; - PyObject *py_funcname = 0; - #if PY_MAJOR_VERSION < 3 - py_srcfile = PyString_FromString(filename); - #else - py_srcfile = PyUnicode_FromString(filename); - #endif - if (!py_srcfile) goto bad; - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - #else - py_funcname = PyUnicode_FromString(funcname); - #endif - } - if (!py_funcname) goto bad; - py_code = __Pyx_PyCode_New( - 0, - 0, - 0, - 0, - 0, - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, - __pyx_empty_bytes /*PyObject *lnotab*/ - ); - Py_DECREF(py_srcfile); - Py_DECREF(py_funcname); - return py_code; -bad: - Py_XDECREF(py_srcfile); - Py_XDECREF(py_funcname); - return NULL; -} -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyFrameObject *py_frame = 0; - py_code = __pyx_find_code_object(c_line ? c_line : py_line); - if (!py_code) { - py_code = __Pyx_CreateCodeObjectForTraceback( - funcname, c_line, py_line, filename); - if (!py_code) goto bad; - __pyx_insert_code_object(c_line ? c_line : py_line, py_code); - } - py_frame = PyFrame_New( - PyThreadState_GET(), /*PyThreadState *tstate,*/ - py_code, /*PyCodeObject *code,*/ - __pyx_d, /*PyObject *globals,*/ - 0 /*PyObject *locals*/ - ); - if (!py_frame) goto bad; - __Pyx_PyFrame_SetLineNumber(py_frame, py_line); - PyTraceBack_Here(py_frame); -bad: - Py_XDECREF(py_code); - Py_XDECREF(py_frame); -} - -#if PY_MAJOR_VERSION < 3 -static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { - if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); - if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); - PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); - return -1; -} -static void __Pyx_ReleaseBuffer(Py_buffer *view) { - PyObject *obj = view->obj; - if (!obj) return; - if (PyObject_CheckBuffer(obj)) { - PyBuffer_Release(view); - return; - } - if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } - Py_DECREF(obj); - view->obj = NULL; -} -#endif - - - /* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(int) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(int) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(int), - little, !is_unsigned); - } -} - -/* CIntFromPyVerify */ - #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) -#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) -#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ - {\ - func_type value = func_value;\ - if (sizeof(target_type) < sizeof(func_type)) {\ - if (unlikely(value != (func_type) (target_type) value)) {\ - func_type zero = 0;\ - if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ - return (target_type) -1;\ - if (is_unsigned && unlikely(value < zero))\ - goto raise_neg_overflow;\ - else\ - goto raise_overflow;\ - }\ - }\ - return (target_type) value;\ - } - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(long) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(long), - little, !is_unsigned); - } -} - -/* Declarations */ - #if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return ::std::complex< float >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return x + y*(__pyx_t_float_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - __pyx_t_float_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -/* Arithmetic */ - #if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - #if 1 - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - if (b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); - } else if (fabsf(b.real) >= fabsf(b.imag)) { - if (b.real == 0 && b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); - } else { - float r = b.imag / b.real; - float s = 1.0 / (b.real + b.imag * r); - return __pyx_t_float_complex_from_parts( - (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); - } - } else { - float r = b.real / b.imag; - float s = 1.0 / (b.imag + b.real * r); - return __pyx_t_float_complex_from_parts( - (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); - } - } - #else - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - if (b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); - } else { - float denom = b.real * b.real + b.imag * b.imag; - return __pyx_t_float_complex_from_parts( - (a.real * b.real + a.imag * b.imag) / denom, - (a.imag * b.real - a.real * b.imag) / denom); - } - } - #endif - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrtf(z.real*z.real + z.imag*z.imag); - #else - return hypotf(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - float r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - float denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(a, a); - case 3: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(z, a); - case 4: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } else if (b.imag == 0) { - z.real = powf(a.real, b.real); - z.imag = 0; - return z; - } else if (a.real > 0) { - r = a.real; - theta = 0; - } else { - r = -a.real; - theta = atan2f(0, -1); - } - } else { - r = __Pyx_c_abs_float(a); - theta = atan2f(a.imag, a.real); - } - lnr = logf(r); - z_r = expf(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cosf(z_theta); - z.imag = z_r * sinf(z_theta); - return z; - } - #endif -#endif - -/* Declarations */ - #if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return ::std::complex< double >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return x + y*(__pyx_t_double_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - __pyx_t_double_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -/* Arithmetic */ - #if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - #if 1 - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - if (b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); - } else if (fabs(b.real) >= fabs(b.imag)) { - if (b.real == 0 && b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); - } else { - double r = b.imag / b.real; - double s = 1.0 / (b.real + b.imag * r); - return __pyx_t_double_complex_from_parts( - (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); - } - } else { - double r = b.real / b.imag; - double s = 1.0 / (b.imag + b.real * r); - return __pyx_t_double_complex_from_parts( - (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); - } - } - #else - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - if (b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); - } else { - double denom = b.real * b.real + b.imag * b.imag; - return __pyx_t_double_complex_from_parts( - (a.real * b.real + a.imag * b.imag) / denom, - (a.imag * b.real - a.real * b.imag) / denom); - } - } - #endif - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrt(z.real*z.real + z.imag*z.imag); - #else - return hypot(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - double r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - double denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(a, a); - case 3: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, a); - case 4: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } else if (b.imag == 0) { - z.real = pow(a.real, b.real); - z.imag = 0; - return z; - } else if (a.real > 0) { - r = a.real; - theta = 0; - } else { - r = -a.real; - theta = atan2(0, -1); - } - } else { - r = __Pyx_c_abs_double(a); - theta = atan2(a.imag, a.real); - } - lnr = log(r); - z_r = exp(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cos(z_theta); - z.imag = z_r * sin(z_theta); - return z; - } - #endif -#endif - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { - const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(enum NPY_TYPES) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(enum NPY_TYPES) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(int) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { - return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { - return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { - return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(int) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) - case -2: - if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - } -#endif - if (sizeof(int) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - int val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to int"); - return (int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; -} - -/* CIntFromPy */ - static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(long) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { - return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { - return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { - return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(long) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) - case -2: - if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - } -#endif - if (sizeof(long) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - long val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (long) -1; - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (long) -1; - val = __Pyx_PyInt_As_long(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to long"); - return (long) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; -} - -/* CheckBinaryVersion */ - static int __Pyx_check_binary_version(void) { - char ctversion[4], rtversion[4]; - PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); - if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { - char message[200]; - PyOS_snprintf(message, sizeof(message), - "compiletime version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - return PyErr_WarnEx(NULL, message, 1); - } - return 0; -} - -/* ModuleImport */ - #ifndef __PYX_HAVE_RT_ImportModule -#define __PYX_HAVE_RT_ImportModule -static PyObject *__Pyx_ImportModule(const char *name) { - PyObject *py_name = 0; - PyObject *py_module = 0; - py_name = __Pyx_PyIdentifier_FromString(name); - if (!py_name) - goto bad; - py_module = PyImport_Import(py_name); - Py_DECREF(py_name); - return py_module; -bad: - Py_XDECREF(py_name); - return 0; -} -#endif - -/* TypeImport */ - #ifndef __PYX_HAVE_RT_ImportType -#define __PYX_HAVE_RT_ImportType -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, - size_t size, int strict) -{ - PyObject *py_module = 0; - PyObject *result = 0; - PyObject *py_name = 0; - char warning[200]; - Py_ssize_t basicsize; -#ifdef Py_LIMITED_API - PyObject *py_basicsize; -#endif - py_module = __Pyx_ImportModule(module_name); - if (!py_module) - goto bad; - py_name = __Pyx_PyIdentifier_FromString(class_name); - if (!py_name) - goto bad; - result = PyObject_GetAttr(py_module, py_name); - Py_DECREF(py_name); - py_name = 0; - Py_DECREF(py_module); - py_module = 0; - if (!result) - goto bad; - if (!PyType_Check(result)) { - PyErr_Format(PyExc_TypeError, - "%.200s.%.200s is not a type object", - module_name, class_name); - goto bad; - } -#ifndef Py_LIMITED_API - basicsize = ((PyTypeObject *)result)->tp_basicsize; -#else - py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); - if (!py_basicsize) - goto bad; - basicsize = PyLong_AsSsize_t(py_basicsize); - Py_DECREF(py_basicsize); - py_basicsize = 0; - if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) - goto bad; -#endif - if (!strict && (size_t)basicsize > size) { - PyOS_snprintf(warning, sizeof(warning), - "%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd", - module_name, class_name, basicsize, size); - if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; - } - else if ((size_t)basicsize != size) { - PyErr_Format(PyExc_ValueError, - "%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd", - module_name, class_name, basicsize, size); - goto bad; - } - return (PyTypeObject *)result; -bad: - Py_XDECREF(py_module); - Py_XDECREF(result); - return NULL; -} -#endif - -/* InitStrings */ - static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION < 3 - if (t->is_unicode) { - *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); - } else if (t->intern) { - *t->p = PyString_InternFromString(t->s); - } else { - *t->p = PyString_FromStringAndSize(t->s, t->n - 1); - } - #else - if (t->is_unicode | t->is_str) { - if (t->intern) { - *t->p = PyUnicode_InternFromString(t->s); - } else if (t->encoding) { - *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); - } else { - *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); - } - } else { - *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); - } - #endif - if (!*t->p) - return -1; - ++t; - } - return 0; -} - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { - return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); -} -static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { - Py_ssize_t ignore; - return __Pyx_PyObject_AsStringAndSize(o, &ignore); -} -static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { -#if CYTHON_COMPILING_IN_CPYTHON && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) - if ( -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - __Pyx_sys_getdefaultencoding_not_ascii && -#endif - PyUnicode_Check(o)) { -#if PY_VERSION_HEX < 0x03030000 - char* defenc_c; - PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); - if (!defenc) return NULL; - defenc_c = PyBytes_AS_STRING(defenc); -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - { - char* end = defenc_c + PyBytes_GET_SIZE(defenc); - char* c; - for (c = defenc_c; c < end; c++) { - if ((unsigned char) (*c) >= 128) { - PyUnicode_AsASCIIString(o); - return NULL; - } - } - } -#endif - *length = PyBytes_GET_SIZE(defenc); - return defenc_c; -#else - if (__Pyx_PyUnicode_READY(o) == -1) return NULL; -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - if (PyUnicode_IS_ASCII(o)) { - *length = PyUnicode_GET_LENGTH(o); - return PyUnicode_AsUTF8(o); - } else { - PyUnicode_AsASCIIString(o); - return NULL; - } -#else - return PyUnicode_AsUTF8AndSize(o, length); -#endif -#endif - } else -#endif -#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) - if (PyByteArray_Check(o)) { - *length = PyByteArray_GET_SIZE(o); - return PyByteArray_AS_STRING(o); - } else -#endif - { - char* result; - int r = PyBytes_AsStringAndSize(o, &result, length); - if (unlikely(r < 0)) { - return NULL; - } else { - return result; - } - } -} -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { - int is_true = x == Py_True; - if (is_true | (x == Py_False) | (x == Py_None)) return is_true; - else return PyObject_IsTrue(x); -} -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { -#if CYTHON_USE_TYPE_SLOTS - PyNumberMethods *m; -#endif - const char *name = NULL; - PyObject *res = NULL; -#if PY_MAJOR_VERSION < 3 - if (PyInt_Check(x) || PyLong_Check(x)) -#else - if (PyLong_Check(x)) -#endif - return __Pyx_NewRef(x); -#if CYTHON_USE_TYPE_SLOTS - m = Py_TYPE(x)->tp_as_number; - #if PY_MAJOR_VERSION < 3 - if (m && m->nb_int) { - name = "int"; - res = PyNumber_Int(x); - } - else if (m && m->nb_long) { - name = "long"; - res = PyNumber_Long(x); - } - #else - if (m && m->nb_int) { - name = "int"; - res = PyNumber_Long(x); - } - #endif -#else - res = PyNumber_Int(x); -#endif - if (res) { -#if PY_MAJOR_VERSION < 3 - if (!PyInt_Check(res) && !PyLong_Check(res)) { -#else - if (!PyLong_Check(res)) { -#endif - PyErr_Format(PyExc_TypeError, - "__%.4s__ returned non-%.4s (type %.200s)", - name, name, Py_TYPE(res)->tp_name); - Py_DECREF(res); - return NULL; - } - } - else if (!PyErr_Occurred()) { - PyErr_SetString(PyExc_TypeError, - "an integer is required"); - } - return res; -} -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { - Py_ssize_t ival; - PyObject *x; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(b))) { - if (sizeof(Py_ssize_t) >= sizeof(long)) - return PyInt_AS_LONG(b); - else - return PyInt_AsSsize_t(x); - } -#endif - if (likely(PyLong_CheckExact(b))) { - #if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)b)->ob_digit; - const Py_ssize_t size = Py_SIZE(b); - if (likely(__Pyx_sst_abs(size) <= 1)) { - ival = likely(size) ? digits[0] : 0; - if (size == -1) ival = -ival; - return ival; - } else { - switch (size) { - case 2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - } - } - #endif - return PyLong_AsSsize_t(b); - } - x = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { - return PyInt_FromSize_t(ival); -} - - -#endif /* Py_PYTHON_H */ diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/nms.pyx b/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/nms.pyx deleted file mode 100755 index 344f66d..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/nms.pyx +++ /dev/null @@ -1,123 +0,0 @@ -# -------------------------------------------------------- -# Fast R-CNN -# Copyright (c) 2015 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Ross Girshick -# -------------------------------------------------------- - -import numpy as np -cimport numpy as np - -cdef inline np.float32_t max(np.float32_t a, np.float32_t b): - return a if a >= b else b - -cdef inline np.float32_t min(np.float32_t a, np.float32_t b): - return a if a <= b else b - -def nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): - cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] - cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] - cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] - cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] - cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] - - cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) - cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1] - - cdef int ndets = dets.shape[0] - cdef np.ndarray[np.int_t, ndim=1] suppressed = \ - np.zeros((ndets), dtype=np.int) - - # nominal indices - cdef int _i, _j - # sorted indices - cdef int i, j - # temp variables for box i's (the box currently under consideration) - cdef np.float32_t ix1, iy1, ix2, iy2, iarea - # variables for computing overlap with box j (lower scoring box) - cdef np.float32_t xx1, yy1, xx2, yy2 - cdef np.float32_t w, h - cdef np.float32_t inter, ovr - - keep = [] - for _i in range(ndets): - i = order[_i] - if suppressed[i] == 1: - continue - keep.append(i) - ix1 = x1[i] - iy1 = y1[i] - ix2 = x2[i] - iy2 = y2[i] - iarea = areas[i] - for _j in range(_i + 1, ndets): - j = order[_j] - if suppressed[j] == 1: - continue - xx1 = max(ix1, x1[j]) - yy1 = max(iy1, y1[j]) - xx2 = min(ix2, x2[j]) - yy2 = min(iy2, y2[j]) - w = max(0.0, xx2 - xx1 + 1) - h = max(0.0, yy2 - yy1 + 1) - inter = w * h - ovr = inter / (iarea + areas[j] - inter) - if ovr >= thresh: - suppressed[j] = 1 - - return keep - -def nms_new(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): - cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] - cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] - cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] - cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] - cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] - - cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) - cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1] - - cdef int ndets = dets.shape[0] - cdef np.ndarray[np.int_t, ndim=1] suppressed = \ - np.zeros((ndets), dtype=np.int) - - # nominal indices - cdef int _i, _j - # sorted indices - cdef int i, j - # temp variables for box i's (the box currently under consideration) - cdef np.float32_t ix1, iy1, ix2, iy2, iarea - # variables for computing overlap with box j (lower scoring box) - cdef np.float32_t xx1, yy1, xx2, yy2 - cdef np.float32_t w, h - cdef np.float32_t inter, ovr - - keep = [] - for _i in range(ndets): - i = order[_i] - if suppressed[i] == 1: - continue - keep.append(i) - ix1 = x1[i] - iy1 = y1[i] - ix2 = x2[i] - iy2 = y2[i] - iarea = areas[i] - for _j in range(_i + 1, ndets): - j = order[_j] - if suppressed[j] == 1: - continue - xx1 = max(ix1, x1[j]) - yy1 = max(iy1, y1[j]) - xx2 = min(ix2, x2[j]) - yy2 = min(iy2, y2[j]) - w = max(0.0, xx2 - xx1 + 1) - h = max(0.0, yy2 - yy1 + 1) - inter = w * h - ovr = inter / (iarea + areas[j] - inter) - ovr1 = inter / iarea - ovr2 = inter / areas[j] - if ovr >= thresh or ovr1 > 0.95 or ovr2 > 0.95: - suppressed[j] = 1 - - return keep diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/setup.py b/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/setup.py deleted file mode 100755 index 1227d34..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/cython_utils/setup.py +++ /dev/null @@ -1,133 +0,0 @@ -# -------------------------------------------------------- -# Fast R-CNN -# Copyright (c) 2015 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Ross Girshick -# -------------------------------------------------------- - -import os -from os.path import join as pjoin -import numpy as np -from distutils.core import setup -from distutils.extension import Extension -from Cython.Distutils import build_ext - -def find_in_path(name, path): - "Find a file in a search path" - #adapted fom http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/ - for dir in path.split(os.pathsep): - binpath = pjoin(dir, name) - if os.path.exists(binpath): - return os.path.abspath(binpath) - return None - -def locate_cuda(): - """Locate the CUDA environment on the system - - Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64' - and values giving the absolute path to each directory. - - Starts by looking for the CUDAHOME env variable. If not found, everything - is based on finding 'nvcc' in the PATH. - """ - - # first check if the CUDAHOME env variable is in use - if 'CUDAHOME' in os.environ: - home = os.environ['CUDAHOME'] - nvcc = pjoin(home, 'bin', 'nvcc') - else: - # otherwise, search the PATH for NVCC - default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin') - nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path) - if nvcc is None: - raise EnvironmentError('The nvcc binary could not be ' - 'located in your $PATH. Either add it to your path, or set $CUDAHOME') - home = os.path.dirname(os.path.dirname(nvcc)) - - cudaconfig = {'home':home, 'nvcc':nvcc, - 'include': pjoin(home, 'include'), - 'lib64': pjoin(home, 'lib64')} - for k, v in cudaconfig.items(): - if not os.path.exists(v): - raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v)) - - return cudaconfig -CUDA = locate_cuda() - -# Obtain the numpy include directory. This logic works across numpy versions. -try: - numpy_include = np.get_include() -except AttributeError: - numpy_include = np.get_numpy_include() - -def customize_compiler_for_nvcc(self): - """inject deep into distutils to customize how the dispatch - to gcc/nvcc works. - - If you subclass UnixCCompiler, it's not trivial to get your subclass - injected in, and still have the right customizations (i.e. - distutils.sysconfig.customize_compiler) run on it. So instead of going - the OO route, I have this. Note, it's kindof like a wierd functional - subclassing going on.""" - - # tell the compiler it can processes .cu - self.src_extensions.append('.cu') - - # save references to the default compiler_so and _comple methods - default_compiler_so = self.compiler_so - super = self._compile - - # now redefine the _compile method. This gets executed for each - # object but distutils doesn't have the ability to change compilers - # based on source extension: we add it. - def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts): - print(extra_postargs) - if os.path.splitext(src)[1] == '.cu': - # use the cuda for .cu files - self.set_executable('compiler_so', CUDA['nvcc']) - # use only a subset of the extra_postargs, which are 1-1 translated - # from the extra_compile_args in the Extension class - postargs = extra_postargs['nvcc'] - else: - postargs = extra_postargs['gcc'] - - super(obj, src, ext, cc_args, postargs, pp_opts) - # reset the default compiler_so, which we might have changed for cuda - self.compiler_so = default_compiler_so - - # inject our redefined _compile method into the class - self._compile = _compile - -# run the customize_compiler -class custom_build_ext(build_ext): - def build_extensions(self): - customize_compiler_for_nvcc(self.compiler) - build_ext.build_extensions(self) - -ext_modules = [ - Extension( - "cython_bbox", - ["bbox.pyx"], - extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, - include_dirs = [numpy_include] - ), - Extension( - "cython_nms", - ["nms.pyx"], - extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, - include_dirs = [numpy_include] - ) - # Extension( - # "cpu_nms", - # ["cpu_nms.pyx"], - # extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, - # include_dirs = [numpy_include] - # ) -] - -setup( - name='tf_faster_rcnn', - ext_modules=ext_modules, - # inject our custom trigger - cmdclass={'build_ext': custom_build_ext}, -) diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/draw_box_in_img.py b/example-apps/PDD/pcb-defect-detection/libs/box_utils/draw_box_in_img.py deleted file mode 100755 index c80c5d2..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/draw_box_in_img.py +++ /dev/null @@ -1,160 +0,0 @@ -# -*- coding: utf-8 -*- - -from __future__ import absolute_import, print_function, division - -import numpy as np - -from PIL import Image, ImageDraw, ImageFont -import cv2 - -from libs.configs import cfgs -from libs.label_name_dict.label_dict import LABEl_NAME_MAP -NOT_DRAW_BOXES = 0 -ONLY_DRAW_BOXES = -1 -ONLY_DRAW_BOXES_WITH_SCORES = -2 - -STANDARD_COLORS = [ - 'AliceBlue', 'Chartreuse', 'Aqua', 'Aquamarine', 'Azure', 'Beige', 'Bisque', - 'BlanchedAlmond', 'BlueViolet', 'BurlyWood', 'CadetBlue', 'AntiqueWhite', - 'Chocolate', 'Coral', 'CornflowerBlue', 'Cornsilk', 'Crimson', 'Cyan', - 'DarkCyan', 'DarkGoldenRod', 'DarkGrey', 'DarkKhaki', 'DarkOrange', - 'DarkOrchid', 'DarkSalmon', 'DarkSeaGreen', 'DarkTurquoise', 'DarkViolet', - 'DeepPink', 'DeepSkyBlue', 'DodgerBlue', 'FireBrick', 'FloralWhite', - 'ForestGreen', 'Fuchsia', 'Gainsboro', 'GhostWhite', 'Gold', 'GoldenRod', - 'Salmon', 'Tan', 'HoneyDew', 'HotPink', 'IndianRed', 'Ivory', 'Khaki', - 'Lavender', 'LavenderBlush', 'LawnGreen', 'LemonChiffon', 'LightBlue', - 'LightCoral', 'LightCyan', 'LightGoldenRodYellow', 'LightGray', 'LightGrey', - 'LightGreen', 'LightPink', 'LightSalmon', 'LightSeaGreen', 'LightSkyBlue', - 'LightSlateGray', 'LightSlateGrey', 'LightSteelBlue', 'LightYellow', 'Lime', - 'LimeGreen', 'Linen', 'Magenta', 'MediumAquaMarine', 'MediumOrchid', - 'MediumPurple', 'MediumSeaGreen', 'MediumSlateBlue', 'MediumSpringGreen', - 'MediumTurquoise', 'MediumVioletRed', 'MintCream', 'MistyRose', 'Moccasin', - 'NavajoWhite', 'OldLace', 'Olive', 'OliveDrab', 'Orange', 'OrangeRed', - 'Orchid', 'PaleGoldenRod', 'PaleGreen', 'PaleTurquoise', 'PaleVioletRed', - 'PapayaWhip', 'PeachPuff', 'Peru', 'Pink', 'Plum', 'PowderBlue', 'Purple', - 'Red', 'RosyBrown', 'RoyalBlue', 'SaddleBrown', 'Green', 'SandyBrown', - 'SeaGreen', 'SeaShell', 'Sienna', 'Silver', 'SkyBlue', 'SlateBlue', - 'SlateGray', 'SlateGrey', 'Snow', 'SpringGreen', 'SteelBlue', 'GreenYellow', - 'Teal', 'Thistle', 'Tomato', 'Turquoise', 'Violet', 'Wheat', 'White', - 'WhiteSmoke', 'Yellow', 'YellowGreen', 'LightBlue', 'LightGreen' -] -FONT = ImageFont.load_default() - - -def draw_a_rectangel_in_img(draw_obj, box, color, width): - ''' - use draw lines to draw rectangle. since the draw_rectangle func can not modify the width of rectangle - :param draw_obj: - :param box: [x1, y1, x2, y2] - :return: - ''' - x1, y1, x2, y2 = box[0], box[1], box[2], box[3] - top_left, top_right = (x1, y1), (x2, y1) - bottom_left, bottom_right = (x1, y2), (x2, y2) - - draw_obj.line(xy=[top_left, top_right], - fill=color, - width=width) - draw_obj.line(xy=[top_left, bottom_left], - fill=color, - width=width) - draw_obj.line(xy=[bottom_left, bottom_right], - fill=color, - width=width) - draw_obj.line(xy=[top_right, bottom_right], - fill=color, - width=width) - - -def only_draw_scores(draw_obj, box, score, color): - - x, y = box[0], box[1] - draw_obj.rectangle(xy=[x, y-10, x+100, y], - fill=color) - draw_obj.text(xy=(x, y-10), - text="obj:" +str(round(score, 2)), - fill='white', - font=FONT) - - -def draw_label_with_scores(draw_obj, box, label, score, color): - x, y = box[0], box[1] - draw_obj.rectangle(xy=[x, y-10, x+100, y], - fill=color) - - txt = LABEl_NAME_MAP[label] + ':' + str(round(score, 2)) - draw_obj.text(xy=(x, y-10), - text=txt, - fill='white', - font=FONT) - - -def draw_boxes_with_label_and_scores(img_array, boxes, labels, scores): - - img_array = img_array + np.array(cfgs.PIXEL_MEAN) - img_array.astype(np.float32) - boxes = boxes.astype(np.int64) - labels = labels.astype(np.int32) - img_array = np.array(img_array * 255 / np.max(img_array), dtype=np.uint8) - - img_obj = Image.fromarray(img_array) - raw_img_obj = img_obj.copy() - - draw_obj = ImageDraw.Draw(img_obj) - num_of_objs = 0 - for box, a_label, a_score in zip(boxes, labels, scores): - - if a_label != NOT_DRAW_BOXES: - num_of_objs += 1 - #draw_a_rectangel_in_img(draw_obj, box, color=STANDARD_COLORS[a_label], width=3) - draw_a_rectangel_in_img(draw_obj, box, color='Red', width=3) - if a_label == ONLY_DRAW_BOXES: # -1 - continue - elif a_label == ONLY_DRAW_BOXES_WITH_SCORES: # -2 - only_draw_scores(draw_obj, box, a_score, color='Yellow') - continue - else: - draw_label_with_scores(draw_obj, box, a_label, a_score, color='DeepSkyBlue') - - out_img_obj = Image.blend(raw_img_obj, img_obj, alpha=0.7) - - return np.array(out_img_obj) - - -if __name__ == '__main__': - img_array = cv2.imread("/home/yjr/PycharmProjects/FPN_TF/tools/inference_image/2.jpg") - img_array = np.array(img_array, np.float32) - np.array(cfgs.PIXEL_MEAN) - boxes = np.array( - [[200, 200, 500, 500], - [300, 300, 400, 400], - [200, 200, 400, 400]] - ) - - # test only draw boxes - labes = np.ones(shape=[len(boxes), ], dtype=np.float32) * ONLY_DRAW_BOXES - scores = np.zeros_like(labes) - imm = draw_boxes_with_label_and_scores(img_array, boxes, labes ,scores) - # imm = np.array(imm) - - cv2.imshow("te", imm) - - # test only draw scores - labes = np.ones(shape=[len(boxes), ], dtype=np.float32) * ONLY_DRAW_BOXES_WITH_SCORES - scores = np.random.rand((len(boxes))) * 10 - imm2 = draw_boxes_with_label_and_scores(img_array, boxes, labes, scores) - - cv2.imshow("te2", imm2) - # test draw label and scores - - labels = np.arange(1, 4) - imm3 = draw_boxes_with_label_and_scores(img_array, boxes, labels, scores) - cv2.imshow("te3", imm3) - - cv2.waitKey(0) - - - - - - - diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/encode_and_decode.py b/example-apps/PDD/pcb-defect-detection/libs/box_utils/encode_and_decode.py deleted file mode 100755 index 66092b2..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/encode_and_decode.py +++ /dev/null @@ -1,165 +0,0 @@ -# -*- coding: utf-8 -*- - -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import tensorflow as tf -import numpy as np - - - -# def encode_boxes(ex_rois, gt_rois, scale_factor=None): -# ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0 -# ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0 -# ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths -# ex_ctr_y = ex_rois[:, 1] + 0.5 * ex_heights -# -# gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0 -# gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0 -# gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths -# gt_ctr_y = gt_rois[:, 1] + 0.5 * gt_heights -# -# targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths -# targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights -# targets_dw = np.log(gt_widths / ex_widths) -# targets_dh = np.log(gt_heights / ex_heights) -# -# if scale_factor: -# targets_dx = targets_dx * scale_factor[0] -# targets_dy = targets_dy * scale_factor[1] -# targets_dw = targets_dw * scale_factor[2] -# targets_dh = targets_dh * scale_factor[3] -# -# targets = np.vstack( -# (targets_dx, targets_dy, targets_dw, targets_dh)).transpose() -# return targets -# -# -# def _concat_new_axis(t1, t2, t3, t4, axis): -# return tf.concat( -# [tf.expand_dims(t1, -1), tf.expand_dims(t2, -1), -# tf.expand_dims(t3, -1), tf.expand_dims(t4, -1)], axis=axis) -# -# -# def decode_boxes(boxes, deltas, scale_factor=None): -# widths = boxes[:, 2] - boxes[:, 0] + 1.0 -# heights = boxes[:, 3] - boxes[:, 1] + 1.0 -# ctr_x = tf.expand_dims(boxes[:, 0] + 0.5 * widths, -1) -# ctr_y = tf.expand_dims(boxes[:, 1] + 0.5 * heights, -1) -# -# dx = deltas[:, 0::4] -# dy = deltas[:, 1::4] -# dw = deltas[:, 2::4] -# dh = deltas[:, 3::4] -# -# if scale_factor: -# dx /= scale_factor[0] -# dy /= scale_factor[1] -# dw /= scale_factor[2] -# dh /= scale_factor[3] -# -# widths = tf.expand_dims(widths, -1) -# heights = tf.expand_dims(heights, -1) -# -# pred_ctr_x = dx * widths + ctr_x -# pred_ctr_y = dy * heights + ctr_y -# pred_w = tf.exp(dw) * widths -# pred_h = tf.exp(dh) * heights -# -# # x1 -# # pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w -# pred_x1 = pred_ctr_x - 0.5 * pred_w -# # y1 -# # pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h -# pred_y1 = pred_ctr_y - 0.5 * pred_h -# # x2 -# # pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w -# pred_x2 = pred_ctr_x + 0.5 * pred_w -# # y2 -# # pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h -# pred_y2 = pred_ctr_y + 0.5 * pred_h -# -# pred_boxes = _concat_new_axis(pred_x1, pred_y1, pred_x2, pred_y2, 2) -# pred_boxes = tf.reshape(pred_boxes, (tf.shape(pred_boxes)[0], -1)) -# return pred_boxes - - -def decode_boxes(encoded_boxes, reference_boxes, scale_factors=None): - ''' - - :param encoded_boxes:[N, 4] - :param reference_boxes: [N, 4] . - :param scale_factors: use for scale. - - in the first stage, reference_boxes are anchors - in the second stage, reference boxes are proposals(decode) produced by first stage - :return:decode boxes [N, 4] - ''' - - t_xcenter, t_ycenter, t_w, t_h = tf.unstack(encoded_boxes, axis=1) - if scale_factors: - t_xcenter /= scale_factors[0] - t_ycenter /= scale_factors[1] - t_w /= scale_factors[2] - t_h /= scale_factors[3] - - reference_xmin, reference_ymin, reference_xmax, reference_ymax = tf.unstack(reference_boxes, axis=1) - # reference boxes are anchors in the first stage - - reference_xcenter = (reference_xmin + reference_xmax) / 2. - reference_ycenter = (reference_ymin + reference_ymax) / 2. - reference_w = reference_xmax - reference_xmin - reference_h = reference_ymax - reference_ymin - - predict_xcenter = t_xcenter * reference_w + reference_xcenter - predict_ycenter = t_ycenter * reference_h + reference_ycenter - predict_w = tf.exp(t_w) * reference_w - predict_h = tf.exp(t_h) * reference_h - - predict_xmin = predict_xcenter - predict_w / 2. - predict_xmax = predict_xcenter + predict_w / 2. - predict_ymin = predict_ycenter - predict_h / 2. - predict_ymax = predict_ycenter + predict_h / 2. - - return tf.transpose(tf.stack([predict_xmin, predict_ymin, - predict_xmax, predict_ymax])) - - -def encode_boxes(unencode_boxes, reference_boxes, scale_factors=None): - ''' - - :param unencode_boxes: [-1, 4] - :param reference_boxes: [-1, 4] - :return: encode_boxes [-1, 4] - ''' - - xmin, ymin, xmax, ymax = unencode_boxes[:, 0], unencode_boxes[:, 1], unencode_boxes[:, 2], unencode_boxes[:, 3] - - reference_xmin, reference_ymin, reference_xmax, reference_ymax = reference_boxes[:, 0], reference_boxes[:, 1], \ - reference_boxes[:, 2], reference_boxes[:, 3] - - x_center = (xmin + xmax) / 2. - y_center = (ymin + ymax) / 2. - w = xmax - xmin + 1e-8 - h = ymax - ymin + 1e-8 - - reference_xcenter = (reference_xmin + reference_xmax) / 2. - reference_ycenter = (reference_ymin + reference_ymax) / 2. - reference_w = reference_xmax - reference_xmin + 1e-8 - reference_h = reference_ymax - reference_ymin + 1e-8 - - # w + 1e-8 to avoid NaN in division and log below - - t_xcenter = (x_center - reference_xcenter) / reference_w - t_ycenter = (y_center - reference_ycenter) / reference_h - t_w = np.log(w/reference_w) - t_h = np.log(h/reference_h) - - if scale_factors: - t_xcenter *= scale_factors[0] - t_ycenter *= scale_factors[1] - t_w *= scale_factors[2] - t_h *= scale_factors[3] - - return np.transpose(np.stack([t_xcenter, t_ycenter, t_w, t_h], axis=0)) diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/show_box_in_tensor.py b/example-apps/PDD/pcb-defect-detection/libs/box_utils/show_box_in_tensor.py deleted file mode 100755 index 37afda0..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/show_box_in_tensor.py +++ /dev/null @@ -1,70 +0,0 @@ -# -*- coding: utf-8 -*- - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf -import numpy as np -import cv2 -from libs.label_name_dict.label_dict import LABEl_NAME_MAP - -from libs.configs import cfgs - -from libs.box_utils import draw_box_in_img - -def only_draw_boxes(img_batch, boxes): - - boxes = tf.stop_gradient(boxes) - img_tensor = tf.squeeze(img_batch, 0) - img_tensor = tf.cast(img_tensor, tf.float32) - labels = tf.ones(shape=(tf.shape(boxes)[0], ), dtype=tf.int32) * draw_box_in_img.ONLY_DRAW_BOXES - scores = tf.zeros_like(labels, dtype=tf.float32) - img_tensor_with_boxes = tf.py_func(draw_box_in_img.draw_boxes_with_label_and_scores, - inp=[img_tensor, boxes, labels, scores], - Tout=tf.uint8) - img_tensor_with_boxes = tf.reshape(img_tensor_with_boxes, tf.shape(img_batch)) # [batch_size, h, w, c] - - return img_tensor_with_boxes - -def draw_boxes_with_scores(img_batch, boxes, scores): - - boxes = tf.stop_gradient(boxes) - scores = tf.stop_gradient(scores) - - img_tensor = tf.squeeze(img_batch, 0) - img_tensor = tf.cast(img_tensor, tf.float32) - labels = tf.ones(shape=(tf.shape(boxes)[0],), dtype=tf.int32) * draw_box_in_img.ONLY_DRAW_BOXES_WITH_SCORES - img_tensor_with_boxes = tf.py_func(draw_box_in_img.draw_boxes_with_label_and_scores, - inp=[img_tensor, boxes, labels, scores], - Tout=[tf.uint8]) - img_tensor_with_boxes = tf.reshape(img_tensor_with_boxes, tf.shape(img_batch)) - return img_tensor_with_boxes - -def draw_boxes_with_categories(img_batch, boxes, labels): - boxes = tf.stop_gradient(boxes) - - img_tensor = tf.squeeze(img_batch, 0) - img_tensor = tf.cast(img_tensor, tf.float32) - scores = tf.ones(shape=(tf.shape(boxes)[0],), dtype=tf.float32) - img_tensor_with_boxes = tf.py_func(draw_box_in_img.draw_boxes_with_label_and_scores, - inp=[img_tensor, boxes, labels, scores], - Tout=[tf.uint8]) - img_tensor_with_boxes = tf.reshape(img_tensor_with_boxes, tf.shape(img_batch)) - return img_tensor_with_boxes - -def draw_boxes_with_categories_and_scores(img_batch, boxes, labels, scores): - boxes = tf.stop_gradient(boxes) - scores = tf.stop_gradient(scores) - - img_tensor = tf.squeeze(img_batch, 0) - img_tensor = tf.cast(img_tensor, tf.float32) - img_tensor_with_boxes = tf.py_func(draw_box_in_img.draw_boxes_with_label_and_scores, - inp=[img_tensor, boxes, labels, scores], - Tout=[tf.uint8]) - img_tensor_with_boxes = tf.reshape(img_tensor_with_boxes, tf.shape(img_batch)) - return img_tensor_with_boxes - -if __name__ == "__main__": - print (1) - diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/tf_ops.py b/example-apps/PDD/pcb-defect-detection/libs/box_utils/tf_ops.py deleted file mode 100755 index 86d945a..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/tf_ops.py +++ /dev/null @@ -1,57 +0,0 @@ -# -*- coding:utf-8 -*- - -from __future__ import absolute_import, print_function, division - -import tensorflow as tf - -''' -all of these ops are derived from tenosrflow Object Detection API -''' -def indices_to_dense_vector(indices, - size, - indices_value=1., - default_value=0, - dtype=tf.float32): - """Creates dense vector with indices set to specific (the para "indices_value" ) and rest to zeros. - - This function exists because it is unclear if it is safe to use - tf.sparse_to_dense(indices, [size], 1, validate_indices=False) - with indices which are not ordered. - This function accepts a dynamic size (e.g. tf.shape(tensor)[0]) - - Args: - indices: 1d Tensor with integer indices which are to be set to - indices_values. - size: scalar with size (integer) of output Tensor. - indices_value: values of elements specified by indices in the output vector - default_value: values of other elements in the output vector. - dtype: data type. - - Returns: - dense 1D Tensor of shape [size] with indices set to indices_values and the - rest set to default_value. - """ - size = tf.to_int32(size) - zeros = tf.ones([size], dtype=dtype) * default_value - values = tf.ones_like(indices, dtype=dtype) * indices_value - - return tf.dynamic_stitch([tf.range(size), tf.to_int32(indices)], - [zeros, values]) - - - - -def test_plt(): - from PIL import Image - import matplotlib.pyplot as plt - import numpy as np - - a = np.random.rand(20, 30) - print (a.shape) - # plt.subplot() - b = plt.imshow(a) - plt.show() - - -if __name__ == '__main__': - test_plt() diff --git a/example-apps/PDD/pcb-defect-detection/libs/configs/__init__.py b/example-apps/PDD/pcb-defect-detection/libs/configs/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs.py b/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs.py deleted file mode 100755 index 93049eb..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs.py +++ /dev/null @@ -1,133 +0,0 @@ -# -*- coding: utf-8 -*- -from __future__ import division, print_function, absolute_import -import os -import tensorflow as tf -# ------------------------------------------------ -VERSION = 'FPN_Res101_0117_OHEM' -NET_NAME = 'resnet_v1_101' -ADD_BOX_IN_TENSORBOARD = True - -# ---------------------------------------- System_config -ROOT_PATH = os.path.abspath('../') -print (20*"++--") -print (ROOT_PATH) -GPU_GROUP = "2" -SHOW_TRAIN_INFO_INTE = 10 -SMRY_ITER = 100 -SAVE_WEIGHTS_INTE = 10000 - -SUMMARY_PATH = ROOT_PATH + '/output/summary' -TEST_SAVE_PATH = ROOT_PATH + '/tools/test_result' -INFERENCE_IMAGE_PATH = ROOT_PATH + '/tools/inference_image' -INFERENCE_SAVE_PATH = ROOT_PATH + '/tools/inference_results' - -if NET_NAME.startswith("resnet"): - weights_name = NET_NAME -elif NET_NAME.startswith("MobilenetV2"): - weights_name = "mobilenet/mobilenet_v2_1.0_224" -else: - raise NotImplementedError - -PRETRAINED_CKPT = ROOT_PATH + '/data/pretrained_weights/' + weights_name + '.ckpt' -TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights') - -EVALUATE_DIR = ROOT_PATH + '/output/evaluate_result_pickle/' -#test_annotate_path = '/home/yjr/DataSet/VOC/VOC_test/VOC2007/Annotations' -test_annotate_path = ROOT_PATH + '/data/pcb_train/Annotations' -# ------------------------------------------ Train config -RESTORE_FROM_RPN = False -IS_FILTER_OUTSIDE_BOXES = False -FIXED_BLOCKS = 0 # allow 0~3 -USE_07_METRIC = False - -RPN_LOCATION_LOSS_WEIGHT = 1. -RPN_CLASSIFICATION_LOSS_WEIGHT = 1.0 - -FAST_RCNN_LOCATION_LOSS_WEIGHT = 1.0 -FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT = 1.0 -RPN_SIGMA = 3.0 -FASTRCNN_SIGMA = 1.0 - -MUTILPY_BIAS_GRADIENT = None # 2.0 # if None, will not multipy -GRADIENT_CLIPPING_BY_NORM = None # 10.0 if None, will not clip - -EPSILON = 1e-5 -MOMENTUM = 0.9 -LR = 0.001 # 0.001 # 0.0003 -#DECAY_STEP = [60000, 80000] # 50000, 70000 -DECAY_STEP = [10000, 20000] # 50000, 70000 -#MAX_ITERATION = 150000 -MAX_ITERATION = 30000 - -# ------------------------------------------- Data_preprocess_config -DATASET_NAME = 'pcb' # 'ship', 'spacenet', 'pascal', 'coco' -# PIXEL_MEAN = [123.68, 116.779, 103.939] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR -PIXEL_MEAN = [21.25, 85.936, 28.729] -IMG_SHORT_SIDE_LEN = 600 # 600 -IMG_MAX_LENGTH = 3000 # 1000 -CLASS_NUM = 6 - -# --------------------------------------------- Network_config -BATCH_SIZE = 1 -INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.01) -BBOX_INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.001) -WEIGHT_DECAY = 0.00004 if NET_NAME.startswith('Mobilenet') else 0.0001 - -# ---------------------------------------------Anchor config -USE_CENTER_OFFSET = False - -LEVLES = ['P2', 'P3', 'P4', 'P5', 'P6'] -# BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512] # addjust the base anchor size for voc. -BASE_ANCHOR_SIZE_LIST = [15, 25, 40, 60, 80] # addjust the base anchor size for voc. -#BASE_ANCHOR_SIZE_LIST = [8, 15, 25, 40, 60] -ANCHOR_STRIDE_LIST = [4, 8, 16, 32, 64] -ANCHOR_SCALES = [2., 3., 4.] -ANCHOR_RATIOS = [2., 3., 4., 5.] -# ANCHOR_SCALES = [1.0] -# ANCHOR_RATIOS = [0.5, 1., 2.0] -ROI_SCALE_FACTORS = [10., 10., 5.0, 5.0] -ANCHOR_SCALE_FACTORS = None - -# --------------------------------------------FPN config -SHARE_HEADS = True -KERNEL_SIZE = 3 -RPN_IOU_POSITIVE_THRESHOLD = 0.7 -RPN_IOU_NEGATIVE_THRESHOLD = 0.3 -TRAIN_RPN_CLOOBER_POSITIVES = False - -RPN_MINIBATCH_SIZE = 256 -RPN_POSITIVE_RATE = 0.5 -RPN_NMS_IOU_THRESHOLD = 0.7 -RPN_TOP_K_NMS_TRAIN = 12000 -#RPN_MAXIMUM_PROPOSAL_TARIN = 2000 -RPN_MAXIMUM_PROPOSAL_TARIN = 2000 - -RPN_TOP_K_NMS_TEST = 6000 -RPN_MAXIMUM_PROPOSAL_TEST = 1000 - -# specific settings for FPN -# FPN_TOP_K_PER_LEVEL_TRAIN = 2000 -# FPN_TOP_K_PER_LEVEL_TEST = 1000 - -# -------------------------------------------Fast-RCNN config -ROI_SIZE = 14 -ROI_POOL_KERNEL_SIZE = 2 -#USE_DROPOUT = False -USE_DROPOUT = True -KEEP_PROB = 1.0 -SHOW_SCORE_THRSHOLD = 0.6 # only show in tensorboard - -#FAST_RCNN_NMS_IOU_THRESHOLD = 0.3 # 0.6 -FAST_RCNN_NMS_IOU_THRESHOLD = 0.3 -FAST_RCNN_NMS_MAX_BOXES_PER_CLASS = 100 -FAST_RCNN_IOU_POSITIVE_THRESHOLD = 0.5 -FAST_RCNN_IOU_NEGATIVE_THRESHOLD = 0.0 # 0.1 < IOU < 0.5 is negative -FAST_RCNN_MINIBATCH_SIZE = 256 # if is -1, that is train with OHEM -# FAST_RCNN_MINIBATCH_SIZE = -1 -FAST_RCNN_POSITIVE_RATE = 0.25 - -#ADD_GTBOXES_TO_TRAIN = False -ADD_GTBOXES_TO_TRAIN = True - - - diff --git a/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs_res101_fpn.py b/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs_res101_fpn.py deleted file mode 100755 index d9daad2..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs_res101_fpn.py +++ /dev/null @@ -1,212 +0,0 @@ -# -*- coding: utf-8 -*- -from __future__ import division, print_function, absolute_import -import os -import tensorflow as tf -''' -cls : person|| Recall: 0.9200530035335689 || Precison: 0.009050166947990866|| AP: 0.8413662097687251 -____________________ -cls : cat|| Recall: 0.9608938547486033 || Precison: 0.0007414480221227399|| AP: 0.886410933036462 -____________________ -cls : horse|| Recall: 0.9626436781609196 || Precison: 0.0007370072226707821|| AP: 0.880462817781879 -____________________ -cls : boat|| Recall: 0.8783269961977186 || Precison: 0.000509740231082238|| AP: 0.6456185469835614 -____________________ -cls : bottle|| Recall: 0.8656716417910447 || Precison: 0.0008665714718695106|| AP: 0.6480626365413494 -____________________ -cls : bicycle|| Recall: 0.9228486646884273 || Precison: 0.0006832296772124229|| AP: 0.8550508887926864 -____________________ -cls : bus|| Recall: 0.9577464788732394 || Precison: 0.00045156820340048565|| AP: 0.8631526839193041 -____________________ -cls : sheep|| Recall: 0.9132231404958677 || Precison: 0.0004864327094081809|| AP: 0.7741568397678364 -____________________ -cls : car|| Recall: 0.9600333055786844 || Precison: 0.0025449055537652685|| AP: 0.8914023804170609 -____________________ -cls : motorbike|| Recall: 0.9538461538461539 || Precison: 0.0006737519288865706|| AP: 0.8495072139551133 -____________________ -cls : chair|| Recall: 0.873015873015873 || Precison: 0.001433311906044233|| AP: 0.5759698175528438 -____________________ -cls : aeroplane|| Recall: 0.9438596491228071 || Precison: 0.0006024690030817745|| AP: 0.8353670052573003 -____________________ -cls : tvmonitor|| Recall: 0.9415584415584416 || Precison: 0.0006278089036291684|| AP: 0.7613581746623427 -____________________ -cls : sofa|| Recall: 0.9707112970711297 || Precison: 0.0005222178954168627|| AP: 0.7987407803525022 -____________________ -cls : bird|| Recall: 0.9281045751633987 || Precison: 0.0009227090390830091|| AP: 0.8159836473038345 -____________________ -cls : dog|| Recall: 0.9611451942740287 || Precison: 0.0010255447933963644|| AP: 0.8951754362513265 -____________________ -cls : cow|| Recall: 0.9467213114754098 || Precison: 0.0005056795917786568|| AP: 0.8497306852549179 -____________________ -cls : diningtable|| Recall: 0.883495145631068 || Precison: 0.0004040099093419522|| AP: 0.7307392356452687 -____________________ -cls : pottedplant|| Recall: 0.7729166666666667 || Precison: 0.0008064077902035582|| AP: 0.4738700691112566 -____________________ -cls : train|| Recall: 0.9290780141843972 || Precison: 0.0005804331981204598|| AP: 0.8427500500899303 -____________________ -mAP is : 0.7857438026222752 (USE_12_METRIC) - -cls : train|| Recall: 0.9290780141843972 || Precison: 0.0005804331981204598|| AP: 0.8101152343436091 -____________________ -cls : bus|| Recall: 0.9577464788732394 || Precison: 0.00045156820340048565|| AP: 0.830722622273239 -____________________ -cls : chair|| Recall: 0.873015873015873 || Precison: 0.001433311906044233|| AP: 0.5698849842652579 -____________________ -cls : pottedplant|| Recall: 0.7729166666666667 || Precison: 0.0008064077902035582|| AP: 0.48047763621440476 -____________________ -cls : horse|| Recall: 0.9626436781609196 || Precison: 0.0007370072226707821|| AP: 0.8512804991519783 -____________________ -cls : person|| Recall: 0.9200530035335689 || Precison: 0.009050166947990866|| AP: 0.8107708491164711 -____________________ -cls : bottle|| Recall: 0.8656716417910447 || Precison: 0.0008665714718695106|| AP: 0.63789938616088 -____________________ -cls : bicycle|| Recall: 0.9228486646884273 || Precison: 0.0006832296772124229|| AP: 0.8166723684624742 -____________________ -cls : dog|| Recall: 0.9611451942740287 || Precison: 0.0010255447933963644|| AP: 0.864470680916449 -____________________ -cls : diningtable|| Recall: 0.883495145631068 || Precison: 0.0004040099093419522|| AP: 0.7122255048941863 -____________________ -cls : bird|| Recall: 0.9281045751633987 || Precison: 0.0009227090390830091|| AP: 0.7832546811459113 -____________________ -cls : sofa|| Recall: 0.9707112970711297 || Precison: 0.0005222178954168627|| AP: 0.778305908921783 -____________________ -cls : sheep|| Recall: 0.9132231404958677 || Precison: 0.0004864327094081809|| AP: 0.7463330859344937 -____________________ -cls : boat|| Recall: 0.8783269961977186 || Precison: 0.000509740231082238|| AP: 0.6291419623367831 -____________________ -cls : car|| Recall: 0.9600333055786844 || Precison: 0.0025449055537652685|| AP: 0.8630428431995184 -____________________ -cls : motorbike|| Recall: 0.9538461538461539 || Precison: 0.0006737519288865706|| AP: 0.8224280778332824 -____________________ -cls : aeroplane|| Recall: 0.9438596491228071 || Precison: 0.0006024690030817745|| AP: 0.8001448356711514 -____________________ -cls : cat|| Recall: 0.9608938547486033 || Precison: 0.0007414480221227399|| AP: 0.8582414148566436 -____________________ -cls : cow|| Recall: 0.9467213114754098 || Precison: 0.0005056795917786568|| AP: 0.8242904910827928 -____________________ -cls : tvmonitor|| Recall: 0.9415584415584416 || Precison: 0.0006278089036291684|| AP: 0.7388745216642896 -____________________ -mAP is : 0.76142887942228 (USE_07_METRIC) - -''' - -# ------------------------------------------------ -VERSION = 'FPN_Res101_20181204' -NET_NAME = 'resnet_v1_101' -ADD_BOX_IN_TENSORBOARD = True - -# ---------------------------------------- System_config -ROOT_PATH = os.path.abspath('../') -print (20*"++--") -print (ROOT_PATH) -GPU_GROUP = "4" -SHOW_TRAIN_INFO_INTE = 10 -SMRY_ITER = 100 -SAVE_WEIGHTS_INTE = 10000 - -SUMMARY_PATH = ROOT_PATH + '/output/summary' -TEST_SAVE_PATH = ROOT_PATH + '/tools/test_result' -INFERENCE_IMAGE_PATH = ROOT_PATH + '/tools/inference_image' -INFERENCE_SAVE_PATH = ROOT_PATH + '/tools/inference_results' - -if NET_NAME.startswith("resnet"): - weights_name = NET_NAME -elif NET_NAME.startswith("MobilenetV2"): - weights_name = "mobilenet/mobilenet_v2_1.0_224" -else: - raise NotImplementedError - -PRETRAINED_CKPT = ROOT_PATH + '/data/pretrained_weights/' + weights_name + '.ckpt' -TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights') - -EVALUATE_DIR = ROOT_PATH + '/output/evaluate_result_pickle/' -#test_annotate_path = '/home/yjr/DataSet/VOC/VOC_test/VOC2007/Annotations' -test_annotate_path = '/home/gq123/dailinhui/FPN_success_dlh/data/pcb/Annotations' - -# ------------------------------------------ Train config -RESTORE_FROM_RPN = False -IS_FILTER_OUTSIDE_BOXES = False -FIXED_BLOCKS = 0 # allow 0~3 -USE_07_METRIC = False - -RPN_LOCATION_LOSS_WEIGHT = 1. -RPN_CLASSIFICATION_LOSS_WEIGHT = 1.0 - -FAST_RCNN_LOCATION_LOSS_WEIGHT = 1.0 -FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT = 1.0 -RPN_SIGMA = 3.0 -FASTRCNN_SIGMA = 1.0 - -MUTILPY_BIAS_GRADIENT = None # 2.0 # if None, will not multipy -GRADIENT_CLIPPING_BY_NORM = None # 10.0 if None, will not clip - -EPSILON = 1e-5 -MOMENTUM = 0.9 -LR = 0.001 # 0.001 # 0.0003 -#DECAY_STEP = [60000, 80000] # 50000, 70000 -DECAY_STEP = [1000, 1500] # 50000, 70000 -#MAX_ITERATION = 150000 -MAX_ITERATION = 2000 - -# ------------------------------------------- Data_preprocess_config -DATASET_NAME = 'pcb' # 'ship', 'spacenet', 'pascal', 'coco' -#PIXEL_MEAN = [123.68, 116.779, 103.939] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR -PIXEL_MEAN = [21.25, 85.936, 28.729] -IMG_SHORT_SIDE_LEN = 1586 # 600 # 600 -IMG_MAX_LENGTH = 3034 # 1000 # 1000 -CLASS_NUM = 6 - -# --------------------------------------------- Network_config -BATCH_SIZE = 1 -INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.01) -BBOX_INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.001) -WEIGHT_DECAY = 0.00004 if NET_NAME.startswith('Mobilenet') else 0.0001 - -# ---------------------------------------------Anchor config -USE_CENTER_OFFSET = False - -LEVLES = ['P2', 'P3', 'P4', 'P5', 'P6'] -BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512] # addjust the base anchor size for voc. -ANCHOR_STRIDE_LIST = [4, 8, 16, 32, 64] -ANCHOR_SCALES = [1.0] -ANCHOR_RATIOS = [0.5, 1., 2.0] -ROI_SCALE_FACTORS = [10., 10., 5.0, 5.0] -ANCHOR_SCALE_FACTORS = None - -# --------------------------------------------FPN config -SHARE_HEADS = True -KERNEL_SIZE = 3 -RPN_IOU_POSITIVE_THRESHOLD = 0.7 -RPN_IOU_NEGATIVE_THRESHOLD = 0.3 -TRAIN_RPN_CLOOBER_POSITIVES = False - -RPN_MINIBATCH_SIZE = 256 -RPN_POSITIVE_RATE = 0.5 -RPN_NMS_IOU_THRESHOLD = 0.7 -RPN_TOP_K_NMS_TRAIN = 12000 -RPN_MAXIMUM_PROPOSAL_TARIN = 2000 - -RPN_TOP_K_NMS_TEST = 6000 -RPN_MAXIMUM_PROPOSAL_TEST = 1000 - -# specific settings for FPN -# FPN_TOP_K_PER_LEVEL_TRAIN = 2000 -# FPN_TOP_K_PER_LEVEL_TEST = 1000 - -# -------------------------------------------Fast-RCNN config -ROI_SIZE = 14 -ROI_POOL_KERNEL_SIZE = 2 -USE_DROPOUT = False -KEEP_PROB = 1.0 -SHOW_SCORE_THRSHOLD = 0.6 # only show in tensorboard - -FAST_RCNN_NMS_IOU_THRESHOLD = 0.3 # 0.6 -FAST_RCNN_NMS_MAX_BOXES_PER_CLASS = 100 -FAST_RCNN_IOU_POSITIVE_THRESHOLD = 0.5 -FAST_RCNN_IOU_NEGATIVE_THRESHOLD = 0.0 # 0.1 < IOU < 0.5 is negative -FAST_RCNN_MINIBATCH_SIZE = 256 # if is -1, that is train with OHEM -FAST_RCNN_POSITIVE_RATE = 0.25 - -ADD_GTBOXES_TO_TRAIN = False - - - diff --git a/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs_res101_fpn_v1.py b/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs_res101_fpn_v1.py deleted file mode 100755 index 15b95f1..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs_res101_fpn_v1.py +++ /dev/null @@ -1,125 +0,0 @@ -# -*- coding: utf-8 -*- -from __future__ import division, print_function, absolute_import -import os -import tensorflow as tf -''' - -''' - -# ------------------------------------------------ -VERSION = 'FPN_Res101_20181201_v1' -NET_NAME = 'resnet_v1_101' -ADD_BOX_IN_TENSORBOARD = True - -# ---------------------------------------- System_config -ROOT_PATH = os.path.abspath('../') -print (20*"++--") -print (ROOT_PATH) -GPU_GROUP = "2" -SHOW_TRAIN_INFO_INTE = 10 -SMRY_ITER = 100 -SAVE_WEIGHTS_INTE = 10000 - -SUMMARY_PATH = ROOT_PATH + '/output/summary' -TEST_SAVE_PATH = ROOT_PATH + '/tools/test_result' -INFERENCE_IMAGE_PATH = ROOT_PATH + '/tools/inference_image' -INFERENCE_SAVE_PATH = ROOT_PATH + '/tools/inference_results' - -if NET_NAME.startswith("resnet"): - weights_name = NET_NAME -elif NET_NAME.startswith("MobilenetV2"): - weights_name = "mobilenet/mobilenet_v2_1.0_224" -else: - raise NotImplementedError - -PRETRAINED_CKPT = ROOT_PATH + '/data/pretrained_weights/' + weights_name + '.ckpt' -TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights') - -EVALUATE_DIR = ROOT_PATH + '/output/evaluate_result_pickle/' -test_annotate_path = '/home/yjr/DataSet/VOC/VOC_test/VOC2007/Annotations' - -# ------------------------------------------ Train config -RESTORE_FROM_RPN = False -IS_FILTER_OUTSIDE_BOXES = False -FIXED_BLOCKS = 0 # allow 0~3 -USE_07_METRIC = False - -RPN_LOCATION_LOSS_WEIGHT = 1. -RPN_CLASSIFICATION_LOSS_WEIGHT = 1.0 - -FAST_RCNN_LOCATION_LOSS_WEIGHT = 1.0 -FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT = 1.0 -RPN_SIGMA = 3.0 -FASTRCNN_SIGMA = 1.0 - -MUTILPY_BIAS_GRADIENT = None # 2.0 # if None, will not multipy -GRADIENT_CLIPPING_BY_NORM = None # 10.0 if None, will not clip - -EPSILON = 1e-5 -MOMENTUM = 0.9 -LR = 0.001 # 0.001 # 0.0003 -DECAY_STEP = [60000, 80000] # 50000, 70000 -MAX_ITERATION = 150000 - -# -------------------------------------------- Data_preprocess_config -DATASET_NAME = 'pascal' # 'ship', 'spacenet', 'pascal', 'coco' -PIXEL_MEAN = [123.68, 116.779, 103.939] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR -IMG_SHORT_SIDE_LEN = 800 # 600 # 600 -IMG_MAX_LENGTH = 1200 # 1000 # 1000 -CLASS_NUM = 20 - -# --------------------------------------------- Network_config -BATCH_SIZE = 1 -INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.01) -BBOX_INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.001) -WEIGHT_DECAY = 0.00004 if NET_NAME.startswith('Mobilenet') else 0.0001 - -# ---------------------------------------------Anchor config -USE_CENTER_OFFSET = False - -LEVLES = ['P2', 'P3', 'P4', 'P5', 'P6'] -BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512] # addjust the base anchor size for voc. -ANCHOR_STRIDE_LIST = [4, 8, 16, 32, 64] -ANCHOR_SCALES = [1.0] -ANCHOR_RATIOS = [0.5, 1., 2.0] -ROI_SCALE_FACTORS = [10., 10., 5.0, 5.0] -ANCHOR_SCALE_FACTORS = None - -# --------------------------------------------FPN config -SHARE_HEADS = True -KERNEL_SIZE = 3 -RPN_IOU_POSITIVE_THRESHOLD = 0.7 -RPN_IOU_NEGATIVE_THRESHOLD = 0.3 -TRAIN_RPN_CLOOBER_POSITIVES = False - -RPN_MINIBATCH_SIZE = 256 -RPN_POSITIVE_RATE = 0.5 -RPN_NMS_IOU_THRESHOLD = 0.7 -RPN_TOP_K_NMS_TRAIN = 12000 -RPN_MAXIMUM_PROPOSAL_TARIN = 2000 - -RPN_TOP_K_NMS_TEST = 6000 -RPN_MAXIMUM_PROPOSAL_TEST = 1000 - -# specific settings for FPN -# FPN_TOP_K_PER_LEVEL_TRAIN = 2000 -# FPN_TOP_K_PER_LEVEL_TEST = 1000 - -# -------------------------------------------Fast-RCNN config -ROI_SIZE = 14 -ROI_POOL_KERNEL_SIZE = 2 -USE_DROPOUT = False -KEEP_PROB = 1.0 -SHOW_SCORE_THRSHOLD = 0.5 # only show in tensorboard - -FAST_RCNN_NMS_IOU_THRESHOLD = 0.3 # 0.6 -FAST_RCNN_NMS_MAX_BOXES_PER_CLASS = 100 -FAST_RCNN_IOU_POSITIVE_THRESHOLD = 0.5 -FAST_RCNN_IOU_NEGATIVE_THRESHOLD = 0.0 # 0.1 < IOU < 0.5 is negative -FAST_RCNN_MINIBATCH_SIZE = 512 # if is -1, that is train with OHEM -FAST_RCNN_POSITIVE_RATE = 0.25 - -ADD_GTBOXES_TO_TRAIN = False - - - diff --git a/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs_res101_fpn_v2.py b/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs_res101_fpn_v2.py deleted file mode 100755 index 963df61..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs_res101_fpn_v2.py +++ /dev/null @@ -1,208 +0,0 @@ -# -*- coding: utf-8 -*- -from __future__ import division, print_function, absolute_import -import os -import tensorflow as tf - -""" -cls : car|| Recall: 0.9542048293089093 || Precison: 0.002486730925298256|| AP: 0.8861557852184543 -____________________ -cls : aeroplane|| Recall: 0.9333333333333333 || Precison: 0.0005925041542114572|| AP: 0.842025283046165 -____________________ -cls : diningtable|| Recall: 0.8932038834951457 || Precison: 0.00040969553387335954|| AP: 0.7380756727563009 -____________________ -cls : cow|| Recall: 0.9672131147540983 || Precison: 0.0005127837421479989|| AP: 0.8598673192007477 -____________________ -cls : boat|| Recall: 0.870722433460076 || Precison: 0.0005048556531707801|| AP: 0.614924861322453 -____________________ -cls : person|| Recall: 0.9112190812720848 || Precison: 0.00890183387270766|| AP: 0.8403573910074297 -____________________ -cls : bottle|| Recall: 0.8550106609808102 || Precison: 0.0008528612324589202|| AP: 0.6431100249936452 -____________________ -cls : bus|| Recall: 0.9624413145539906 || Precison: 0.00045113940207524124|| AP: 0.8462278674877455 -____________________ -cls : chair|| Recall: 0.8465608465608465 || Precison: 0.0013795928045612787|| AP: 0.5683751395230314 -____________________ -cls : train|| Recall: 0.9361702127659575 || Precison: 0.0005837401825514753|| AP: 0.8364460305413468 -____________________ -cls : dog|| Recall: 0.9754601226993865 || Precison: 0.0010383537847668929|| AP: 0.9033364857501106 -____________________ -cls : motorbike|| Recall: 0.92 || Precison: 0.0006513139551094382|| AP: 0.8295629935581179 -____________________ -cls : cat|| Recall: 0.9608938547486033 || Precison: 0.0007397197236372707|| AP: 0.8871965890034861 -____________________ -cls : sofa|| Recall: 0.9539748953974896 || Precison: 0.0005096076691483964|| AP: 0.8040267075520897 -____________________ -cls : bird|| Recall: 0.9150326797385621 || Precison: 0.0009065830883400464|| AP: 0.8237404489482196 -____________________ -cls : horse|| Recall: 0.9568965517241379 || Precison: 0.0007317362585204424|| AP: 0.8735567893480496 -____________________ -cls : pottedplant|| Recall: 0.7895833333333333 || Precison: 0.0008196455411498826|| AP: 0.4870711669635938 -____________________ -cls : bicycle|| Recall: 0.9109792284866469 || Precison: 0.0006712950308861314|| AP: 0.8361496835573192 -____________________ -cls : tvmonitor|| Recall: 0.9512987012987013 || Precison: 0.0006329305331737686|| AP: 0.7944641602539069 -____________________ -cls : sheep|| Recall: 0.9214876033057852 || Precison: 0.0004898504308706817|| AP: 0.7823429444259854 -____________________ -mAP is : 0.7848506672229101 USE_12_METRIC - -cls : tvmonitor|| Recall: 0.9512987012987013 || Precison: 0.0006329305331737686|| AP: 0.7680923930233127 -____________________ -cls : sheep|| Recall: 0.9214876033057852 || Precison: 0.0004898504308706817|| AP: 0.7605331894258903 -____________________ -cls : cat|| Recall: 0.9608938547486033 || Precison: 0.0007397197236372707|| AP: 0.8561115936556246 -____________________ -cls : train|| Recall: 0.9361702127659575 || Precison: 0.0005837401825514753|| AP: 0.7978443126776701 -____________________ -cls : aeroplane|| Recall: 0.9333333333333333 || Precison: 0.0005925041542114572|| AP: 0.8033498986573708 -____________________ -cls : motorbike|| Recall: 0.92 || Precison: 0.0006513139551094382|| AP: 0.7991141448766482 -____________________ -cls : bus|| Recall: 0.9624413145539906 || Precison: 0.00045113940207524124|| AP: 0.8181027979596772 -____________________ -cls : bird|| Recall: 0.9150326797385621 || Precison: 0.0009065830883400464|| AP: 0.7851247014320806 -____________________ -cls : pottedplant|| Recall: 0.7895833333333333 || Precison: 0.0008196455411498826|| AP: 0.49033575375142174 -____________________ -cls : cow|| Recall: 0.9672131147540983 || Precison: 0.0005127837421479989|| AP: 0.8304367006298838 -____________________ -cls : person|| Recall: 0.9112190812720848 || Precison: 0.00890183387270766|| AP: 0.797530517185023 -____________________ -cls : bottle|| Recall: 0.8550106609808102 || Precison: 0.0008528612324589202|| AP: 0.6320745719617634 -____________________ -cls : sofa|| Recall: 0.9539748953974896 || Precison: 0.0005096076691483964|| AP: 0.7868518534567335 -____________________ -cls : boat|| Recall: 0.870722433460076 || Precison: 0.0005048556531707801|| AP: 0.6036612374959088 -____________________ -cls : car|| Recall: 0.9542048293089093 || Precison: 0.002486730925298256|| AP: 0.8623955910304107 -____________________ -cls : bicycle|| Recall: 0.9109792284866469 || Precison: 0.0006712950308861314|| AP: 0.8029062441256611 -____________________ -cls : dog|| Recall: 0.9754601226993865 || Precison: 0.0010383537847668929|| AP: 0.8661350949646617 -____________________ -cls : diningtable|| Recall: 0.8932038834951457 || Precison: 0.00040969553387335954|| AP: 0.7127169697539509 -____________________ -cls : horse|| Recall: 0.9568965517241379 || Precison: 0.0007317362585204424|| AP: 0.8422342978325045 -____________________ -cls : chair|| Recall: 0.8465608465608465 || Precison: 0.0013795928045612787|| AP: 0.563170854101135 -____________________ -mAP is : 0.7589361358998666 USE_07_METRIC -""" - -# ------------------------------------------------ -VERSION = 'FPN_Res101_20181201_v2' -NET_NAME = 'resnet_v1_101' -ADD_BOX_IN_TENSORBOARD = True - -# ---------------------------------------- System_config -ROOT_PATH = os.path.abspath('../') -print (20*"++--") -print (ROOT_PATH) -GPU_GROUP = "3" -SHOW_TRAIN_INFO_INTE = 10 -SMRY_ITER = 100 -SAVE_WEIGHTS_INTE = 10000 - -SUMMARY_PATH = ROOT_PATH + '/output/summary' -TEST_SAVE_PATH = ROOT_PATH + '/tools/test_result' -INFERENCE_IMAGE_PATH = ROOT_PATH + '/tools/inference_image' -INFERENCE_SAVE_PATH = ROOT_PATH + '/tools/inference_results' - -if NET_NAME.startswith("resnet"): - weights_name = NET_NAME -elif NET_NAME.startswith("MobilenetV2"): - weights_name = "mobilenet/mobilenet_v2_1.0_224" -else: - raise NotImplementedError - -PRETRAINED_CKPT = ROOT_PATH + '/data/pretrained_weights/' + weights_name + '.ckpt' -TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights') - -EVALUATE_DIR = ROOT_PATH + '/output/evaluate_result_pickle/' -test_annotate_path = '/home/yjr/DataSet/VOC/VOC_test/VOC2007/Annotations' - -# ------------------------------------------ Train config -RESTORE_FROM_RPN = False -IS_FILTER_OUTSIDE_BOXES = False -FIXED_BLOCKS = 0 # allow 0~3 -USE_07_METRIC = False - -RPN_LOCATION_LOSS_WEIGHT = 1. -RPN_CLASSIFICATION_LOSS_WEIGHT = 1.0 - -FAST_RCNN_LOCATION_LOSS_WEIGHT = 1.0 -FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT = 1.0 -RPN_SIGMA = 3.0 -FASTRCNN_SIGMA = 1.0 - -MUTILPY_BIAS_GRADIENT = None # 2.0 # if None, will not multipy -GRADIENT_CLIPPING_BY_NORM = None # 10.0 if None, will not clip - -EPSILON = 1e-5 -MOMENTUM = 0.9 -LR = 0.001 # 0.001 # 0.0003 -DECAY_STEP = [60000, 80000] # 50000, 70000 -MAX_ITERATION = 150000 - -# -------------------------------------------- Data_preprocess_config -DATASET_NAME = 'pascal' # 'ship', 'spacenet', 'pascal', 'coco' -PIXEL_MEAN = [123.68, 116.779, 103.939] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR -IMG_SHORT_SIDE_LEN = 600 # 600 # 600 -IMG_MAX_LENGTH = 1000 # 1000 # 1000 -CLASS_NUM = 20 - -# --------------------------------------------- Network_config -BATCH_SIZE = 1 -INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.01) -BBOX_INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.001) -WEIGHT_DECAY = 0.00004 if NET_NAME.startswith('Mobilenet') else 0.0001 - -# ---------------------------------------------Anchor config -USE_CENTER_OFFSET = False - -LEVLES = ['P2', 'P3', 'P4', 'P5', 'P6'] -BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512] # addjust the base anchor size for voc. -ANCHOR_STRIDE_LIST = [4, 8, 16, 32, 64] -ANCHOR_SCALES = [1.0] -ANCHOR_RATIOS = [0.5, 1., 2.0] -ROI_SCALE_FACTORS = [10., 10., 5.0, 5.0] -ANCHOR_SCALE_FACTORS = None - -# --------------------------------------------FPN config -SHARE_HEADS = False -KERNEL_SIZE = 3 -RPN_IOU_POSITIVE_THRESHOLD = 0.7 -RPN_IOU_NEGATIVE_THRESHOLD = 0.3 -TRAIN_RPN_CLOOBER_POSITIVES = False - -RPN_MINIBATCH_SIZE = 256 -RPN_POSITIVE_RATE = 0.5 -RPN_NMS_IOU_THRESHOLD = 0.7 -RPN_TOP_K_NMS_TRAIN = 12000 -RPN_MAXIMUM_PROPOSAL_TARIN = 2000 - -RPN_TOP_K_NMS_TEST = 6000 -RPN_MAXIMUM_PROPOSAL_TEST = 1000 - -# specific settings for FPN -# FPN_TOP_K_PER_LEVEL_TRAIN = 2000 -# FPN_TOP_K_PER_LEVEL_TEST = 1000 - -# -------------------------------------------Fast-RCNN config -ROI_SIZE = 14 -ROI_POOL_KERNEL_SIZE = 2 -USE_DROPOUT = False -KEEP_PROB = 1.0 -SHOW_SCORE_THRSHOLD = 0.5 # only show in tensorboard - -FAST_RCNN_NMS_IOU_THRESHOLD = 0.3 # 0.6 -FAST_RCNN_NMS_MAX_BOXES_PER_CLASS = 100 -FAST_RCNN_IOU_POSITIVE_THRESHOLD = 0.5 -FAST_RCNN_IOU_NEGATIVE_THRESHOLD = 0.0 # 0.1 < IOU < 0.5 is negative -FAST_RCNN_MINIBATCH_SIZE = 256 # if is -1, that is train with OHEM -FAST_RCNN_POSITIVE_RATE = 0.25 - -ADD_GTBOXES_TO_TRAIN = False - - - diff --git a/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs_res50_fpn.py b/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs_res50_fpn.py deleted file mode 100755 index 31f8eb1..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs_res50_fpn.py +++ /dev/null @@ -1,208 +0,0 @@ -# -*- coding: utf-8 -*- -from __future__ import division, print_function, absolute_import -import os -import tensorflow as tf -''' -cls : aeroplane|| Recall: 0.9473684210526315 || Precison: 0.0006199030196164867|| AP: 0.826992691184208 -____________________ -cls : cow|| Recall: 0.9631147540983607 || Precison: 0.0005354526625668462|| AP: 0.8344652186720717 -____________________ -cls : dog|| Recall: 0.9652351738241309 || Precison: 0.0010528593384385115|| AP: 0.8848104631457077 -____________________ -cls : pottedplant|| Recall: 0.7708333333333334 || Precison: 0.000823124000293655|| AP: 0.4527288299945802 -____________________ -cls : diningtable|| Recall: 0.8980582524271845 || Precison: 0.00042887810125232407|| AP: 0.6700510019755388 -____________________ -cls : bird|| Recall: 0.9237472766884531 || Precison: 0.0009519832235409285|| AP: 0.7858394634006082 -____________________ -cls : tvmonitor|| Recall: 0.9415584415584416 || Precison: 0.0006423247726945524|| AP: 0.7532342429791412 -____________________ -cls : chair|| Recall: 0.8452380952380952 || Precison: 0.0014212159291838572|| AP: 0.5629849133883229 -____________________ -cls : train|| Recall: 0.925531914893617 || Precison: 0.0006022581218315107|| AP: 0.81368729431196 -____________________ -cls : horse|| Recall: 0.9454022988505747 || Precison: 0.0007562505602920185|| AP: 0.8603450848286776 -____________________ -cls : cat|| Recall: 0.9608938547486033 || Precison: 0.0007696817008175631|| AP: 0.8780370107529119 -____________________ -cls : sofa|| Recall: 0.9623430962343096 || Precison: 0.0005431753558979397|| AP: 0.748024582610825 -____________________ -cls : bottle|| Recall: 0.8571428571428571 || Precison: 0.0008744472166693132|| AP: 0.6253912291817303 -____________________ -cls : person|| Recall: 0.9149734982332155 || Precison: 0.009253199981238986|| AP: 0.8351147684067881 -____________________ -cls : car|| Recall: 0.9533721898417985 || Precison: 0.00259228066362385|| AP: 0.8841614814276471 -____________________ -cls : boat|| Recall: 0.8821292775665399 || Precison: 0.0005342347777629379|| AP: 0.6106671555293245 -____________________ -cls : motorbike|| Recall: 0.9323076923076923 || Precison: 0.0006731029825348658|| AP: 0.8421918380864666 -____________________ -cls : bicycle|| Recall: 0.9317507418397626 || Precison: 0.0007036524942688176|| AP: 0.8552669093308443 -____________________ -cls : bus|| Recall: 0.9765258215962441 || Precison: 0.00047651993823568495|| AP: 0.8420876315549962 -____________________ -cls : sheep|| Recall: 0.9049586776859504 || Precison: 0.000502333902950925|| AP: 0.7647489734437813 -____________________ -mAP is : 0.7665415392103065 USE_12_METRIC - -cls : bicycle|| Recall: 0.9317507418397626 || Precison: 0.0007036524942688176|| AP: 0.8298982119397122 -____________________ -cls : sofa|| Recall: 0.9623430962343096 || Precison: 0.0005431753558979397|| AP: 0.7272523895735249 -____________________ -cls : bus|| Recall: 0.9765258215962441 || Precison: 0.00047651993823568495|| AP: 0.8137027123104137 -____________________ -cls : diningtable|| Recall: 0.8980582524271845 || Precison: 0.00042887810125232407|| AP: 0.6530525394835751 -____________________ -cls : person|| Recall: 0.9149734982332155 || Precison: 0.009253199981238986|| AP: 0.803256081733613 -____________________ -cls : car|| Recall: 0.9533721898417985 || Precison: 0.00259228066362385|| AP: 0.8577825832291308 -____________________ -cls : boat|| Recall: 0.8821292775665399 || Precison: 0.0005342347777629379|| AP: 0.5979719282542533 -____________________ -cls : chair|| Recall: 0.8452380952380952 || Precison: 0.0014212159291838572|| AP: 0.5599343653732526 -____________________ -cls : aeroplane|| Recall: 0.9473684210526315 || Precison: 0.0006199030196164867|| AP: 0.7917730109896329 -____________________ -cls : cat|| Recall: 0.9608938547486033 || Precison: 0.0007696817008175631|| AP: 0.8475644227001603 -____________________ -cls : sheep|| Recall: 0.9049586776859504 || Precison: 0.000502333902950925|| AP: 0.7327379110779253 -____________________ -cls : train|| Recall: 0.925531914893617 || Precison: 0.0006022581218315107|| AP: 0.7743045860493956 -____________________ -cls : horse|| Recall: 0.9454022988505747 || Precison: 0.0007562505602920185|| AP: 0.8223412836194737 -____________________ -cls : cow|| Recall: 0.9631147540983607 || Precison: 0.0005354526625668462|| AP: 0.8058877343148467 -____________________ -cls : tvmonitor|| Recall: 0.9415584415584416 || Precison: 0.0006423247726945524|| AP: 0.7310441973657807 -____________________ -cls : pottedplant|| Recall: 0.7708333333333334 || Precison: 0.000823124000293655|| AP: 0.4646864671975241 -____________________ -cls : dog|| Recall: 0.9652351738241309 || Precison: 0.0010528593384385115|| AP: 0.8525619478862897 -____________________ -cls : bird|| Recall: 0.9237472766884531 || Precison: 0.0009519832235409285|| AP: 0.7610720209528306 -____________________ -cls : bottle|| Recall: 0.8571428571428571 || Precison: 0.0008744472166693132|| AP: 0.6127328834288011 -____________________ -cls : motorbike|| Recall: 0.9323076923076923 || Precison: 0.0006731029825348658|| AP: 0.8119378019468331 -____________________ -mAP is : 0.7425747539713485 USE_07_METRIC - -''' - -# ------------------------------------------------ -VERSION = 'FPN_Res50_20181201' -NET_NAME = 'resnet_v1_50' -ADD_BOX_IN_TENSORBOARD = True - -# ---------------------------------------- System_config -ROOT_PATH = os.path.abspath('../') -print (20*"++--") -print (ROOT_PATH) -GPU_GROUP = "1" -SHOW_TRAIN_INFO_INTE = 10 -SMRY_ITER = 100 -SAVE_WEIGHTS_INTE = 10000 - -SUMMARY_PATH = ROOT_PATH + '/output/summary' -TEST_SAVE_PATH = ROOT_PATH + '/tools/test_result' -INFERENCE_IMAGE_PATH = ROOT_PATH + '/tools/inference_image' -INFERENCE_SAVE_PATH = ROOT_PATH + '/tools/inference_results' - -if NET_NAME.startswith("resnet"): - weights_name = NET_NAME -elif NET_NAME.startswith("MobilenetV2"): - weights_name = "mobilenet/mobilenet_v2_1.0_224" -else: - raise NotImplementedError - -PRETRAINED_CKPT = ROOT_PATH + '/data/pretrained_weights/' + weights_name + '.ckpt' -TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights') - -EVALUATE_DIR = ROOT_PATH + '/output/evaluate_result_pickle/' -test_annotate_path = '/home/yjr/DataSet/VOC/VOC_test/VOC2007/Annotations' - -# ------------------------------------------ Train config -RESTORE_FROM_RPN = False -IS_FILTER_OUTSIDE_BOXES = False -FIXED_BLOCKS = 0 # allow 0~3 -USE_07_METRIC = False - -RPN_LOCATION_LOSS_WEIGHT = 1. -RPN_CLASSIFICATION_LOSS_WEIGHT = 1.0 - -FAST_RCNN_LOCATION_LOSS_WEIGHT = 1.0 -FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT = 1.0 -RPN_SIGMA = 3.0 -FASTRCNN_SIGMA = 1.0 - -MUTILPY_BIAS_GRADIENT = None # 2.0 # if None, will not multipy -GRADIENT_CLIPPING_BY_NORM = None # 10.0 if None, will not clip - -EPSILON = 1e-5 -MOMENTUM = 0.9 -LR = 0.001 # 0.001 # 0.0003 -DECAY_STEP = [60000, 80000] # 50000, 70000 -MAX_ITERATION = 150000 - -# -------------------------------------------- Data_preprocess_config -DATASET_NAME = 'pascal' # 'ship', 'spacenet', 'pascal', 'coco' -PIXEL_MEAN = [123.68, 116.779, 103.939] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR -IMG_SHORT_SIDE_LEN = 600 # 600 # 600 -IMG_MAX_LENGTH = 1000 # 1000 # 1000 -CLASS_NUM = 20 - -# --------------------------------------------- Network_config -BATCH_SIZE = 1 -INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.01) -BBOX_INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.001) -WEIGHT_DECAY = 0.00004 if NET_NAME.startswith('Mobilenet') else 0.0001 - -# ---------------------------------------------Anchor config -USE_CENTER_OFFSET = False - -LEVLES = ['P2', 'P3', 'P4', 'P5', 'P6'] -BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512] # addjust the base anchor size for voc. -ANCHOR_STRIDE_LIST = [4, 8, 16, 32, 64] -ANCHOR_SCALES = [1.0] -ANCHOR_RATIOS = [0.5, 1., 2.0] -ROI_SCALE_FACTORS = [10., 10., 5.0, 5.0] -ANCHOR_SCALE_FACTORS = None - -# --------------------------------------------FPN config -SHARE_HEADS = True -KERNEL_SIZE = 3 -RPN_IOU_POSITIVE_THRESHOLD = 0.7 -RPN_IOU_NEGATIVE_THRESHOLD = 0.3 -TRAIN_RPN_CLOOBER_POSITIVES = False - -RPN_MINIBATCH_SIZE = 256 -RPN_POSITIVE_RATE = 0.5 -RPN_NMS_IOU_THRESHOLD = 0.7 -RPN_TOP_K_NMS_TRAIN = 12000 -RPN_MAXIMUM_PROPOSAL_TARIN = 2000 - -RPN_TOP_K_NMS_TEST = 6000 -RPN_MAXIMUM_PROPOSAL_TEST = 1000 - -# specific settings for FPN -# FPN_TOP_K_PER_LEVEL_TRAIN = 2000 -# FPN_TOP_K_PER_LEVEL_TEST = 1000 - -# -------------------------------------------Fast-RCNN config -ROI_SIZE = 14 -ROI_POOL_KERNEL_SIZE = 2 -USE_DROPOUT = False -KEEP_PROB = 1.0 -SHOW_SCORE_THRSHOLD = 0.5 # only show in tensorboard - -FAST_RCNN_NMS_IOU_THRESHOLD = 0.3 # 0.6 -FAST_RCNN_NMS_MAX_BOXES_PER_CLASS = 100 -FAST_RCNN_IOU_POSITIVE_THRESHOLD = 0.5 -FAST_RCNN_IOU_NEGATIVE_THRESHOLD = 0.0 # 0.1 < IOU < 0.5 is negative -FAST_RCNN_MINIBATCH_SIZE = 256 # if is -1, that is train with OHEM -FAST_RCNN_POSITIVE_RATE = 0.25 - -ADD_GTBOXES_TO_TRAIN = False - - - diff --git a/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/__init__.py b/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/anchor_target_layer_without_boxweight.py b/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/anchor_target_layer_without_boxweight.py deleted file mode 100755 index 6179bc0..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/anchor_target_layer_without_boxweight.py +++ /dev/null @@ -1,125 +0,0 @@ -# -------------------------------------------------------- -# Faster R-CNN -# Copyright (c) 2015 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Ross Girshick and Xinlei Chen -# -------------------------------------------------------- -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -from libs.configs import cfgs -import numpy as np -import numpy.random as npr -from libs.box_utils.cython_utils.cython_bbox import bbox_overlaps -from libs.box_utils import encode_and_decode - - -def anchor_target_layer( - gt_boxes, img_shape, all_anchors, is_restrict_bg=False): - """Same as the anchor target layer in original Fast/er RCNN """ - - total_anchors = all_anchors.shape[0] - img_h, img_w = img_shape[1], img_shape[2] - gt_boxes = gt_boxes[:, :-1] # remove class label - - - # allow boxes to sit over the edge by a small amount - _allowed_border = 0 - - # only keep anchors inside the image - if cfgs.IS_FILTER_OUTSIDE_BOXES: - inds_inside = np.where( - (all_anchors[:, 0] >= -_allowed_border) & - (all_anchors[:, 1] >= -_allowed_border) & - (all_anchors[:, 2] < img_w + _allowed_border) & # width - (all_anchors[:, 3] < img_h + _allowed_border) # height - )[0] - else: - inds_inside = range(all_anchors.shape[0]) - - anchors = all_anchors[inds_inside, :] - - # label: 1 is positive, 0 is negative, -1 is dont care - labels = np.empty((len(inds_inside),), dtype=np.float32) - labels.fill(-1) - - # overlaps between the anchors and the gt boxes - overlaps = bbox_overlaps( - np.ascontiguousarray(anchors, dtype=np.float), - np.ascontiguousarray(gt_boxes, dtype=np.float)) - - argmax_overlaps = overlaps.argmax(axis=1) - max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps] - gt_argmax_overlaps = overlaps.argmax(axis=0) - gt_max_overlaps = overlaps[ - gt_argmax_overlaps, np.arange(overlaps.shape[1])] - gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0] - - if not cfgs.TRAIN_RPN_CLOOBER_POSITIVES: - labels[max_overlaps < cfgs.RPN_IOU_NEGATIVE_THRESHOLD] = 0 - - labels[gt_argmax_overlaps] = 1 - labels[max_overlaps >= cfgs.RPN_IOU_POSITIVE_THRESHOLD] = 1 - - if cfgs.TRAIN_RPN_CLOOBER_POSITIVES: - labels[max_overlaps < cfgs.RPN_IOU_NEGATIVE_THRESHOLD] = 0 - - num_fg = int(cfgs.RPN_MINIBATCH_SIZE * cfgs.RPN_POSITIVE_RATE) - fg_inds = np.where(labels == 1)[0] - if len(fg_inds) > num_fg: - disable_inds = npr.choice( - fg_inds, size=(len(fg_inds) - num_fg), replace=False) - labels[disable_inds] = -1 - - num_bg = cfgs.RPN_MINIBATCH_SIZE - np.sum(labels == 1) - if is_restrict_bg: - num_bg = max(num_bg, num_fg * 1.5) - bg_inds = np.where(labels == 0)[0] - if len(bg_inds) > num_bg: - disable_inds = npr.choice( - bg_inds, size=(len(bg_inds) - num_bg), replace=False) - labels[disable_inds] = -1 - - bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :]) - - # map up to original set of anchors - labels = _unmap(labels, total_anchors, inds_inside, fill=-1) - bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0) - - # labels = labels.reshape((1, height, width, A)) - rpn_labels = labels.reshape((-1, 1)) - - # bbox_targets - bbox_targets = bbox_targets.reshape((-1, 4)) - rpn_bbox_targets = bbox_targets - - return rpn_labels, rpn_bbox_targets - - -def _unmap(data, count, inds, fill=0): - """ Unmap a subset of item (data) back to the original set of items (of - size count) """ - if len(data.shape) == 1: - ret = np.empty((count,), dtype=np.float32) - ret.fill(fill) - ret[inds] = data - else: - ret = np.empty((count,) + data.shape[1:], dtype=np.float32) - ret.fill(fill) - ret[inds, :] = data - return ret - - -def _compute_targets(ex_rois, gt_rois): - """Compute bounding-box regression targets for an image.""" - # targets = bbox_transform(ex_rois, gt_rois[:, :4]).astype( - # np.float32, copy=False) - targets = encode_and_decode.encode_boxes(unencode_boxes=gt_rois, - reference_boxes=ex_rois, - scale_factors=cfgs.ANCHOR_SCALE_FACTORS) - # targets = encode_and_decode.encode_boxes(ex_rois=ex_rois, - # gt_rois=gt_rois, - # scale_factor=None) - return targets diff --git a/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/proposal_opr.py b/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/proposal_opr.py deleted file mode 100755 index 54cf9b8..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/proposal_opr.py +++ /dev/null @@ -1,66 +0,0 @@ -# encoding: utf-8 -""" -@author: zeming li -@contact: zengarden2009@gmail.com -""" - -from libs.configs import cfgs -from libs.box_utils import encode_and_decode -from libs.box_utils import boxes_utils -import tensorflow as tf -import numpy as np - - -def postprocess_rpn_proposals(rpn_bbox_pred, rpn_cls_prob, img_shape, anchors, is_training): - ''' - - :param rpn_bbox_pred: [-1, 4] - :param rpn_cls_prob: [-1, 2] - :param img_shape: - :param anchors:[-1, 4] - :param is_training: - :return: - ''' - - if is_training: - pre_nms_topN = cfgs.RPN_TOP_K_NMS_TRAIN - post_nms_topN = cfgs.RPN_MAXIMUM_PROPOSAL_TARIN - # pre_nms_topN = cfgs.FPN_TOP_K_PER_LEVEL_TRAIN - # post_nms_topN = pre_nms_topN - else: - pre_nms_topN = cfgs.RPN_TOP_K_NMS_TEST - post_nms_topN = cfgs.RPN_MAXIMUM_PROPOSAL_TEST - # pre_nms_topN = cfgs.FPN_TOP_K_PER_LEVEL_TEST - # post_nms_topN = pre_nms_topN - - nms_thresh = cfgs.RPN_NMS_IOU_THRESHOLD - - cls_prob = rpn_cls_prob[:, 1] - - # 1. decode boxes - decode_boxes = encode_and_decode.decode_boxes(encoded_boxes=rpn_bbox_pred, - reference_boxes=anchors, - scale_factors=cfgs.ANCHOR_SCALE_FACTORS) - - # 2. clip to img boundaries - decode_boxes = boxes_utils.clip_boxes_to_img_boundaries(decode_boxes=decode_boxes, - img_shape=img_shape) - - # 3. get top N to NMS - if pre_nms_topN > 0: - pre_nms_topN = tf.minimum(pre_nms_topN, tf.shape(decode_boxes)[0], name='avoid_unenough_boxes') - cls_prob, top_k_indices = tf.nn.top_k(cls_prob, k=pre_nms_topN) - decode_boxes = tf.gather(decode_boxes, top_k_indices) - - # 4. NMS - keep = tf.image.non_max_suppression( - boxes=decode_boxes, - scores=cls_prob, - max_output_size=post_nms_topN, - iou_threshold=nms_thresh) - - final_boxes = tf.gather(decode_boxes, keep) - final_probs = tf.gather(cls_prob, keep) - - return final_boxes, final_probs - diff --git a/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/proposal_target_layer.py b/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/proposal_target_layer.py deleted file mode 100755 index bbfaaca..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/proposal_target_layer.py +++ /dev/null @@ -1,145 +0,0 @@ -# -------------------------------------------------------- -# Faster R-CNN -# Copyright (c) 2015 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Ross Girshick -# -------------------------------------------------------- -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from libs.configs import cfgs -import numpy as np -import numpy.random as npr - -from libs.box_utils import encode_and_decode -from libs.box_utils.cython_utils.cython_bbox import bbox_overlaps - - -def proposal_target_layer(rpn_rois, gt_boxes): - """ - Assign object detection proposals to ground-truth targets. Produces proposal - classification labels and bounding-box regression targets. - """ - # Proposal ROIs (x1, y1, x2, y2) coming from RPN - # gt_boxes (x1, y1, x2, y2, label) - - if cfgs.ADD_GTBOXES_TO_TRAIN: - all_rois = np.vstack((rpn_rois, gt_boxes[:, :-1])) - else: - all_rois = rpn_rois - - rois_per_image = np.inf if cfgs.FAST_RCNN_MINIBATCH_SIZE == -1 else cfgs.FAST_RCNN_MINIBATCH_SIZE - - fg_rois_per_image = np.round(cfgs.FAST_RCNN_POSITIVE_RATE * rois_per_image) - - # Sample rois with classification labels and bounding box regression - labels, rois, bbox_targets = _sample_rois(all_rois, gt_boxes, fg_rois_per_image, - rois_per_image, cfgs.CLASS_NUM+1) - - rois = rois.reshape(-1, 4) - labels = labels.reshape(-1) - bbox_targets = bbox_targets.reshape(-1, (cfgs.CLASS_NUM+1) * 4) - - return rois, labels, bbox_targets - - -def _get_bbox_regression_labels(bbox_target_data, num_classes): - """Bounding-box regression targets (bbox_target_data) are stored in a - compact form N x (class, tx, ty, tw, th) - - This function expands those targets into the 4-of-4*K representation used - by the network (i.e. only one class has non-zero targets). - - Returns: - bbox_target (ndarray): N x 4K blob of regression targets - """ - - clss = bbox_target_data[:, 0] - bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32) - inds = np.where(clss > 0)[0] - for ind in inds: - cls = clss[ind] - start = int(4 * cls) - end = start + 4 - bbox_targets[ind, start:end] = bbox_target_data[ind, 1:] - - return bbox_targets - - -def _compute_targets(ex_rois, gt_rois, labels): - """Compute bounding-box regression targets for an image. - that is : [label, tx, ty, tw, th] - """ - - assert ex_rois.shape[0] == gt_rois.shape[0] - assert ex_rois.shape[1] == 4 - assert gt_rois.shape[1] == 4 - - targets = encode_and_decode.encode_boxes(unencode_boxes=gt_rois, - reference_boxes=ex_rois, - scale_factors=cfgs.ROI_SCALE_FACTORS) - # targets = encode_and_decode.encode_boxes(ex_rois=ex_rois, - # gt_rois=gt_rois, - # scale_factor=cfgs.ROI_SCALE_FACTORS) - - return np.hstack( - (labels[:, np.newaxis], targets)).astype(np.float32, copy=False) - - -def _sample_rois(all_rois, gt_boxes, fg_rois_per_image, - rois_per_image, num_classes): - """Generate a random sample of RoIs comprising foreground and background - examples. - - all_rois shape is [-1, 4] - gt_boxes shape is [-1, 5]. that is [x1, y1, x2, y2, label] - """ - # overlaps: (rois x gt_boxes) - overlaps = bbox_overlaps( - np.ascontiguousarray(all_rois, dtype=np.float), - np.ascontiguousarray(gt_boxes[:, :-1], dtype=np.float)) - gt_assignment = overlaps.argmax(axis=1) - max_overlaps = overlaps.max(axis=1) - labels = gt_boxes[gt_assignment, -1] - - # Select foreground RoIs as those with >= FG_THRESH overlap - fg_inds = np.where(max_overlaps >= cfgs.FAST_RCNN_IOU_POSITIVE_THRESHOLD)[0] - # Guard against the case when an image has fewer than fg_rois_per_image - # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI) - bg_inds = np.where((max_overlaps < cfgs.FAST_RCNN_IOU_POSITIVE_THRESHOLD) & - (max_overlaps >= cfgs.FAST_RCNN_IOU_NEGATIVE_THRESHOLD))[0] - # print("first fileter, fg_size: {} || bg_size: {}".format(fg_inds.shape, bg_inds.shape)) - # Guard against the case when an image has fewer than fg_rois_per_image - # foreground RoIs - fg_rois_per_this_image = min(fg_rois_per_image, fg_inds.size) - - # Sample foreground regions without replacement - if fg_inds.size > 0: - fg_inds = npr.choice(fg_inds, size=int(fg_rois_per_this_image), replace=False) - # Compute number of background RoIs to take from this image (guarding - # against there being fewer than desired) - bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image - bg_rois_per_this_image = min(bg_rois_per_this_image, bg_inds.size) - # Sample background regions without replacement - if bg_inds.size > 0: - bg_inds = npr.choice(bg_inds, size=int(bg_rois_per_this_image), replace=False) - - # print("second fileter, fg_size: {} || bg_size: {}".format(fg_inds.shape, bg_inds.shape)) - # The indices that we're selecting (both fg and bg) - keep_inds = np.append(fg_inds, bg_inds) - - - # Select sampled values from various arrays: - labels = labels[keep_inds] - - # Clamp labels for the background RoIs to 0 - labels[int(fg_rois_per_this_image):] = 0 - rois = all_rois[keep_inds] - - bbox_target_data = _compute_targets( - rois, gt_boxes[gt_assignment[keep_inds], :-1], labels) - - bbox_targets = \ - _get_bbox_regression_labels(bbox_target_data, num_classes) - - return labels, rois, bbox_targets diff --git a/example-apps/PDD/pcb-defect-detection/libs/export_pbs/__init__.py b/example-apps/PDD/pcb-defect-detection/libs/export_pbs/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/libs/export_pbs/exportPb.py b/example-apps/PDD/pcb-defect-detection/libs/export_pbs/exportPb.py deleted file mode 100755 index 8f4b217..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/export_pbs/exportPb.py +++ /dev/null @@ -1,87 +0,0 @@ -# -*- coding: utf-8 -*- - -from __future__ import absolute_import, print_function, division - -import os, sys -import tensorflow as tf -import tensorflow.contrib.slim as slim -from tensorflow.python.tools import freeze_graph - -sys.path.append('../../') -from data.io.image_preprocess import short_side_resize_for_inference_data -from libs.configs import cfgs -from libs.networks import build_whole_network - -CKPT_PATH = '/home/yjr/PycharmProjects/Faster-RCNN_Tensorflow/output/trained_weights/FasterRCNN_20180517/voc_200000model.ckpt' -OUT_DIR = '../../output/Pbs' -PB_NAME = 'FasterRCNN_Res101_Pascal.pb' - - -def build_detection_graph(): - # 1. preprocess img - img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3], - name='input_img') # is RGB. not GBR - raw_shape = tf.shape(img_plac) - raw_h, raw_w = tf.to_float(raw_shape[0]), tf.to_float(raw_shape[1]) - - img_batch = tf.cast(img_plac, tf.float32) - img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, - target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, - length_limitation=cfgs.IMG_MAX_LENGTH) - img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) - img_batch = tf.expand_dims(img_batch, axis=0) # [1, None, None, 3] - - det_net = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, - is_training=False) - - detected_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( - input_img_batch=img_batch, - gtboxes_batch=None) - - xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \ - detected_boxes[:, 2], detected_boxes[:, 3] - - resized_shape = tf.shape(img_batch) - resized_h, resized_w = tf.to_float(resized_shape[1]), tf.to_float(resized_shape[2]) - - xmin = xmin * raw_w / resized_w - xmax = xmax * raw_w / resized_w - - ymin = ymin * raw_h / resized_h - ymax = ymax * raw_h / resized_h - - boxes = tf.transpose(tf.stack([xmin, ymin, xmax, ymax])) - dets = tf.concat([tf.reshape(detection_category, [-1, 1]), - tf.reshape(detection_scores, [-1, 1]), - boxes], axis=1, name='DetResults') - - return dets - - -def export_frozenPB(): - - tf.reset_default_graph() - - dets = build_detection_graph() - - saver = tf.train.Saver() - - with tf.Session() as sess: - print("we have restred the weights from =====>>\n", CKPT_PATH) - saver.restore(sess, CKPT_PATH) - - tf.train.write_graph(sess.graph_def, OUT_DIR, PB_NAME) - freeze_graph.freeze_graph(input_graph=os.path.join(OUT_DIR, PB_NAME), - input_saver='', - input_binary=False, - input_checkpoint=CKPT_PATH, - output_node_names="DetResults", - restore_op_name="save/restore_all", - filename_tensor_name='save/Const:0', - output_graph=os.path.join(OUT_DIR, PB_NAME.replace('.pb', '_Frozen.pb')), - clear_devices=False, - initializer_nodes='') - -if __name__ == '__main__': - os.environ["CUDA_VISIBLE_DEVICES"] = '' - export_frozenPB() diff --git a/example-apps/PDD/pcb-defect-detection/libs/export_pbs/test_exportPb.py b/example-apps/PDD/pcb-defect-detection/libs/export_pbs/test_exportPb.py deleted file mode 100755 index ad78e42..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/export_pbs/test_exportPb.py +++ /dev/null @@ -1,81 +0,0 @@ -# -*- coding:utf-8 -*- - -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import os, sys -import tensorflow as tf -import time -import cv2 -import argparse -import numpy as np -sys.path.append("../") - -from data.io.image_preprocess import short_side_resize_for_inference_data -from libs.configs import cfgs -from libs.networks import build_whole_network -from libs.box_utils import draw_box_in_img -from help_utils import tools - - - - - -def load_graph(frozen_graph_file): - - # we parse the graph_def file - with tf.gfile.GFile(frozen_graph_file, 'rb') as f: - graph_def = tf.GraphDef() - graph_def.ParseFromString(f.read()) - - # we load the graph_def in the default graph - - with tf.Graph().as_default() as graph: - tf.import_graph_def(graph_def, - input_map=None, - return_elements=None, - name="", - op_dict=None, - producer_op_list=None) - return graph - - -def test(frozen_graph_path, test_dir): - - graph = load_graph(frozen_graph_path) - print("we are testing ====>>>>", frozen_graph_path) - - img = graph.get_tensor_by_name("input_img:0") - dets = graph.get_tensor_by_name("DetResults:0") - - with tf.Session(graph=graph) as sess: - for img_path in os.listdir(test_dir): - a_img = cv2.imread(os.path.join(test_dir, img_path))[:, :, ::-1] - st = time.time() - dets_val = sess.run(dets, feed_dict={img: a_img}) - - show_indices = dets_val[:, 1] >= 0.5 - dets_val = dets_val[show_indices] - final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(a_img, - boxes=dets_val[:, 2:], - labels=dets_val[:, 0], - scores=dets_val[:, 1]) - cv2.imwrite(img_path, - final_detections[:, :, ::-1]) - print ("%s cost time: %f" % (img_path, time.time() - st)) - -if __name__ == '__main__': - test('/home/yjr/PycharmProjects/Faster-RCNN_Tensorflow/output/Pbs/FasterRCNN_Res101_Pascal_Frozen.pb', - '/home/yjr/PycharmProjects/Faster-RCNN_Tensorflow/tools/demos') - - - - - - - - - - - diff --git a/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/__init__.py b/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/__init__.pyc b/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/__init__.pyc deleted file mode 100755 index 64b67a2..0000000 Binary files a/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/__init__.pyc and /dev/null differ diff --git a/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/coco_dict.py b/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/coco_dict.py deleted file mode 100755 index d4c190f..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/coco_dict.py +++ /dev/null @@ -1,54 +0,0 @@ -# -*- coding: utf-8 -*- - -from __future__ import absolute_import, print_function, division - -class_names = [ - 'back_ground', 'person', 'bicycle', 'car', 'motorcycle', - 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', - 'fire hydrant', 'stop sign', 'parking meter', 'bench', - 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', - 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', - 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', - 'sports ball', 'kite', 'baseball bat', 'baseball glove', - 'skateboard', 'surfboard', 'tennis racket', 'bottle', - 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', - 'banana', 'apple', 'sandwich', 'orange', 'broccoli', - 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', - 'couch', 'potted plant', 'bed', 'dining table', 'toilet', - 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', - 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', - 'book', 'clock', 'vase', 'scissors', 'teddy bear', - 'hair drier', 'toothbrush'] - - -classes_originID = { - 'person': 1, 'bicycle': 2, 'car': 3, 'motorcycle': 4, - 'airplane': 5, 'bus': 6, 'train': 7, 'truck': 8, 'boat': 9, - 'traffic light': 10, 'fire hydrant': 11, 'stop sign': 13, - 'parking meter': 14, 'bench': 15, 'bird': 16, 'cat': 17, - 'dog': 18, 'horse': 19, 'sheep': 20, 'cow': 21, 'elephant': 22, - 'bear': 23, 'zebra': 24, 'giraffe': 25, 'backpack': 27, - 'umbrella': 28, 'handbag': 31, 'tie': 32, 'suitcase': 33, - 'frisbee': 34, 'skis': 35, 'snowboard': 36, 'sports ball': 37, - 'kite': 38, 'baseball bat': 39, 'baseball glove': 40, - 'skateboard': 41, 'surfboard': 42, 'tennis racket': 43, - 'bottle': 44, 'wine glass': 46, 'cup': 47, 'fork': 48, - 'knife': 49, 'spoon': 50, 'bowl': 51, 'banana': 52, 'apple': 53, - 'sandwich': 54, 'orange': 55, 'broccoli': 56, 'carrot': 57, - 'hot dog': 58, 'pizza': 59, 'donut': 60, 'cake': 61, - 'chair': 62, 'couch': 63, 'potted plant': 64, 'bed': 65, - 'dining table': 67, 'toilet': 70, 'tv': 72, 'laptop': 73, - 'mouse': 74, 'remote': 75, 'keyboard': 76, 'cell phone': 77, - 'microwave': 78, 'oven': 79, 'toaster': 80, 'sink': 81, - 'refrigerator': 82, 'book': 84, 'clock': 85, 'vase': 86, - 'scissors': 87, 'teddy bear': 88, 'hair drier': 89, - 'toothbrush': 90} - -originID_classes = {item: key for key, item in classes_originID.items()} -NAME_LABEL_MAP = dict(zip(class_names, range(len(class_names)))) -LABEL_NAME_MAP = dict(zip(range(len(class_names)), class_names)) - -# print (originID_classes) - - - diff --git a/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/label_dict.py b/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/label_dict.py deleted file mode 100755 index dc5d504..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/label_dict.py +++ /dev/null @@ -1,98 +0,0 @@ -# -*- coding: utf-8 -*- -from __future__ import division, print_function, absolute_import - -from libs.configs import cfgs - -if cfgs.DATASET_NAME == 'ship': - NAME_LABEL_MAP = { - 'back_ground': 0, - 'ship': 1 - } -elif cfgs.DATASET_NAME == 'aeroplane': - NAME_LABEL_MAP = { - 'back_ground': 0, - 'aeroplane': 1 - } -elif cfgs.DATASET_NAME == 'WIDER': - NAME_LABEL_MAP = { - 'back_ground': 0, - 'face': 1 - } -elif cfgs.DATASET_NAME == 'jyzdata': - NAME_LABEL_MAP = { - 'back_ground': 0, - 'classone': 1, - 'boli': 2, - 'dangeboli': 3, - 'taoci': 4, - 'taoci2': 4 - } -elif cfgs.DATASET_NAME == 'icdar': - NAME_LABEL_MAP = { - 'back_ground': 0, - 'text': 1 - } -elif cfgs.DATASET_NAME.startswith('DOTA'): - NAME_LABEL_MAP = { - 'back_ground': 0, - 'roundabout': 1, - 'tennis-court': 2, - 'swimming-pool': 3, - 'storage-tank': 4, - 'soccer-ball-field': 5, - 'small-vehicle': 6, - 'ship': 7, - 'plane': 8, - 'large-vehicle': 9, - 'helicopter': 10, - 'harbor': 11, - 'ground-track-field': 12, - 'bridge': 13, - 'basketball-court': 14, - 'baseball-diamond': 15 - } -elif cfgs.DATASET_NAME == 'pascal': - NAME_LABEL_MAP = { - 'back_ground': 0, - 'aeroplane': 1, - 'bicycle': 2, - 'bird': 3, - 'boat': 4, - 'bottle': 5, - 'bus': 6, - 'car': 7, - 'cat': 8, - 'chair': 9, - 'cow': 10, - 'diningtable': 11, - 'dog': 12, - 'horse': 13, - 'motorbike': 14, - 'person': 15, - 'pottedplant': 16, - 'sheep': 17, - 'sofa': 18, - 'train': 19, - 'tvmonitor': 20 - } -elif cfgs.DATASET_NAME == 'pcb': - NAME_LABEL_MAP = { - 'back_ground': 0, - 'missing_hole': 1, - 'mouse_bite': 2, - 'open_circuit': 3, - 'short': 4, - 'spur': 5, - 'spurious_copper': 6 - } -else: - assert 'please set label dict!' - - -def get_label_name_map(): - reverse_dict = {} - for name, label in NAME_LABEL_MAP.items(): - reverse_dict[label] = name - return reverse_dict - -LABEl_NAME_MAP = get_label_name_map() \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/label_dict.pyc b/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/label_dict.pyc deleted file mode 100755 index 38a1e54..0000000 Binary files a/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/label_dict.pyc and /dev/null differ diff --git a/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/remote_sensing_dict.py b/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/remote_sensing_dict.py deleted file mode 100755 index 5257bc9..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/label_name_dict/remote_sensing_dict.py +++ /dev/null @@ -1,15 +0,0 @@ -# -*- coding: utf-8 -*- - -NAME_LABEL_MAP = { - 'back_ground': 0, - 'building': 1 -} - - -def get_label_name_map(): - reverse_dict = {} - for name, label in NAME_LABEL_MAP.items(): - reverse_dict[label] = name - return reverse_dict - -LABEl_NAME_MAP = get_label_name_map() \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/libs/losses/__init__.py b/example-apps/PDD/pcb-defect-detection/libs/losses/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/libs/losses/losses.py b/example-apps/PDD/pcb-defect-detection/libs/losses/losses.py deleted file mode 100755 index 32c9429..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/losses/losses.py +++ /dev/null @@ -1,135 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@author: jemmy li -@contact: zengarden2009@gmail.com -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - - -def _smooth_l1_loss_base(bbox_pred, bbox_targets, sigma=1.0): - ''' - - :param bbox_pred: [-1, 4] in RPN. [-1, cls_num+1, 4] in Fast-rcnn - :param bbox_targets: shape is same as bbox_pred - :param sigma: - :return: - ''' - sigma_2 = sigma**2 - - box_diff = bbox_pred - bbox_targets - - abs_box_diff = tf.abs(box_diff) - - smoothL1_sign = tf.stop_gradient( - tf.to_float(tf.less(abs_box_diff, 1. / sigma_2))) - loss_box = tf.pow(box_diff, 2) * (sigma_2 / 2.0) * smoothL1_sign \ - + (abs_box_diff - (0.5 / sigma_2)) * (1.0 - smoothL1_sign) - return loss_box - -def smooth_l1_loss_rpn(bbox_pred, bbox_targets, label, sigma=1.0): - ''' - - :param bbox_pred: [-1, 4] - :param bbox_targets: [-1, 4] - :param label: [-1] - :param sigma: - :return: - ''' - value = _smooth_l1_loss_base(bbox_pred, bbox_targets, sigma=sigma) - value = tf.reduce_sum(value, axis=1) # to sum in axis 1 - rpn_positive = tf.where(tf.greater(label, 0)) - - # rpn_select = tf.stop_gradient(rpn_select) # to avoid - selected_value = tf.gather(value, rpn_positive) - non_ignored_mask = tf.stop_gradient( - 1.0 - tf.to_float(tf.equal(label, -1))) # positve is 1.0 others is 0.0 - - bbox_loss = tf.reduce_sum(selected_value) / tf.maximum(1.0, tf.reduce_sum(non_ignored_mask)) - - return bbox_loss - -def smooth_l1_loss_rcnn(bbox_pred, bbox_targets, label, num_classes, sigma=1.0): - ''' - - :param bbox_pred: [-1, (cfgs.CLS_NUM +1) * 4] - :param bbox_targets:[-1, (cfgs.CLS_NUM +1) * 4] - :param label:[-1] - :param num_classes: - :param sigma: - :return: - ''' - - outside_mask = tf.stop_gradient(tf.to_float(tf.greater(label, 0))) - - bbox_pred = tf.reshape(bbox_pred, [-1, num_classes, 4]) - bbox_targets = tf.reshape(bbox_targets, [-1, num_classes, 4]) - - value = _smooth_l1_loss_base(bbox_pred, - bbox_targets, - sigma=sigma) - value = tf.reduce_sum(value, 2) - value = tf.reshape(value, [-1, num_classes]) - - inside_mask = tf.one_hot(tf.reshape(label, [-1, 1]), - depth=num_classes, axis=1) - - inside_mask = tf.stop_gradient( - tf.to_float(tf.reshape(inside_mask, [-1, num_classes]))) - - normalizer = tf.to_float(tf.shape(bbox_pred)[0]) - bbox_loss = tf.reduce_sum( - tf.reduce_sum(value * inside_mask, 1)*outside_mask) / normalizer - - return bbox_loss - -def sum_ohem_loss(cls_score, label, bbox_pred, bbox_targets, - num_classes, num_ohem_samples=256, sigma=1.0): - - ''' - :param cls_score: [-1, cls_num+1] - :param label: [-1] - :param bbox_pred: [-1, 4*(cls_num+1)] - :param bbox_targets: [-1, 4*(cls_num+1)] - :param num_ohem_samples: 256 by default - :param num_classes: cls_num+1 - :param sigma: - :return: - ''' - cls_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=cls_score, labels=label) # [-1, ] - # cls_loss = tf.Print(cls_loss, [tf.shape(cls_loss)], summarize=10, message='CLS losss shape ****') - - outside_mask = tf.stop_gradient(tf.to_float(tf.greater(label, 0))) - bbox_pred = tf.reshape(bbox_pred, [-1, num_classes, 4]) - bbox_targets = tf.reshape(bbox_targets, [-1, num_classes, 4]) - - value = _smooth_l1_loss_base(bbox_pred, - bbox_targets, - sigma=sigma) - value = tf.reduce_sum(value, 2) - value = tf.reshape(value, [-1, num_classes]) - - inside_mask = tf.one_hot(tf.reshape(label, [-1, 1]), - depth=num_classes, axis=1) - - inside_mask = tf.stop_gradient( - tf.to_float(tf.reshape(inside_mask, [-1, num_classes]))) - loc_loss = tf.reduce_sum(value * inside_mask, 1)*outside_mask - # loc_loss = tf.Print(loc_loss, [tf.shape(loc_loss)], summarize=10, message='loc_loss shape***') - - sum_loss = cls_loss + loc_loss - - num_ohem_samples = tf.stop_gradient(tf.minimum(num_ohem_samples, tf.shape(sum_loss)[0])) - _, top_k_indices = tf.nn.top_k(sum_loss, k=num_ohem_samples) - - cls_loss_ohem = tf.gather(cls_loss, top_k_indices) - cls_loss_ohem = tf.reduce_mean(cls_loss_ohem) - - loc_loss_ohem = tf.gather(loc_loss, top_k_indices) - normalizer = tf.to_float(num_ohem_samples) - loc_loss_ohem = tf.reduce_sum(loc_loss_ohem) / normalizer - - return cls_loss_ohem, loc_loss_ohem \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/__init__.py b/example-apps/PDD/pcb-defect-detection/libs/networks/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/build_whole_network.py b/example-apps/PDD/pcb-defect-detection/libs/networks/build_whole_network.py deleted file mode 100755 index e5d4c17..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/build_whole_network.py +++ /dev/null @@ -1,636 +0,0 @@ -# -*-coding: utf-8 -*- - -from __future__ import absolute_import, division, print_function - -import os -import tensorflow as tf -import tensorflow.contrib.slim as slim -import numpy as np - -from libs.networks import resnet -from libs.networks import mobilenet_v2 -from libs.box_utils import encode_and_decode -from libs.box_utils import boxes_utils -from libs.box_utils import anchor_utils -from libs.configs import cfgs -from libs.losses import losses -from libs.box_utils import show_box_in_tensor -from libs.detection_oprations.proposal_opr import postprocess_rpn_proposals -from libs.detection_oprations.anchor_target_layer_without_boxweight import anchor_target_layer -from libs.detection_oprations.proposal_target_layer import proposal_target_layer - - -class DetectionNetwork(object): - - def __init__(self, base_network_name, is_training): - - self.base_network_name = base_network_name - self.is_training = is_training - self.num_anchors_per_location = len(cfgs.ANCHOR_SCALES) * len(cfgs.ANCHOR_RATIOS) - - def build_base_network(self, input_img_batch): - - if self.base_network_name.startswith('resnet_v1'): - return resnet.resnet_base(input_img_batch, scope_name=self.base_network_name, is_training=self.is_training) - - elif self.base_network_name.startswith('MobilenetV2'): - return mobilenet_v2.mobilenetv2_base(input_img_batch, is_training=self.is_training) - - else: - raise ValueError('Sry, we only support resnet or mobilenet_v2') - - def postprocess_fastrcnn(self, rois, bbox_ppred, scores, img_shape): - ''' - - :param rois:[-1, 4] - :param bbox_ppred: [-1, (cfgs.Class_num+1) * 4] - :param scores: [-1, cfgs.Class_num + 1] - :return: - ''' - - with tf.name_scope('postprocess_fastrcnn'): - rois = tf.stop_gradient(rois) - scores = tf.stop_gradient(scores) - bbox_ppred = tf.reshape(bbox_ppred, [-1, cfgs.CLASS_NUM + 1, 4]) - bbox_ppred = tf.stop_gradient(bbox_ppred) - - bbox_pred_list = tf.unstack(bbox_ppred, axis=1) - score_list = tf.unstack(scores, axis=1) - - allclasses_boxes = [] - allclasses_scores = [] - categories = [] - for i in range(1, cfgs.CLASS_NUM+1): - - # 1. decode boxes in each class - tmp_encoded_box = bbox_pred_list[i] - tmp_score = score_list[i] - tmp_decoded_boxes = encode_and_decode.decode_boxes(encoded_boxes=tmp_encoded_box, - reference_boxes=rois, - scale_factors=cfgs.ROI_SCALE_FACTORS) - # tmp_decoded_boxes = encode_and_decode.decode_boxes(boxes=rois, - # deltas=tmp_encoded_box, - # scale_factor=cfgs.ROI_SCALE_FACTORS) - - # 2. clip to img boundaries - tmp_decoded_boxes = boxes_utils.clip_boxes_to_img_boundaries(decode_boxes=tmp_decoded_boxes, - img_shape=img_shape) - - # 3. NMS - keep = tf.image.non_max_suppression( - boxes=tmp_decoded_boxes, - scores=tmp_score, - max_output_size=cfgs.FAST_RCNN_NMS_MAX_BOXES_PER_CLASS, - iou_threshold=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD) - - perclass_boxes = tf.gather(tmp_decoded_boxes, keep) - perclass_scores = tf.gather(tmp_score, keep) - - allclasses_boxes.append(perclass_boxes) - allclasses_scores.append(perclass_scores) - categories.append(tf.ones_like(perclass_scores) * i) - - final_boxes = tf.concat(allclasses_boxes, axis=0) - final_scores = tf.concat(allclasses_scores, axis=0) - final_category = tf.concat(categories, axis=0) - - if self.is_training: - ''' - in training. We should show the detecitons in the tensorboard. So we add this. - ''' - kept_indices = tf.reshape(tf.where(tf.greater_equal(final_scores, cfgs.SHOW_SCORE_THRSHOLD)), [-1]) - - final_boxes = tf.gather(final_boxes, kept_indices) - final_scores = tf.gather(final_scores, kept_indices) - final_category = tf.gather(final_category, kept_indices) - - return final_boxes, final_scores, final_category - - def roi_pooling(self, feature_maps, rois, img_shape, scope): - ''' - Here use roi warping as roi_pooling - - :param featuremaps_dict: feature map to crop - :param rois: shape is [-1, 4]. [x1, y1, x2, y2] - :return: - ''' - - with tf.variable_scope('ROI_Warping_'+scope): - img_h, img_w = tf.cast(img_shape[1], tf.float32), tf.cast(img_shape[2], tf.float32) - N = tf.shape(rois)[0] - x1, y1, x2, y2 = tf.unstack(rois, axis=1) - - normalized_x1 = x1 / img_w - normalized_x2 = x2 / img_w - normalized_y1 = y1 / img_h - normalized_y2 = y2 / img_h - - normalized_rois = tf.transpose( - tf.stack([normalized_y1, normalized_x1, normalized_y2, normalized_x2]), name='get_normalized_rois') - - normalized_rois = tf.stop_gradient(normalized_rois) - - cropped_roi_features = tf.image.crop_and_resize(feature_maps, normalized_rois, - box_ind=tf.zeros(shape=[N, ], - dtype=tf.int32), - crop_size=[cfgs.ROI_SIZE, cfgs.ROI_SIZE], - name='CROP_AND_RESIZE' - ) - roi_features = slim.max_pool2d(cropped_roi_features, - [cfgs.ROI_POOL_KERNEL_SIZE, cfgs.ROI_POOL_KERNEL_SIZE], - stride=cfgs.ROI_POOL_KERNEL_SIZE) - - return roi_features - - def build_fastrcnn(self, P_list, rois_list, img_shape): - - with tf.variable_scope('Fast-RCNN'): - # 5. ROI Pooling - with tf.variable_scope('rois_pooling'): - pooled_features_list = [] - for level_name, p, rois in zip(cfgs.LEVLES, P_list, rois_list): # exclude P6_rois - # p = tf.Print(p, [tf.shape(p)], summarize=10, message=level_name+'SHPAE***') - pooled_features = self.roi_pooling(feature_maps=p, rois=rois, img_shape=img_shape, - scope=level_name) - pooled_features_list.append(pooled_features) - - pooled_features = tf.concat(pooled_features_list, axis=0) # [minibatch_size, H, W, C] - - # 6. inferecne rois in Fast-RCNN to obtain fc_flatten features - if self.base_network_name.startswith('resnet'): - fc_flatten = resnet.restnet_head(inputs=pooled_features, - is_training=self.is_training, - scope_name=self.base_network_name) - elif self.base_network_name.startswith('Mobile'): - fc_flatten = mobilenet_v2.mobilenetv2_head(inputs=pooled_features, - is_training=self.is_training) - else: - raise NotImplementedError('only support resnet and mobilenet') - - # 7. cls and reg in Fast-RCNN - with slim.arg_scope([slim.fully_connected], weights_regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY)): - - cls_score = slim.fully_connected(fc_flatten, - num_outputs=cfgs.CLASS_NUM+1, - weights_initializer=cfgs.INITIALIZER, - activation_fn=None, trainable=self.is_training, - scope='cls_fc') - - bbox_pred = slim.fully_connected(fc_flatten, - num_outputs=(cfgs.CLASS_NUM+1)*4, - weights_initializer=cfgs.BBOX_INITIALIZER, - activation_fn=None, trainable=self.is_training, - scope='reg_fc') - # for convient. It also produce (cls_num +1) bboxes - - cls_score = tf.reshape(cls_score, [-1, cfgs.CLASS_NUM+1]) - bbox_pred = tf.reshape(bbox_pred, [-1, 4*(cfgs.CLASS_NUM+1)]) - - return bbox_pred, cls_score - - def assign_levels(self, all_rois, labels=None, bbox_targets=None): - ''' - - :param all_rois: - :param labels: - :param bbox_targets: - :return: - ''' - with tf.name_scope('assign_levels'): - # all_rois = tf.Print(all_rois, [tf.shape(all_rois)], summarize=10, message='ALL_ROIS_SHAPE*****') - xmin, ymin, xmax, ymax = tf.unstack(all_rois, axis=1) - - h = tf.maximum(0., ymax - ymin) - w = tf.maximum(0., xmax - xmin) - - levels = tf.floor(4. + tf.log(tf.sqrt(w * h + 1e-8) / 224.0) / tf.log(2.)) # 4 + log_2(***) - # use floor instead of round - - min_level = int(cfgs.LEVLES[0][-1]) - max_level = min(5, int(cfgs.LEVLES[-1][-1])) - levels = tf.maximum(levels, tf.ones_like(levels) * min_level) # level minimum is 2 - levels = tf.minimum(levels, tf.ones_like(levels) * max_level) # level maximum is 5 - - levels = tf.stop_gradient(tf.reshape(levels, [-1])) - - def get_rois(levels, level_i, rois, labels, bbox_targets): - - level_i_indices = tf.reshape(tf.where(tf.equal(levels, level_i)), [-1]) - # level_i_indices = tf.Print(level_i_indices, [tf.shape(tf.where(tf.equal(levels, level_i)))[0]], message="SHAPE%d***"%level_i, - # summarize=10) - tf.summary.scalar('LEVEL/LEVEL_%d_rois_NUM'%level_i, tf.shape(level_i_indices)[0]) - level_i_rois = tf.gather(rois, level_i_indices) - - if self.is_training: - # If you use low version tensorflow, you may uncomment these code. - level_i_rois = tf.stop_gradient(tf.concat([level_i_rois, [[0, 0, 0., 0.]]], axis=0)) - # # to avoid the num of level i rois is 0.0, which will broken the BP in tf - # - level_i_labels = tf.gather(labels, level_i_indices) - level_i_labels = tf.stop_gradient(tf.concat([level_i_labels, [0]], axis=0)) - - level_i_targets = tf.gather(bbox_targets, level_i_indices) - level_i_targets = tf.stop_gradient(tf.concat([level_i_targets, - tf.zeros(shape=(1, 4*(cfgs.CLASS_NUM+1)), dtype=tf.float32)], - axis=0)) - #level_i_rois = tf.stop_gradient(level_i_rois) - #level_i_labels = tf.gather(labels, level_i_indices) - - #level_i_targets = tf.gather(bbox_targets, level_i_indices) - - return level_i_rois, level_i_labels, level_i_targets - else: - return level_i_rois, None, None - - rois_list = [] - labels_list = [] - targets_list = [] - for i in range(min_level, max_level+1): - P_i_rois, P_i_labels, P_i_targets = get_rois(levels, level_i=i, rois=all_rois, - labels=labels, - bbox_targets=bbox_targets) - rois_list.append(P_i_rois) - labels_list.append(P_i_labels) - targets_list.append(P_i_targets) - - if self.is_training: - all_labels = tf.concat(labels_list, axis=0) - all_targets = tf.concat(targets_list, axis=0) - return rois_list, all_labels, all_targets - else: - return rois_list # [P2_rois, P3_rois, P4_rois, P5_rois] Note: P6 do not assign rois - - def add_anchor_img_smry(self, img, anchors, labels): - - positive_anchor_indices = tf.reshape(tf.where(tf.greater_equal(labels, 1)), [-1]) - negative_anchor_indices = tf.reshape(tf.where(tf.equal(labels, 0)), [-1]) - - positive_anchor = tf.gather(anchors, positive_anchor_indices) - negative_anchor = tf.gather(anchors, negative_anchor_indices) - - pos_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, - boxes=positive_anchor) - neg_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, - boxes=negative_anchor) - - tf.summary.image('positive_anchor', pos_in_img) - tf.summary.image('negative_anchors', neg_in_img) - - def add_roi_batch_img_smry(self, img, rois, labels): - positive_roi_indices = tf.reshape(tf.where(tf.greater_equal(labels, 1)), [-1]) - - negative_roi_indices = tf.reshape(tf.where(tf.equal(labels, 0)), [-1]) - - pos_roi = tf.gather(rois, positive_roi_indices) - neg_roi = tf.gather(rois, negative_roi_indices) - - - pos_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, - boxes=pos_roi) - neg_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, - boxes=neg_roi) - tf.summary.image('pos_rois', pos_in_img) - tf.summary.image('neg_rois', neg_in_img) - - def build_loss(self, rpn_box_pred, rpn_bbox_targets, rpn_cls_score, rpn_labels, - bbox_pred, bbox_targets, cls_score, labels): - ''' - - :param rpn_box_pred: [-1, 4] - :param rpn_bbox_targets: [-1, 4] - :param rpn_cls_score: [-1] - :param rpn_labels: [-1] - :param bbox_pred: [-1, 4*(cls_num+1)] - :param bbox_targets: [-1, 4*(cls_num+1)] - :param cls_score: [-1, cls_num+1] - :param labels: [-1] - :return: - ''' - with tf.variable_scope('build_loss') as sc: - with tf.variable_scope('rpn_loss'): - - rpn_bbox_loss = losses.smooth_l1_loss_rpn(bbox_pred=rpn_box_pred, - bbox_targets=rpn_bbox_targets, - label=rpn_labels, - sigma=cfgs.RPN_SIGMA) - # rpn_cls_loss: - # rpn_cls_score = tf.reshape(rpn_cls_score, [-1, 2]) - # rpn_labels = tf.reshape(rpn_labels, [-1]) - # ensure rpn_labels shape is [-1] - rpn_select = tf.reshape(tf.where(tf.not_equal(rpn_labels, -1)), [-1]) - rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2]) - rpn_labels = tf.reshape(tf.gather(rpn_labels, rpn_select), [-1]) - rpn_cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, - labels=rpn_labels)) - - rpn_cls_loss = rpn_cls_loss * cfgs.RPN_CLASSIFICATION_LOSS_WEIGHT - rpn_bbox_loss = rpn_bbox_loss * cfgs.RPN_LOCATION_LOSS_WEIGHT - - with tf.variable_scope('FastRCNN_loss'): - if not cfgs.FAST_RCNN_MINIBATCH_SIZE == -1: - bbox_loss = losses.smooth_l1_loss_rcnn(bbox_pred=bbox_pred, - bbox_targets=bbox_targets, - label=labels, - num_classes=cfgs.CLASS_NUM + 1, - sigma=cfgs.FASTRCNN_SIGMA) - - # cls_score = tf.reshape(cls_score, [-1, cfgs.CLASS_NUM + 1]) - # labels = tf.reshape(labels, [-1]) - cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=cls_score, - labels=labels)) # beacause already sample before - else: - ''' - applying OHEM here - ''' - print(20 * "@@") - print("@@" + 10 * " " + "TRAIN WITH OHEM ...") - print(20 * "@@") - cls_loss, bbox_loss = losses.sum_ohem_loss( - cls_score=cls_score, - label=labels, - bbox_targets=bbox_targets, - bbox_pred=bbox_pred, - num_ohem_samples=128, - num_classes=cfgs.CLASS_NUM + 1) - cls_loss = cls_loss * cfgs.FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT - bbox_loss = bbox_loss * cfgs.FAST_RCNN_LOCATION_LOSS_WEIGHT - loss_dict = { - 'rpn_cls_loss': rpn_cls_loss, - 'rpn_loc_loss': rpn_bbox_loss, - 'fastrcnn_cls_loss': cls_loss, - 'fastrcnn_loc_loss': bbox_loss - } - return loss_dict - - def build_whole_detection_network(self, input_img_batch, gtboxes_batch): - - if self.is_training: - # ensure shape is [M, 5] - gtboxes_batch = tf.reshape(gtboxes_batch, [-1, 5]) - gtboxes_batch = tf.cast(gtboxes_batch, tf.float32) - - img_shape = tf.shape(input_img_batch) - - # 1. build base network - P_list = self.build_base_network(input_img_batch) # [P2, P3, P4, P5, P6] - - # 2. build rpn - with tf.variable_scope('build_rpn', - regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY)): - - fpn_cls_score =[] - fpn_box_pred = [] - for level_name, p in zip(cfgs.LEVLES, P_list): - if cfgs.SHARE_HEADS: - reuse_flag = None if level_name==cfgs.LEVLES[0] else True - scope_list=['rpn_conv/3x3', 'rpn_cls_score', 'rpn_bbox_pred'] - else: - reuse_flag = None - scope_list= ['rpn_conv/3x3_%s' % level_name, 'rpn_cls_score_%s' % level_name, 'rpn_bbox_pred_%s' % level_name] - rpn_conv3x3 = slim.conv2d( - p, 512, [3, 3], - trainable=self.is_training, weights_initializer=cfgs.INITIALIZER, padding="SAME", - activation_fn=tf.nn.relu, - scope=scope_list[0], - reuse=reuse_flag) - rpn_cls_score = slim.conv2d(rpn_conv3x3, self.num_anchors_per_location*2, [1, 1], stride=1, - trainable=self.is_training, weights_initializer=cfgs.INITIALIZER, - activation_fn=None, padding="VALID", - scope=scope_list[1], - reuse=reuse_flag) - rpn_box_pred = slim.conv2d(rpn_conv3x3, self.num_anchors_per_location*4, [1, 1], stride=1, - trainable=self.is_training, weights_initializer=cfgs.BBOX_INITIALIZER, - activation_fn=None, padding="VALID", - scope=scope_list[2], - reuse=reuse_flag) - rpn_box_pred = tf.reshape(rpn_box_pred, [-1, 4]) - rpn_cls_score = tf.reshape(rpn_cls_score, [-1, 2]) - - fpn_cls_score.append(rpn_cls_score) - fpn_box_pred.append(rpn_box_pred) - - fpn_cls_score = tf.concat(fpn_cls_score, axis=0, name='fpn_cls_score') - fpn_box_pred = tf.concat(fpn_box_pred, axis=0, name='fpn_box_pred') - fpn_cls_prob = slim.softmax(fpn_cls_score, scope='fpn_cls_prob') - - # 3. generate_anchors - all_anchors = [] - for i in range(len(cfgs.LEVLES)): - level_name, p = cfgs.LEVLES[i], P_list[i] - - p_h, p_w = tf.shape(p)[1], tf.shape(p)[2] - featuremap_height = tf.cast(p_h, tf.float32) - featuremap_width = tf.cast(p_w, tf.float32) - anchors = anchor_utils.make_anchors(base_anchor_size=cfgs.BASE_ANCHOR_SIZE_LIST[i], - anchor_scales=cfgs.ANCHOR_SCALES, - anchor_ratios=cfgs.ANCHOR_RATIOS, - featuremap_height=featuremap_height, - featuremap_width=featuremap_width, - stride=cfgs.ANCHOR_STRIDE_LIST[i], - name="make_anchors_for%s" % level_name) - all_anchors.append(anchors) - all_anchors = tf.concat(all_anchors, axis=0, name='all_anchors_of_FPN') - - # 4. postprocess rpn proposals. such as: decode, clip, NMS - with tf.variable_scope('postprocess_FPN'): - rois, roi_scores = postprocess_rpn_proposals(rpn_bbox_pred=fpn_box_pred, - rpn_cls_prob=fpn_cls_prob, - img_shape=img_shape, - anchors=all_anchors, - is_training=self.is_training) - # rois shape [-1, 4] - # +++++++++++++++++++++++++++++++++++++add img smry+++++++++++++++++++++++++++++++++++++++++++++++++++++++ - - if self.is_training: - score_gre_05 = tf.reshape(tf.where(tf.greater_equal(roi_scores, 0.5)), [-1]) - score_gre_05_rois = tf.gather(rois, score_gre_05) - score_gre_05_score = tf.gather(roi_scores, score_gre_05) - score_gre_05_in_img = show_box_in_tensor.draw_boxes_with_scores(img_batch=input_img_batch, - boxes=score_gre_05_rois, - scores=score_gre_05_score) - tf.summary.image('score_greater_05_rois', score_gre_05_in_img) - # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - - if self.is_training: - with tf.variable_scope('sample_anchors_minibatch'): - fpn_labels, fpn_bbox_targets = \ - tf.py_func( - anchor_target_layer, - [gtboxes_batch, img_shape, all_anchors], - [tf.float32, tf.float32]) - fpn_bbox_targets = tf.reshape(fpn_bbox_targets, [-1, 4]) - fpn_labels = tf.to_int32(fpn_labels, name="to_int32") - fpn_labels = tf.reshape(fpn_labels, [-1]) - self.add_anchor_img_smry(input_img_batch, all_anchors, fpn_labels) - - # --------------------------------------add smry----------------------------------------------------------- - - fpn_cls_category = tf.argmax(fpn_cls_prob, axis=1) - kept_rpppn = tf.reshape(tf.where(tf.not_equal(fpn_labels, -1)), [-1]) - fpn_cls_category = tf.gather(fpn_cls_category, kept_rpppn) - acc = tf.reduce_mean(tf.to_float(tf.equal(fpn_cls_category, - tf.to_int64(tf.gather(fpn_labels, kept_rpppn))))) - tf.summary.scalar('ACC/fpn_accuracy', acc) - - with tf.control_dependencies([fpn_labels]): - with tf.variable_scope('sample_RCNN_minibatch'): - rois, labels, bbox_targets = \ - tf.py_func(proposal_target_layer, - [rois, gtboxes_batch], - [tf.float32, tf.float32, tf.float32]) - rois = tf.reshape(rois, [-1, 4]) - labels = tf.to_int32(labels) - labels = tf.reshape(labels, [-1]) - bbox_targets = tf.reshape(bbox_targets, [-1, 4*(cfgs.CLASS_NUM+1)]) - self.add_roi_batch_img_smry(input_img_batch, rois, labels) - if self.is_training: - rois_list, labels, bbox_targets = self.assign_levels(all_rois=rois, - labels=labels, - bbox_targets=bbox_targets) - else: - rois_list = self.assign_levels(all_rois=rois) # rois_list: [P2_rois, P3_rois, P4_rois, P5_rois] - - # -------------------------------------------------------------------------------------------------------------# - # Fast-RCNN # - # -------------------------------------------------------------------------------------------------------------# - - # 5. build Fast-RCNN - # rois = tf.Print(rois, [tf.shape(rois)], 'rois shape', summarize=10) - bbox_pred, cls_score = self.build_fastrcnn(P_list=P_list, rois_list=rois_list, - img_shape=img_shape) - # bbox_pred shape: [-1, 4*(cls_num+1)]. - # cls_score shapeï¼?[-1, cls_num+1] - - cls_prob = slim.softmax(cls_score, 'cls_prob') - - - # ----------------------------------------------add smry------------------------------------------------------- - if self.is_training: - cls_category = tf.argmax(cls_prob, axis=1) - fast_acc = tf.reduce_mean(tf.to_float(tf.equal(cls_category, tf.to_int64(labels)))) - tf.summary.scalar('ACC/fast_acc', fast_acc) - - rois = tf.concat(rois_list, axis=0, name='concat_rois') - # 6. postprocess_fastrcnn - if not self.is_training: - return self.postprocess_fastrcnn(rois=rois, bbox_ppred=bbox_pred, scores=cls_prob, img_shape=img_shape) - else: - ''' - when trian. We need build Loss - ''' - loss_dict = self.build_loss(rpn_box_pred=fpn_box_pred, - rpn_bbox_targets=fpn_bbox_targets, - rpn_cls_score=fpn_cls_score, - rpn_labels=fpn_labels, - bbox_pred=bbox_pred, - bbox_targets=bbox_targets, - cls_score=cls_score, - labels=labels) - - final_bbox, final_scores, final_category = self.postprocess_fastrcnn(rois=rois, - bbox_ppred=bbox_pred, - scores=cls_prob, - img_shape=img_shape) - return final_bbox, final_scores, final_category, loss_dict - - def get_restorer(self): - checkpoint_path = tf.train.latest_checkpoint(os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION)) - - if checkpoint_path != None: - restorer = tf.train.Saver() - print("model restore from :", checkpoint_path) - else: - checkpoint_path = cfgs.PRETRAINED_CKPT - print("model restore from pretrained mode, path is :", checkpoint_path) - - model_variables = slim.get_model_variables() - # for var in model_variables: - # print(var.name) - # print(20*"__++__++__") - - def name_in_ckpt_rpn(var): - return var.op.name - - def name_in_ckpt_fastrcnn_head(var): - ''' - Fast-RCNN/resnet_v1_50/block4 -->resnet_v1_50/block4 - Fast-RCNN/MobilenetV2/** -- > MobilenetV2 ** - :param var: - :return: - ''' - return '/'.join(var.op.name.split('/')[1:]) - nameInCkpt_Var_dict = {} - for var in model_variables: - if var.name.startswith(self.base_network_name): - var_name_in_ckpt = name_in_ckpt_rpn(var) - nameInCkpt_Var_dict[var_name_in_ckpt] = var - restore_variables = nameInCkpt_Var_dict - for key, item in restore_variables.items(): - print("var_in_graph: ", item.name) - print("var_in_ckpt: ", key) - print(20*"___") - restorer = tf.train.Saver(restore_variables) - print(20 * "****") - print("restore from pretrained_weighs in IMAGE_NET") - return restorer, checkpoint_path - - def get_gradients(self, optimizer, loss): - ''' - - :param optimizer: - :param loss: - :return: - - return vars and grads that not be fixed - ''' - - # if cfgs.FIXED_BLOCKS > 0: - # trainable_vars = tf.trainable_variables() - # # trained_vars = slim.get_trainable_variables() - # start_names = [cfgs.NET_NAME + '/block%d'%i for i in range(1, cfgs.FIXED_BLOCKS+1)] + \ - # [cfgs.NET_NAME + '/conv1'] - # start_names = tuple(start_names) - # trained_var_list = [] - # for var in trainable_vars: - # if not var.name.startswith(start_names): - # trained_var_list.append(var) - # # slim.learning.train() - # grads = optimizer.compute_gradients(loss, var_list=trained_var_list) - # return grads - # else: - # return optimizer.compute_gradients(loss) - return optimizer.compute_gradients(loss) - - def enlarge_gradients_for_bias(self, gradients): - - final_gradients = [] - with tf.variable_scope("Gradient_Mult") as scope: - for grad, var in gradients: - scale = 1.0 - if cfgs.MUTILPY_BIAS_GRADIENT and './biases' in var.name: - scale = scale * cfgs.MUTILPY_BIAS_GRADIENT - if not np.allclose(scale, 1.0): - grad = tf.multiply(grad, scale) - final_gradients.append((grad, var)) - return final_gradients - - - - - - - - - - - - - - - - - - - - diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/build_whole_network2.py b/example-apps/PDD/pcb-defect-detection/libs/networks/build_whole_network2.py deleted file mode 100755 index 8281440..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/build_whole_network2.py +++ /dev/null @@ -1,637 +0,0 @@ -# -*-coding: utf-8 -*- - -from __future__ import absolute_import, division, print_function - -import os -import tensorflow as tf -import tensorflow.contrib.slim as slim -import numpy as np - -from libs.networks import resnet -from libs.networks import mobilenet_v2 -from libs.box_utils import encode_and_decode -from libs.box_utils import boxes_utils -from libs.box_utils import anchor_utils -from libs.configs import cfgs -from libs.losses import losses -from libs.box_utils import show_box_in_tensor -from libs.detection_oprations.proposal_opr import postprocess_rpn_proposals -from libs.detection_oprations.anchor_target_layer_without_boxweight import anchor_target_layer -from libs.detection_oprations.proposal_target_layer import proposal_target_layer - - -class DetectionNetwork(object): - - def __init__(self, base_network_name, is_training): - - self.base_network_name = base_network_name - self.is_training = is_training - self.num_anchors_per_location = len(cfgs.ANCHOR_SCALES) * len(cfgs.ANCHOR_RATIOS) - - def build_base_network(self, input_img_batch): - - if self.base_network_name.startswith('resnet_v1'): - return resnet.resnet_base(input_img_batch, scope_name=self.base_network_name, is_training=self.is_training) - - elif self.base_network_name.startswith('MobilenetV2'): - return mobilenet_v2.mobilenetv2_base(input_img_batch, is_training=self.is_training) - - else: - raise ValueError('Sry, we only support resnet or mobilenet_v2') - - def postprocess_fastrcnn(self, rois, bbox_ppred, scores, img_shape): - ''' - - :param rois:[-1, 4] - :param bbox_ppred: [-1, (cfgs.Class_num+1) * 4] - :param scores: [-1, cfgs.Class_num + 1] - :return: - ''' - - with tf.name_scope('postprocess_fastrcnn'): - rois = tf.stop_gradient(rois) - scores = tf.stop_gradient(scores) - bbox_ppred = tf.reshape(bbox_ppred, [-1, cfgs.CLASS_NUM + 1, 4]) - bbox_ppred = tf.stop_gradient(bbox_ppred) - - bbox_pred_list = tf.unstack(bbox_ppred, axis=1) - score_list = tf.unstack(scores, axis=1) - - allclasses_boxes = [] - allclasses_scores = [] - categories = [] - for i in range(1, cfgs.CLASS_NUM+1): - - # 1. decode boxes in each class - tmp_encoded_box = bbox_pred_list[i] - tmp_score = score_list[i] - tmp_decoded_boxes = encode_and_decode.decode_boxes(encoded_boxes=tmp_encoded_box, - reference_boxes=rois, - scale_factors=cfgs.ROI_SCALE_FACTORS) - # tmp_decoded_boxes = encode_and_decode.decode_boxes(boxes=rois, - # deltas=tmp_encoded_box, - # scale_factor=cfgs.ROI_SCALE_FACTORS) - - # 2. clip to img boundaries - tmp_decoded_boxes = boxes_utils.clip_boxes_to_img_boundaries(decode_boxes=tmp_decoded_boxes, - img_shape=img_shape) - - # 3. NMS - keep = tf.image.non_max_suppression( - boxes=tmp_decoded_boxes, - scores=tmp_score, - max_output_size=cfgs.FAST_RCNN_NMS_MAX_BOXES_PER_CLASS, - iou_threshold=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD) - - perclass_boxes = tf.gather(tmp_decoded_boxes, keep) - perclass_scores = tf.gather(tmp_score, keep) - - allclasses_boxes.append(perclass_boxes) - allclasses_scores.append(perclass_scores) - categories.append(tf.ones_like(perclass_scores) * i) - - final_boxes = tf.concat(allclasses_boxes, axis=0) - final_scores = tf.concat(allclasses_scores, axis=0) - final_category = tf.concat(categories, axis=0) - - if self.is_training: - ''' - in training. We should show the detecitons in the tensorboard. So we add this. - ''' - kept_indices = tf.reshape(tf.where(tf.greater_equal(final_scores, cfgs.SHOW_SCORE_THRSHOLD)), [-1]) - - final_boxes = tf.gather(final_boxes, kept_indices) - final_scores = tf.gather(final_scores, kept_indices) - final_category = tf.gather(final_category, kept_indices) - - return final_boxes, final_scores, final_category - - def roi_pooling(self, feature_maps, rois, img_shape, scope): - ''' - Here use roi warping as roi_pooling - - :param featuremaps_dict: feature map to crop - :param rois: shape is [-1, 4]. [x1, y1, x2, y2] - :return: - ''' - - with tf.variable_scope('ROI_Warping_'+scope): - img_h, img_w = tf.cast(img_shape[1], tf.float32), tf.cast(img_shape[2], tf.float32) - N = tf.shape(rois)[0] - x1, y1, x2, y2 = tf.unstack(rois, axis=1) - - normalized_x1 = x1 / img_w - normalized_x2 = x2 / img_w - normalized_y1 = y1 / img_h - normalized_y2 = y2 / img_h - - normalized_rois = tf.transpose( - tf.stack([normalized_y1, normalized_x1, normalized_y2, normalized_x2]), name='get_normalized_rois') - - normalized_rois = tf.stop_gradient(normalized_rois) - - cropped_roi_features = tf.image.crop_and_resize(feature_maps, normalized_rois, - box_ind=tf.zeros(shape=[N], - dtype=tf.int32), - crop_size=[cfgs.ROI_SIZE, cfgs.ROI_SIZE], - name='CROP_AND_RESIZE' - ) - roi_features = slim.max_pool2d(cropped_roi_features, - [cfgs.ROI_POOL_KERNEL_SIZE, cfgs.ROI_POOL_KERNEL_SIZE], - stride=cfgs.ROI_POOL_KERNEL_SIZE) - - return roi_features - - def build_fastrcnn(self, P_list, rois_list, img_shape): - - with tf.variable_scope('Fast-RCNN'): - # 5. ROI Pooling - with tf.variable_scope('rois_pooling'): - pooled_features_list = [] - for level_name, p, rois in zip(cfgs.LEVLES, P_list, rois_list): # exclude P6_rois - # p = tf.Print(p, [tf.shape(p)], summarize=10, message=level_name+'SHPAE***') - pooled_features = self.roi_pooling(feature_maps=p, rois=rois, img_shape=img_shape, - scope=level_name) - pooled_features_list.append(pooled_features) - - pooled_features = tf.concat(pooled_features_list, axis=0) # [minibatch_size, H, W, C] - - # 6. inferecne rois in Fast-RCNN to obtain fc_flatten features - if self.base_network_name.startswith('resnet'): - fc_flatten = resnet.restnet_head(inputs=pooled_features, - is_training=self.is_training, - scope_name=self.base_network_name) - elif self.base_network_name.startswith('Mobile'): - fc_flatten = mobilenet_v2.mobilenetv2_head(inputs=pooled_features, - is_training=self.is_training) - else: - raise NotImplementedError('only support resnet and mobilenet') - - # 7. cls and reg in Fast-RCNN - with slim.arg_scope([slim.fully_connected], weights_regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY)): - - cls_score = slim.fully_connected(fc_flatten, - num_outputs=cfgs.CLASS_NUM+1, - weights_initializer=cfgs.INITIALIZER, - activation_fn=None, trainable=self.is_training, - scope='cls_fc') - - bbox_pred = slim.fully_connected(fc_flatten, - num_outputs=(cfgs.CLASS_NUM+1)*4, - weights_initializer=cfgs.BBOX_INITIALIZER, - activation_fn=None, trainable=self.is_training, - scope='reg_fc') - # for convient. It also produce (cls_num +1) bboxes - - cls_score = tf.reshape(cls_score, [-1, cfgs.CLASS_NUM+1]) - bbox_pred = tf.reshape(bbox_pred, [-1, 4*(cfgs.CLASS_NUM+1)]) - - return bbox_pred, cls_score - - def assign_levels(self, all_rois, labels=None, bbox_targets=None): - ''' - - :param all_rois: - :param labels: - :param bbox_targets: - :return: - ''' - with tf.name_scope('assign_levels'): - # all_rois = tf.Print(all_rois, [tf.shape(all_rois)], summarize=10, message='ALL_ROIS_SHAPE*****') - xmin, ymin, xmax, ymax = tf.unstack(all_rois, axis=1) - - h = tf.maximum(0., ymax - ymin) - w = tf.maximum(0., xmax - xmin) - - levels = tf.floor(4. + tf.log(tf.sqrt(w * h + 1e-8) / 224.0) / tf.log(2.)) # 4 + log_2(***) - # use floor instead of round - - min_level = int(cfgs.LEVLES[0][-1]) - max_level = min(5, int(cfgs.LEVLES[-1][-1])) - levels = tf.maximum(levels, tf.ones_like(levels) * min_level) # level minimum is 2 - levels = tf.minimum(levels, tf.ones_like(levels) * max_level) # level maximum is 5 - - levels = tf.stop_gradient(tf.reshape(levels, [-1])) - - def get_rois(levels, level_i, rois, labels, bbox_targets): - - level_i_indices = tf.reshape(tf.where(tf.equal(levels, level_i)), [-1]) - tf.summary.scalar('LEVEL/LEVEL_%d_rois_NUM'%level_i, tf.shape(level_i_indices)[0]) - level_i_rois = tf.gather(rois, level_i_indices) - - # if self.is_training: - # level_i_rois = tf.stop_gradient(tf.concat([level_i_rois, [[0, 0, 0., 0.]]], axis=0)) - # # to avoid the num of level i rois is 0.0, which will broken the BP in tf - # - # level_i_labels = tf.gather(labels, level_i_indices) - # level_i_labels = tf.stop_gradient(tf.concat([level_i_labels, [0]], axis=0)) - # - # level_i_targets = tf.gather(bbox_targets, level_i_indices) - # level_i_targets = tf.stop_gradient(tf.concat([level_i_targets, - # tf.zeros(shape=(1, 4*(cfgs.CLASS_NUM+1)), dtype=tf.float32)], - # axis=0)) - # - # return level_i_rois, level_i_labels, level_i_targets - if self.is_training: - level_i_labels = tf.gather(labels, level_i_indices) - level_i_targets = tf.gather(bbox_targets, level_i_indices) - return level_i_rois, level_i_labels, level_i_targets - else: - return level_i_rois, None, None - - rois_list = [] - labels_list = [] - targets_list = [] - for i in range(min_level, max_level+1): - P_i_rois, P_i_labels, P_i_targets = get_rois(levels, level_i=i, rois=all_rois, - labels=labels, - bbox_targets=bbox_targets) - rois_list.append(P_i_rois) - labels_list.append(P_i_labels) - targets_list.append(P_i_targets) - - if self.is_training: - all_labels = tf.concat(labels_list, axis=0) - all_targets = tf.concat(targets_list, axis=0) - - return rois_list, \ - tf.stop_gradient(all_labels), \ - tf.stop_gradient(all_targets) - else: - return rois_list # [P2_rois, P3_rois, P4_rois, P5_rois] Note: P6 do not assign rois - # --------------------------------------------------------------------------------------- - - def add_anchor_img_smry(self, img, anchors, labels): - - positive_anchor_indices = tf.reshape(tf.where(tf.greater_equal(labels, 1)), [-1]) - negative_anchor_indices = tf.reshape(tf.where(tf.equal(labels, 0)), [-1]) - - positive_anchor = tf.gather(anchors, positive_anchor_indices) - negative_anchor = tf.gather(anchors, negative_anchor_indices) - - pos_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, - boxes=positive_anchor) - neg_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, - boxes=negative_anchor) - - tf.summary.image('positive_anchor', pos_in_img) - tf.summary.image('negative_anchors', neg_in_img) - - def add_roi_batch_img_smry(self, img, rois, labels): - positive_roi_indices = tf.reshape(tf.where(tf.greater_equal(labels, 1)), [-1]) - - negative_roi_indices = tf.reshape(tf.where(tf.equal(labels, 0)), [-1]) - - pos_roi = tf.gather(rois, positive_roi_indices) - neg_roi = tf.gather(rois, negative_roi_indices) - - - pos_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, - boxes=pos_roi) - neg_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, - boxes=neg_roi) - tf.summary.image('pos_rois', pos_in_img) - tf.summary.image('neg_rois', neg_in_img) - - def build_loss(self, rpn_box_pred, rpn_bbox_targets, rpn_cls_score, rpn_labels, - bbox_pred, bbox_targets, cls_score, labels): - ''' - - :param rpn_box_pred: [-1, 4] - :param rpn_bbox_targets: [-1, 4] - :param rpn_cls_score: [-1] - :param rpn_labels: [-1] - :param bbox_pred: [-1, 4*(cls_num+1)] - :param bbox_targets: [-1, 4*(cls_num+1)] - :param cls_score: [-1, cls_num+1] - :param labels: [-1] - :return: - ''' - with tf.variable_scope('build_loss') as sc: - with tf.variable_scope('rpn_loss'): - - rpn_bbox_loss = losses.smooth_l1_loss_rpn(bbox_pred=rpn_box_pred, - bbox_targets=rpn_bbox_targets, - label=rpn_labels, - sigma=cfgs.RPN_SIGMA) - # rpn_cls_loss: - # rpn_cls_score = tf.reshape(rpn_cls_score, [-1, 2]) - # rpn_labels = tf.reshape(rpn_labels, [-1]) - # ensure rpn_labels shape is [-1] - rpn_select = tf.reshape(tf.where(tf.not_equal(rpn_labels, -1)), [-1]) - rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2]) - rpn_labels = tf.reshape(tf.gather(rpn_labels, rpn_select), [-1]) - rpn_cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, - labels=rpn_labels)) - - rpn_cls_loss = rpn_cls_loss * cfgs.RPN_CLASSIFICATION_LOSS_WEIGHT - rpn_bbox_loss = rpn_bbox_loss * cfgs.RPN_LOCATION_LOSS_WEIGHT - - with tf.variable_scope('FastRCNN_loss'): - if not cfgs.FAST_RCNN_MINIBATCH_SIZE == -1: - bbox_loss = losses.smooth_l1_loss_rcnn(bbox_pred=bbox_pred, - bbox_targets=bbox_targets, - label=labels, - num_classes=cfgs.CLASS_NUM + 1, - sigma=cfgs.FASTRCNN_SIGMA) - - # cls_score = tf.reshape(cls_score, [-1, cfgs.CLASS_NUM + 1]) - # labels = tf.reshape(labels, [-1]) - cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=cls_score, - labels=labels)) # beacause already sample before - else: - ''' - applying OHEM here - ''' - print(20 * "@@") - print("@@" + 10 * " " + "TRAIN WITH OHEM ...") - print(20 * "@@") - cls_loss = bbox_loss = losses.sum_ohem_loss( - cls_score=cls_score, - label=labels, - bbox_targets=bbox_targets, - nr_ohem_sampling=128, - nr_classes=cfgs.CLASS_NUM + 1) - cls_loss = cls_loss * cfgs.FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT - bbox_loss = bbox_loss * cfgs.FAST_RCNN_LOCATION_LOSS_WEIGHT - loss_dict = { - 'rpn_cls_loss': rpn_cls_loss, - 'rpn_loc_loss': rpn_bbox_loss, - 'fastrcnn_cls_loss': cls_loss, - 'fastrcnn_loc_loss': bbox_loss - } - return loss_dict - - - def build_whole_detection_network(self, input_img_batch, gtboxes_batch): - - if self.is_training: - # ensure shape is [M, 5] - gtboxes_batch = tf.reshape(gtboxes_batch, [-1, 5]) - gtboxes_batch = tf.cast(gtboxes_batch, tf.float32) - - img_shape = tf.shape(input_img_batch) - - # 1. build base network - P_dict = self.build_base_network(input_img_batch) # [P2, P3, P4, P5, P6] - - # 2. build fpn by build rpn for each level - with tf.variable_scope("build_FPN", regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY)): - fpn_box_delta = {} - fpn_cls_score = {} - fpn_cls_prob = {} - for key in cfgs.LEVLES: - if cfgs.SHARE_HEADS: - reuse_flag = None if key == cfgs.LEVLES[0] else True - scope_list = ['fpn_conv/3x3', 'fpn_cls_score', 'fpn_bbox_pred'] - else: - reuse_flag = None - scope_list= ['fpn_conv/3x3_%s' % key, 'fpn_cls_score_%s' % key, 'fpn_bbox_pred_%s' % key] - rpn_conv3x3 = slim.conv2d( - P_dict[key], 512, [3, 3], - trainable=self.is_training, weights_initializer=cfgs.INITIALIZER, padding="SAME", - activation_fn=tf.nn.relu, - scope=scope_list[0], - reuse=reuse_flag) - rpn_cls_score = slim.conv2d(rpn_conv3x3, self.num_anchors_per_location*2, [1, 1], stride=1, - trainable=self.is_training, weights_initializer=cfgs.INITIALIZER, - activation_fn=None, padding="VALID", - scope=scope_list[1], - reuse=reuse_flag) - rpn_box_delta = slim.conv2d(rpn_conv3x3, self.num_anchors_per_location*4, [1, 1], stride=1, - trainable=self.is_training, weights_initializer=cfgs.BBOX_INITIALIZER, - activation_fn=None, padding="VALID", - scope=scope_list[2], - reuse=reuse_flag) - fpn_box_delta[key] = tf.reshape(rpn_box_delta, [-1, 4]) - fpn_cls_score[key] = tf.reshape(rpn_cls_score, [-1, 2]) - fpn_cls_prob[key] = slim.softmax(fpn_cls_score[key]) - - # 3. generate anchors for fpn. (by generate for each level) - anchors_dict = {} - anchor_list = [] - with tf.name_scope("generate_FPN_anchors"): - for key in cfgs.LEVLES: - p_h, p_w = tf.to_float(tf.shape(P_dict[key])[1]), tf.to_float(tf.shape(P_dict[key])[2]) - id_ = int(key[-1]) - int(cfgs.LEVLES[0][-1]) # such as : 2-2, 3-3 - tmp_anchors = anchor_utils.make_anchors(base_anchor_size=cfgs.BASE_ANCHOR_SIZE_LIST[id_], - anchor_scales=cfgs.ANCHOR_SCALES, - anchor_ratios=cfgs.ANCHOR_RATIOS, - stride=cfgs.ANCHOR_STRIDE_LIST[id_], - featuremap_height=p_h, - featuremap_width=p_w, - name='%s_make_anchors' % key) - anchors_dict[key] = tmp_anchors - anchor_list.append(tmp_anchors) - all_anchors = tf.concat(anchor_list, axis=0) - - # 4. postprocess fpn proposals. such as: decode, clip, NMS - # Need to Note: Here we NMS for each level instead of NMS for all anchors. - rois_list = [] - rois_scores_list = [] - with tf.name_scope("postproces_fpn"): - for key in cfgs.LEVLES: - tmp_rois, tmp_roi_scores = postprocess_rpn_proposals(rpn_bbox_pred=fpn_box_delta[key], - rpn_cls_prob=fpn_cls_prob[key], - img_shape=img_shape, - anchors=anchors_dict[key], - is_training=self.is_training) - rois_list.append(tmp_rois) - rois_scores_list.append(tmp_roi_scores) - allrois = tf.concat(rois_list, axis=0) - allrois_scores = tf.concat(rois_scores_list, axis=0) - fpn_topk = cfgs.FPN_TOP_K_PER_LEVEL_TRAIN if self.is_training else cfgs.FPN_TOP_K_PER_LEVEL_TEST - topk = tf.minimum(fpn_topk, tf.shape(allrois)[0]) - - rois_scores, topk_indices = tf.nn.top_k(allrois_scores, k=topk) - - rois = tf.stop_gradient(tf.gather(allrois, topk_indices)) - rois_scores = tf.stop_gradient(rois_scores) - - # +++++++++++++++++++++++++++++++++++++add img smry+++++++++++++++++++++++++++++++++++++++++++++++++++++++ - if self.is_training: - score_gre_05 = tf.reshape(tf.where(tf.greater_equal(rois_scores, 0.5)), [-1]) - score_gre_05_rois = tf.gather(rois, score_gre_05) - score_gre_05_score = tf.gather(rois_scores, score_gre_05) - score_gre_05_in_img = show_box_in_tensor.draw_boxes_with_scores(img_batch=input_img_batch, - boxes=score_gre_05_rois, - scores=score_gre_05_score) - tf.summary.image('score_greater_05_rois', score_gre_05_in_img) - # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - - # sample for fpn. We should concat all the anchors - if self.is_training: - with tf.variable_scope('sample_anchors_minibatch'): - fpn_labels, fpn_bbox_targets = \ - tf.py_func( - anchor_target_layer, - [gtboxes_batch, img_shape, all_anchors], - [tf.float32, tf.float32]) - fpn_bbox_targets = tf.reshape(fpn_bbox_targets, [-1, 4]) - fpn_labels = tf.to_int32(fpn_labels, name="to_int32") - fpn_labels = tf.reshape(fpn_labels, [-1]) - self.add_anchor_img_smry(input_img_batch, all_anchors, fpn_labels) - - with tf.control_dependencies([fpn_labels]): - with tf.variable_scope('sample_RCNN_minibatch'): - rois, labels, bbox_targets = \ - tf.py_func(proposal_target_layer, - [rois, gtboxes_batch], - [tf.float32, tf.float32, tf.float32]) - rois = tf.reshape(rois, [-1, 4]) - labels = tf.to_int32(labels) - labels = tf.reshape(labels, [-1]) - bbox_targets = tf.reshape(bbox_targets, [-1, 4*(cfgs.CLASS_NUM+1)]) - self.add_roi_batch_img_smry(input_img_batch, rois, labels) - if self.is_training: - rois_list, labels, bbox_targets = self.assign_levels(all_rois=rois, - labels=labels, - bbox_targets=bbox_targets) - else: - rois_list = self.assign_levels(all_rois=rois) # rois_list: [P2_rois, P3_rois, P4_rois, P5_rois] - - # -------------------------------------------------------------------------------------------------------------# - # Fast-RCNN # - # -------------------------------------------------------------------------------------------------------------# - - # 5. build Fast-RCNN - # rois = tf.Print(rois, [tf.shape(rois)], 'rois shape', summarize=10) - bbox_pred, cls_score = self.build_fastrcnn(P_list=[P_dict[key] for key in cfgs.LEVLES], - rois_list=rois_list, - img_shape=img_shape) - # bbox_pred shape: [-1, 4*(cls_num+1)]. - # cls_score shape： [-1, cls_num+1] - - cls_prob = slim.softmax(cls_score, 'cls_prob') - - - # ----------------------------------------------add smry------------------------------------------------------- - if self.is_training: - cls_category = tf.argmax(cls_prob, axis=1) - fast_acc = tf.reduce_mean(tf.to_float(tf.equal(cls_category, tf.to_int64(labels)))) - tf.summary.scalar('ACC/fast_acc', fast_acc) - - rois = tf.concat(rois_list, axis=0, name='concat_rois') - # 6. postprocess_fastrcnn - if not self.is_training: - return self.postprocess_fastrcnn(rois=rois, bbox_ppred=bbox_pred, scores=cls_prob, img_shape=img_shape) - else: - ''' - when trian. We need build Loss - ''' - loss_dict = self.build_loss(rpn_box_pred=tf.concat([fpn_box_delta[key] for key in cfgs.LEVLES], axis=0), - rpn_bbox_targets=fpn_bbox_targets, - rpn_cls_score=tf.concat([fpn_cls_score[key] for key in cfgs.LEVLES], axis=0), - rpn_labels=fpn_labels, - bbox_pred=bbox_pred, - bbox_targets=bbox_targets, - cls_score=cls_score, - labels=labels) - - final_bbox, final_scores, final_category = self.postprocess_fastrcnn(rois=rois, - bbox_ppred=bbox_pred, - scores=cls_prob, - img_shape=img_shape) - return final_bbox, final_scores, final_category, loss_dict - - def get_restorer(self): - checkpoint_path = tf.train.latest_checkpoint(os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION)) - - if checkpoint_path != None: - restorer = tf.train.Saver() - print("model restore from :", checkpoint_path) - else: - checkpoint_path = cfgs.PRETRAINED_CKPT - print("model restore from pretrained mode, path is :", checkpoint_path) - - model_variables = slim.get_model_variables() - # for var in model_variables: - # print(var.name) - # print(20*"__++__++__") - - def name_in_ckpt_rpn(var): - return var.op.name - - def name_in_ckpt_fastrcnn_head(var): - ''' - Fast-RCNN/resnet_v1_50/block4 -->resnet_v1_50/block4 - Fast-RCNN/MobilenetV2/** -- > MobilenetV2 ** - :param var: - :return: - ''' - return '/'.join(var.op.name.split('/')[1:]) - nameInCkpt_Var_dict = {} - for var in model_variables: - if var.name.startswith(self.base_network_name): - var_name_in_ckpt = name_in_ckpt_rpn(var) - nameInCkpt_Var_dict[var_name_in_ckpt] = var - restore_variables = nameInCkpt_Var_dict - for key, item in restore_variables.items(): - print("var_in_graph: ", item.name) - print("var_in_ckpt: ", key) - print(20*"___") - restorer = tf.train.Saver(restore_variables) - print(20 * "****") - print("restore from pretrained_weighs in IMAGE_NET") - return restorer, checkpoint_path - - def get_gradients(self, optimizer, loss): - ''' - - :param optimizer: - :param loss: - :return: - - return vars and grads that not be fixed - ''' - - # if cfgs.FIXED_BLOCKS > 0: - # trainable_vars = tf.trainable_variables() - # # trained_vars = slim.get_trainable_variables() - # start_names = [cfgs.NET_NAME + '/block%d'%i for i in range(1, cfgs.FIXED_BLOCKS+1)] + \ - # [cfgs.NET_NAME + '/conv1'] - # start_names = tuple(start_names) - # trained_var_list = [] - # for var in trainable_vars: - # if not var.name.startswith(start_names): - # trained_var_list.append(var) - # # slim.learning.train() - # grads = optimizer.compute_gradients(loss, var_list=trained_var_list) - # return grads - # else: - # return optimizer.compute_gradients(loss) - return optimizer.compute_gradients(loss) - - def enlarge_gradients_for_bias(self, gradients): - - final_gradients = [] - with tf.variable_scope("Gradient_Mult") as scope: - for grad, var in gradients: - scale = 1.0 - if cfgs.MUTILPY_BIAS_GRADIENT and './biases' in var.name: - scale = scale * cfgs.MUTILPY_BIAS_GRADIENT - if not np.allclose(scale, 1.0): - grad = tf.multiply(grad, scale) - final_gradients.append((grad, var)) - return final_gradients - - - - - - - - - - - - - - - - - - - - diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/README.md b/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/README.md deleted file mode 100755 index ba62406..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/README.md +++ /dev/null @@ -1,12 +0,0 @@ -# Mobilenet V2 -This folder contains building code for Mobilenet V2, based on -[Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation] -(https://arxiv.org/abs/1801.04381) - -# Pretrained model -TODO - -# Example -TODO - - diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/__init__.py b/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/conv_blocks.py b/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/conv_blocks.py deleted file mode 100755 index 029bd11..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/conv_blocks.py +++ /dev/null @@ -1,352 +0,0 @@ -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Convolution blocks for mobilenet.""" -import contextlib -import functools - -import tensorflow as tf - -slim = tf.contrib.slim - - -def _fixed_padding(inputs, kernel_size, rate=1): - """Pads the input along the spatial dimensions independently of input size. - - Pads the input such that if it was used in a convolution with 'VALID' padding, - the output would have the same dimensions as if the unpadded input was used - in a convolution with 'SAME' padding. - - Args: - inputs: A tensor of size [batch, height_in, width_in, channels]. - kernel_size: The kernel to be used in the conv2d or max_pool2d operation. - rate: An integer, rate for atrous convolution. - - Returns: - output: A tensor of size [batch, height_out, width_out, channels] with the - input, either intact (if kernel_size == 1) or padded (if kernel_size > 1). - """ - kernel_size_effective = [kernel_size[0] + (kernel_size[0] - 1) * (rate - 1), - kernel_size[0] + (kernel_size[0] - 1) * (rate - 1)] - pad_total = [kernel_size_effective[0] - 1, kernel_size_effective[1] - 1] - pad_beg = [pad_total[0] // 2, pad_total[1] // 2] - pad_end = [pad_total[0] - pad_beg[0], pad_total[1] - pad_beg[1]] - padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg[0], pad_end[0]], - [pad_beg[1], pad_end[1]], [0, 0]]) - return padded_inputs - - -def _make_divisible(v, divisor, min_value=None): - if min_value is None: - min_value = divisor - new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) - # Make sure that round down does not go down by more than 10%. - if new_v < 0.9 * v: - new_v += divisor - return new_v - - -def _split_divisible(num, num_ways, divisible_by=8): - """Evenly splits num, num_ways so each piece is a multiple of divisible_by.""" - assert num % divisible_by == 0 - assert num / num_ways >= divisible_by - # Note: want to round down, we adjust each split to match the total. - base = num // num_ways // divisible_by * divisible_by - result = [] - accumulated = 0 - for i in range(num_ways): - r = base - while accumulated + r < num * (i + 1) / num_ways: - r += divisible_by - result.append(r) - accumulated += r - assert accumulated == num - return result - - -@contextlib.contextmanager -def _v1_compatible_scope_naming(scope): - if scope is None: # Create uniqified separable blocks. - with tf.variable_scope(None, default_name='separable') as s, \ - tf.name_scope(s.original_name_scope): - yield '' - else: - # We use scope_depthwise, scope_pointwise for compatibility with V1 ckpts. - # which provide numbered scopes. - scope += '_' - yield scope - - -@slim.add_arg_scope -def split_separable_conv2d(input_tensor, - num_outputs, - scope=None, - normalizer_fn=None, - stride=1, - rate=1, - endpoints=None, - use_explicit_padding=False): - """Separable mobilenet V1 style convolution. - - Depthwise convolution, with default non-linearity, - followed by 1x1 depthwise convolution. This is similar to - slim.separable_conv2d, but differs in tha it applies batch - normalization and non-linearity to depthwise. This matches - the basic building of Mobilenet Paper - (https://arxiv.org/abs/1704.04861) - - Args: - input_tensor: input - num_outputs: number of outputs - scope: optional name of the scope. Note if provided it will use - scope_depthwise for deptwhise, and scope_pointwise for pointwise. - normalizer_fn: which normalizer function to use for depthwise/pointwise - stride: stride - rate: output rate (also known as dilation rate) - endpoints: optional, if provided, will export additional tensors to it. - use_explicit_padding: Use 'VALID' padding for convolutions, but prepad - inputs so that the output dimensions are the same as if 'SAME' padding - were used. - - Returns: - output tesnor - """ - - with _v1_compatible_scope_naming(scope) as scope: - dw_scope = scope + 'depthwise' - endpoints = endpoints if endpoints is not None else {} - kernel_size = [3, 3] - padding = 'SAME' - if use_explicit_padding: - padding = 'VALID' - input_tensor = _fixed_padding(input_tensor, kernel_size, rate) - net = slim.separable_conv2d( - input_tensor, - None, - kernel_size, - depth_multiplier=1, - stride=stride, - rate=rate, - normalizer_fn=normalizer_fn, - padding=padding, - scope=dw_scope) - - endpoints[dw_scope] = net - - pw_scope = scope + 'pointwise' - net = slim.conv2d( - net, - num_outputs, [1, 1], - stride=1, - normalizer_fn=normalizer_fn, - scope=pw_scope) - endpoints[pw_scope] = net - return net - - -def expand_input_by_factor(n, divisible_by=8): - return lambda num_inputs, **_: _make_divisible(num_inputs * n, divisible_by) - - -@slim.add_arg_scope -def expanded_conv(input_tensor, - num_outputs, - expansion_size=expand_input_by_factor(6), - stride=1, - rate=1, - kernel_size=(3, 3), - residual=True, - normalizer_fn=None, - split_projection=1, - split_expansion=1, - expansion_transform=None, - depthwise_location='expansion', - depthwise_channel_multiplier=1, - endpoints=None, - use_explicit_padding=False, - scope=None): - """Depthwise Convolution Block with expansion. - - Builds a composite convolution that has the following structure - expansion (1x1) -> depthwise (kernel_size) -> projection (1x1) - - Args: - input_tensor: input - num_outputs: number of outputs in the final layer. - expansion_size: the size of expansion, could be a constant or a callable. - If latter it will be provided 'num_inputs' as an input. For forward - compatibility it should accept arbitrary keyword arguments. - Default will expand the input by factor of 6. - stride: depthwise stride - rate: depthwise rate - kernel_size: depthwise kernel - residual: whether to include residual connection between input - and output. - normalizer_fn: batchnorm or otherwise - split_projection: how many ways to split projection operator - (that is conv expansion->bottleneck) - split_expansion: how many ways to split expansion op - (that is conv bottleneck->expansion) ops will keep depth divisible - by this value. - expansion_transform: Optional function that takes expansion - as a single input and returns output. - depthwise_location: where to put depthwise covnvolutions supported - values None, 'input', 'output', 'expansion' - depthwise_channel_multiplier: depthwise channel multiplier: - each input will replicated (with different filters) - that many times. So if input had c channels, - output will have c x depthwise_channel_multpilier. - endpoints: An optional dictionary into which intermediate endpoints are - placed. The keys "expansion_output", "depthwise_output", - "projection_output" and "expansion_transform" are always populated, even - if the corresponding functions are not invoked. - use_explicit_padding: Use 'VALID' padding for convolutions, but prepad - inputs so that the output dimensions are the same as if 'SAME' padding - were used. - scope: optional scope. - - Returns: - Tensor of depth num_outputs - - Raises: - TypeError: on inval - """ - with tf.variable_scope(scope, default_name='expanded_conv') as s, \ - tf.name_scope(s.original_name_scope): - prev_depth = input_tensor.get_shape().as_list()[3] - if depthwise_location not in [None, 'input', 'output', 'expansion']: - raise TypeError('%r is unknown value for depthwise_location' % - depthwise_location) - padding = 'SAME' - if use_explicit_padding: - padding = 'VALID' - depthwise_func = functools.partial( - slim.separable_conv2d, - num_outputs=None, - kernel_size=kernel_size, - depth_multiplier=depthwise_channel_multiplier, - stride=stride, - rate=rate, - normalizer_fn=normalizer_fn, - padding=padding, - scope='depthwise') - # b1 -> b2 * r -> b2 - # i -> (o * r) (bottleneck) -> o - input_tensor = tf.identity(input_tensor, 'input') - net = input_tensor - - if depthwise_location == 'input': - if use_explicit_padding: - net = _fixed_padding(net, kernel_size, rate) - net = depthwise_func(net, activation_fn=None) - - if callable(expansion_size): - inner_size = expansion_size(num_inputs=prev_depth) - else: - inner_size = expansion_size - - if inner_size > net.shape[3]: - net = split_conv( - net, - inner_size, - num_ways=split_expansion, - scope='expand', - stride=1, - normalizer_fn=normalizer_fn) - net = tf.identity(net, 'expansion_output') - if endpoints is not None: - endpoints['expansion_output'] = net - - if depthwise_location == 'expansion': - if use_explicit_padding: - net = _fixed_padding(net, kernel_size, rate) - net = depthwise_func(net) - - net = tf.identity(net, name='depthwise_output') - if endpoints is not None: - endpoints['depthwise_output'] = net - if expansion_transform: - net = expansion_transform(expansion_tensor=net, input_tensor=input_tensor) - # Note in contrast with expansion, we always have - # projection to produce the desired output size. - net = split_conv( - net, - num_outputs, - num_ways=split_projection, - stride=1, - scope='project', - normalizer_fn=normalizer_fn, - activation_fn=tf.identity) - if endpoints is not None: - endpoints['projection_output'] = net - if depthwise_location == 'output': - if use_explicit_padding: - net = _fixed_padding(net, kernel_size, rate) - net = depthwise_func(net, activation_fn=None) - - if callable(residual): # custom residual - net = residual(input_tensor=input_tensor, output_tensor=net) - elif (residual and - # stride check enforces that we don't add residuals when spatial - # dimensions are None - stride == 1 and - # Depth matches - net.get_shape().as_list()[3] == - input_tensor.get_shape().as_list()[3]): - net += input_tensor - return tf.identity(net, name='output') - - -def split_conv(input_tensor, - num_outputs, - num_ways, - scope, - divisible_by=8, - **kwargs): - """Creates a split convolution. - - Split convolution splits the input and output into - 'num_blocks' blocks of approximately the same size each, - and only connects $i$-th input to $i$ output. - - Args: - input_tensor: input tensor - num_outputs: number of output filters - num_ways: num blocks to split by. - scope: scope for all the operators. - divisible_by: make sure that every part is divisiable by this. - **kwargs: will be passed directly into conv2d operator - Returns: - tensor - """ - b = input_tensor.get_shape().as_list()[3] - - if num_ways == 1 or min(b // num_ways, - num_outputs // num_ways) < divisible_by: - # Don't do any splitting if we end up with less than 8 filters - # on either side. - return slim.conv2d(input_tensor, num_outputs, [1, 1], scope=scope, **kwargs) - - outs = [] - input_splits = _split_divisible(b, num_ways, divisible_by=divisible_by) - output_splits = _split_divisible( - num_outputs, num_ways, divisible_by=divisible_by) - inputs = tf.split(input_tensor, input_splits, axis=3, name='split_' + scope) - base = scope - for i, (input_tensor, out_size) in enumerate(zip(inputs, output_splits)): - scope = base + '_part_%d' % (i,) - n = slim.conv2d(input_tensor, out_size, [1, 1], scope=scope, **kwargs) - n = tf.identity(n, scope + '_output') - outs.append(n) - return tf.concat(outs, 3, name=scope + '_concat') diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/mobilenet.py b/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/mobilenet.py deleted file mode 100755 index 3c8ca50..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/mobilenet.py +++ /dev/null @@ -1,433 +0,0 @@ -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Mobilenet Base Class.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -import collections -import contextlib -import copy -import os - -import tensorflow as tf - - -slim = tf.contrib.slim - - -@slim.add_arg_scope -def apply_activation(x, name=None, activation_fn=None): - return activation_fn(x, name=name) if activation_fn else x - - -def _fixed_padding(inputs, kernel_size, rate=1): - """Pads the input along the spatial dimensions independently of input size. - - Pads the input such that if it was used in a convolution with 'VALID' padding, - the output would have the same dimensions as if the unpadded input was used - in a convolution with 'SAME' padding. - - Args: - inputs: A tensor of size [batch, height_in, width_in, channels]. - kernel_size: The kernel to be used in the conv2d or max_pool2d operation. - rate: An integer, rate for atrous convolution. - - Returns: - output: A tensor of size [batch, height_out, width_out, channels] with the - input, either intact (if kernel_size == 1) or padded (if kernel_size > 1). - """ - kernel_size_effective = [kernel_size[0] + (kernel_size[0] - 1) * (rate - 1), - kernel_size[0] + (kernel_size[0] - 1) * (rate - 1)] - pad_total = [kernel_size_effective[0] - 1, kernel_size_effective[1] - 1] - pad_beg = [pad_total[0] // 2, pad_total[1] // 2] - pad_end = [pad_total[0] - pad_beg[0], pad_total[1] - pad_beg[1]] - padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg[0], pad_end[0]], - [pad_beg[1], pad_end[1]], [0, 0]]) - return padded_inputs - - -def _make_divisible(v, divisor, min_value=None): - if min_value is None: - min_value = divisor - new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) - # Make sure that round down does not go down by more than 10%. - if new_v < 0.9 * v: - new_v += divisor - return new_v - - -@contextlib.contextmanager -def _set_arg_scope_defaults(defaults): - """Sets arg scope defaults for all items present in defaults. - - Args: - defaults: dictionary/list of pairs, containing a mapping from - function to a dictionary of default args. - - Yields: - context manager where all defaults are set. - """ - if hasattr(defaults, 'items'): - items = defaults.items() - else: - items = defaults - if not items: - yield - else: - func, default_arg = items[0] - with slim.arg_scope(func, **default_arg): - with _set_arg_scope_defaults(items[1:]): - yield - - -@slim.add_arg_scope -def depth_multiplier(output_params, - multiplier, - divisible_by=8, - min_depth=8, - **unused_kwargs): - if 'num_outputs' not in output_params: - return - d = output_params['num_outputs'] - output_params['num_outputs'] = _make_divisible(d * multiplier, divisible_by, - min_depth) - - -_Op = collections.namedtuple('Op', ['op', 'params', 'multiplier_func']) - - -def op(opfunc, **params): - multiplier = params.pop('multiplier_transorm', depth_multiplier) - return _Op(opfunc, params=params, multiplier_func=multiplier) - - -@slim.add_arg_scope -def mobilenet_base( # pylint: disable=invalid-name - inputs, - conv_defs, - multiplier=1.0, - final_endpoint=None, - output_stride=None, - use_explicit_padding=False, - scope=None, - is_training=False): - """Mobilenet base network. - - Constructs a network from inputs to the given final endpoint. By default - the network is constructed in inference mode. To create network - in training mode use: - - with slim.arg_scope(mobilenet.training_scope()): - logits, endpoints = mobilenet_base(...) - - Args: - inputs: a tensor of shape [batch_size, height, width, channels]. - conv_defs: A list of op(...) layers specifying the net architecture. - multiplier: Float multiplier for the depth (number of channels) - for all convolution ops. The value must be greater than zero. Typical - usage will be to set this value in (0, 1) to reduce the number of - parameters or computation cost of the model. - final_endpoint: The name of last layer, for early termination for - for V1-based networks: last layer is "layer_14", for V2: "layer_20" - output_stride: An integer that specifies the requested ratio of input to - output spatial resolution. If not None, then we invoke atrous convolution - if necessary to prevent the network from reducing the spatial resolution - of the activation maps. Allowed values are 1 or any even number, excluding - zero. Typical values are 8 (accurate fully convolutional mode), 16 - (fast fully convolutional mode), and 32 (classification mode). - - NOTE- output_stride relies on all consequent operators to support dilated - operators via "rate" parameter. This might require wrapping non-conv - operators to operate properly. - - use_explicit_padding: Use 'VALID' padding for convolutions, but prepad - inputs so that the output dimensions are the same as if 'SAME' padding - were used. - scope: optional variable scope. - is_training: How to setup batch_norm and other ops. Note: most of the time - this does not need be set directly. Use mobilenet.training_scope() to set - up training instead. This parameter is here for backward compatibility - only. It is safe to set it to the value matching - training_scope(is_training=...). It is also safe to explicitly set - it to False, even if there is outer training_scope set to to training. - (The network will be built in inference mode). - Returns: - tensor_out: output tensor. - end_points: a set of activations for external use, for example summaries or - losses. - - Raises: - ValueError: depth_multiplier <= 0, or the target output_stride is not - allowed. - """ - if multiplier <= 0: - raise ValueError('multiplier is not greater than zero.') - - # Set conv defs defaults and overrides. - conv_defs_defaults = conv_defs.get('defaults', {}) - conv_defs_overrides = conv_defs.get('overrides', {}) - if use_explicit_padding: - conv_defs_overrides = copy.deepcopy(conv_defs_overrides) - conv_defs_overrides[ - (slim.conv2d, slim.separable_conv2d)] = {'padding': 'VALID'} - - if output_stride is not None: - if output_stride == 0 or (output_stride > 1 and output_stride % 2): - raise ValueError('Output stride must be None, 1 or a multiple of 2.') - - # a) Set the tensorflow scope - # b) set padding to default: note we might consider removing this - # since it is also set by mobilenet_scope - # c) set all defaults - # d) set all extra overrides. - with _scope_all(scope, default_scope='Mobilenet'), \ - slim.arg_scope([slim.batch_norm], is_training=is_training), \ - _set_arg_scope_defaults(conv_defs_defaults), \ - _set_arg_scope_defaults(conv_defs_overrides): - # The current_stride variable keeps track of the output stride of the - # activations, i.e., the running product of convolution strides up to the - # current network layer. This allows us to invoke atrous convolution - # whenever applying the next convolution would result in the activations - # having output stride larger than the target output_stride. - current_stride = 1 - - # The atrous convolution rate parameter. - rate = 1 - - net = inputs - # Insert default parameters before the base scope which includes - # any custom overrides set in mobilenet. - end_points = {} - scopes = {} - for i, opdef in enumerate(conv_defs['spec']): - params = dict(opdef.params) - opdef.multiplier_func(params, multiplier) - stride = params.get('stride', 1) - if output_stride is not None and current_stride == output_stride: - # If we have reached the target output_stride, then we need to employ - # atrous convolution with stride=1 and multiply the atrous rate by the - # current unit's stride for use in subsequent layers. - layer_stride = 1 - layer_rate = rate - rate *= stride - else: - layer_stride = stride - layer_rate = 1 - current_stride *= stride - # Update params. - params['stride'] = layer_stride - # Only insert rate to params if rate > 1. - if layer_rate > 1: - params['rate'] = layer_rate - # Set padding - if use_explicit_padding: - if 'kernel_size' in params: - net = _fixed_padding(net, params['kernel_size'], layer_rate) - else: - params['use_explicit_padding'] = True - - end_point = 'layer_%d' % (i + 1) - try: - net = opdef.op(net, **params) - except Exception: - print('Failed to create op %i: %r params: %r' % (i, opdef, params)) - raise - end_points[end_point] = net - scope = os.path.dirname(net.name) - scopes[scope] = end_point - if final_endpoint is not None and end_point == final_endpoint: - break - - # Add all tensors that end with 'output' to - # endpoints - for t in net.graph.get_operations(): - scope = os.path.dirname(t.name) - bn = os.path.basename(t.name) - if scope in scopes and t.name.endswith('output'): - end_points[scopes[scope] + '/' + bn] = t.outputs[0] - return net, end_points - - -@contextlib.contextmanager -def _scope_all(scope, default_scope=None): - with tf.variable_scope(scope, default_name=default_scope) as s,\ - tf.name_scope(s.original_name_scope): - yield s - - -@slim.add_arg_scope -def mobilenet(inputs, - num_classes=1001, - prediction_fn=slim.softmax, - reuse=None, - scope='Mobilenet', - base_only=False, - **mobilenet_args): - """Mobilenet model for classification, supports both V1 and V2. - - Note: default mode is inference, use mobilenet.training_scope to create - training network. - - - Args: - inputs: a tensor of shape [batch_size, height, width, channels]. - num_classes: number of predicted classes. If 0 or None, the logits layer - is omitted and the input features to the logits layer (before dropout) - are returned instead. - prediction_fn: a function to get predictions out of logits - (default softmax). - reuse: whether or not the network and its variables should be reused. To be - able to reuse 'scope' must be given. - scope: Optional variable_scope. - base_only: if True will only create the base of the network (no pooling - and no logits). - **mobilenet_args: passed to mobilenet_base verbatim. - - conv_defs: list of conv defs - - multiplier: Float multiplier for the depth (number of channels) - for all convolution ops. The value must be greater than zero. Typical - usage will be to set this value in (0, 1) to reduce the number of - parameters or computation cost of the model. - - output_stride: will ensure that the last layer has at most total stride. - If the architecture calls for more stride than that provided - (e.g. output_stride=16, but the architecture has 5 stride=2 operators), - it will replace output_stride with fractional convolutions using Atrous - Convolutions. - - Returns: - logits: the pre-softmax activations, a tensor of size - [batch_size, num_classes] - end_points: a dictionary from components of the network to the corresponding - activation tensor. - - Raises: - ValueError: Input rank is invalid. - """ - is_training = mobilenet_args.get('is_training', False) - input_shape = inputs.get_shape().as_list() - if len(input_shape) != 4: - raise ValueError('Expected rank 4 input, was: %d' % len(input_shape)) - - with tf.variable_scope(scope, 'Mobilenet', reuse=reuse) as scope: - inputs = tf.identity(inputs, 'input') - net, end_points = mobilenet_base(inputs, scope=scope, **mobilenet_args) - if base_only: - return net, end_points - - net = tf.identity(net, name='embedding') - - with tf.variable_scope('Logits'): - net = global_pool(net) - end_points['global_pool'] = net - if not num_classes: - return net, end_points - net = slim.dropout(net, scope='Dropout', is_training=is_training) - # 1 x 1 x num_classes - # Note: legacy scope name. - logits = slim.conv2d( - net, - num_classes, [1, 1], - activation_fn=None, - normalizer_fn=None, - biases_initializer=tf.zeros_initializer(), - scope='Conv2d_1c_1x1') - - logits = tf.squeeze(logits, [1, 2]) - - logits = tf.identity(logits, name='output') - end_points['Logits'] = logits - if prediction_fn: - end_points['Predictions'] = prediction_fn(logits, 'Predictions') - return logits, end_points - - -def global_pool(input_tensor, pool_op=tf.nn.avg_pool): - """Applies avg pool to produce 1x1 output. - - NOTE: This function is funcitonally equivalenet to reduce_mean, but it has - baked in average pool which has better support across hardware. - - Args: - input_tensor: input tensor - pool_op: pooling op (avg pool is default) - Returns: - a tensor batch_size x 1 x 1 x depth. - """ - shape = input_tensor.get_shape().as_list() - if shape[1] is None or shape[2] is None: - kernel_size = tf.convert_to_tensor( - [1, tf.shape(input_tensor)[1], - tf.shape(input_tensor)[2], 1]) - else: - kernel_size = [1, shape[1], shape[2], 1] - output = pool_op( - input_tensor, ksize=kernel_size, strides=[1, 1, 1, 1], padding='VALID') - # Recover output shape, for unknown shape. - output.set_shape([None, 1, 1, None]) - return output - - -def training_scope(is_training=True, - weight_decay=0.00004, - stddev=0.09, - dropout_keep_prob=0.8, - bn_decay=0.997): - """Defines Mobilenet training scope. - - Usage: - with tf.contrib.slim.arg_scope(mobilenet.training_scope()): - logits, endpoints = mobilenet_v2.mobilenet(input_tensor) - - # the network created will be trainble with dropout/batch norm - # initialized appropriately. - Args: - is_training: if set to False this will ensure that all customizations are - set to non-training mode. This might be helpful for code that is reused - across both training/evaluation, but most of the time training_scope with - value False is not needed. - - weight_decay: The weight decay to use for regularizing the model. - stddev: Standard deviation for initialization, if negative uses xavier. - dropout_keep_prob: dropout keep probability - bn_decay: decay for the batch norm moving averages. - - Returns: - An argument scope to use via arg_scope. - """ - # Note: do not introduce parameters that would change the inference - # model here (for example whether to use bias), modify conv_def instead. - batch_norm_params = { - 'is_training': is_training, - 'decay': bn_decay, - } - - if stddev < 0: - weight_intitializer = slim.initializers.xavier_initializer() - else: - weight_intitializer = tf.truncated_normal_initializer(stddev=stddev) - - # Set weight_decay for weights in Conv and FC layers. - with slim.arg_scope( - [slim.conv2d, slim.fully_connected, slim.separable_conv2d], - weights_initializer=weight_intitializer, - normalizer_fn=slim.batch_norm), \ - slim.arg_scope([mobilenet_base, mobilenet], is_training=is_training),\ - slim.arg_scope([slim.batch_norm], **batch_norm_params), \ - slim.arg_scope([slim.dropout], is_training=is_training, - keep_prob=dropout_keep_prob), \ - slim.arg_scope([slim.conv2d], \ - weights_regularizer=slim.l2_regularizer(weight_decay)), \ - slim.arg_scope([slim.separable_conv2d], weights_regularizer=None) as s: - return s diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/mobilenet_v2.py b/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/mobilenet_v2.py deleted file mode 100755 index 84300cd..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/mobilenet_v2.py +++ /dev/null @@ -1,188 +0,0 @@ -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Implementation of Mobilenet V2. - -Architecture: https://arxiv.org/abs/1801.04381 - -The base model gives 72.2% accuracy on ImageNet, with 300MMadds, -3.4 M parameters. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import copy - -import tensorflow as tf - -from libs.networks.mobilenet import conv_blocks as ops -from libs.networks.mobilenet import mobilenet as lib - -slim = tf.contrib.slim -op = lib.op - -expand_input = ops.expand_input_by_factor - -# pyformat: disable -# Architecture: https://arxiv.org/abs/1801.04381 -V2_DEF = dict( - defaults={ - # Note: these parameters of batch norm affect the architecture - # that's why they are here and not in training_scope. - (slim.batch_norm,): {'center': True, 'scale': True}, - (slim.conv2d, slim.fully_connected, slim.separable_conv2d): { - 'normalizer_fn': slim.batch_norm, 'activation_fn': tf.nn.relu6 - }, - (ops.expanded_conv,): { - 'expansion_size': expand_input(6), - 'split_expansion': 1, - 'normalizer_fn': slim.batch_norm, - 'residual': True - }, - (slim.conv2d, slim.separable_conv2d): {'padding': 'SAME'} - }, - spec=[ - op(slim.conv2d, stride=2, num_outputs=32, kernel_size=[3, 3]), - op(ops.expanded_conv, - expansion_size=expand_input(1, divisible_by=1), - num_outputs=16), - op(ops.expanded_conv, stride=2, num_outputs=24), - op(ops.expanded_conv, stride=1, num_outputs=24), - op(ops.expanded_conv, stride=2, num_outputs=32), - op(ops.expanded_conv, stride=1, num_outputs=32), - op(ops.expanded_conv, stride=1, num_outputs=32), - op(ops.expanded_conv, stride=2, num_outputs=64), - op(ops.expanded_conv, stride=1, num_outputs=64), - op(ops.expanded_conv, stride=1, num_outputs=64), - op(ops.expanded_conv, stride=1, num_outputs=64), - op(ops.expanded_conv, stride=1, num_outputs=96), - op(ops.expanded_conv, stride=1, num_outputs=96), - op(ops.expanded_conv, stride=1, num_outputs=96), - op(ops.expanded_conv, stride=2, num_outputs=160), - op(ops.expanded_conv, stride=1, num_outputs=160), - op(ops.expanded_conv, stride=1, num_outputs=160), - op(ops.expanded_conv, stride=1, num_outputs=320), - op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=1280) - ], -) -# pyformat: enable - - -@slim.add_arg_scope -def mobilenet(input_tensor, - num_classes=1001, - depth_multiplier=1.0, - scope='MobilenetV2', - conv_defs=None, - finegrain_classification_mode=False, - min_depth=None, - divisible_by=None, - **kwargs): - """Creates mobilenet V2 network. - - Inference mode is created by default. To create training use training_scope - below. - - with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()): - logits, endpoints = mobilenet_v2.mobilenet(input_tensor) - - Args: - input_tensor: The input tensor - num_classes: number of classes - depth_multiplier: The multiplier applied to scale number of - channels in each layer. Note: this is called depth multiplier in the - paper but the name is kept for consistency with slim's model builder. - scope: Scope of the operator - conv_defs: Allows to override default conv def. - finegrain_classification_mode: When set to True, the model - will keep the last layer large even for small multipliers. Following - https://arxiv.org/abs/1801.04381 - suggests that it improves performance for ImageNet-type of problems. - *Note* ignored if final_endpoint makes the builder exit earlier. - min_depth: If provided, will ensure that all layers will have that - many channels after application of depth multiplier. - divisible_by: If provided will ensure that all layers # channels - will be divisible by this number. - **kwargs: passed directly to mobilenet.mobilenet: - prediciton_fn- what prediction function to use. - reuse-: whether to reuse variables (if reuse set to true, scope - must be given). - Returns: - logits/endpoints pair - - Raises: - ValueError: On invalid arguments - """ - if conv_defs is None: - conv_defs = V2_DEF - if 'multiplier' in kwargs: - raise ValueError('mobilenetv2 doesn\'t support generic ' - 'multiplier parameter use "depth_multiplier" instead.') - if finegrain_classification_mode: - conv_defs = copy.deepcopy(conv_defs) - if depth_multiplier < 1: - conv_defs['spec'][-1].params['num_outputs'] /= depth_multiplier - - depth_args = {} - # NB: do not set depth_args unless they are provided to avoid overriding - # whatever default depth_multiplier might have thanks to arg_scope. - if min_depth is not None: - depth_args['min_depth'] = min_depth - if divisible_by is not None: - depth_args['divisible_by'] = divisible_by - - with slim.arg_scope((lib.depth_multiplier,), **depth_args): - return lib.mobilenet( - input_tensor, - num_classes=num_classes, - conv_defs=conv_defs, - scope=scope, - multiplier=depth_multiplier, - **kwargs) - - -@slim.add_arg_scope -def mobilenet_base(input_tensor, depth_multiplier=1.0, **kwargs): - """Creates base of the mobilenet (no pooling and no logits) .""" - return mobilenet(input_tensor, - depth_multiplier=depth_multiplier, - base_only=True, **kwargs) - - -def training_scope(**kwargs): - """Defines MobilenetV2 training scope. - - Usage: - with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()): - logits, endpoints = mobilenet_v2.mobilenet(input_tensor) - - with slim. - - Args: - **kwargs: Passed to mobilenet.training_scope. The following parameters - are supported: - weight_decay- The weight decay to use for regularizing the model. - stddev- Standard deviation for initialization, if negative uses xavier. - dropout_keep_prob- dropout keep probability - bn_decay- decay for the batch norm moving averages. - - Returns: - An `arg_scope` to use for the mobilenet v2 model. - """ - return lib.training_scope(**kwargs) - - -__all__ = ['training_scope', 'mobilenet_base', 'mobilenet', 'V2_DEF'] diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/mobilenet_v2_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/mobilenet_v2_test.py deleted file mode 100755 index 40e48fa..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/mobilenet_v2_test.py +++ /dev/null @@ -1,176 +0,0 @@ -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for mobilenet_v2.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -import copy -import tensorflow as tf -from nets.mobilenet import conv_blocks as ops -from nets.mobilenet import mobilenet -from nets.mobilenet import mobilenet_v2 - - -slim = tf.contrib.slim - - -def find_ops(optype): - """Find ops of a given type in graphdef or a graph. - - Args: - optype: operation type (e.g. Conv2D) - Returns: - List of operations. - """ - gd = tf.get_default_graph() - return [var for var in gd.get_operations() if var.type == optype] - - -class MobilenetV2Test(tf.test.TestCase): - - def setUp(self): - tf.reset_default_graph() - - def testCreation(self): - spec = dict(mobilenet_v2.V2_DEF) - _, ep = mobilenet.mobilenet( - tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec) - num_convs = len(find_ops('Conv2D')) - - # This is mostly a sanity test. No deep reason for these particular - # constants. - # - # All but first 2 and last one have two convolutions, and there is one - # extra conv that is not in the spec. (logits) - self.assertEqual(num_convs, len(spec['spec']) * 2 - 2) - # Check that depthwise are exposed. - for i in range(2, 17): - self.assertIn('layer_%d/depthwise_output' % i, ep) - - def testCreationNoClasses(self): - spec = copy.deepcopy(mobilenet_v2.V2_DEF) - net, ep = mobilenet.mobilenet( - tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec, - num_classes=None) - self.assertIs(net, ep['global_pool']) - - def testImageSizes(self): - for input_size, output_size in [(224, 7), (192, 6), (160, 5), - (128, 4), (96, 3)]: - tf.reset_default_graph() - _, ep = mobilenet_v2.mobilenet( - tf.placeholder(tf.float32, (10, input_size, input_size, 3))) - - self.assertEqual(ep['layer_18/output'].get_shape().as_list()[1:3], - [output_size] * 2) - - def testWithSplits(self): - spec = copy.deepcopy(mobilenet_v2.V2_DEF) - spec['overrides'] = { - (ops.expanded_conv,): dict(split_expansion=2), - } - _, _ = mobilenet.mobilenet( - tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec) - num_convs = len(find_ops('Conv2D')) - # All but 3 op has 3 conv operatore, the remainign 3 have one - # and there is one unaccounted. - self.assertEqual(num_convs, len(spec['spec']) * 3 - 5) - - def testWithOutputStride8(self): - out, _ = mobilenet.mobilenet_base( - tf.placeholder(tf.float32, (10, 224, 224, 16)), - conv_defs=mobilenet_v2.V2_DEF, - output_stride=8, - scope='MobilenetV2') - self.assertEqual(out.get_shape().as_list()[1:3], [28, 28]) - - def testDivisibleBy(self): - tf.reset_default_graph() - mobilenet_v2.mobilenet( - tf.placeholder(tf.float32, (10, 224, 224, 16)), - conv_defs=mobilenet_v2.V2_DEF, - divisible_by=16, - min_depth=32) - s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')] - s = set(s) - self.assertSameElements([32, 64, 96, 160, 192, 320, 384, 576, 960, 1280, - 1001], s) - - def testDivisibleByWithArgScope(self): - tf.reset_default_graph() - # Verifies that depth_multiplier arg scope actually works - # if no default min_depth is provided. - with slim.arg_scope((mobilenet.depth_multiplier,), min_depth=32): - mobilenet_v2.mobilenet( - tf.placeholder(tf.float32, (10, 224, 224, 2)), - conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.1) - s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')] - s = set(s) - self.assertSameElements(s, [32, 192, 128, 1001]) - - def testFineGrained(self): - tf.reset_default_graph() - # Verifies that depth_multiplier arg scope actually works - # if no default min_depth is provided. - - mobilenet_v2.mobilenet( - tf.placeholder(tf.float32, (10, 224, 224, 2)), - conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.01, - finegrain_classification_mode=True) - s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')] - s = set(s) - # All convolutions will be 8->48, except for the last one. - self.assertSameElements(s, [8, 48, 1001, 1280]) - - def testMobilenetBase(self): - tf.reset_default_graph() - # Verifies that mobilenet_base returns pre-pooling layer. - with slim.arg_scope((mobilenet.depth_multiplier,), min_depth=32): - net, _ = mobilenet_v2.mobilenet_base( - tf.placeholder(tf.float32, (10, 224, 224, 16)), - conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.1) - self.assertEqual(net.get_shape().as_list(), [10, 7, 7, 128]) - - def testWithOutputStride16(self): - tf.reset_default_graph() - out, _ = mobilenet.mobilenet_base( - tf.placeholder(tf.float32, (10, 224, 224, 16)), - conv_defs=mobilenet_v2.V2_DEF, - output_stride=16) - self.assertEqual(out.get_shape().as_list()[1:3], [14, 14]) - - def testWithOutputStride8AndExplicitPadding(self): - tf.reset_default_graph() - out, _ = mobilenet.mobilenet_base( - tf.placeholder(tf.float32, (10, 224, 224, 16)), - conv_defs=mobilenet_v2.V2_DEF, - output_stride=8, - use_explicit_padding=True, - scope='MobilenetV2') - self.assertEqual(out.get_shape().as_list()[1:3], [28, 28]) - - def testWithOutputStride16AndExplicitPadding(self): - tf.reset_default_graph() - out, _ = mobilenet.mobilenet_base( - tf.placeholder(tf.float32, (10, 224, 224, 16)), - conv_defs=mobilenet_v2.V2_DEF, - output_stride=16, - use_explicit_padding=True) - self.assertEqual(out.get_shape().as_list()[1:3], [14, 14]) - - -if __name__ == '__main__': - tf.test.main() diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet_v2.py b/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet_v2.py deleted file mode 100755 index eced27c..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet_v2.py +++ /dev/null @@ -1,126 +0,0 @@ -# -*- coding: utf-8 -*- - -from __future__ import absolute_import, print_function, division -import tensorflow.contrib.slim as slim -import tensorflow as tf - -from libs.networks.mobilenet import mobilenet_v2 -from libs.networks.mobilenet.mobilenet import training_scope -from libs.networks.mobilenet.mobilenet_v2 import op -from libs.networks.mobilenet.mobilenet_v2 import ops -expand_input = ops.expand_input_by_factor - -V2_BASE_DEF = dict( - defaults={ - # Note: these parameters of batch norm affect the architecture - # that's why they are here and not in training_scope. - (slim.batch_norm,): {'center': True, 'scale': True}, - (slim.conv2d, slim.fully_connected, slim.separable_conv2d): { - 'normalizer_fn': slim.batch_norm, 'activation_fn': tf.nn.relu6 - }, - (ops.expanded_conv,): { - 'expansion_size': expand_input(6), - 'split_expansion': 1, - 'normalizer_fn': slim.batch_norm, - 'residual': True - }, - (slim.conv2d, slim.separable_conv2d): {'padding': 'SAME'} - }, - spec=[ - op(slim.conv2d, stride=2, num_outputs=32, kernel_size=[3, 3]), - op(ops.expanded_conv, - expansion_size=expand_input(1, divisible_by=1), - num_outputs=16, scope='expanded_conv'), - op(ops.expanded_conv, stride=2, num_outputs=24, scope='expanded_conv_1'), - op(ops.expanded_conv, stride=1, num_outputs=24, scope='expanded_conv_2'), - op(ops.expanded_conv, stride=2, num_outputs=32, scope='expanded_conv_3'), - op(ops.expanded_conv, stride=1, num_outputs=32, scope='expanded_conv_4'), - op(ops.expanded_conv, stride=1, num_outputs=32, scope='expanded_conv_5'), - op(ops.expanded_conv, stride=2, num_outputs=64, scope='expanded_conv_6'), - op(ops.expanded_conv, stride=1, num_outputs=64, scope='expanded_conv_7'), - op(ops.expanded_conv, stride=1, num_outputs=64, scope='expanded_conv_8'), - op(ops.expanded_conv, stride=1, num_outputs=64, scope='expanded_conv_9'), - op(ops.expanded_conv, stride=1, num_outputs=96, scope='expanded_conv_10'), - op(ops.expanded_conv, stride=1, num_outputs=96, scope='expanded_conv_11'), - op(ops.expanded_conv, stride=1, num_outputs=96, scope='expanded_conv_12') - ], -) - - -V2_HEAD_DEF = dict( - defaults={ - # Note: these parameters of batch norm affect the architecture - # that's why they are here and not in training_scope. - (slim.batch_norm,): {'center': True, 'scale': True}, - (slim.conv2d, slim.fully_connected, slim.separable_conv2d): { - 'normalizer_fn': slim.batch_norm, 'activation_fn': tf.nn.relu6 - }, - (ops.expanded_conv,): { - 'expansion_size': expand_input(6), - 'split_expansion': 1, - 'normalizer_fn': slim.batch_norm, - 'residual': True - }, - (slim.conv2d, slim.separable_conv2d): {'padding': 'SAME'} - }, - spec=[ - op(ops.expanded_conv, stride=2, num_outputs=160, scope='expanded_conv_13'), - op(ops.expanded_conv, stride=1, num_outputs=160, scope='expanded_conv_14'), - op(ops.expanded_conv, stride=1, num_outputs=160, scope='expanded_conv_15'), - op(ops.expanded_conv, stride=1, num_outputs=320, scope='expanded_conv_16'), - op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=1280, scope='Conv_1') - ], -) -def mobilenetv2_scope(is_training=True, - trainable=True, - weight_decay=0.00004, - stddev=0.09, - dropout_keep_prob=0.8, - bn_decay=0.997): - """Defines Mobilenet training scope. - In default. We do not use BN - - ReWrite the scope. - """ - batch_norm_params = { - 'is_training': False, - 'trainable': False, - 'decay': bn_decay, - } - with slim.arg_scope(training_scope(is_training=is_training, weight_decay=weight_decay)): - with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.separable_conv2d], - trainable=trainable): - with slim.arg_scope([slim.batch_norm], **batch_norm_params) as sc: - return sc - - - -def mobilenetv2_base(img_batch, is_training=True): - - with slim.arg_scope(mobilenetv2_scope(is_training=is_training, trainable=True)): - - feature_to_crop, endpoints = mobilenet_v2.mobilenet_base(input_tensor=img_batch, - num_classes=None, - is_training=False, - depth_multiplier=1.0, - scope='MobilenetV2', - conv_defs=V2_BASE_DEF, - finegrain_classification_mode=False) - - # feature_to_crop = tf.Print(feature_to_crop, [tf.shape(feature_to_crop)], summarize=10, message='rpn_shape') - return feature_to_crop - - -def mobilenetv2_head(inputs, is_training=True): - with slim.arg_scope(mobilenetv2_scope(is_training=is_training, trainable=True)): - net, _ = mobilenet_v2.mobilenet(input_tensor=inputs, - num_classes=None, - is_training=False, - depth_multiplier=1.0, - scope='MobilenetV2', - conv_defs=V2_HEAD_DEF, - finegrain_classification_mode=False) - - net = tf.squeeze(net, [1, 2]) - - return net \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/resnet.py b/example-apps/PDD/pcb-defect-detection/libs/networks/resnet.py deleted file mode 100755 index 4aaa9b9..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/resnet.py +++ /dev/null @@ -1,268 +0,0 @@ -# -*- coding: utf-8 -*- - -from __future__ import absolute_import, print_function, division - - -import tensorflow as tf -import tensorflow.contrib.slim as slim -from libs.configs import cfgs -from tensorflow.contrib.slim.nets import resnet_v1 -from tensorflow.contrib.slim.nets import resnet_utils -from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block -import tfplot as tfp - - -def resnet_arg_scope( - is_training=True, weight_decay=cfgs.WEIGHT_DECAY, batch_norm_decay=0.997, - batch_norm_epsilon=1e-5, batch_norm_scale=True): - ''' - - In Default, we do not use BN to train resnet, since batch_size is too small. - So is_training is False and trainable is False in the batch_norm params. - - ''' - batch_norm_params = { - 'is_training': False, 'decay': batch_norm_decay, - 'epsilon': batch_norm_epsilon, 'scale': batch_norm_scale, - 'trainable': False, - 'updates_collections': tf.GraphKeys.UPDATE_OPS - } - - with slim.arg_scope( - [slim.conv2d], - weights_regularizer=slim.l2_regularizer(weight_decay), - weights_initializer=slim.variance_scaling_initializer(), - trainable=is_training, - activation_fn=tf.nn.relu, - normalizer_fn=slim.batch_norm, - normalizer_params=batch_norm_params): - with slim.arg_scope([slim.batch_norm], **batch_norm_params) as arg_sc: - return arg_sc - - -def fusion_two_layer(C_i, P_j, scope): - ''' - i = j+1 - :param C_i: shape is [1, h, w, c] - :param P_j: shape is [1, h/2, w/2, 256] - :return: - P_i - ''' - with tf.variable_scope(scope): - level_name = scope.split('_')[1] - h, w = tf.shape(C_i)[1], tf.shape(C_i)[2] - upsample_p = tf.image.resize_bilinear(P_j, - size=[h, w], - name='up_sample_'+level_name) - - reduce_dim_c = slim.conv2d(C_i, - num_outputs=256, - kernel_size=[1, 1], stride=1, - scope='reduce_dim_'+level_name) - - add_f = 0.5*upsample_p + 0.5*reduce_dim_c - - # P_i = slim.conv2d(add_f, - # num_outputs=256, kernel_size=[3, 3], stride=1, - # padding='SAME', - # scope='fusion_'+level_name) - return add_f - - -def add_heatmap(feature_maps, name): - ''' - - :param feature_maps:[B, H, W, C] - :return: - ''' - - def figure_attention(activation): - fig, ax = tfp.subplots() - im = ax.imshow(activation, cmap='jet') - fig.colorbar(im) - return fig - - heatmap = tf.reduce_sum(feature_maps, axis=-1) - heatmap = tf.squeeze(heatmap, axis=0) - tfp.summary.plot(name, figure_attention, [heatmap]) - - -def resnet_base(img_batch, scope_name, is_training=True): - ''' - this code is derived from light-head rcnn. - https://github.com/zengarden/light_head_rcnn - - It is convenient to freeze blocks. So we adapt this mode. - ''' - if scope_name == 'resnet_v1_50': - middle_num_units = 6 - elif scope_name == 'resnet_v1_101': - middle_num_units = 23 - else: - raise NotImplementedError('We only support resnet_v1_50 or resnet_v1_101. Check your network name....yjr') - - blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), - resnet_v1_block('block2', base_depth=128, num_units=4, stride=2), - resnet_v1_block('block3', base_depth=256, num_units=middle_num_units, stride=2), - resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)] - # when use fpn . stride list is [1, 2, 2] - - with slim.arg_scope(resnet_arg_scope(is_training=False)): - with tf.variable_scope(scope_name, scope_name): - # Do the first few layers manually, because 'SAME' padding can behave inconsistently - # for images of different sizes: sometimes 0, sometimes 1 - net = resnet_utils.conv2d_same( - img_batch, 64, 7, stride=2, scope='conv1') - net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]]) - net = slim.max_pool2d( - net, [3, 3], stride=2, padding='VALID', scope='pool1') - - not_freezed = [False] * cfgs.FIXED_BLOCKS + (4-cfgs.FIXED_BLOCKS)*[True] - # Fixed_Blocks can be 1~3 - - with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[0]))): - C2, end_points_C2 = resnet_v1.resnet_v1(net, - blocks[0:1], - global_pool=False, - include_root_block=False, - scope=scope_name) - - # C2 = tf.Print(C2, [tf.shape(C2)], summarize=10, message='C2_shape') - add_heatmap(C2, name='Layer2/C2_heat') - - with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[1]))): - C3, end_points_C3 = resnet_v1.resnet_v1(C2, - blocks[1:2], - global_pool=False, - include_root_block=False, - scope=scope_name) - - # C3 = tf.Print(C3, [tf.shape(C3)], summarize=10, message='C3_shape') - add_heatmap(C3, name='Layer3/C3_heat') - with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[2]))): - C4, end_points_C4 = resnet_v1.resnet_v1(C3, - blocks[2:3], - global_pool=False, - include_root_block=False, - scope=scope_name) - - add_heatmap(C4, name='Layer4/C4_heat') - - # C4 = tf.Print(C4, [tf.shape(C4)], summarize=10, message='C4_shape') - with slim.arg_scope(resnet_arg_scope(is_training=is_training)): - C5, end_points_C5 = resnet_v1.resnet_v1(C4, - blocks[3:4], - global_pool=False, - include_root_block=False, - scope=scope_name) - # C5 = tf.Print(C5, [tf.shape(C5)], summarize=10, message='C5_shape') - add_heatmap(C5, name='Layer5/C5_heat') - - feature_dict = {'C2': end_points_C2['{}/block1/unit_2/bottleneck_v1'.format(scope_name)], - 'C3': end_points_C3['{}/block2/unit_3/bottleneck_v1'.format(scope_name)], - 'C4': end_points_C4['{}/block3/unit_{}/bottleneck_v1'.format(scope_name, middle_num_units - 1)], - 'C5': end_points_C5['{}/block4/unit_3/bottleneck_v1'.format(scope_name)], - # 'C5': end_points_C5['{}/block4'.format(scope_name)], - } - - # feature_dict = {'C2': C2, - # 'C3': C3, - # 'C4': C4, - # 'C5': C5} - - pyramid_dict = {} - with tf.variable_scope('build_pyramid'): - with slim.arg_scope([slim.conv2d], weights_regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY), - activation_fn=None, normalizer_fn=None): - - P5 = slim.conv2d(C5, - num_outputs=256, - kernel_size=[1, 1], - stride=1, scope='build_P5') - if "P6" in cfgs.LEVLES: - P6 = slim.max_pool2d(P5, kernel_size=[1, 1], stride=2, scope='build_P6') - pyramid_dict['P6'] = P6 - - pyramid_dict['P5'] = P5 - - for level in range(4, 1, -1): # build [P4, P3, P2] - - pyramid_dict['P%d' % level] = fusion_two_layer(C_i=feature_dict["C%d" % level], - P_j=pyramid_dict["P%d" % (level+1)], - scope='build_P%d' % level) - for level in range(4, 1, -1): - pyramid_dict['P%d' % level] = slim.conv2d(pyramid_dict['P%d' % level], - num_outputs=256, kernel_size=[3, 3], padding="SAME", - stride=1, scope="fuse_P%d" % level) - for level in range(5, 1, -1): - add_heatmap(pyramid_dict['P%d' % level], name='Layer%d/P%d_heat' % (level, level)) - - # return [P2, P3, P4, P5, P6] - print("we are in Pyramid::-======>>>>") - print(cfgs.LEVLES) - print("base_anchor_size are: ", cfgs.BASE_ANCHOR_SIZE_LIST) - print(20 * "__") - return [pyramid_dict[level_name] for level_name in cfgs.LEVLES] - # return pyramid_dict # return the dict. And get each level by key. But ensure the levels are consitant - # return list rather than dict, to avoid dict is unordered - - - -def restnet_head(inputs, is_training, scope_name): - ''' - - :param inputs: [minibatch_size, 7, 7, 256] - :param is_training: - :param scope_name: - :return: - ''' - - with tf.variable_scope('build_fc_layers'): - - # fc1 = slim.conv2d(inputs=inputs, - # num_outputs=1024, - # kernel_size=[7, 7], - # padding='VALID', - # scope='fc1') # shape is [minibatch_size, 1, 1, 1024] - # fc1 = tf.squeeze(fc1, [1, 2], name='squeeze_fc1') - - inputs = slim.flatten(inputs=inputs, scope='flatten_inputs') - - fc1 = slim.fully_connected(inputs, num_outputs=1024, scope='fc1') - - fc2 = slim.fully_connected(fc1, num_outputs=1024, scope='fc2') - - # fc3 = slim.fully_connected(fc2, num_outputs=1024, scope='fc3') - - # we add fc3 to increase the ability of fast-rcnn head - return fc2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/__init__.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/__init__.py deleted file mode 100755 index 8b13789..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/alexnet.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/alexnet.py deleted file mode 100755 index 4e7e563..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/alexnet.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains a model definition for AlexNet. - -This work was first described in: - ImageNet Classification with Deep Convolutional Neural Networks - Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton - -and later refined in: - One weird trick for parallelizing convolutional neural networks - Alex Krizhevsky, 2014 - -Here we provide the implementation proposed in "One weird trick" and not -"ImageNet Classification", as per the paper, the LRN layers have been removed. - -Usage: - with slim.arg_scope(alexnet.alexnet_v2_arg_scope()): - outputs, end_points = alexnet.alexnet_v2(inputs) - -@@alexnet_v2 -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -slim = tf.contrib.slim -trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) - - -def alexnet_v2_arg_scope(weight_decay=0.0005): - with slim.arg_scope([slim.conv2d, slim.fully_connected], - activation_fn=tf.nn.relu, - biases_initializer=tf.constant_initializer(0.1), - weights_regularizer=slim.l2_regularizer(weight_decay)): - with slim.arg_scope([slim.conv2d], padding='SAME'): - with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc: - return arg_sc - - -def alexnet_v2(inputs, - num_classes=1000, - is_training=True, - dropout_keep_prob=0.5, - spatial_squeeze=True, - scope='alexnet_v2'): - """AlexNet version 2. - - Described in: http://arxiv.org/pdf/1404.5997v2.pdf - Parameters from: - github.com/akrizhevsky/cuda-convnet2/blob/master/layers/ - layers-imagenet-1gpu.cfg - - Note: All the fully_connected layers have been transformed to conv2d layers. - To use in classification mode, resize input to 224x224. To use in fully - convolutional mode, set spatial_squeeze to false. - The LRN layers have been removed and change the initializers from - random_normal_initializer to xavier_initializer. - - Args: - inputs: a tensor of size [batch_size, height, width, channels]. - num_classes: number of predicted classes. - is_training: whether or not the model is being trained. - dropout_keep_prob: the probability that activations are kept in the dropout - layers during training. - spatial_squeeze: whether or not should squeeze the spatial dimensions of the - outputs. Useful to remove unnecessary dimensions for classification. - scope: Optional scope for the variables. - - Returns: - the last op containing the log predictions and end_points dict. - """ - with tf.variable_scope(scope, 'alexnet_v2', [inputs]) as sc: - end_points_collection = sc.name + '_end_points' - # Collect outputs for conv2d, fully_connected and max_pool2d. - with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], - outputs_collections=[end_points_collection]): - net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID', - scope='conv1') - net = slim.max_pool2d(net, [3, 3], 2, scope='pool1') - net = slim.conv2d(net, 192, [5, 5], scope='conv2') - net = slim.max_pool2d(net, [3, 3], 2, scope='pool2') - net = slim.conv2d(net, 384, [3, 3], scope='conv3') - net = slim.conv2d(net, 384, [3, 3], scope='conv4') - net = slim.conv2d(net, 256, [3, 3], scope='conv5') - net = slim.max_pool2d(net, [3, 3], 2, scope='pool5') - - # Use conv2d instead of fully_connected layers. - with slim.arg_scope([slim.conv2d], - weights_initializer=trunc_normal(0.005), - biases_initializer=tf.constant_initializer(0.1)): - net = slim.conv2d(net, 4096, [5, 5], padding='VALID', - scope='fc6') - net = slim.dropout(net, dropout_keep_prob, is_training=is_training, - scope='dropout6') - net = slim.conv2d(net, 4096, [1, 1], scope='fc7') - net = slim.dropout(net, dropout_keep_prob, is_training=is_training, - scope='dropout7') - net = slim.conv2d(net, num_classes, [1, 1], - activation_fn=None, - normalizer_fn=None, - biases_initializer=tf.zeros_initializer(), - scope='fc8') - - # Convert end_points_collection into a end_point dict. - end_points = slim.utils.convert_collection_to_dict(end_points_collection) - if spatial_squeeze: - net = tf.squeeze(net, [1, 2], name='fc8/squeezed') - end_points[sc.name + '/fc8'] = net - return net, end_points -alexnet_v2.default_image_size = 224 diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/alexnet_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/alexnet_test.py deleted file mode 100755 index 6fc9a05..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/alexnet_test.py +++ /dev/null @@ -1,145 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for slim.slim_nets.alexnet.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from nets import alexnet - -slim = tf.contrib.slim - - -class AlexnetV2Test(tf.test.TestCase): - - def testBuild(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = alexnet.alexnet_v2(inputs, num_classes) - self.assertEquals(logits.op.name, 'alexnet_v2/fc8/squeezed') - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - - def testFullyConvolutional(self): - batch_size = 1 - height, width = 300, 400 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = alexnet.alexnet_v2(inputs, num_classes, spatial_squeeze=False) - self.assertEquals(logits.op.name, 'alexnet_v2/fc8/BiasAdd') - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, 4, 7, num_classes]) - - def testEndPoints(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = alexnet.alexnet_v2(inputs, num_classes) - expected_names = ['alexnet_v2/conv1', - 'alexnet_v2/pool1', - 'alexnet_v2/conv2', - 'alexnet_v2/pool2', - 'alexnet_v2/conv3', - 'alexnet_v2/conv4', - 'alexnet_v2/conv5', - 'alexnet_v2/pool5', - 'alexnet_v2/fc6', - 'alexnet_v2/fc7', - 'alexnet_v2/fc8' - ] - self.assertSetEqual(set(end_points.keys()), set(expected_names)) - - def testModelVariables(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - alexnet.alexnet_v2(inputs, num_classes) - expected_names = ['alexnet_v2/conv1/weights', - 'alexnet_v2/conv1/biases', - 'alexnet_v2/conv2/weights', - 'alexnet_v2/conv2/biases', - 'alexnet_v2/conv3/weights', - 'alexnet_v2/conv3/biases', - 'alexnet_v2/conv4/weights', - 'alexnet_v2/conv4/biases', - 'alexnet_v2/conv5/weights', - 'alexnet_v2/conv5/biases', - 'alexnet_v2/fc6/weights', - 'alexnet_v2/fc6/biases', - 'alexnet_v2/fc7/weights', - 'alexnet_v2/fc7/biases', - 'alexnet_v2/fc8/weights', - 'alexnet_v2/fc8/biases', - ] - model_variables = [v.op.name for v in slim.get_model_variables()] - self.assertSetEqual(set(model_variables), set(expected_names)) - - def testEvaluation(self): - batch_size = 2 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - eval_inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = alexnet.alexnet_v2(eval_inputs, is_training=False) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - predictions = tf.argmax(logits, 1) - self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) - - def testTrainEvalWithReuse(self): - train_batch_size = 2 - eval_batch_size = 1 - train_height, train_width = 224, 224 - eval_height, eval_width = 300, 400 - num_classes = 1000 - with self.test_session(): - train_inputs = tf.random_uniform( - (train_batch_size, train_height, train_width, 3)) - logits, _ = alexnet.alexnet_v2(train_inputs) - self.assertListEqual(logits.get_shape().as_list(), - [train_batch_size, num_classes]) - tf.get_variable_scope().reuse_variables() - eval_inputs = tf.random_uniform( - (eval_batch_size, eval_height, eval_width, 3)) - logits, _ = alexnet.alexnet_v2(eval_inputs, is_training=False, - spatial_squeeze=False) - self.assertListEqual(logits.get_shape().as_list(), - [eval_batch_size, 4, 7, num_classes]) - logits = tf.reduce_mean(logits, [1, 2]) - predictions = tf.argmax(logits, 1) - self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) - - def testForward(self): - batch_size = 1 - height, width = 224, 224 - with self.test_session() as sess: - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = alexnet.alexnet_v2(inputs) - sess.run(tf.global_variables_initializer()) - output = sess.run(logits) - self.assertTrue(output.any()) - -if __name__ == '__main__': - tf.test.main() diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/cifarnet.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/cifarnet.py deleted file mode 100755 index 44ca0fe..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/cifarnet.py +++ /dev/null @@ -1,112 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains a variant of the CIFAR-10 model definition.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -slim = tf.contrib.slim - -trunc_normal = lambda stddev: tf.truncated_normal_initializer(stddev=stddev) - - -def cifarnet(images, num_classes=10, is_training=False, - dropout_keep_prob=0.5, - prediction_fn=slim.softmax, - scope='CifarNet'): - """Creates a variant of the CifarNet model. - - Note that since the output is a set of 'logits', the values fall in the - interval of (-infinity, infinity). Consequently, to convert the outputs to a - probability distribution over the characters, one will need to convert them - using the softmax function: - - logits = cifarnet.cifarnet(images, is_training=False) - probabilities = tf.nn.softmax(logits) - predictions = tf.argmax(logits, 1) - - Args: - images: A batch of `Tensors` of size [batch_size, height, width, channels]. - num_classes: the number of classes in the dataset. - is_training: specifies whether or not we're currently training the model. - This variable will determine the behaviour of the dropout layer. - dropout_keep_prob: the percentage of activation values that are retained. - prediction_fn: a function to get predictions out of logits. - scope: Optional variable_scope. - - Returns: - logits: the pre-softmax activations, a tensor of size - [batch_size, `num_classes`] - end_points: a dictionary from components of the network to the corresponding - activation. - """ - end_points = {} - - with tf.variable_scope(scope, 'CifarNet', [images, num_classes]): - net = slim.conv2d(images, 64, [5, 5], scope='conv1') - end_points['conv1'] = net - net = slim.max_pool2d(net, [2, 2], 2, scope='pool1') - end_points['pool1'] = net - net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm1') - net = slim.conv2d(net, 64, [5, 5], scope='conv2') - end_points['conv2'] = net - net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm2') - net = slim.max_pool2d(net, [2, 2], 2, scope='pool2') - end_points['pool2'] = net - net = slim.flatten(net) - end_points['Flatten'] = net - net = slim.fully_connected(net, 384, scope='fc3') - end_points['fc3'] = net - net = slim.dropout(net, dropout_keep_prob, is_training=is_training, - scope='dropout3') - net = slim.fully_connected(net, 192, scope='fc4') - end_points['fc4'] = net - logits = slim.fully_connected(net, num_classes, - biases_initializer=tf.zeros_initializer(), - weights_initializer=trunc_normal(1/192.0), - weights_regularizer=None, - activation_fn=None, - scope='logits') - - end_points['Logits'] = logits - end_points['Predictions'] = prediction_fn(logits, scope='Predictions') - - return logits, end_points -cifarnet.default_image_size = 32 - - -def cifarnet_arg_scope(weight_decay=0.004): - """Defines the default cifarnet argument scope. - - Args: - weight_decay: The weight decay to use for regularizing the model. - - Returns: - An `arg_scope` to use for the inception v3 model. - """ - with slim.arg_scope( - [slim.conv2d], - weights_initializer=tf.truncated_normal_initializer(stddev=5e-2), - activation_fn=tf.nn.relu): - with slim.arg_scope( - [slim.fully_connected], - biases_initializer=tf.constant_initializer(0.1), - weights_initializer=trunc_normal(0.04), - weights_regularizer=slim.l2_regularizer(weight_decay), - activation_fn=tf.nn.relu) as sc: - return sc diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception.py deleted file mode 100755 index b69cd2a..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception.py +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Brings all inception models under one namespace.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -# pylint: disable=unused-import -from nets.inception_resnet_v2 import inception_resnet_v2 -from nets.inception_resnet_v2 import inception_resnet_v2_arg_scope -from nets.inception_resnet_v2 import inception_resnet_v2_base -from nets.inception_v1 import inception_v1 -from nets.inception_v1 import inception_v1_arg_scope -from nets.inception_v1 import inception_v1_base -from nets.inception_v2 import inception_v2 -from nets.inception_v2 import inception_v2_arg_scope -from nets.inception_v2 import inception_v2_base -from nets.inception_v3 import inception_v3 -from nets.inception_v3 import inception_v3_arg_scope -from nets.inception_v3 import inception_v3_base -from nets.inception_v4 import inception_v4 -from nets.inception_v4 import inception_v4_arg_scope -from nets.inception_v4 import inception_v4_base -# pylint: enable=unused-import diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_resnet_v2.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_resnet_v2.py deleted file mode 100755 index ddd9392..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_resnet_v2.py +++ /dev/null @@ -1,359 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains the definition of the Inception Resnet V2 architecture. - -As described in http://arxiv.org/abs/1602.07261. - - Inception-v4, Inception-ResNet and the Impact of Residual Connections - on Learning - Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - - -import tensorflow as tf - -slim = tf.contrib.slim - - -def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None): - """Builds the 35x35 resnet block.""" - with tf.variable_scope(scope, 'Block35', [net], reuse=reuse): - with tf.variable_scope('Branch_0'): - tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1') - with tf.variable_scope('Branch_1'): - tower_conv1_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1') - tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - tower_conv2_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1') - tower_conv2_1 = slim.conv2d(tower_conv2_0, 48, 3, scope='Conv2d_0b_3x3') - tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope='Conv2d_0c_3x3') - mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_1, tower_conv2_2]) - up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None, - activation_fn=None, scope='Conv2d_1x1') - net += scale * up - if activation_fn: - net = activation_fn(net) - return net - - -def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None): - """Builds the 17x17 resnet block.""" - with tf.variable_scope(scope, 'Block17', [net], reuse=reuse): - with tf.variable_scope('Branch_0'): - tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1') - with tf.variable_scope('Branch_1'): - tower_conv1_0 = slim.conv2d(net, 128, 1, scope='Conv2d_0a_1x1') - tower_conv1_1 = slim.conv2d(tower_conv1_0, 160, [1, 7], - scope='Conv2d_0b_1x7') - tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [7, 1], - scope='Conv2d_0c_7x1') - mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2]) - up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None, - activation_fn=None, scope='Conv2d_1x1') - net += scale * up - if activation_fn: - net = activation_fn(net) - return net - - -def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None): - """Builds the 8x8 resnet block.""" - with tf.variable_scope(scope, 'Block8', [net], reuse=reuse): - with tf.variable_scope('Branch_0'): - tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1') - with tf.variable_scope('Branch_1'): - tower_conv1_0 = slim.conv2d(net, 192, 1, scope='Conv2d_0a_1x1') - tower_conv1_1 = slim.conv2d(tower_conv1_0, 224, [1, 3], - scope='Conv2d_0b_1x3') - tower_conv1_2 = slim.conv2d(tower_conv1_1, 256, [3, 1], - scope='Conv2d_0c_3x1') - mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2]) - up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None, - activation_fn=None, scope='Conv2d_1x1') - net += scale * up - if activation_fn: - net = activation_fn(net) - return net - - -def inception_resnet_v2_base(inputs, - final_endpoint='Conv2d_7b_1x1', - output_stride=16, - align_feature_maps=False, - scope=None): - """Inception model from http://arxiv.org/abs/1602.07261. - - Constructs an Inception Resnet v2 network from inputs to the given final - endpoint. This method can construct the network up to the final inception - block Conv2d_7b_1x1. - - Args: - inputs: a tensor of size [batch_size, height, width, channels]. - final_endpoint: specifies the endpoint to construct the network up to. It - can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', - 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3', - 'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1'] - output_stride: A scalar that specifies the requested ratio of input to - output spatial resolution. Only supports 8 and 16. - align_feature_maps: When true, changes all the VALID paddings in the network - to SAME padding so that the feature maps are aligned. - scope: Optional variable_scope. - - Returns: - tensor_out: output tensor corresponding to the final_endpoint. - end_points: a set of activations for external use, for example summaries or - losses. - - Raises: - ValueError: if final_endpoint is not set to one of the predefined values, - or if the output_stride is not 8 or 16, or if the output_stride is 8 and - we request an end point after 'PreAuxLogits'. - """ - if output_stride != 8 and output_stride != 16: - raise ValueError('output_stride must be 8 or 16.') - - padding = 'SAME' if align_feature_maps else 'VALID' - - end_points = {} - - def add_and_check_final(name, net): - end_points[name] = net - return name == final_endpoint - - with tf.variable_scope(scope, 'InceptionResnetV2', [inputs]): - with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], - stride=1, padding='SAME'): - # 149 x 149 x 32 - net = slim.conv2d(inputs, 32, 3, stride=2, padding=padding, - scope='Conv2d_1a_3x3') - if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points - - # 147 x 147 x 32 - net = slim.conv2d(net, 32, 3, padding=padding, - scope='Conv2d_2a_3x3') - if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points - # 147 x 147 x 64 - net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3') - if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points - # 73 x 73 x 64 - net = slim.max_pool2d(net, 3, stride=2, padding=padding, - scope='MaxPool_3a_3x3') - if add_and_check_final('MaxPool_3a_3x3', net): return net, end_points - # 73 x 73 x 80 - net = slim.conv2d(net, 80, 1, padding=padding, - scope='Conv2d_3b_1x1') - if add_and_check_final('Conv2d_3b_1x1', net): return net, end_points - # 71 x 71 x 192 - net = slim.conv2d(net, 192, 3, padding=padding, - scope='Conv2d_4a_3x3') - if add_and_check_final('Conv2d_4a_3x3', net): return net, end_points - # 35 x 35 x 192 - net = slim.max_pool2d(net, 3, stride=2, padding=padding, - scope='MaxPool_5a_3x3') - if add_and_check_final('MaxPool_5a_3x3', net): return net, end_points - - # 35 x 35 x 320 - with tf.variable_scope('Mixed_5b'): - with tf.variable_scope('Branch_0'): - tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1') - with tf.variable_scope('Branch_1'): - tower_conv1_0 = slim.conv2d(net, 48, 1, scope='Conv2d_0a_1x1') - tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5, - scope='Conv2d_0b_5x5') - with tf.variable_scope('Branch_2'): - tower_conv2_0 = slim.conv2d(net, 64, 1, scope='Conv2d_0a_1x1') - tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3, - scope='Conv2d_0b_3x3') - tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3, - scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - tower_pool = slim.avg_pool2d(net, 3, stride=1, padding='SAME', - scope='AvgPool_0a_3x3') - tower_pool_1 = slim.conv2d(tower_pool, 64, 1, - scope='Conv2d_0b_1x1') - net = tf.concat( - [tower_conv, tower_conv1_1, tower_conv2_2, tower_pool_1], 3) - - if add_and_check_final('Mixed_5b', net): return net, end_points - # TODO(alemi): Register intermediate endpoints - net = slim.repeat(net, 10, block35, scale=0.17) - - # 17 x 17 x 1088 if output_stride == 8, - # 33 x 33 x 1088 if output_stride == 16 - use_atrous = output_stride == 8 - - with tf.variable_scope('Mixed_6a'): - with tf.variable_scope('Branch_0'): - tower_conv = slim.conv2d(net, 384, 3, stride=1 if use_atrous else 2, - padding=padding, - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - tower_conv1_0 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1') - tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3, - scope='Conv2d_0b_3x3') - tower_conv1_2 = slim.conv2d(tower_conv1_1, 384, 3, - stride=1 if use_atrous else 2, - padding=padding, - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_2'): - tower_pool = slim.max_pool2d(net, 3, stride=1 if use_atrous else 2, - padding=padding, - scope='MaxPool_1a_3x3') - net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3) - - if add_and_check_final('Mixed_6a', net): return net, end_points - - # TODO(alemi): register intermediate endpoints - with slim.arg_scope([slim.conv2d], rate=2 if use_atrous else 1): - net = slim.repeat(net, 20, block17, scale=0.10) - if add_and_check_final('PreAuxLogits', net): return net, end_points - - if output_stride == 8: - # TODO(gpapan): Properly support output_stride for the rest of the net. - raise ValueError('output_stride==8 is only supported up to the ' - 'PreAuxlogits end_point for now.') - - # 8 x 8 x 2080 - with tf.variable_scope('Mixed_7a'): - with tf.variable_scope('Branch_0'): - tower_conv = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1') - tower_conv_1 = slim.conv2d(tower_conv, 384, 3, stride=2, - padding=padding, - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - tower_conv1 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1') - tower_conv1_1 = slim.conv2d(tower_conv1, 288, 3, stride=2, - padding=padding, - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_2'): - tower_conv2 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1') - tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3, - scope='Conv2d_0b_3x3') - tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 3, stride=2, - padding=padding, - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_3'): - tower_pool = slim.max_pool2d(net, 3, stride=2, - padding=padding, - scope='MaxPool_1a_3x3') - net = tf.concat( - [tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3) - - if add_and_check_final('Mixed_7a', net): return net, end_points - - # TODO(alemi): register intermediate endpoints - net = slim.repeat(net, 9, block8, scale=0.20) - net = block8(net, activation_fn=None) - - # 8 x 8 x 1536 - net = slim.conv2d(net, 1536, 1, scope='Conv2d_7b_1x1') - if add_and_check_final('Conv2d_7b_1x1', net): return net, end_points - - raise ValueError('final_endpoint (%s) not recognized', final_endpoint) - - -def inception_resnet_v2(inputs, num_classes=1001, is_training=True, - dropout_keep_prob=0.8,#0.8 - reuse=None, - scope='InceptionResnetV2', - create_aux_logits=True): - """Creates the Inception Resnet V2 model. - - Args: - inputs: a 4-D tensor of size [batch_size, height, width, 3]. - num_classes: number of predicted classes. - is_training: whether is training or not. - dropout_keep_prob: float, the fraction to keep before final layer. - reuse: whether or not the network and its variables should be reused. To be - able to reuse 'scope' must be given. - scope: Optional variable_scope. - create_aux_logits: Whether to include the auxilliary logits. - - Returns: - logits: the logits outputs of the model. - end_points: the set of end_points from the inception model. - """ - end_points = {} - - with tf.variable_scope(scope, 'InceptionResnetV2', [inputs, num_classes], - reuse=reuse) as scope: - with slim.arg_scope([slim.batch_norm, slim.dropout], - is_training=is_training): - - net, end_points = inception_resnet_v2_base(inputs, scope=scope) - - if create_aux_logits: - with tf.variable_scope('AuxLogits'): - aux = end_points['PreAuxLogits'] - aux = slim.avg_pool2d(aux, 5, stride=3, padding='VALID', - scope='Conv2d_1a_3x3') - aux = slim.conv2d(aux, 128, 1, scope='Conv2d_1b_1x1') - aux = slim.conv2d(aux, 768, aux.get_shape()[1:3], - padding='VALID', scope='Conv2d_2a_5x5') - aux = slim.flatten(aux) - aux = slim.fully_connected(aux, num_classes, activation_fn=None, - scope='Logits') - end_points['AuxLogits'] = aux - - with tf.variable_scope('Logits'): - net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID', - scope='AvgPool_1a_8x8') - net = slim.flatten(net) - - net = slim.dropout(net, dropout_keep_prob, is_training=is_training, - scope='Dropout') - - end_points['PreLogitsFlatten'] = net - # end_points['yjr_feature'] = tf.squeeze(net, axis=0) - - logits = slim.fully_connected(net, num_classes, activation_fn=None, - scope='Logits') - end_points['Logits'] = logits - end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions') - - return logits, end_points -inception_resnet_v2.default_image_size = 299 - - -def inception_resnet_v2_arg_scope(weight_decay=0.00004, - batch_norm_decay=0.9997, - batch_norm_epsilon=0.001): - """Yields the scope with the default parameters for inception_resnet_v2. - - Args: - weight_decay: the weight decay for weights variables. - batch_norm_decay: decay for the moving average of batch_norm momentums. - batch_norm_epsilon: small float added to variance to avoid dividing by zero. - - Returns: - a arg_scope with the parameters needed for inception_resnet_v2. - """ - # Set weight_decay for weights in conv2d and fully_connected layers. - with slim.arg_scope([slim.conv2d, slim.fully_connected], - weights_regularizer=slim.l2_regularizer(weight_decay), - biases_regularizer=slim.l2_regularizer(weight_decay)): - - batch_norm_params = { - 'decay': batch_norm_decay, - 'epsilon': batch_norm_epsilon, - } - # Set activation_fn and parameters for batch_norm. - with slim.arg_scope([slim.conv2d], activation_fn=tf.nn.relu, - normalizer_fn=slim.batch_norm, - normalizer_params=batch_norm_params) as scope: - return scope diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_resnet_v2_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_resnet_v2_test.py deleted file mode 100755 index c369ed9..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_resnet_v2_test.py +++ /dev/null @@ -1,265 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for slim.inception_resnet_v2.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from nets import inception - - -class InceptionTest(tf.test.TestCase): - - def testBuildLogits(self): - batch_size = 5 - height, width = 299, 299 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, endpoints = inception.inception_resnet_v2(inputs, num_classes) - self.assertTrue('AuxLogits' in endpoints) - auxlogits = endpoints['AuxLogits'] - self.assertTrue( - auxlogits.op.name.startswith('InceptionResnetV2/AuxLogits')) - self.assertListEqual(auxlogits.get_shape().as_list(), - [batch_size, num_classes]) - self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - - def testBuildWithoutAuxLogits(self): - batch_size = 5 - height, width = 299, 299 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, endpoints = inception.inception_resnet_v2(inputs, num_classes, - create_aux_logits=False) - self.assertTrue('AuxLogits' not in endpoints) - self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - - def testBuildEndPoints(self): - batch_size = 5 - height, width = 299, 299 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_resnet_v2(inputs, num_classes) - self.assertTrue('Logits' in end_points) - logits = end_points['Logits'] - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - self.assertTrue('AuxLogits' in end_points) - aux_logits = end_points['AuxLogits'] - self.assertListEqual(aux_logits.get_shape().as_list(), - [batch_size, num_classes]) - pre_pool = end_points['Conv2d_7b_1x1'] - self.assertListEqual(pre_pool.get_shape().as_list(), - [batch_size, 8, 8, 1536]) - - def testBuildBaseNetwork(self): - batch_size = 5 - height, width = 299, 299 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - net, end_points = inception.inception_resnet_v2_base(inputs) - self.assertTrue(net.op.name.startswith('InceptionResnetV2/Conv2d_7b_1x1')) - self.assertListEqual(net.get_shape().as_list(), - [batch_size, 8, 8, 1536]) - expected_endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', - 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', - 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_6a', - 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1'] - self.assertItemsEqual(end_points.keys(), expected_endpoints) - - def testBuildOnlyUptoFinalEndpoint(self): - batch_size = 5 - height, width = 299, 299 - endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', - 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', - 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_6a', - 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1'] - for index, endpoint in enumerate(endpoints): - with tf.Graph().as_default(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - out_tensor, end_points = inception.inception_resnet_v2_base( - inputs, final_endpoint=endpoint) - if endpoint != 'PreAuxLogits': - self.assertTrue(out_tensor.op.name.startswith( - 'InceptionResnetV2/' + endpoint)) - self.assertItemsEqual(endpoints[:index+1], end_points) - - def testBuildAndCheckAllEndPointsUptoPreAuxLogits(self): - batch_size = 5 - height, width = 299, 299 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_resnet_v2_base( - inputs, final_endpoint='PreAuxLogits') - endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32], - 'Conv2d_2a_3x3': [5, 147, 147, 32], - 'Conv2d_2b_3x3': [5, 147, 147, 64], - 'MaxPool_3a_3x3': [5, 73, 73, 64], - 'Conv2d_3b_1x1': [5, 73, 73, 80], - 'Conv2d_4a_3x3': [5, 71, 71, 192], - 'MaxPool_5a_3x3': [5, 35, 35, 192], - 'Mixed_5b': [5, 35, 35, 320], - 'Mixed_6a': [5, 17, 17, 1088], - 'PreAuxLogits': [5, 17, 17, 1088] - } - - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name in endpoints_shapes: - expected_shape = endpoints_shapes[endpoint_name] - self.assertTrue(endpoint_name in end_points) - self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), - expected_shape) - - def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithAlignedFeatureMaps(self): - batch_size = 5 - height, width = 299, 299 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_resnet_v2_base( - inputs, final_endpoint='PreAuxLogits', align_feature_maps=True) - endpoints_shapes = {'Conv2d_1a_3x3': [5, 150, 150, 32], - 'Conv2d_2a_3x3': [5, 150, 150, 32], - 'Conv2d_2b_3x3': [5, 150, 150, 64], - 'MaxPool_3a_3x3': [5, 75, 75, 64], - 'Conv2d_3b_1x1': [5, 75, 75, 80], - 'Conv2d_4a_3x3': [5, 75, 75, 192], - 'MaxPool_5a_3x3': [5, 38, 38, 192], - 'Mixed_5b': [5, 38, 38, 320], - 'Mixed_6a': [5, 19, 19, 1088], - 'PreAuxLogits': [5, 19, 19, 1088] - } - - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name in endpoints_shapes: - expected_shape = endpoints_shapes[endpoint_name] - self.assertTrue(endpoint_name in end_points) - self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), - expected_shape) - - def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithOutputStrideEight(self): - batch_size = 5 - height, width = 299, 299 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_resnet_v2_base( - inputs, final_endpoint='PreAuxLogits', output_stride=8) - endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32], - 'Conv2d_2a_3x3': [5, 147, 147, 32], - 'Conv2d_2b_3x3': [5, 147, 147, 64], - 'MaxPool_3a_3x3': [5, 73, 73, 64], - 'Conv2d_3b_1x1': [5, 73, 73, 80], - 'Conv2d_4a_3x3': [5, 71, 71, 192], - 'MaxPool_5a_3x3': [5, 35, 35, 192], - 'Mixed_5b': [5, 35, 35, 320], - 'Mixed_6a': [5, 33, 33, 1088], - 'PreAuxLogits': [5, 33, 33, 1088] - } - - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name in endpoints_shapes: - expected_shape = endpoints_shapes[endpoint_name] - self.assertTrue(endpoint_name in end_points) - self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), - expected_shape) - - def testVariablesSetDevice(self): - batch_size = 5 - height, width = 299, 299 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - # Force all Variables to reside on the device. - with tf.variable_scope('on_cpu'), tf.device('/cpu:0'): - inception.inception_resnet_v2(inputs, num_classes) - with tf.variable_scope('on_gpu'), tf.device('/gpu:0'): - inception.inception_resnet_v2(inputs, num_classes) - for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'): - self.assertDeviceEqual(v.device, '/cpu:0') - for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'): - self.assertDeviceEqual(v.device, '/gpu:0') - - def testHalfSizeImages(self): - batch_size = 5 - height, width = 150, 150 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, end_points = inception.inception_resnet_v2(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - pre_pool = end_points['Conv2d_7b_1x1'] - self.assertListEqual(pre_pool.get_shape().as_list(), - [batch_size, 3, 3, 1536]) - - def testUnknownBatchSize(self): - batch_size = 1 - height, width = 299, 299 - num_classes = 1000 - with self.test_session() as sess: - inputs = tf.placeholder(tf.float32, (None, height, width, 3)) - logits, _ = inception.inception_resnet_v2(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [None, num_classes]) - images = tf.random_uniform((batch_size, height, width, 3)) - sess.run(tf.global_variables_initializer()) - output = sess.run(logits, {inputs: images.eval()}) - self.assertEquals(output.shape, (batch_size, num_classes)) - - def testEvaluation(self): - batch_size = 2 - height, width = 299, 299 - num_classes = 1000 - with self.test_session() as sess: - eval_inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = inception.inception_resnet_v2(eval_inputs, - num_classes, - is_training=False) - predictions = tf.argmax(logits, 1) - sess.run(tf.global_variables_initializer()) - output = sess.run(predictions) - self.assertEquals(output.shape, (batch_size,)) - - def testTrainEvalWithReuse(self): - train_batch_size = 5 - eval_batch_size = 2 - height, width = 150, 150 - num_classes = 1000 - with self.test_session() as sess: - train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) - inception.inception_resnet_v2(train_inputs, num_classes) - eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) - logits, _ = inception.inception_resnet_v2(eval_inputs, - num_classes, - is_training=False, - reuse=True) - predictions = tf.argmax(logits, 1) - sess.run(tf.global_variables_initializer()) - output = sess.run(predictions) - self.assertEquals(output.shape, (eval_batch_size,)) - - -if __name__ == '__main__': - tf.test.main() diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_utils.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_utils.py deleted file mode 100755 index 66ee41f..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_utils.py +++ /dev/null @@ -1,71 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains common code shared by all inception models. - -Usage of arg scope: - with slim.arg_scope(inception_arg_scope()): - logits, end_points = inception.inception_v3(images, num_classes, - is_training=is_training) - -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -slim = tf.contrib.slim - - -def inception_arg_scope(weight_decay=0.00004, - use_batch_norm=True, - batch_norm_decay=0.9997, - batch_norm_epsilon=0.001): - """Defines the default arg scope for inception models. - - Args: - weight_decay: The weight decay to use for regularizing the model. - use_batch_norm: "If `True`, batch_norm is applied after each convolution. - batch_norm_decay: Decay for batch norm moving average. - batch_norm_epsilon: Small float added to variance to avoid dividing by zero - in batch norm. - - Returns: - An `arg_scope` to use for the inception models. - """ - batch_norm_params = { - # Decay for the moving averages. - 'decay': batch_norm_decay, - # epsilon to prevent 0s in variance. - 'epsilon': batch_norm_epsilon, - # collection containing update_ops. - 'updates_collections': tf.GraphKeys.UPDATE_OPS, - } - if use_batch_norm: - normalizer_fn = slim.batch_norm - normalizer_params = batch_norm_params - else: - normalizer_fn = None - normalizer_params = {} - # Set weight_decay for weights in Conv and FC layers. - with slim.arg_scope([slim.conv2d, slim.fully_connected], - weights_regularizer=slim.l2_regularizer(weight_decay)): - with slim.arg_scope( - [slim.conv2d], - weights_initializer=slim.variance_scaling_initializer(), - activation_fn=tf.nn.relu, - normalizer_fn=normalizer_fn, - normalizer_params=normalizer_params) as sc: - return sc diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v1.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v1.py deleted file mode 100755 index 4207c2a..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v1.py +++ /dev/null @@ -1,305 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains the definition for inception v1 classification network.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from nets import inception_utils - -slim = tf.contrib.slim -trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) - - -def inception_v1_base(inputs, - final_endpoint='Mixed_5c', - scope='InceptionV1'): - """Defines the Inception V1 base architecture. - - This architecture is defined in: - Going deeper with convolutions - Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, - Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich. - http://arxiv.org/pdf/1409.4842v1.pdf. - - Args: - inputs: a tensor of size [batch_size, height, width, channels]. - final_endpoint: specifies the endpoint to construct the network up to. It - can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', - 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', - 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', - 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c'] - scope: Optional variable_scope. - - Returns: - A dictionary from components of the network to the corresponding activation. - - Raises: - ValueError: if final_endpoint is not set to one of the predefined values. - """ - end_points = {} - with tf.variable_scope(scope, 'InceptionV1', [inputs]): - with slim.arg_scope( - [slim.conv2d, slim.fully_connected], - weights_initializer=trunc_normal(0.01)): - with slim.arg_scope([slim.conv2d, slim.max_pool2d], - stride=1, padding='SAME'): - end_point = 'Conv2d_1a_7x7' - net = slim.conv2d(inputs, 64, [7, 7], stride=2, scope=end_point) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - end_point = 'MaxPool_2a_3x3' - net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - end_point = 'Conv2d_2b_1x1' - net = slim.conv2d(net, 64, [1, 1], scope=end_point) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - end_point = 'Conv2d_2c_3x3' - net = slim.conv2d(net, 192, [3, 3], scope=end_point) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - end_point = 'MaxPool_3a_3x3' - net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - - end_point = 'Mixed_3b' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 32, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - - end_point = 'Mixed_3c' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 192, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - - end_point = 'MaxPool_4a_3x3' - net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - - end_point = 'Mixed_4b' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 208, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 48, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - - end_point = 'Mixed_4c' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - - end_point = 'Mixed_4d' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 256, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - - end_point = 'Mixed_4e' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, 144, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 288, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - - end_point = 'Mixed_4f' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - - end_point = 'MaxPool_5a_2x2' - net = slim.max_pool2d(net, [2, 2], stride=2, scope=end_point) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - - end_point = 'Mixed_5b' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0a_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - - end_point = 'Mixed_5c' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 384, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if final_endpoint == end_point: return net, end_points - raise ValueError('Unknown final endpoint %s' % final_endpoint) - - -def inception_v1(inputs, - num_classes=1000, - is_training=True, - dropout_keep_prob=0.8, - prediction_fn=slim.softmax, - spatial_squeeze=True, - reuse=None, - scope='InceptionV1'): - """Defines the Inception V1 architecture. - - This architecture is defined in: - - Going deeper with convolutions - Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, - Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich. - http://arxiv.org/pdf/1409.4842v1.pdf. - - The default image size used to train this network is 224x224. - - Args: - inputs: a tensor of size [batch_size, height, width, channels]. - num_classes: number of predicted classes. - is_training: whether is training or not. - dropout_keep_prob: the percentage of activation values that are retained. - prediction_fn: a function to get predictions out of logits. - spatial_squeeze: if True, logits is of shape [B, C], if false logits is - of shape [B, 1, 1, C], where B is batch_size and C is number of classes. - reuse: whether or not the network and its variables should be reused. To be - able to reuse 'scope' must be given. - scope: Optional variable_scope. - - Returns: - logits: the pre-softmax activations, a tensor of size - [batch_size, num_classes] - end_points: a dictionary from components of the network to the corresponding - activation. - """ - # Final pooling and prediction - with tf.variable_scope(scope, 'InceptionV1', [inputs, num_classes], - reuse=reuse) as scope: - with slim.arg_scope([slim.batch_norm, slim.dropout], - is_training=is_training): - net, end_points = inception_v1_base(inputs, scope=scope) - with tf.variable_scope('Logits'): - net = slim.avg_pool2d(net, [7, 7], stride=1, scope='AvgPool_0a_7x7') - net = slim.dropout(net, - dropout_keep_prob, scope='Dropout_0b') - logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, - normalizer_fn=None, scope='Conv2d_0c_1x1') - if spatial_squeeze: - logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze') - - end_points['Logits'] = logits - end_points['Predictions'] = prediction_fn(logits, scope='Predictions') - return logits, end_points -inception_v1.default_image_size = 224 - -inception_v1_arg_scope = inception_utils.inception_arg_scope diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v1_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v1_test.py deleted file mode 100755 index 11eb14e..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v1_test.py +++ /dev/null @@ -1,210 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for slim_nets.inception_v1.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np -import tensorflow as tf - -from nets import inception - -slim = tf.contrib.slim - - -class InceptionV1Test(tf.test.TestCase): - - def testBuildClassificationNetwork(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, end_points = inception.inception_v1(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV1/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - self.assertTrue('Predictions' in end_points) - self.assertListEqual(end_points['Predictions'].get_shape().as_list(), - [batch_size, num_classes]) - - def testBuildBaseNetwork(self): - batch_size = 5 - height, width = 224, 224 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - mixed_6c, end_points = inception.inception_v1_base(inputs) - self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c')) - self.assertListEqual(mixed_6c.get_shape().as_list(), - [batch_size, 7, 7, 1024]) - expected_endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', - 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', - 'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', - 'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', - 'Mixed_5b', 'Mixed_5c'] - self.assertItemsEqual(end_points.keys(), expected_endpoints) - - def testBuildOnlyUptoFinalEndpoint(self): - batch_size = 5 - height, width = 224, 224 - endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', - 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', - 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', - 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', - 'Mixed_5c'] - for index, endpoint in enumerate(endpoints): - with tf.Graph().as_default(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - out_tensor, end_points = inception.inception_v1_base( - inputs, final_endpoint=endpoint) - self.assertTrue(out_tensor.op.name.startswith( - 'InceptionV1/' + endpoint)) - self.assertItemsEqual(endpoints[:index+1], end_points) - - def testBuildAndCheckAllEndPointsUptoMixed5c(self): - batch_size = 5 - height, width = 224, 224 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_v1_base(inputs, - final_endpoint='Mixed_5c') - endpoints_shapes = {'Conv2d_1a_7x7': [5, 112, 112, 64], - 'MaxPool_2a_3x3': [5, 56, 56, 64], - 'Conv2d_2b_1x1': [5, 56, 56, 64], - 'Conv2d_2c_3x3': [5, 56, 56, 192], - 'MaxPool_3a_3x3': [5, 28, 28, 192], - 'Mixed_3b': [5, 28, 28, 256], - 'Mixed_3c': [5, 28, 28, 480], - 'MaxPool_4a_3x3': [5, 14, 14, 480], - 'Mixed_4b': [5, 14, 14, 512], - 'Mixed_4c': [5, 14, 14, 512], - 'Mixed_4d': [5, 14, 14, 512], - 'Mixed_4e': [5, 14, 14, 528], - 'Mixed_4f': [5, 14, 14, 832], - 'MaxPool_5a_2x2': [5, 7, 7, 832], - 'Mixed_5b': [5, 7, 7, 832], - 'Mixed_5c': [5, 7, 7, 1024]} - - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name in endpoints_shapes: - expected_shape = endpoints_shapes[endpoint_name] - self.assertTrue(endpoint_name in end_points) - self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), - expected_shape) - - def testModelHasExpectedNumberOfParameters(self): - batch_size = 5 - height, width = 224, 224 - inputs = tf.random_uniform((batch_size, height, width, 3)) - with slim.arg_scope(inception.inception_v1_arg_scope()): - inception.inception_v1_base(inputs) - total_params, _ = slim.model_analyzer.analyze_vars( - slim.get_model_variables()) - self.assertAlmostEqual(5607184, total_params) - - def testHalfSizeImages(self): - batch_size = 5 - height, width = 112, 112 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - mixed_5c, _ = inception.inception_v1_base(inputs) - self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c')) - self.assertListEqual(mixed_5c.get_shape().as_list(), - [batch_size, 4, 4, 1024]) - - def testUnknownImageShape(self): - tf.reset_default_graph() - batch_size = 2 - height, width = 224, 224 - num_classes = 1000 - input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) - with self.test_session() as sess: - inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3)) - logits, end_points = inception.inception_v1(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV1/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - pre_pool = end_points['Mixed_5c'] - feed_dict = {inputs: input_np} - tf.global_variables_initializer().run() - pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) - self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024]) - - def testUnknowBatchSize(self): - batch_size = 1 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.placeholder(tf.float32, (None, height, width, 3)) - logits, _ = inception.inception_v1(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV1/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [None, num_classes]) - images = tf.random_uniform((batch_size, height, width, 3)) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(logits, {inputs: images.eval()}) - self.assertEquals(output.shape, (batch_size, num_classes)) - - def testEvaluation(self): - batch_size = 2 - height, width = 224, 224 - num_classes = 1000 - - eval_inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = inception.inception_v1(eval_inputs, num_classes, - is_training=False) - predictions = tf.argmax(logits, 1) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(predictions) - self.assertEquals(output.shape, (batch_size,)) - - def testTrainEvalWithReuse(self): - train_batch_size = 5 - eval_batch_size = 2 - height, width = 224, 224 - num_classes = 1000 - - train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) - inception.inception_v1(train_inputs, num_classes) - eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) - logits, _ = inception.inception_v1(eval_inputs, num_classes, reuse=True) - predictions = tf.argmax(logits, 1) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(predictions) - self.assertEquals(output.shape, (eval_batch_size,)) - - def testLogitsNotSqueezed(self): - num_classes = 25 - images = tf.random_uniform([1, 224, 224, 3]) - logits, _ = inception.inception_v1(images, - num_classes=num_classes, - spatial_squeeze=False) - - with self.test_session() as sess: - tf.global_variables_initializer().run() - logits_out = sess.run(logits) - self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes]) - - -if __name__ == '__main__': - tf.test.main() diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v2.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v2.py deleted file mode 100755 index 2651f71..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v2.py +++ /dev/null @@ -1,520 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains the definition for inception v2 classification network.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from nets import inception_utils - -slim = tf.contrib.slim -trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) - - -def inception_v2_base(inputs, - final_endpoint='Mixed_5c', - min_depth=16, - depth_multiplier=1.0, - scope=None): - """Inception v2 (6a2). - - Constructs an Inception v2 network from inputs to the given final endpoint. - This method can construct the network up to the layer inception(5b) as - described in http://arxiv.org/abs/1502.03167. - - Args: - inputs: a tensor of shape [batch_size, height, width, channels]. - final_endpoint: specifies the endpoint to construct the network up to. It - can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', - 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', 'Mixed_4a', - 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', 'Mixed_5b', - 'Mixed_5c']. - min_depth: Minimum depth value (number of channels) for all convolution ops. - Enforced when depth_multiplier < 1, and not an active constraint when - depth_multiplier >= 1. - depth_multiplier: Float multiplier for the depth (number of channels) - for all convolution ops. The value must be greater than zero. Typical - usage will be to set this value in (0, 1) to reduce the number of - parameters or computation cost of the model. - scope: Optional variable_scope. - - Returns: - tensor_out: output tensor corresponding to the final_endpoint. - end_points: a set of activations for external use, for example summaries or - losses. - - Raises: - ValueError: if final_endpoint is not set to one of the predefined values, - or depth_multiplier <= 0 - """ - - # end_points will collect relevant activations for external use, for example - # summaries or losses. - end_points = {} - - # Used to find thinned depths for each layer. - if depth_multiplier <= 0: - raise ValueError('depth_multiplier is not greater than zero.') - depth = lambda d: max(int(d * depth_multiplier), min_depth) - - with tf.variable_scope(scope, 'InceptionV2', [inputs]): - with slim.arg_scope( - [slim.conv2d, slim.max_pool2d, slim.avg_pool2d, slim.separable_conv2d], - stride=1, padding='SAME'): - - # Note that sizes in the comments below assume an input spatial size of - # 224x224, however, the inputs can be of any size greater 32x32. - - # 224 x 224 x 3 - end_point = 'Conv2d_1a_7x7' - # depthwise_multiplier here is different from depth_multiplier. - # depthwise_multiplier determines the output channels of the initial - # depthwise conv (see docs for tf.nn.separable_conv2d), while - # depth_multiplier controls the # channels of the subsequent 1x1 - # convolution. Must have - # in_channels * depthwise_multipler <= out_channels - # so that the separable convolution is not overparameterized. - depthwise_multiplier = min(int(depth(64) / 3), 8) - net = slim.separable_conv2d( - inputs, depth(64), [7, 7], depth_multiplier=depthwise_multiplier, - stride=2, weights_initializer=trunc_normal(1.0), - scope=end_point) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 112 x 112 x 64 - end_point = 'MaxPool_2a_3x3' - net = slim.max_pool2d(net, [3, 3], scope=end_point, stride=2) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 56 x 56 x 64 - end_point = 'Conv2d_2b_1x1' - net = slim.conv2d(net, depth(64), [1, 1], scope=end_point, - weights_initializer=trunc_normal(0.1)) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 56 x 56 x 64 - end_point = 'Conv2d_2c_3x3' - net = slim.conv2d(net, depth(192), [3, 3], scope=end_point) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 56 x 56 x 192 - end_point = 'MaxPool_3a_3x3' - net = slim.max_pool2d(net, [3, 3], scope=end_point, stride=2) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 28 x 28 x 192 - # Inception module. - end_point = 'Mixed_3b' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(64), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(64), [3, 3], - scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(64), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], - scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d( - branch_3, depth(32), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 28 x 28 x 256 - end_point = 'Mixed_3c' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(64), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], - scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(64), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], - scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d( - branch_3, depth(64), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 28 x 28 x 320 - end_point = 'Mixed_4a' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d( - net, depth(128), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_0 = slim.conv2d(branch_0, depth(160), [3, 3], stride=2, - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(64), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d( - branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3') - branch_1 = slim.conv2d( - branch_1, depth(96), [3, 3], stride=2, scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.max_pool2d( - net, [3, 3], stride=2, scope='MaxPool_1a_3x3') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 14 x 14 x 576 - end_point = 'Mixed_4b' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(224), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(64), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d( - branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(96), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(128), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(128), [3, 3], - scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d( - branch_3, depth(128), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 14 x 14 x 576 - end_point = 'Mixed_4c' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(96), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(128), [3, 3], - scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(96), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(128), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(128), [3, 3], - scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d( - branch_3, depth(128), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 14 x 14 x 576 - end_point = 'Mixed_4d' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(128), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(160), [3, 3], - scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(128), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(160), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(160), [3, 3], - scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d( - branch_3, depth(96), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - - # 14 x 14 x 576 - end_point = 'Mixed_4e' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(96), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(128), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(192), [3, 3], - scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(160), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(192), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(192), [3, 3], - scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d( - branch_3, depth(96), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 14 x 14 x 576 - end_point = 'Mixed_5a' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d( - net, depth(128), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_0 = slim.conv2d(branch_0, depth(192), [3, 3], stride=2, - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(192), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(256), [3, 3], - scope='Conv2d_0b_3x3') - branch_1 = slim.conv2d(branch_1, depth(256), [3, 3], stride=2, - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.max_pool2d(net, [3, 3], stride=2, - scope='MaxPool_1a_3x3') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 7 x 7 x 1024 - end_point = 'Mixed_5b' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(352), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(192), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(320), [3, 3], - scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(160), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(224), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(224), [3, 3], - scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d( - branch_3, depth(128), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - - # 7 x 7 x 1024 - end_point = 'Mixed_5c' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(352), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(192), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(320), [3, 3], - scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(192), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(224), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(224), [3, 3], - scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') - branch_3 = slim.conv2d( - branch_3, depth(128), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - raise ValueError('Unknown final endpoint %s' % final_endpoint) - - -def inception_v2(inputs, - num_classes=1000, - is_training=True, - dropout_keep_prob=0.8, - min_depth=16, - depth_multiplier=1.0, - prediction_fn=slim.softmax, - spatial_squeeze=True, - reuse=None, - scope='InceptionV2'): - """Inception v2 model for classification. - - Constructs an Inception v2 network for classification as described in - http://arxiv.org/abs/1502.03167. - - The default image size used to train this network is 224x224. - - Args: - inputs: a tensor of shape [batch_size, height, width, channels]. - num_classes: number of predicted classes. - is_training: whether is training or not. - dropout_keep_prob: the percentage of activation values that are retained. - min_depth: Minimum depth value (number of channels) for all convolution ops. - Enforced when depth_multiplier < 1, and not an active constraint when - depth_multiplier >= 1. - depth_multiplier: Float multiplier for the depth (number of channels) - for all convolution ops. The value must be greater than zero. Typical - usage will be to set this value in (0, 1) to reduce the number of - parameters or computation cost of the model. - prediction_fn: a function to get predictions out of logits. - spatial_squeeze: if True, logits is of shape [B, C], if false logits is - of shape [B, 1, 1, C], where B is batch_size and C is number of classes. - reuse: whether or not the network and its variables should be reused. To be - able to reuse 'scope' must be given. - scope: Optional variable_scope. - - Returns: - logits: the pre-softmax activations, a tensor of size - [batch_size, num_classes] - end_points: a dictionary from components of the network to the corresponding - activation. - - Raises: - ValueError: if final_endpoint is not set to one of the predefined values, - or depth_multiplier <= 0 - """ - if depth_multiplier <= 0: - raise ValueError('depth_multiplier is not greater than zero.') - - # Final pooling and prediction - with tf.variable_scope(scope, 'InceptionV2', [inputs, num_classes], - reuse=reuse) as scope: - with slim.arg_scope([slim.batch_norm, slim.dropout], - is_training=is_training): - net, end_points = inception_v2_base( - inputs, scope=scope, min_depth=min_depth, - depth_multiplier=depth_multiplier) - with tf.variable_scope('Logits'): - kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7]) - net = slim.avg_pool2d(net, kernel_size, padding='VALID', - scope='AvgPool_1a_{}x{}'.format(*kernel_size)) - # 1 x 1 x 1024 - net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b') - logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, - normalizer_fn=None, scope='Conv2d_1c_1x1') - if spatial_squeeze: - logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze') - end_points['Logits'] = logits - end_points['Predictions'] = prediction_fn(logits, scope='Predictions') - return logits, end_points -inception_v2.default_image_size = 224 - - -def _reduced_kernel_size_for_small_input(input_tensor, kernel_size): - """Define kernel size which is automatically reduced for small input. - - If the shape of the input images is unknown at graph construction time this - function assumes that the input images are is large enough. - - Args: - input_tensor: input tensor of size [batch_size, height, width, channels]. - kernel_size: desired kernel size of length 2: [kernel_height, kernel_width] - - Returns: - a tensor with the kernel size. - - TODO(jrru): Make this function work with unknown shapes. Theoretically, this - can be done with the code below. Problems are two-fold: (1) If the shape was - known, it will be lost. (2) inception.slim.ops._two_element_tuple cannot - handle tensors that define the kernel size. - shape = tf.shape(input_tensor) - return = tf.pack([tf.minimum(shape[1], kernel_size[0]), - tf.minimum(shape[2], kernel_size[1])]) - - """ - shape = input_tensor.get_shape().as_list() - if shape[1] is None or shape[2] is None: - kernel_size_out = kernel_size - else: - kernel_size_out = [min(shape[1], kernel_size[0]), - min(shape[2], kernel_size[1])] - return kernel_size_out - - -inception_v2_arg_scope = inception_utils.inception_arg_scope diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v2_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v2_test.py deleted file mode 100755 index 397aa50..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v2_test.py +++ /dev/null @@ -1,262 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for slim_nets.inception_v2.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np -import tensorflow as tf - -from nets import inception - -slim = tf.contrib.slim - - -class InceptionV2Test(tf.test.TestCase): - - def testBuildClassificationNetwork(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, end_points = inception.inception_v2(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - self.assertTrue('Predictions' in end_points) - self.assertListEqual(end_points['Predictions'].get_shape().as_list(), - [batch_size, num_classes]) - - def testBuildBaseNetwork(self): - batch_size = 5 - height, width = 224, 224 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - mixed_5c, end_points = inception.inception_v2_base(inputs) - self.assertTrue(mixed_5c.op.name.startswith('InceptionV2/Mixed_5c')) - self.assertListEqual(mixed_5c.get_shape().as_list(), - [batch_size, 7, 7, 1024]) - expected_endpoints = ['Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b', - 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', - 'Mixed_5b', 'Mixed_5c', 'Conv2d_1a_7x7', - 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3', - 'MaxPool_3a_3x3'] - self.assertItemsEqual(end_points.keys(), expected_endpoints) - - def testBuildOnlyUptoFinalEndpoint(self): - batch_size = 5 - height, width = 224, 224 - endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', - 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', - 'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', - 'Mixed_5a', 'Mixed_5b', 'Mixed_5c'] - for index, endpoint in enumerate(endpoints): - with tf.Graph().as_default(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - out_tensor, end_points = inception.inception_v2_base( - inputs, final_endpoint=endpoint) - self.assertTrue(out_tensor.op.name.startswith( - 'InceptionV2/' + endpoint)) - self.assertItemsEqual(endpoints[:index+1], end_points) - - def testBuildAndCheckAllEndPointsUptoMixed5c(self): - batch_size = 5 - height, width = 224, 224 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_v2_base(inputs, - final_endpoint='Mixed_5c') - endpoints_shapes = {'Mixed_3b': [batch_size, 28, 28, 256], - 'Mixed_3c': [batch_size, 28, 28, 320], - 'Mixed_4a': [batch_size, 14, 14, 576], - 'Mixed_4b': [batch_size, 14, 14, 576], - 'Mixed_4c': [batch_size, 14, 14, 576], - 'Mixed_4d': [batch_size, 14, 14, 576], - 'Mixed_4e': [batch_size, 14, 14, 576], - 'Mixed_5a': [batch_size, 7, 7, 1024], - 'Mixed_5b': [batch_size, 7, 7, 1024], - 'Mixed_5c': [batch_size, 7, 7, 1024], - 'Conv2d_1a_7x7': [batch_size, 112, 112, 64], - 'MaxPool_2a_3x3': [batch_size, 56, 56, 64], - 'Conv2d_2b_1x1': [batch_size, 56, 56, 64], - 'Conv2d_2c_3x3': [batch_size, 56, 56, 192], - 'MaxPool_3a_3x3': [batch_size, 28, 28, 192]} - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name in endpoints_shapes: - expected_shape = endpoints_shapes[endpoint_name] - self.assertTrue(endpoint_name in end_points) - self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), - expected_shape) - - def testModelHasExpectedNumberOfParameters(self): - batch_size = 5 - height, width = 224, 224 - inputs = tf.random_uniform((batch_size, height, width, 3)) - with slim.arg_scope(inception.inception_v2_arg_scope()): - inception.inception_v2_base(inputs) - total_params, _ = slim.model_analyzer.analyze_vars( - slim.get_model_variables()) - self.assertAlmostEqual(10173112, total_params) - - def testBuildEndPointsWithDepthMultiplierLessThanOne(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_v2(inputs, num_classes) - - endpoint_keys = [key for key in end_points.keys() - if key.startswith('Mixed') or key.startswith('Conv')] - - _, end_points_with_multiplier = inception.inception_v2( - inputs, num_classes, scope='depth_multiplied_net', - depth_multiplier=0.5) - - for key in endpoint_keys: - original_depth = end_points[key].get_shape().as_list()[3] - new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] - self.assertEqual(0.5 * original_depth, new_depth) - - def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_v2(inputs, num_classes) - - endpoint_keys = [key for key in end_points.keys() - if key.startswith('Mixed') or key.startswith('Conv')] - - _, end_points_with_multiplier = inception.inception_v2( - inputs, num_classes, scope='depth_multiplied_net', - depth_multiplier=2.0) - - for key in endpoint_keys: - original_depth = end_points[key].get_shape().as_list()[3] - new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] - self.assertEqual(2.0 * original_depth, new_depth) - - def testRaiseValueErrorWithInvalidDepthMultiplier(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - with self.assertRaises(ValueError): - _ = inception.inception_v2(inputs, num_classes, depth_multiplier=-0.1) - with self.assertRaises(ValueError): - _ = inception.inception_v2(inputs, num_classes, depth_multiplier=0.0) - - def testHalfSizeImages(self): - batch_size = 5 - height, width = 112, 112 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, end_points = inception.inception_v2(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - pre_pool = end_points['Mixed_5c'] - self.assertListEqual(pre_pool.get_shape().as_list(), - [batch_size, 4, 4, 1024]) - - def testUnknownImageShape(self): - tf.reset_default_graph() - batch_size = 2 - height, width = 224, 224 - num_classes = 1000 - input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) - with self.test_session() as sess: - inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3)) - logits, end_points = inception.inception_v2(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - pre_pool = end_points['Mixed_5c'] - feed_dict = {inputs: input_np} - tf.global_variables_initializer().run() - pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) - self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024]) - - def testUnknowBatchSize(self): - batch_size = 1 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.placeholder(tf.float32, (None, height, width, 3)) - logits, _ = inception.inception_v2(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [None, num_classes]) - images = tf.random_uniform((batch_size, height, width, 3)) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(logits, {inputs: images.eval()}) - self.assertEquals(output.shape, (batch_size, num_classes)) - - def testEvaluation(self): - batch_size = 2 - height, width = 224, 224 - num_classes = 1000 - - eval_inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = inception.inception_v2(eval_inputs, num_classes, - is_training=False) - predictions = tf.argmax(logits, 1) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(predictions) - self.assertEquals(output.shape, (batch_size,)) - - def testTrainEvalWithReuse(self): - train_batch_size = 5 - eval_batch_size = 2 - height, width = 150, 150 - num_classes = 1000 - - train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) - inception.inception_v2(train_inputs, num_classes) - eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) - logits, _ = inception.inception_v2(eval_inputs, num_classes, reuse=True) - predictions = tf.argmax(logits, 1) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(predictions) - self.assertEquals(output.shape, (eval_batch_size,)) - - def testLogitsNotSqueezed(self): - num_classes = 25 - images = tf.random_uniform([1, 224, 224, 3]) - logits, _ = inception.inception_v2(images, - num_classes=num_classes, - spatial_squeeze=False) - - with self.test_session() as sess: - tf.global_variables_initializer().run() - logits_out = sess.run(logits) - self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes]) - - -if __name__ == '__main__': - tf.test.main() diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v3.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v3.py deleted file mode 100755 index d64bcfd..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v3.py +++ /dev/null @@ -1,560 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains the definition for inception v3 classification network.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from nets import inception_utils - -slim = tf.contrib.slim -trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) - - -def inception_v3_base(inputs, - final_endpoint='Mixed_7c', - min_depth=16, - depth_multiplier=1.0, - scope=None): - """Inception model from http://arxiv.org/abs/1512.00567. - - Constructs an Inception v3 network from inputs to the given final endpoint. - This method can construct the network up to the final inception block - Mixed_7c. - - Note that the names of the layers in the paper do not correspond to the names - of the endpoints registered by this function although they build the same - network. - - Here is a mapping from the old_names to the new names: - Old name | New name - ======================================= - conv0 | Conv2d_1a_3x3 - conv1 | Conv2d_2a_3x3 - conv2 | Conv2d_2b_3x3 - pool1 | MaxPool_3a_3x3 - conv3 | Conv2d_3b_1x1 - conv4 | Conv2d_4a_3x3 - pool2 | MaxPool_5a_3x3 - mixed_35x35x256a | Mixed_5b - mixed_35x35x288a | Mixed_5c - mixed_35x35x288b | Mixed_5d - mixed_17x17x768a | Mixed_6a - mixed_17x17x768b | Mixed_6b - mixed_17x17x768c | Mixed_6c - mixed_17x17x768d | Mixed_6d - mixed_17x17x768e | Mixed_6e - mixed_8x8x1280a | Mixed_7a - mixed_8x8x2048a | Mixed_7b - mixed_8x8x2048b | Mixed_7c - - Args: - inputs: a tensor of size [batch_size, height, width, channels]. - final_endpoint: specifies the endpoint to construct the network up to. It - can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', - 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3', - 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', - 'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c']. - min_depth: Minimum depth value (number of channels) for all convolution ops. - Enforced when depth_multiplier < 1, and not an active constraint when - depth_multiplier >= 1. - depth_multiplier: Float multiplier for the depth (number of channels) - for all convolution ops. The value must be greater than zero. Typical - usage will be to set this value in (0, 1) to reduce the number of - parameters or computation cost of the model. - scope: Optional variable_scope. - - Returns: - tensor_out: output tensor corresponding to the final_endpoint. - end_points: a set of activations for external use, for example summaries or - losses. - - Raises: - ValueError: if final_endpoint is not set to one of the predefined values, - or depth_multiplier <= 0 - """ - # end_points will collect relevant activations for external use, for example - # summaries or losses. - end_points = {} - - if depth_multiplier <= 0: - raise ValueError('depth_multiplier is not greater than zero.') - depth = lambda d: max(int(d * depth_multiplier), min_depth) - - with tf.variable_scope(scope, 'InceptionV3', [inputs]): - with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], - stride=1, padding='VALID'): - # 299 x 299 x 3 - end_point = 'Conv2d_1a_3x3' - net = slim.conv2d(inputs, depth(32), [3, 3], stride=2, scope=end_point) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 149 x 149 x 32 - end_point = 'Conv2d_2a_3x3' - net = slim.conv2d(net, depth(32), [3, 3], scope=end_point) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 147 x 147 x 32 - end_point = 'Conv2d_2b_3x3' - net = slim.conv2d(net, depth(64), [3, 3], padding='SAME', scope=end_point) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 147 x 147 x 64 - end_point = 'MaxPool_3a_3x3' - net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 73 x 73 x 64 - end_point = 'Conv2d_3b_1x1' - net = slim.conv2d(net, depth(80), [1, 1], scope=end_point) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 73 x 73 x 80. - end_point = 'Conv2d_4a_3x3' - net = slim.conv2d(net, depth(192), [3, 3], scope=end_point) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 71 x 71 x 192. - end_point = 'MaxPool_5a_3x3' - net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 35 x 35 x 192. - - # Inception blocks - with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], - stride=1, padding='SAME'): - # mixed: 35 x 35 x 256. - end_point = 'Mixed_5b' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(64), [5, 5], - scope='Conv2d_0b_5x5') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], - scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, depth(32), [1, 1], - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - - # mixed_1: 35 x 35 x 288. - end_point = 'Mixed_5c' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0b_1x1') - branch_1 = slim.conv2d(branch_1, depth(64), [5, 5], - scope='Conv_1_0c_5x5') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, depth(64), [1, 1], - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], - scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, depth(64), [1, 1], - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - - # mixed_2: 35 x 35 x 288. - end_point = 'Mixed_5d' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(64), [5, 5], - scope='Conv2d_0b_5x5') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], - scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, depth(64), [1, 1], - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - - # mixed_3: 17 x 17 x 768. - end_point = 'Mixed_6a' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(384), [3, 3], stride=2, - padding='VALID', scope='Conv2d_1a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], - scope='Conv2d_0b_3x3') - branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], stride=2, - padding='VALID', scope='Conv2d_1a_1x1') - with tf.variable_scope('Branch_2'): - branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', - scope='MaxPool_1a_3x3') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - - # mixed4: 17 x 17 x 768. - end_point = 'Mixed_6b' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(128), [1, 7], - scope='Conv2d_0b_1x7') - branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], - scope='Conv2d_0c_7x1') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(128), [7, 1], - scope='Conv2d_0b_7x1') - branch_2 = slim.conv2d(branch_2, depth(128), [1, 7], - scope='Conv2d_0c_1x7') - branch_2 = slim.conv2d(branch_2, depth(128), [7, 1], - scope='Conv2d_0d_7x1') - branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], - scope='Conv2d_0e_1x7') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - - # mixed_5: 17 x 17 x 768. - end_point = 'Mixed_6c' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(160), [1, 7], - scope='Conv2d_0b_1x7') - branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], - scope='Conv2d_0c_7x1') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], - scope='Conv2d_0b_7x1') - branch_2 = slim.conv2d(branch_2, depth(160), [1, 7], - scope='Conv2d_0c_1x7') - branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], - scope='Conv2d_0d_7x1') - branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], - scope='Conv2d_0e_1x7') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # mixed_6: 17 x 17 x 768. - end_point = 'Mixed_6d' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(160), [1, 7], - scope='Conv2d_0b_1x7') - branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], - scope='Conv2d_0c_7x1') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], - scope='Conv2d_0b_7x1') - branch_2 = slim.conv2d(branch_2, depth(160), [1, 7], - scope='Conv2d_0c_1x7') - branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], - scope='Conv2d_0d_7x1') - branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], - scope='Conv2d_0e_1x7') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - - # mixed_7: 17 x 17 x 768. - end_point = 'Mixed_6e' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(192), [1, 7], - scope='Conv2d_0b_1x7') - branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], - scope='Conv2d_0c_7x1') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(192), [7, 1], - scope='Conv2d_0b_7x1') - branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], - scope='Conv2d_0c_1x7') - branch_2 = slim.conv2d(branch_2, depth(192), [7, 1], - scope='Conv2d_0d_7x1') - branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], - scope='Conv2d_0e_1x7') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - - # mixed_8: 8 x 8 x 1280. - end_point = 'Mixed_7a' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') - branch_0 = slim.conv2d(branch_0, depth(320), [3, 3], stride=2, - padding='VALID', scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(192), [1, 7], - scope='Conv2d_0b_1x7') - branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], - scope='Conv2d_0c_7x1') - branch_1 = slim.conv2d(branch_1, depth(192), [3, 3], stride=2, - padding='VALID', scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', - scope='MaxPool_1a_3x3') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # mixed_9: 8 x 8 x 2048. - end_point = 'Mixed_7b' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1') - branch_1 = tf.concat(axis=3, values=[ - slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'), - slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0b_3x1')]) - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d( - branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3') - branch_2 = tf.concat(axis=3, values=[ - slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'), - slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')]) - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d( - branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - - # mixed_10: 8 x 8 x 2048. - end_point = 'Mixed_7c' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1') - branch_1 = tf.concat(axis=3, values=[ - slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'), - slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0c_3x1')]) - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d( - branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3') - branch_2 = tf.concat(axis=3, values=[ - slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'), - slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')]) - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d( - branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - raise ValueError('Unknown final endpoint %s' % final_endpoint) - - -def inception_v3(inputs, - num_classes=1000, - is_training=True, - dropout_keep_prob=0.8, - min_depth=16, - depth_multiplier=1.0, - prediction_fn=slim.softmax, - spatial_squeeze=True, - reuse=None, - scope='InceptionV3'): - """Inception model from http://arxiv.org/abs/1512.00567. - - "Rethinking the Inception Architecture for Computer Vision" - - Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, - Zbigniew Wojna. - - With the default arguments this method constructs the exact model defined in - the paper. However, one can experiment with variations of the inception_v3 - network by changing arguments dropout_keep_prob, min_depth and - depth_multiplier. - - The default image size used to train this network is 299x299. - - Args: - inputs: a tensor of size [batch_size, height, width, channels]. - num_classes: number of predicted classes. - is_training: whether is training or not. - dropout_keep_prob: the percentage of activation values that are retained. - min_depth: Minimum depth value (number of channels) for all convolution ops. - Enforced when depth_multiplier < 1, and not an active constraint when - depth_multiplier >= 1. - depth_multiplier: Float multiplier for the depth (number of channels) - for all convolution ops. The value must be greater than zero. Typical - usage will be to set this value in (0, 1) to reduce the number of - parameters or computation cost of the model. - prediction_fn: a function to get predictions out of logits. - spatial_squeeze: if True, logits is of shape [B, C], if false logits is - of shape [B, 1, 1, C], where B is batch_size and C is number of classes. - reuse: whether or not the network and its variables should be reused. To be - able to reuse 'scope' must be given. - scope: Optional variable_scope. - - Returns: - logits: the pre-softmax activations, a tensor of size - [batch_size, num_classes] - end_points: a dictionary from components of the network to the corresponding - activation. - - Raises: - ValueError: if 'depth_multiplier' is less than or equal to zero. - """ - if depth_multiplier <= 0: - raise ValueError('depth_multiplier is not greater than zero.') - depth = lambda d: max(int(d * depth_multiplier), min_depth) - - with tf.variable_scope(scope, 'InceptionV3', [inputs, num_classes], - reuse=reuse) as scope: - with slim.arg_scope([slim.batch_norm, slim.dropout], - is_training=is_training): - net, end_points = inception_v3_base( - inputs, scope=scope, min_depth=min_depth, - depth_multiplier=depth_multiplier) - - # Auxiliary Head logits - with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], - stride=1, padding='SAME'): - aux_logits = end_points['Mixed_6e'] - with tf.variable_scope('AuxLogits'): - aux_logits = slim.avg_pool2d( - aux_logits, [5, 5], stride=3, padding='VALID', - scope='AvgPool_1a_5x5') - aux_logits = slim.conv2d(aux_logits, depth(128), [1, 1], - scope='Conv2d_1b_1x1') - - # Shape of feature map before the final layer. - kernel_size = _reduced_kernel_size_for_small_input( - aux_logits, [5, 5]) - aux_logits = slim.conv2d( - aux_logits, depth(768), kernel_size, - weights_initializer=trunc_normal(0.01), - padding='VALID', scope='Conv2d_2a_{}x{}'.format(*kernel_size)) - aux_logits = slim.conv2d( - aux_logits, num_classes, [1, 1], activation_fn=None, - normalizer_fn=None, weights_initializer=trunc_normal(0.001), - scope='Conv2d_2b_1x1') - if spatial_squeeze: - aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze') - end_points['AuxLogits'] = aux_logits - - # Final pooling and prediction - with tf.variable_scope('Logits'): - kernel_size = _reduced_kernel_size_for_small_input(net, [8, 8]) - net = slim.avg_pool2d(net, kernel_size, padding='VALID', - scope='AvgPool_1a_{}x{}'.format(*kernel_size)) - # 1 x 1 x 2048 - net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b') - end_points['PreLogits'] = net - # 2048 - logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, - normalizer_fn=None, scope='Conv2d_1c_1x1') - if spatial_squeeze: - logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze') - # 1000 - end_points['Logits'] = logits - end_points['Predictions'] = prediction_fn(logits, scope='Predictions') - return logits, end_points -inception_v3.default_image_size = 299 - - -def _reduced_kernel_size_for_small_input(input_tensor, kernel_size): - """Define kernel size which is automatically reduced for small input. - - If the shape of the input images is unknown at graph construction time this - function assumes that the input images are is large enough. - - Args: - input_tensor: input tensor of size [batch_size, height, width, channels]. - kernel_size: desired kernel size of length 2: [kernel_height, kernel_width] - - Returns: - a tensor with the kernel size. - - TODO(jrru): Make this function work with unknown shapes. Theoretically, this - can be done with the code below. Problems are two-fold: (1) If the shape was - known, it will be lost. (2) inception.slim.ops._two_element_tuple cannot - handle tensors that define the kernel size. - shape = tf.shape(input_tensor) - return = tf.pack([tf.minimum(shape[1], kernel_size[0]), - tf.minimum(shape[2], kernel_size[1])]) - - """ - shape = input_tensor.get_shape().as_list() - if shape[1] is None or shape[2] is None: - kernel_size_out = kernel_size - else: - kernel_size_out = [min(shape[1], kernel_size[0]), - min(shape[2], kernel_size[1])] - return kernel_size_out - - -inception_v3_arg_scope = inception_utils.inception_arg_scope diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v3_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v3_test.py deleted file mode 100755 index a6f3c95..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v3_test.py +++ /dev/null @@ -1,292 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for slim_nets.inception_v1.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np -import tensorflow as tf - -from nets import inception - -slim = tf.contrib.slim - - -class InceptionV3Test(tf.test.TestCase): - - def testBuildClassificationNetwork(self): - batch_size = 5 - height, width = 299, 299 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, end_points = inception.inception_v3(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV3/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - self.assertTrue('Predictions' in end_points) - self.assertListEqual(end_points['Predictions'].get_shape().as_list(), - [batch_size, num_classes]) - - def testBuildBaseNetwork(self): - batch_size = 5 - height, width = 299, 299 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - final_endpoint, end_points = inception.inception_v3_base(inputs) - self.assertTrue(final_endpoint.op.name.startswith( - 'InceptionV3/Mixed_7c')) - self.assertListEqual(final_endpoint.get_shape().as_list(), - [batch_size, 8, 8, 2048]) - expected_endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', - 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', - 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', - 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', - 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'] - self.assertItemsEqual(end_points.keys(), expected_endpoints) - - def testBuildOnlyUptoFinalEndpoint(self): - batch_size = 5 - height, width = 299, 299 - endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', - 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', - 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', - 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', - 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'] - - for index, endpoint in enumerate(endpoints): - with tf.Graph().as_default(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - out_tensor, end_points = inception.inception_v3_base( - inputs, final_endpoint=endpoint) - self.assertTrue(out_tensor.op.name.startswith( - 'InceptionV3/' + endpoint)) - self.assertItemsEqual(endpoints[:index+1], end_points) - - def testBuildAndCheckAllEndPointsUptoMixed7c(self): - batch_size = 5 - height, width = 299, 299 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_v3_base( - inputs, final_endpoint='Mixed_7c') - endpoints_shapes = {'Conv2d_1a_3x3': [batch_size, 149, 149, 32], - 'Conv2d_2a_3x3': [batch_size, 147, 147, 32], - 'Conv2d_2b_3x3': [batch_size, 147, 147, 64], - 'MaxPool_3a_3x3': [batch_size, 73, 73, 64], - 'Conv2d_3b_1x1': [batch_size, 73, 73, 80], - 'Conv2d_4a_3x3': [batch_size, 71, 71, 192], - 'MaxPool_5a_3x3': [batch_size, 35, 35, 192], - 'Mixed_5b': [batch_size, 35, 35, 256], - 'Mixed_5c': [batch_size, 35, 35, 288], - 'Mixed_5d': [batch_size, 35, 35, 288], - 'Mixed_6a': [batch_size, 17, 17, 768], - 'Mixed_6b': [batch_size, 17, 17, 768], - 'Mixed_6c': [batch_size, 17, 17, 768], - 'Mixed_6d': [batch_size, 17, 17, 768], - 'Mixed_6e': [batch_size, 17, 17, 768], - 'Mixed_7a': [batch_size, 8, 8, 1280], - 'Mixed_7b': [batch_size, 8, 8, 2048], - 'Mixed_7c': [batch_size, 8, 8, 2048]} - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name in endpoints_shapes: - expected_shape = endpoints_shapes[endpoint_name] - self.assertTrue(endpoint_name in end_points) - self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), - expected_shape) - - def testModelHasExpectedNumberOfParameters(self): - batch_size = 5 - height, width = 299, 299 - inputs = tf.random_uniform((batch_size, height, width, 3)) - with slim.arg_scope(inception.inception_v3_arg_scope()): - inception.inception_v3_base(inputs) - total_params, _ = slim.model_analyzer.analyze_vars( - slim.get_model_variables()) - self.assertAlmostEqual(21802784, total_params) - - def testBuildEndPoints(self): - batch_size = 5 - height, width = 299, 299 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_v3(inputs, num_classes) - self.assertTrue('Logits' in end_points) - logits = end_points['Logits'] - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - self.assertTrue('AuxLogits' in end_points) - aux_logits = end_points['AuxLogits'] - self.assertListEqual(aux_logits.get_shape().as_list(), - [batch_size, num_classes]) - self.assertTrue('Mixed_7c' in end_points) - pre_pool = end_points['Mixed_7c'] - self.assertListEqual(pre_pool.get_shape().as_list(), - [batch_size, 8, 8, 2048]) - self.assertTrue('PreLogits' in end_points) - pre_logits = end_points['PreLogits'] - self.assertListEqual(pre_logits.get_shape().as_list(), - [batch_size, 1, 1, 2048]) - - def testBuildEndPointsWithDepthMultiplierLessThanOne(self): - batch_size = 5 - height, width = 299, 299 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_v3(inputs, num_classes) - - endpoint_keys = [key for key in end_points.keys() - if key.startswith('Mixed') or key.startswith('Conv')] - - _, end_points_with_multiplier = inception.inception_v3( - inputs, num_classes, scope='depth_multiplied_net', - depth_multiplier=0.5) - - for key in endpoint_keys: - original_depth = end_points[key].get_shape().as_list()[3] - new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] - self.assertEqual(0.5 * original_depth, new_depth) - - def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self): - batch_size = 5 - height, width = 299, 299 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_v3(inputs, num_classes) - - endpoint_keys = [key for key in end_points.keys() - if key.startswith('Mixed') or key.startswith('Conv')] - - _, end_points_with_multiplier = inception.inception_v3( - inputs, num_classes, scope='depth_multiplied_net', - depth_multiplier=2.0) - - for key in endpoint_keys: - original_depth = end_points[key].get_shape().as_list()[3] - new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] - self.assertEqual(2.0 * original_depth, new_depth) - - def testRaiseValueErrorWithInvalidDepthMultiplier(self): - batch_size = 5 - height, width = 299, 299 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - with self.assertRaises(ValueError): - _ = inception.inception_v3(inputs, num_classes, depth_multiplier=-0.1) - with self.assertRaises(ValueError): - _ = inception.inception_v3(inputs, num_classes, depth_multiplier=0.0) - - def testHalfSizeImages(self): - batch_size = 5 - height, width = 150, 150 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, end_points = inception.inception_v3(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV3/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - pre_pool = end_points['Mixed_7c'] - self.assertListEqual(pre_pool.get_shape().as_list(), - [batch_size, 3, 3, 2048]) - - def testUnknownImageShape(self): - tf.reset_default_graph() - batch_size = 2 - height, width = 299, 299 - num_classes = 1000 - input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) - with self.test_session() as sess: - inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3)) - logits, end_points = inception.inception_v3(inputs, num_classes) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - pre_pool = end_points['Mixed_7c'] - feed_dict = {inputs: input_np} - tf.global_variables_initializer().run() - pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) - self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048]) - - def testUnknowBatchSize(self): - batch_size = 1 - height, width = 299, 299 - num_classes = 1000 - - inputs = tf.placeholder(tf.float32, (None, height, width, 3)) - logits, _ = inception.inception_v3(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV3/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [None, num_classes]) - images = tf.random_uniform((batch_size, height, width, 3)) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(logits, {inputs: images.eval()}) - self.assertEquals(output.shape, (batch_size, num_classes)) - - def testEvaluation(self): - batch_size = 2 - height, width = 299, 299 - num_classes = 1000 - - eval_inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = inception.inception_v3(eval_inputs, num_classes, - is_training=False) - predictions = tf.argmax(logits, 1) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(predictions) - self.assertEquals(output.shape, (batch_size,)) - - def testTrainEvalWithReuse(self): - train_batch_size = 5 - eval_batch_size = 2 - height, width = 150, 150 - num_classes = 1000 - - train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) - inception.inception_v3(train_inputs, num_classes) - eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) - logits, _ = inception.inception_v3(eval_inputs, num_classes, - is_training=False, reuse=True) - predictions = tf.argmax(logits, 1) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(predictions) - self.assertEquals(output.shape, (eval_batch_size,)) - - def testLogitsNotSqueezed(self): - num_classes = 25 - images = tf.random_uniform([1, 299, 299, 3]) - logits, _ = inception.inception_v3(images, - num_classes=num_classes, - spatial_squeeze=False) - - with self.test_session() as sess: - tf.global_variables_initializer().run() - logits_out = sess.run(logits) - self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes]) - - -if __name__ == '__main__': - tf.test.main() diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v4.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v4.py deleted file mode 100755 index b4f07ea..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v4.py +++ /dev/null @@ -1,323 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains the definition of the Inception V4 architecture. - -As described in http://arxiv.org/abs/1602.07261. - - Inception-v4, Inception-ResNet and the Impact of Residual Connections - on Learning - Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from nets import inception_utils - -slim = tf.contrib.slim - - -def block_inception_a(inputs, scope=None, reuse=None): - """Builds Inception-A block for Inception v4 network.""" - # By default use stride=1 and SAME padding - with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], - stride=1, padding='SAME'): - with tf.variable_scope(scope, 'BlockInceptionA', [inputs], reuse=reuse): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(inputs, 96, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 96, [1, 1], scope='Conv2d_0b_1x1') - return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - - -def block_reduction_a(inputs, scope=None, reuse=None): - """Builds Reduction-A block for Inception v4 network.""" - # By default use stride=1 and SAME padding - with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], - stride=1, padding='SAME'): - with tf.variable_scope(scope, 'BlockReductionA', [inputs], reuse=reuse): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(inputs, 384, [3, 3], stride=2, padding='VALID', - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3') - branch_1 = slim.conv2d(branch_1, 256, [3, 3], stride=2, - padding='VALID', scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID', - scope='MaxPool_1a_3x3') - return tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) - - -def block_inception_b(inputs, scope=None, reuse=None): - """Builds Inception-B block for Inception v4 network.""" - # By default use stride=1 and SAME padding - with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], - stride=1, padding='SAME'): - with tf.variable_scope(scope, 'BlockInceptionB', [inputs], reuse=reuse): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 224, [1, 7], scope='Conv2d_0b_1x7') - branch_1 = slim.conv2d(branch_1, 256, [7, 1], scope='Conv2d_0c_7x1') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1') - branch_2 = slim.conv2d(branch_2, 224, [1, 7], scope='Conv2d_0c_1x7') - branch_2 = slim.conv2d(branch_2, 224, [7, 1], scope='Conv2d_0d_7x1') - branch_2 = slim.conv2d(branch_2, 256, [1, 7], scope='Conv2d_0e_1x7') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1') - return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - - -def block_reduction_b(inputs, scope=None, reuse=None): - """Builds Reduction-B block for Inception v4 network.""" - # By default use stride=1 and SAME padding - with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], - stride=1, padding='SAME'): - with tf.variable_scope(scope, 'BlockReductionB', [inputs], reuse=reuse): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') - branch_0 = slim.conv2d(branch_0, 192, [3, 3], stride=2, - padding='VALID', scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 256, [1, 7], scope='Conv2d_0b_1x7') - branch_1 = slim.conv2d(branch_1, 320, [7, 1], scope='Conv2d_0c_7x1') - branch_1 = slim.conv2d(branch_1, 320, [3, 3], stride=2, - padding='VALID', scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID', - scope='MaxPool_1a_3x3') - return tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) - - -def block_inception_c(inputs, scope=None, reuse=None): - """Builds Inception-C block for Inception v4 network.""" - # By default use stride=1 and SAME padding - with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], - stride=1, padding='SAME'): - with tf.variable_scope(scope, 'BlockInceptionC', [inputs], reuse=reuse): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = tf.concat(axis=3, values=[ - slim.conv2d(branch_1, 256, [1, 3], scope='Conv2d_0b_1x3'), - slim.conv2d(branch_1, 256, [3, 1], scope='Conv2d_0c_3x1')]) - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 448, [3, 1], scope='Conv2d_0b_3x1') - branch_2 = slim.conv2d(branch_2, 512, [1, 3], scope='Conv2d_0c_1x3') - branch_2 = tf.concat(axis=3, values=[ - slim.conv2d(branch_2, 256, [1, 3], scope='Conv2d_0d_1x3'), - slim.conv2d(branch_2, 256, [3, 1], scope='Conv2d_0e_3x1')]) - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 256, [1, 1], scope='Conv2d_0b_1x1') - return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - - -def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None): - """Creates the Inception V4 network up to the given final endpoint. - - Args: - inputs: a 4-D tensor of size [batch_size, height, width, 3]. - final_endpoint: specifies the endpoint to construct the network up to. - It can be one of [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', - 'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', - 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', - 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c', - 'Mixed_7d'] - scope: Optional variable_scope. - - Returns: - logits: the logits outputs of the model. - end_points: the set of end_points from the inception model. - - Raises: - ValueError: if final_endpoint is not set to one of the predefined values, - """ - end_points = {} - - def add_and_check_final(name, net): - end_points[name] = net - return name == final_endpoint - - with tf.variable_scope(scope, 'InceptionV4', [inputs]): - with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], - stride=1, padding='SAME'): - # 299 x 299 x 3 - net = slim.conv2d(inputs, 32, [3, 3], stride=2, - padding='VALID', scope='Conv2d_1a_3x3') - if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points - # 149 x 149 x 32 - net = slim.conv2d(net, 32, [3, 3], padding='VALID', - scope='Conv2d_2a_3x3') - if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points - # 147 x 147 x 32 - net = slim.conv2d(net, 64, [3, 3], scope='Conv2d_2b_3x3') - if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points - # 147 x 147 x 64 - with tf.variable_scope('Mixed_3a'): - with tf.variable_scope('Branch_0'): - branch_0 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', - scope='MaxPool_0a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, 96, [3, 3], stride=2, padding='VALID', - scope='Conv2d_0a_3x3') - net = tf.concat(axis=3, values=[branch_0, branch_1]) - if add_and_check_final('Mixed_3a', net): return net, end_points - - # 73 x 73 x 160 - with tf.variable_scope('Mixed_4a'): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1') - branch_0 = slim.conv2d(branch_0, 96, [3, 3], padding='VALID', - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 64, [1, 7], scope='Conv2d_0b_1x7') - branch_1 = slim.conv2d(branch_1, 64, [7, 1], scope='Conv2d_0c_7x1') - branch_1 = slim.conv2d(branch_1, 96, [3, 3], padding='VALID', - scope='Conv2d_1a_3x3') - net = tf.concat(axis=3, values=[branch_0, branch_1]) - if add_and_check_final('Mixed_4a', net): return net, end_points - - # 71 x 71 x 192 - with tf.variable_scope('Mixed_5a'): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, 192, [3, 3], stride=2, padding='VALID', - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', - scope='MaxPool_1a_3x3') - net = tf.concat(axis=3, values=[branch_0, branch_1]) - if add_and_check_final('Mixed_5a', net): return net, end_points - - # 35 x 35 x 384 - # 4 x Inception-A blocks - for idx in range(4): - block_scope = 'Mixed_5' + chr(ord('b') + idx) - net = block_inception_a(net, block_scope) - if add_and_check_final(block_scope, net): return net, end_points - - # 35 x 35 x 384 - # Reduction-A block - net = block_reduction_a(net, 'Mixed_6a') - if add_and_check_final('Mixed_6a', net): return net, end_points - - # 17 x 17 x 1024 - # 7 x Inception-B blocks - for idx in range(7): - block_scope = 'Mixed_6' + chr(ord('b') + idx) - net = block_inception_b(net, block_scope) - if add_and_check_final(block_scope, net): return net, end_points - - # 17 x 17 x 1024 - # Reduction-B block - net = block_reduction_b(net, 'Mixed_7a') - if add_and_check_final('Mixed_7a', net): return net, end_points - - # 8 x 8 x 1536 - # 3 x Inception-C blocks - for idx in range(3): - block_scope = 'Mixed_7' + chr(ord('b') + idx) - net = block_inception_c(net, block_scope) - if add_and_check_final(block_scope, net): return net, end_points - raise ValueError('Unknown final endpoint %s' % final_endpoint) - - -def inception_v4(inputs, num_classes=1001, is_training=True, - dropout_keep_prob=0.8, - reuse=None, - scope='InceptionV4', - create_aux_logits=True): - """Creates the Inception V4 model. - - Args: - inputs: a 4-D tensor of size [batch_size, height, width, 3]. - num_classes: number of predicted classes. - is_training: whether is training or not. - dropout_keep_prob: float, the fraction to keep before final layer. - reuse: whether or not the network and its variables should be reused. To be - able to reuse 'scope' must be given. - scope: Optional variable_scope. - create_aux_logits: Whether to include the auxiliary logits. - - Returns: - logits: the logits outputs of the model. - end_points: the set of end_points from the inception model. - """ - end_points = {} - with tf.variable_scope(scope, 'InceptionV4', [inputs], reuse=reuse) as scope: - with slim.arg_scope([slim.batch_norm, slim.dropout], - is_training=is_training): - net, end_points = inception_v4_base(inputs, scope=scope) - - with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], - stride=1, padding='SAME'): - # Auxiliary Head logits - if create_aux_logits: - with tf.variable_scope('AuxLogits'): - # 17 x 17 x 1024 - aux_logits = end_points['Mixed_6h'] - aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3, - padding='VALID', - scope='AvgPool_1a_5x5') - aux_logits = slim.conv2d(aux_logits, 128, [1, 1], - scope='Conv2d_1b_1x1') - aux_logits = slim.conv2d(aux_logits, 768, - aux_logits.get_shape()[1:3], - padding='VALID', scope='Conv2d_2a') - aux_logits = slim.flatten(aux_logits) - aux_logits = slim.fully_connected(aux_logits, num_classes, - activation_fn=None, - scope='Aux_logits') - end_points['AuxLogits'] = aux_logits - - # Final pooling and prediction - with tf.variable_scope('Logits'): - # 8 x 8 x 1536 - net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID', - scope='AvgPool_1a') - # 1 x 1 x 1536 - net = slim.dropout(net, dropout_keep_prob, scope='Dropout_1b') - net = slim.flatten(net, scope='PreLogitsFlatten') - end_points['PreLogitsFlatten'] = net - # 1536 - logits = slim.fully_connected(net, num_classes, activation_fn=None, - scope='Logits') - end_points['Logits'] = logits - end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions') - return logits, end_points -inception_v4.default_image_size = 299 - - -inception_v4_arg_scope = inception_utils.inception_arg_scope diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v4_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v4_test.py deleted file mode 100755 index 11cffb6..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v4_test.py +++ /dev/null @@ -1,216 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for slim.inception_v4.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from nets import inception - - -class InceptionTest(tf.test.TestCase): - - def testBuildLogits(self): - batch_size = 5 - height, width = 299, 299 - num_classes = 1000 - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, end_points = inception.inception_v4(inputs, num_classes) - auxlogits = end_points['AuxLogits'] - predictions = end_points['Predictions'] - self.assertTrue(auxlogits.op.name.startswith('InceptionV4/AuxLogits')) - self.assertListEqual(auxlogits.get_shape().as_list(), - [batch_size, num_classes]) - self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - self.assertTrue(predictions.op.name.startswith( - 'InceptionV4/Logits/Predictions')) - self.assertListEqual(predictions.get_shape().as_list(), - [batch_size, num_classes]) - - def testBuildWithoutAuxLogits(self): - batch_size = 5 - height, width = 299, 299 - num_classes = 1000 - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, endpoints = inception.inception_v4(inputs, num_classes, - create_aux_logits=False) - self.assertFalse('AuxLogits' in endpoints) - self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - - def testAllEndPointsShapes(self): - batch_size = 5 - height, width = 299, 299 - num_classes = 1000 - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_v4(inputs, num_classes) - endpoints_shapes = {'Conv2d_1a_3x3': [batch_size, 149, 149, 32], - 'Conv2d_2a_3x3': [batch_size, 147, 147, 32], - 'Conv2d_2b_3x3': [batch_size, 147, 147, 64], - 'Mixed_3a': [batch_size, 73, 73, 160], - 'Mixed_4a': [batch_size, 71, 71, 192], - 'Mixed_5a': [batch_size, 35, 35, 384], - # 4 x Inception-A blocks - 'Mixed_5b': [batch_size, 35, 35, 384], - 'Mixed_5c': [batch_size, 35, 35, 384], - 'Mixed_5d': [batch_size, 35, 35, 384], - 'Mixed_5e': [batch_size, 35, 35, 384], - # Reduction-A block - 'Mixed_6a': [batch_size, 17, 17, 1024], - # 7 x Inception-B blocks - 'Mixed_6b': [batch_size, 17, 17, 1024], - 'Mixed_6c': [batch_size, 17, 17, 1024], - 'Mixed_6d': [batch_size, 17, 17, 1024], - 'Mixed_6e': [batch_size, 17, 17, 1024], - 'Mixed_6f': [batch_size, 17, 17, 1024], - 'Mixed_6g': [batch_size, 17, 17, 1024], - 'Mixed_6h': [batch_size, 17, 17, 1024], - # Reduction-A block - 'Mixed_7a': [batch_size, 8, 8, 1536], - # 3 x Inception-C blocks - 'Mixed_7b': [batch_size, 8, 8, 1536], - 'Mixed_7c': [batch_size, 8, 8, 1536], - 'Mixed_7d': [batch_size, 8, 8, 1536], - # Logits and predictions - 'AuxLogits': [batch_size, num_classes], - 'PreLogitsFlatten': [batch_size, 1536], - 'Logits': [batch_size, num_classes], - 'Predictions': [batch_size, num_classes]} - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name in endpoints_shapes: - expected_shape = endpoints_shapes[endpoint_name] - self.assertTrue(endpoint_name in end_points) - self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), - expected_shape) - - def testBuildBaseNetwork(self): - batch_size = 5 - height, width = 299, 299 - inputs = tf.random_uniform((batch_size, height, width, 3)) - net, end_points = inception.inception_v4_base(inputs) - self.assertTrue(net.op.name.startswith( - 'InceptionV4/Mixed_7d')) - self.assertListEqual(net.get_shape().as_list(), [batch_size, 8, 8, 1536]) - expected_endpoints = [ - 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a', - 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', - 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', - 'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', - 'Mixed_7b', 'Mixed_7c', 'Mixed_7d'] - self.assertItemsEqual(end_points.keys(), expected_endpoints) - for name, op in end_points.iteritems(): - self.assertTrue(op.name.startswith('InceptionV4/' + name)) - - def testBuildOnlyUpToFinalEndpoint(self): - batch_size = 5 - height, width = 299, 299 - all_endpoints = [ - 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a', - 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', - 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', - 'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', - 'Mixed_7b', 'Mixed_7c', 'Mixed_7d'] - for index, endpoint in enumerate(all_endpoints): - with tf.Graph().as_default(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - out_tensor, end_points = inception.inception_v4_base( - inputs, final_endpoint=endpoint) - self.assertTrue(out_tensor.op.name.startswith( - 'InceptionV4/' + endpoint)) - self.assertItemsEqual(all_endpoints[:index+1], end_points) - - def testVariablesSetDevice(self): - batch_size = 5 - height, width = 299, 299 - num_classes = 1000 - inputs = tf.random_uniform((batch_size, height, width, 3)) - # Force all Variables to reside on the device. - with tf.variable_scope('on_cpu'), tf.device('/cpu:0'): - inception.inception_v4(inputs, num_classes) - with tf.variable_scope('on_gpu'), tf.device('/gpu:0'): - inception.inception_v4(inputs, num_classes) - for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'): - self.assertDeviceEqual(v.device, '/cpu:0') - for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'): - self.assertDeviceEqual(v.device, '/gpu:0') - - def testHalfSizeImages(self): - batch_size = 5 - height, width = 150, 150 - num_classes = 1000 - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, end_points = inception.inception_v4(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - pre_pool = end_points['Mixed_7d'] - self.assertListEqual(pre_pool.get_shape().as_list(), - [batch_size, 3, 3, 1536]) - - def testUnknownBatchSize(self): - batch_size = 1 - height, width = 299, 299 - num_classes = 1000 - with self.test_session() as sess: - inputs = tf.placeholder(tf.float32, (None, height, width, 3)) - logits, _ = inception.inception_v4(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [None, num_classes]) - images = tf.random_uniform((batch_size, height, width, 3)) - sess.run(tf.global_variables_initializer()) - output = sess.run(logits, {inputs: images.eval()}) - self.assertEquals(output.shape, (batch_size, num_classes)) - - def testEvaluation(self): - batch_size = 2 - height, width = 299, 299 - num_classes = 1000 - with self.test_session() as sess: - eval_inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = inception.inception_v4(eval_inputs, - num_classes, - is_training=False) - predictions = tf.argmax(logits, 1) - sess.run(tf.global_variables_initializer()) - output = sess.run(predictions) - self.assertEquals(output.shape, (batch_size,)) - - def testTrainEvalWithReuse(self): - train_batch_size = 5 - eval_batch_size = 2 - height, width = 150, 150 - num_classes = 1000 - with self.test_session() as sess: - train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) - inception.inception_v4(train_inputs, num_classes) - eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) - logits, _ = inception.inception_v4(eval_inputs, - num_classes, - is_training=False, - reuse=True) - predictions = tf.argmax(logits, 1) - sess.run(tf.global_variables_initializer()) - output = sess.run(predictions) - self.assertEquals(output.shape, (eval_batch_size,)) - - -if __name__ == '__main__': - tf.test.main() diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/lenet.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/lenet.py deleted file mode 100755 index 789d2bd..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/lenet.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains a variant of the LeNet model definition.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -slim = tf.contrib.slim - - -def lenet(images, num_classes=10, is_training=False, - dropout_keep_prob=0.5, - prediction_fn=slim.softmax, - scope='LeNet'): - """Creates a variant of the LeNet model. - - Note that since the output is a set of 'logits', the values fall in the - interval of (-infinity, infinity). Consequently, to convert the outputs to a - probability distribution over the characters, one will need to convert them - using the softmax function: - - logits = lenet.lenet(images, is_training=False) - probabilities = tf.nn.softmax(logits) - predictions = tf.argmax(logits, 1) - - Args: - images: A batch of `Tensors` of size [batch_size, height, width, channels]. - num_classes: the number of classes in the dataset. - is_training: specifies whether or not we're currently training the model. - This variable will determine the behaviour of the dropout layer. - dropout_keep_prob: the percentage of activation values that are retained. - prediction_fn: a function to get predictions out of logits. - scope: Optional variable_scope. - - Returns: - logits: the pre-softmax activations, a tensor of size - [batch_size, `num_classes`] - end_points: a dictionary from components of the network to the corresponding - activation. - """ - end_points = {} - - with tf.variable_scope(scope, 'LeNet', [images, num_classes]): - net = slim.conv2d(images, 32, [5, 5], scope='conv1') - net = slim.max_pool2d(net, [2, 2], 2, scope='pool1') - net = slim.conv2d(net, 64, [5, 5], scope='conv2') - net = slim.max_pool2d(net, [2, 2], 2, scope='pool2') - net = slim.flatten(net) - end_points['Flatten'] = net - - net = slim.fully_connected(net, 1024, scope='fc3') - net = slim.dropout(net, dropout_keep_prob, is_training=is_training, - scope='dropout3') - logits = slim.fully_connected(net, num_classes, activation_fn=None, - scope='fc4') - - end_points['Logits'] = logits - end_points['Predictions'] = prediction_fn(logits, scope='Predictions') - - return logits, end_points -lenet.default_image_size = 28 - - -def lenet_arg_scope(weight_decay=0.0): - """Defines the default lenet argument scope. - - Args: - weight_decay: The weight decay to use for regularizing the model. - - Returns: - An `arg_scope` to use for the inception v3 model. - """ - with slim.arg_scope( - [slim.conv2d, slim.fully_connected], - weights_regularizer=slim.l2_regularizer(weight_decay), - weights_initializer=tf.truncated_normal_initializer(stddev=0.1), - activation_fn=tf.nn.relu) as sc: - return sc diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1.md b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1.md deleted file mode 100755 index 342f305..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1.md +++ /dev/null @@ -1,47 +0,0 @@ -# MobileNet_v1 - -[MobileNets](https://arxiv.org/abs/1704.04861) are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices with [TensorFlow Mobile](https://www.tensorflow.org/mobile/). - -MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature. - -![alt text](mobilenet_v1.png "MobileNet Graph") - -# Pre-trained Models - -Choose the right MobileNet model to fit your latency and size budget. The size of the network in memory and on disk is proportional to the number of parameters. The latency and power usage of the network scales with the number of Multiply-Accumulates (MACs) which measures the number of fused Multiplication and Addition operations. These MobileNet models have been trained on the -[ILSVRC-2012-CLS](http://www.image-net.org/challenges/LSVRC/2012/) -image classification dataset. Accuracies were computed by evaluating using a single image crop. - -Model Checkpoint | Million MACs | Million Parameters | Top-1 Accuracy| Top-5 Accuracy | -:----:|:------------:|:----------:|:-------:|:-------:| -[MobileNet_v1_1.0_224](http://download.tensorflow.org/models/mobilenet_v1_1.0_224_2017_06_14.tar.gz)|569|4.24|70.7|89.5| -[MobileNet_v1_1.0_192](http://download.tensorflow.org/models/mobilenet_v1_1.0_192_2017_06_14.tar.gz)|418|4.24|69.3|88.9| -[MobileNet_v1_1.0_160](http://download.tensorflow.org/models/mobilenet_v1_1.0_160_2017_06_14.tar.gz)|291|4.24|67.2|87.5| -[MobileNet_v1_1.0_128](http://download.tensorflow.org/models/mobilenet_v1_1.0_128_2017_06_14.tar.gz)|186|4.24|64.1|85.3| -[MobileNet_v1_0.75_224](http://download.tensorflow.org/models/mobilenet_v1_0.75_224_2017_06_14.tar.gz)|317|2.59|68.4|88.2| -[MobileNet_v1_0.75_192](http://download.tensorflow.org/models/mobilenet_v1_0.75_192_2017_06_14.tar.gz)|233|2.59|67.4|87.3| -[MobileNet_v1_0.75_160](http://download.tensorflow.org/models/mobilenet_v1_0.75_160_2017_06_14.tar.gz)|162|2.59|65.2|86.1| -[MobileNet_v1_0.75_128](http://download.tensorflow.org/models/mobilenet_v1_0.75_128_2017_06_14.tar.gz)|104|2.59|61.8|83.6| -[MobileNet_v1_0.50_224](http://download.tensorflow.org/models/mobilenet_v1_0.50_224_2017_06_14.tar.gz)|150|1.34|64.0|85.4| -[MobileNet_v1_0.50_192](http://download.tensorflow.org/models/mobilenet_v1_0.50_192_2017_06_14.tar.gz)|110|1.34|62.1|84.0| -[MobileNet_v1_0.50_160](http://download.tensorflow.org/models/mobilenet_v1_0.50_160_2017_06_14.tar.gz)|77|1.34|59.9|82.5| -[MobileNet_v1_0.50_128](http://download.tensorflow.org/models/mobilenet_v1_0.50_128_2017_06_14.tar.gz)|49|1.34|56.2|79.6| -[MobileNet_v1_0.25_224](http://download.tensorflow.org/models/mobilenet_v1_0.25_224_2017_06_14.tar.gz)|41|0.47|50.6|75.0| -[MobileNet_v1_0.25_192](http://download.tensorflow.org/models/mobilenet_v1_0.25_192_2017_06_14.tar.gz)|34|0.47|49.0|73.6| -[MobileNet_v1_0.25_160](http://download.tensorflow.org/models/mobilenet_v1_0.25_160_2017_06_14.tar.gz)|21|0.47|46.0|70.7| -[MobileNet_v1_0.25_128](http://download.tensorflow.org/models/mobilenet_v1_0.25_128_2017_06_14.tar.gz)|14|0.47|41.3|66.2| - - -Here is an example of how to download the MobileNet_v1_1.0_224 checkpoint: - -```shell -$ CHECKPOINT_DIR=/tmp/checkpoints -$ mkdir ${CHECKPOINT_DIR} -$ wget http://download.tensorflow.org/models/mobilenet_v1_1.0_224_2017_06_14.tar.gz -$ tar -xvf mobilenet_v1_1.0_224_2017_06_14.tar.gz -$ mv mobilenet_v1_1.0_224.ckpt.* ${CHECKPOINT_DIR} -$ rm mobilenet_v1_1.0_224_2017_06_14.tar.gz -``` -More information on integrating MobileNets into your project can be found at the [TF-Slim Image Classification Library](https://github.com/tensorflow/models/blob/master/slim/README.md). - -To get started running models on-device go to [TensorFlow Mobile](https://www.tensorflow.org/mobile/). diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1.png b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1.png deleted file mode 100755 index a458345..0000000 Binary files a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1.png and /dev/null differ diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1.py deleted file mode 100755 index 9b25145..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1.py +++ /dev/null @@ -1,397 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================= -"""MobileNet v1. - -MobileNet is a general architecture and can be used for multiple use cases. -Depending on the use case, it can use different input layer size and different -head (for example: embeddings, localization and classification). - -As described in https://arxiv.org/abs/1704.04861. - - MobileNets: Efficient Convolutional Neural Networks for - Mobile Vision Applications - Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, - Tobias Weyand, Marco Andreetto, Hartwig Adam - -100% Mobilenet V1 (base) with input size 224x224: - -Layer params macs --------------------------------------------------------------------------------- -MobilenetV1/Conv2d_0/Conv2D: 864 10,838,016 -MobilenetV1/Conv2d_1_depthwise/depthwise: 288 3,612,672 -MobilenetV1/Conv2d_1_pointwise/Conv2D: 2,048 25,690,112 -MobilenetV1/Conv2d_2_depthwise/depthwise: 576 1,806,336 -MobilenetV1/Conv2d_2_pointwise/Conv2D: 8,192 25,690,112 -MobilenetV1/Conv2d_3_depthwise/depthwise: 1,152 3,612,672 -MobilenetV1/Conv2d_3_pointwise/Conv2D: 16,384 51,380,224 -MobilenetV1/Conv2d_4_depthwise/depthwise: 1,152 903,168 -MobilenetV1/Conv2d_4_pointwise/Conv2D: 32,768 25,690,112 -MobilenetV1/Conv2d_5_depthwise/depthwise: 2,304 1,806,336 -MobilenetV1/Conv2d_5_pointwise/Conv2D: 65,536 51,380,224 -MobilenetV1/Conv2d_6_depthwise/depthwise: 2,304 451,584 -MobilenetV1/Conv2d_6_pointwise/Conv2D: 131,072 25,690,112 -MobilenetV1/Conv2d_7_depthwise/depthwise: 4,608 903,168 -MobilenetV1/Conv2d_7_pointwise/Conv2D: 262,144 51,380,224 -MobilenetV1/Conv2d_8_depthwise/depthwise: 4,608 903,168 -MobilenetV1/Conv2d_8_pointwise/Conv2D: 262,144 51,380,224 -MobilenetV1/Conv2d_9_depthwise/depthwise: 4,608 903,168 -MobilenetV1/Conv2d_9_pointwise/Conv2D: 262,144 51,380,224 -MobilenetV1/Conv2d_10_depthwise/depthwise: 4,608 903,168 -MobilenetV1/Conv2d_10_pointwise/Conv2D: 262,144 51,380,224 -MobilenetV1/Conv2d_11_depthwise/depthwise: 4,608 903,168 -MobilenetV1/Conv2d_11_pointwise/Conv2D: 262,144 51,380,224 -MobilenetV1/Conv2d_12_depthwise/depthwise: 4,608 225,792 -MobilenetV1/Conv2d_12_pointwise/Conv2D: 524,288 25,690,112 -MobilenetV1/Conv2d_13_depthwise/depthwise: 9,216 451,584 -MobilenetV1/Conv2d_13_pointwise/Conv2D: 1,048,576 51,380,224 --------------------------------------------------------------------------------- -Total: 3,185,088 567,716,352 - - -75% Mobilenet V1 (base) with input size 128x128: - -Layer params macs --------------------------------------------------------------------------------- -MobilenetV1/Conv2d_0/Conv2D: 648 2,654,208 -MobilenetV1/Conv2d_1_depthwise/depthwise: 216 884,736 -MobilenetV1/Conv2d_1_pointwise/Conv2D: 1,152 4,718,592 -MobilenetV1/Conv2d_2_depthwise/depthwise: 432 442,368 -MobilenetV1/Conv2d_2_pointwise/Conv2D: 4,608 4,718,592 -MobilenetV1/Conv2d_3_depthwise/depthwise: 864 884,736 -MobilenetV1/Conv2d_3_pointwise/Conv2D: 9,216 9,437,184 -MobilenetV1/Conv2d_4_depthwise/depthwise: 864 221,184 -MobilenetV1/Conv2d_4_pointwise/Conv2D: 18,432 4,718,592 -MobilenetV1/Conv2d_5_depthwise/depthwise: 1,728 442,368 -MobilenetV1/Conv2d_5_pointwise/Conv2D: 36,864 9,437,184 -MobilenetV1/Conv2d_6_depthwise/depthwise: 1,728 110,592 -MobilenetV1/Conv2d_6_pointwise/Conv2D: 73,728 4,718,592 -MobilenetV1/Conv2d_7_depthwise/depthwise: 3,456 221,184 -MobilenetV1/Conv2d_7_pointwise/Conv2D: 147,456 9,437,184 -MobilenetV1/Conv2d_8_depthwise/depthwise: 3,456 221,184 -MobilenetV1/Conv2d_8_pointwise/Conv2D: 147,456 9,437,184 -MobilenetV1/Conv2d_9_depthwise/depthwise: 3,456 221,184 -MobilenetV1/Conv2d_9_pointwise/Conv2D: 147,456 9,437,184 -MobilenetV1/Conv2d_10_depthwise/depthwise: 3,456 221,184 -MobilenetV1/Conv2d_10_pointwise/Conv2D: 147,456 9,437,184 -MobilenetV1/Conv2d_11_depthwise/depthwise: 3,456 221,184 -MobilenetV1/Conv2d_11_pointwise/Conv2D: 147,456 9,437,184 -MobilenetV1/Conv2d_12_depthwise/depthwise: 3,456 55,296 -MobilenetV1/Conv2d_12_pointwise/Conv2D: 294,912 4,718,592 -MobilenetV1/Conv2d_13_depthwise/depthwise: 6,912 110,592 -MobilenetV1/Conv2d_13_pointwise/Conv2D: 589,824 9,437,184 --------------------------------------------------------------------------------- -Total: 1,800,144 106,002,432 - -""" - -# Tensorflow mandates these. -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from collections import namedtuple - -import tensorflow as tf - -slim = tf.contrib.slim - -# Conv and DepthSepConv namedtuple define layers of the MobileNet architecture -# Conv defines 3x3 convolution layers -# DepthSepConv defines 3x3 depthwise convolution followed by 1x1 convolution. -# stride is the stride of the convolution -# depth is the number of channels or filters in a layer -Conv = namedtuple('Conv', ['kernel', 'stride', 'depth']) -DepthSepConv = namedtuple('DepthSepConv', ['kernel', 'stride', 'depth']) - -# _CONV_DEFS specifies the MobileNet body -_CONV_DEFS = [ - Conv(kernel=[3, 3], stride=2, depth=32), - DepthSepConv(kernel=[3, 3], stride=1, depth=64), - DepthSepConv(kernel=[3, 3], stride=2, depth=128), - DepthSepConv(kernel=[3, 3], stride=1, depth=128), - DepthSepConv(kernel=[3, 3], stride=2, depth=256), - DepthSepConv(kernel=[3, 3], stride=1, depth=256), - DepthSepConv(kernel=[3, 3], stride=2, depth=512), - DepthSepConv(kernel=[3, 3], stride=1, depth=512), - DepthSepConv(kernel=[3, 3], stride=1, depth=512), - DepthSepConv(kernel=[3, 3], stride=1, depth=512), - DepthSepConv(kernel=[3, 3], stride=1, depth=512), - DepthSepConv(kernel=[3, 3], stride=1, depth=512), - DepthSepConv(kernel=[3, 3], stride=2, depth=1024), - DepthSepConv(kernel=[3, 3], stride=1, depth=1024) -] - - -def mobilenet_v1_base(inputs, - final_endpoint='Conv2d_13_pointwise', - min_depth=8, - depth_multiplier=1.0, - conv_defs=None, - output_stride=None, - scope=None): - """Mobilenet v1. - - Constructs a Mobilenet v1 network from inputs to the given final endpoint. - - Args: - inputs: a tensor of shape [batch_size, height, width, channels]. - final_endpoint: specifies the endpoint to construct the network up to. It - can be one of ['Conv2d_0', 'Conv2d_1_pointwise', 'Conv2d_2_pointwise', - 'Conv2d_3_pointwise', 'Conv2d_4_pointwise', 'Conv2d_5'_pointwise, - 'Conv2d_6_pointwise', 'Conv2d_7_pointwise', 'Conv2d_8_pointwise', - 'Conv2d_9_pointwise', 'Conv2d_10_pointwise', 'Conv2d_11_pointwise', - 'Conv2d_12_pointwise', 'Conv2d_13_pointwise']. - min_depth: Minimum depth value (number of channels) for all convolution ops. - Enforced when depth_multiplier < 1, and not an active constraint when - depth_multiplier >= 1. - depth_multiplier: Float multiplier for the depth (number of channels) - for all convolution ops. The value must be greater than zero. Typical - usage will be to set this value in (0, 1) to reduce the number of - parameters or computation cost of the model. - conv_defs: A list of ConvDef namedtuples specifying the net architecture. - output_stride: An integer that specifies the requested ratio of input to - output spatial resolution. If not None, then we invoke atrous convolution - if necessary to prevent the network from reducing the spatial resolution - of the activation maps. Allowed values are 8 (accurate fully convolutional - mode), 16 (fast fully convolutional mode), 32 (classification mode). - scope: Optional variable_scope. - - Returns: - tensor_out: output tensor corresponding to the final_endpoint. - end_points: a set of activations for external use, for example summaries or - losses. - - Raises: - ValueError: if final_endpoint is not set to one of the predefined values, - or depth_multiplier <= 0, or the target output_stride is not - allowed. - """ - depth = lambda d: max(int(d * depth_multiplier), min_depth) - end_points = {} - - # Used to find thinned depths for each layer. - if depth_multiplier <= 0: - raise ValueError('depth_multiplier is not greater than zero.') - - if conv_defs is None: - conv_defs = _CONV_DEFS - - if output_stride is not None and output_stride not in [8, 16, 32]: - raise ValueError('Only allowed output_stride values are 8, 16, 32.') - - with tf.variable_scope(scope, 'MobilenetV1', [inputs]): - with slim.arg_scope([slim.conv2d, slim.separable_conv2d], padding='SAME'): - # The current_stride variable keeps track of the output stride of the - # activations, i.e., the running product of convolution strides up to the - # current network layer. This allows us to invoke atrous convolution - # whenever applying the next convolution would result in the activations - # having output stride larger than the target output_stride. - current_stride = 1 - - # The atrous convolution rate parameter. - rate = 1 - - net = inputs - for i, conv_def in enumerate(conv_defs): - end_point_base = 'Conv2d_%d' % i - - if output_stride is not None and current_stride == output_stride: - # If we have reached the target output_stride, then we need to employ - # atrous convolution with stride=1 and multiply the atrous rate by the - # current unit's stride for use in subsequent layers. - layer_stride = 1 - layer_rate = rate - rate *= conv_def.stride - else: - layer_stride = conv_def.stride - layer_rate = 1 - current_stride *= conv_def.stride - - if isinstance(conv_def, Conv): - end_point = end_point_base - net = slim.conv2d(net, depth(conv_def.depth), conv_def.kernel, - stride=conv_def.stride, - normalizer_fn=slim.batch_norm, - scope=end_point) - end_points[end_point] = net - if end_point == final_endpoint: - return net, end_points - - elif isinstance(conv_def, DepthSepConv): - end_point = end_point_base + '_depthwise' - - # By passing filters=None - # separable_conv2d produces only a depthwise convolution layer - net = slim.separable_conv2d(net, None, conv_def.kernel, - depth_multiplier=1, - stride=layer_stride, - rate=layer_rate, - normalizer_fn=slim.batch_norm, - scope=end_point) - - end_points[end_point] = net - if end_point == final_endpoint: - return net, end_points - - end_point = end_point_base + '_pointwise' - - net = slim.conv2d(net, depth(conv_def.depth), [1, 1], - stride=1, - normalizer_fn=slim.batch_norm, - scope=end_point) - - end_points[end_point] = net - if end_point == final_endpoint: - return net, end_points - else: - raise ValueError('Unknown convolution type %s for layer %d' - % (conv_def.ltype, i)) - raise ValueError('Unknown final endpoint %s' % final_endpoint) - - -def mobilenet_v1(inputs, - num_classes=1000, - dropout_keep_prob=0.999, - is_training=True, - min_depth=8, - depth_multiplier=1.0, - conv_defs=None, - prediction_fn=tf.contrib.layers.softmax, - spatial_squeeze=True, - reuse=None, - scope='MobilenetV1'): - """Mobilenet v1 model for classification. - - Args: - inputs: a tensor of shape [batch_size, height, width, channels]. - num_classes: number of predicted classes. - dropout_keep_prob: the percentage of activation values that are retained. - is_training: whether is training or not. - min_depth: Minimum depth value (number of channels) for all convolution ops. - Enforced when depth_multiplier < 1, and not an active constraint when - depth_multiplier >= 1. - depth_multiplier: Float multiplier for the depth (number of channels) - for all convolution ops. The value must be greater than zero. Typical - usage will be to set this value in (0, 1) to reduce the number of - parameters or computation cost of the model. - conv_defs: A list of ConvDef namedtuples specifying the net architecture. - prediction_fn: a function to get predictions out of logits. - spatial_squeeze: if True, logits is of shape is [B, C], if false logits is - of shape [B, 1, 1, C], where B is batch_size and C is number of classes. - reuse: whether or not the network and its variables should be reused. To be - able to reuse 'scope' must be given. - scope: Optional variable_scope. - - Returns: - logits: the pre-softmax activations, a tensor of size - [batch_size, num_classes] - end_points: a dictionary from components of the network to the corresponding - activation. - - Raises: - ValueError: Input rank is invalid. - """ - input_shape = inputs.get_shape().as_list() - if len(input_shape) != 4: - raise ValueError('Invalid input tensor rank, expected 4, was: %d' % - len(input_shape)) - - with tf.variable_scope(scope, 'MobilenetV1', [inputs, num_classes], - reuse=reuse) as scope: - with slim.arg_scope([slim.batch_norm, slim.dropout], - is_training=is_training): - net, end_points = mobilenet_v1_base(inputs, scope=scope, - min_depth=min_depth, - depth_multiplier=depth_multiplier, - conv_defs=conv_defs) - with tf.variable_scope('Logits'): - kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7]) - net = slim.avg_pool2d(net, kernel_size, padding='VALID', - scope='AvgPool_1a') - end_points['AvgPool_1a'] = net - # 1 x 1 x 1024 - net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b') - logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, - normalizer_fn=None, scope='Conv2d_1c_1x1') - if spatial_squeeze: - logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze') - end_points['Logits'] = logits - if prediction_fn: - end_points['Predictions'] = prediction_fn(logits, scope='Predictions') - return logits, end_points - -mobilenet_v1.default_image_size = 224 - - -def _reduced_kernel_size_for_small_input(input_tensor, kernel_size): - """Define kernel size which is automatically reduced for small input. - - If the shape of the input images is unknown at graph construction time this - function assumes that the input images are large enough. - - Args: - input_tensor: input tensor of size [batch_size, height, width, channels]. - kernel_size: desired kernel size of length 2: [kernel_height, kernel_width] - - Returns: - a tensor with the kernel size. - """ - shape = input_tensor.get_shape().as_list() - if shape[1] is None or shape[2] is None: - kernel_size_out = kernel_size - else: - kernel_size_out = [min(shape[1], kernel_size[0]), - min(shape[2], kernel_size[1])] - return kernel_size_out - - -def mobilenet_v1_arg_scope(is_training=True, - weight_decay=0.00004, - stddev=0.09, - regularize_depthwise=False): - """Defines the default MobilenetV1 arg scope. - - Args: - is_training: Whether or not we're training the model. - weight_decay: The weight decay to use for regularizing the model. - stddev: The standard deviation of the trunctated normal weight initializer. - regularize_depthwise: Whether or not apply regularization on depthwise. - - Returns: - An `arg_scope` to use for the mobilenet v1 model. - """ - batch_norm_params = { - 'is_training': is_training, - 'center': True, - 'scale': True, - 'decay': 0.9997, - 'epsilon': 0.001, - } - - # Set weight_decay for weights in Conv and DepthSepConv layers. - weights_init = tf.truncated_normal_initializer(stddev=stddev) - regularizer = tf.contrib.layers.l2_regularizer(weight_decay) - if regularize_depthwise: - depthwise_regularizer = regularizer - else: - depthwise_regularizer = None - with slim.arg_scope([slim.conv2d, slim.separable_conv2d], - weights_initializer=weights_init, - activation_fn=tf.nn.relu6, normalizer_fn=slim.batch_norm): - with slim.arg_scope([slim.batch_norm], **batch_norm_params): - with slim.arg_scope([slim.conv2d], weights_regularizer=regularizer): - with slim.arg_scope([slim.separable_conv2d], - weights_regularizer=depthwise_regularizer) as sc: - return sc diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1_test.py deleted file mode 100755 index 44e6644..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1_test.py +++ /dev/null @@ -1,450 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================= -"""Tests for MobileNet v1.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np -import tensorflow as tf - -from nets import mobilenet_v1 - -slim = tf.contrib.slim - - -class MobilenetV1Test(tf.test.TestCase): - - def testBuildClassificationNetwork(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - self.assertTrue('Predictions' in end_points) - self.assertListEqual(end_points['Predictions'].get_shape().as_list(), - [batch_size, num_classes]) - - def testBuildBaseNetwork(self): - batch_size = 5 - height, width = 224, 224 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - net, end_points = mobilenet_v1.mobilenet_v1_base(inputs) - self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_13')) - self.assertListEqual(net.get_shape().as_list(), - [batch_size, 7, 7, 1024]) - expected_endpoints = ['Conv2d_0', - 'Conv2d_1_depthwise', 'Conv2d_1_pointwise', - 'Conv2d_2_depthwise', 'Conv2d_2_pointwise', - 'Conv2d_3_depthwise', 'Conv2d_3_pointwise', - 'Conv2d_4_depthwise', 'Conv2d_4_pointwise', - 'Conv2d_5_depthwise', 'Conv2d_5_pointwise', - 'Conv2d_6_depthwise', 'Conv2d_6_pointwise', - 'Conv2d_7_depthwise', 'Conv2d_7_pointwise', - 'Conv2d_8_depthwise', 'Conv2d_8_pointwise', - 'Conv2d_9_depthwise', 'Conv2d_9_pointwise', - 'Conv2d_10_depthwise', 'Conv2d_10_pointwise', - 'Conv2d_11_depthwise', 'Conv2d_11_pointwise', - 'Conv2d_12_depthwise', 'Conv2d_12_pointwise', - 'Conv2d_13_depthwise', 'Conv2d_13_pointwise'] - self.assertItemsEqual(end_points.keys(), expected_endpoints) - - def testBuildOnlyUptoFinalEndpoint(self): - batch_size = 5 - height, width = 224, 224 - endpoints = ['Conv2d_0', - 'Conv2d_1_depthwise', 'Conv2d_1_pointwise', - 'Conv2d_2_depthwise', 'Conv2d_2_pointwise', - 'Conv2d_3_depthwise', 'Conv2d_3_pointwise', - 'Conv2d_4_depthwise', 'Conv2d_4_pointwise', - 'Conv2d_5_depthwise', 'Conv2d_5_pointwise', - 'Conv2d_6_depthwise', 'Conv2d_6_pointwise', - 'Conv2d_7_depthwise', 'Conv2d_7_pointwise', - 'Conv2d_8_depthwise', 'Conv2d_8_pointwise', - 'Conv2d_9_depthwise', 'Conv2d_9_pointwise', - 'Conv2d_10_depthwise', 'Conv2d_10_pointwise', - 'Conv2d_11_depthwise', 'Conv2d_11_pointwise', - 'Conv2d_12_depthwise', 'Conv2d_12_pointwise', - 'Conv2d_13_depthwise', 'Conv2d_13_pointwise'] - for index, endpoint in enumerate(endpoints): - with tf.Graph().as_default(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - out_tensor, end_points = mobilenet_v1.mobilenet_v1_base( - inputs, final_endpoint=endpoint) - self.assertTrue(out_tensor.op.name.startswith( - 'MobilenetV1/' + endpoint)) - self.assertItemsEqual(endpoints[:index+1], end_points) - - def testBuildCustomNetworkUsingConvDefs(self): - batch_size = 5 - height, width = 224, 224 - conv_defs = [ - mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32), - mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64), - mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128), - mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512) - ] - - inputs = tf.random_uniform((batch_size, height, width, 3)) - net, end_points = mobilenet_v1.mobilenet_v1_base( - inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs) - self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3')) - self.assertListEqual(net.get_shape().as_list(), - [batch_size, 56, 56, 512]) - expected_endpoints = ['Conv2d_0', - 'Conv2d_1_depthwise', 'Conv2d_1_pointwise', - 'Conv2d_2_depthwise', 'Conv2d_2_pointwise', - 'Conv2d_3_depthwise', 'Conv2d_3_pointwise'] - self.assertItemsEqual(end_points.keys(), expected_endpoints) - - def testBuildAndCheckAllEndPointsUptoConv2d_13(self): - batch_size = 5 - height, width = 224, 224 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - with slim.arg_scope([slim.conv2d, slim.separable_conv2d], - normalizer_fn=slim.batch_norm): - _, end_points = mobilenet_v1.mobilenet_v1_base( - inputs, final_endpoint='Conv2d_13_pointwise') - endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32], - 'Conv2d_1_depthwise': [batch_size, 112, 112, 32], - 'Conv2d_1_pointwise': [batch_size, 112, 112, 64], - 'Conv2d_2_depthwise': [batch_size, 56, 56, 64], - 'Conv2d_2_pointwise': [batch_size, 56, 56, 128], - 'Conv2d_3_depthwise': [batch_size, 56, 56, 128], - 'Conv2d_3_pointwise': [batch_size, 56, 56, 128], - 'Conv2d_4_depthwise': [batch_size, 28, 28, 128], - 'Conv2d_4_pointwise': [batch_size, 28, 28, 256], - 'Conv2d_5_depthwise': [batch_size, 28, 28, 256], - 'Conv2d_5_pointwise': [batch_size, 28, 28, 256], - 'Conv2d_6_depthwise': [batch_size, 14, 14, 256], - 'Conv2d_6_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_7_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_7_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_8_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_8_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_9_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_9_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_10_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_10_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_11_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_11_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_12_depthwise': [batch_size, 7, 7, 512], - 'Conv2d_12_pointwise': [batch_size, 7, 7, 1024], - 'Conv2d_13_depthwise': [batch_size, 7, 7, 1024], - 'Conv2d_13_pointwise': [batch_size, 7, 7, 1024]} - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name, expected_shape in endpoints_shapes.iteritems(): - self.assertTrue(endpoint_name in end_points) - self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), - expected_shape) - - def testOutputStride16BuildAndCheckAllEndPointsUptoConv2d_13(self): - batch_size = 5 - height, width = 224, 224 - output_stride = 16 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - with slim.arg_scope([slim.conv2d, slim.separable_conv2d], - normalizer_fn=slim.batch_norm): - _, end_points = mobilenet_v1.mobilenet_v1_base( - inputs, output_stride=output_stride, - final_endpoint='Conv2d_13_pointwise') - endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32], - 'Conv2d_1_depthwise': [batch_size, 112, 112, 32], - 'Conv2d_1_pointwise': [batch_size, 112, 112, 64], - 'Conv2d_2_depthwise': [batch_size, 56, 56, 64], - 'Conv2d_2_pointwise': [batch_size, 56, 56, 128], - 'Conv2d_3_depthwise': [batch_size, 56, 56, 128], - 'Conv2d_3_pointwise': [batch_size, 56, 56, 128], - 'Conv2d_4_depthwise': [batch_size, 28, 28, 128], - 'Conv2d_4_pointwise': [batch_size, 28, 28, 256], - 'Conv2d_5_depthwise': [batch_size, 28, 28, 256], - 'Conv2d_5_pointwise': [batch_size, 28, 28, 256], - 'Conv2d_6_depthwise': [batch_size, 14, 14, 256], - 'Conv2d_6_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_7_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_7_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_8_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_8_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_9_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_9_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_10_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_10_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_11_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_11_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_12_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_12_pointwise': [batch_size, 14, 14, 1024], - 'Conv2d_13_depthwise': [batch_size, 14, 14, 1024], - 'Conv2d_13_pointwise': [batch_size, 14, 14, 1024]} - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name, expected_shape in endpoints_shapes.iteritems(): - self.assertTrue(endpoint_name in end_points) - self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), - expected_shape) - - def testOutputStride8BuildAndCheckAllEndPointsUptoConv2d_13(self): - batch_size = 5 - height, width = 224, 224 - output_stride = 8 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - with slim.arg_scope([slim.conv2d, slim.separable_conv2d], - normalizer_fn=slim.batch_norm): - _, end_points = mobilenet_v1.mobilenet_v1_base( - inputs, output_stride=output_stride, - final_endpoint='Conv2d_13_pointwise') - endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32], - 'Conv2d_1_depthwise': [batch_size, 112, 112, 32], - 'Conv2d_1_pointwise': [batch_size, 112, 112, 64], - 'Conv2d_2_depthwise': [batch_size, 56, 56, 64], - 'Conv2d_2_pointwise': [batch_size, 56, 56, 128], - 'Conv2d_3_depthwise': [batch_size, 56, 56, 128], - 'Conv2d_3_pointwise': [batch_size, 56, 56, 128], - 'Conv2d_4_depthwise': [batch_size, 28, 28, 128], - 'Conv2d_4_pointwise': [batch_size, 28, 28, 256], - 'Conv2d_5_depthwise': [batch_size, 28, 28, 256], - 'Conv2d_5_pointwise': [batch_size, 28, 28, 256], - 'Conv2d_6_depthwise': [batch_size, 28, 28, 256], - 'Conv2d_6_pointwise': [batch_size, 28, 28, 512], - 'Conv2d_7_depthwise': [batch_size, 28, 28, 512], - 'Conv2d_7_pointwise': [batch_size, 28, 28, 512], - 'Conv2d_8_depthwise': [batch_size, 28, 28, 512], - 'Conv2d_8_pointwise': [batch_size, 28, 28, 512], - 'Conv2d_9_depthwise': [batch_size, 28, 28, 512], - 'Conv2d_9_pointwise': [batch_size, 28, 28, 512], - 'Conv2d_10_depthwise': [batch_size, 28, 28, 512], - 'Conv2d_10_pointwise': [batch_size, 28, 28, 512], - 'Conv2d_11_depthwise': [batch_size, 28, 28, 512], - 'Conv2d_11_pointwise': [batch_size, 28, 28, 512], - 'Conv2d_12_depthwise': [batch_size, 28, 28, 512], - 'Conv2d_12_pointwise': [batch_size, 28, 28, 1024], - 'Conv2d_13_depthwise': [batch_size, 28, 28, 1024], - 'Conv2d_13_pointwise': [batch_size, 28, 28, 1024]} - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name, expected_shape in endpoints_shapes.iteritems(): - self.assertTrue(endpoint_name in end_points) - self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), - expected_shape) - - def testBuildAndCheckAllEndPointsApproximateFaceNet(self): - batch_size = 5 - height, width = 128, 128 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - with slim.arg_scope([slim.conv2d, slim.separable_conv2d], - normalizer_fn=slim.batch_norm): - _, end_points = mobilenet_v1.mobilenet_v1_base( - inputs, final_endpoint='Conv2d_13_pointwise', depth_multiplier=0.75) - # For the Conv2d_0 layer FaceNet has depth=16 - endpoints_shapes = {'Conv2d_0': [batch_size, 64, 64, 24], - 'Conv2d_1_depthwise': [batch_size, 64, 64, 24], - 'Conv2d_1_pointwise': [batch_size, 64, 64, 48], - 'Conv2d_2_depthwise': [batch_size, 32, 32, 48], - 'Conv2d_2_pointwise': [batch_size, 32, 32, 96], - 'Conv2d_3_depthwise': [batch_size, 32, 32, 96], - 'Conv2d_3_pointwise': [batch_size, 32, 32, 96], - 'Conv2d_4_depthwise': [batch_size, 16, 16, 96], - 'Conv2d_4_pointwise': [batch_size, 16, 16, 192], - 'Conv2d_5_depthwise': [batch_size, 16, 16, 192], - 'Conv2d_5_pointwise': [batch_size, 16, 16, 192], - 'Conv2d_6_depthwise': [batch_size, 8, 8, 192], - 'Conv2d_6_pointwise': [batch_size, 8, 8, 384], - 'Conv2d_7_depthwise': [batch_size, 8, 8, 384], - 'Conv2d_7_pointwise': [batch_size, 8, 8, 384], - 'Conv2d_8_depthwise': [batch_size, 8, 8, 384], - 'Conv2d_8_pointwise': [batch_size, 8, 8, 384], - 'Conv2d_9_depthwise': [batch_size, 8, 8, 384], - 'Conv2d_9_pointwise': [batch_size, 8, 8, 384], - 'Conv2d_10_depthwise': [batch_size, 8, 8, 384], - 'Conv2d_10_pointwise': [batch_size, 8, 8, 384], - 'Conv2d_11_depthwise': [batch_size, 8, 8, 384], - 'Conv2d_11_pointwise': [batch_size, 8, 8, 384], - 'Conv2d_12_depthwise': [batch_size, 4, 4, 384], - 'Conv2d_12_pointwise': [batch_size, 4, 4, 768], - 'Conv2d_13_depthwise': [batch_size, 4, 4, 768], - 'Conv2d_13_pointwise': [batch_size, 4, 4, 768]} - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name, expected_shape in endpoints_shapes.iteritems(): - self.assertTrue(endpoint_name in end_points) - self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), - expected_shape) - - def testModelHasExpectedNumberOfParameters(self): - batch_size = 5 - height, width = 224, 224 - inputs = tf.random_uniform((batch_size, height, width, 3)) - with slim.arg_scope([slim.conv2d, slim.separable_conv2d], - normalizer_fn=slim.batch_norm): - mobilenet_v1.mobilenet_v1_base(inputs) - total_params, _ = slim.model_analyzer.analyze_vars( - slim.get_model_variables()) - self.assertAlmostEqual(3217920L, total_params) - - def testBuildEndPointsWithDepthMultiplierLessThanOne(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) - - endpoint_keys = [key for key in end_points.keys() if key.startswith('Conv')] - - _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1( - inputs, num_classes, scope='depth_multiplied_net', - depth_multiplier=0.5) - - for key in endpoint_keys: - original_depth = end_points[key].get_shape().as_list()[3] - new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] - self.assertEqual(0.5 * original_depth, new_depth) - - def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) - - endpoint_keys = [key for key in end_points.keys() - if key.startswith('Mixed') or key.startswith('Conv')] - - _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1( - inputs, num_classes, scope='depth_multiplied_net', - depth_multiplier=2.0) - - for key in endpoint_keys: - original_depth = end_points[key].get_shape().as_list()[3] - new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] - self.assertEqual(2.0 * original_depth, new_depth) - - def testRaiseValueErrorWithInvalidDepthMultiplier(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - with self.assertRaises(ValueError): - _ = mobilenet_v1.mobilenet_v1( - inputs, num_classes, depth_multiplier=-0.1) - with self.assertRaises(ValueError): - _ = mobilenet_v1.mobilenet_v1( - inputs, num_classes, depth_multiplier=0.0) - - def testHalfSizeImages(self): - batch_size = 5 - height, width = 112, 112 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - pre_pool = end_points['Conv2d_13_pointwise'] - self.assertListEqual(pre_pool.get_shape().as_list(), - [batch_size, 4, 4, 1024]) - - def testUnknownImageShape(self): - tf.reset_default_graph() - batch_size = 2 - height, width = 224, 224 - num_classes = 1000 - input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) - with self.test_session() as sess: - inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3)) - logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - pre_pool = end_points['Conv2d_13_pointwise'] - feed_dict = {inputs: input_np} - tf.global_variables_initializer().run() - pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) - self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024]) - - def testUnknowBatchSize(self): - batch_size = 1 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.placeholder(tf.float32, (None, height, width, 3)) - logits, _ = mobilenet_v1.mobilenet_v1(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [None, num_classes]) - images = tf.random_uniform((batch_size, height, width, 3)) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(logits, {inputs: images.eval()}) - self.assertEquals(output.shape, (batch_size, num_classes)) - - def testEvaluation(self): - batch_size = 2 - height, width = 224, 224 - num_classes = 1000 - - eval_inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes, - is_training=False) - predictions = tf.argmax(logits, 1) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(predictions) - self.assertEquals(output.shape, (batch_size,)) - - def testTrainEvalWithReuse(self): - train_batch_size = 5 - eval_batch_size = 2 - height, width = 150, 150 - num_classes = 1000 - - train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) - mobilenet_v1.mobilenet_v1(train_inputs, num_classes) - eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) - logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes, - reuse=True) - predictions = tf.argmax(logits, 1) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(predictions) - self.assertEquals(output.shape, (eval_batch_size,)) - - def testLogitsNotSqueezed(self): - num_classes = 25 - images = tf.random_uniform([1, 224, 224, 3]) - logits, _ = mobilenet_v1.mobilenet_v1(images, - num_classes=num_classes, - spatial_squeeze=False) - - with self.test_session() as sess: - tf.global_variables_initializer().run() - logits_out = sess.run(logits) - self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes]) - - -if __name__ == '__main__': - tf.test.main() diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/nets_factory.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/nets_factory.py deleted file mode 100755 index 7c04161..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/nets_factory.py +++ /dev/null @@ -1,112 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains a factory for building various models.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -import functools - -import tensorflow as tf - -from nets import alexnet -from nets import cifarnet -from nets import inception -from nets import lenet -from nets import mobilenet_v1 -from nets import overfeat -from nets import resnet_v1 -from nets import resnet_v2 -from nets import vgg - -slim = tf.contrib.slim - -networks_map = {'alexnet_v2': alexnet.alexnet_v2, - 'cifarnet': cifarnet.cifarnet, - 'overfeat': overfeat.overfeat, - 'vgg_a': vgg.vgg_a, - 'vgg_16': vgg.vgg_16, - 'vgg_19': vgg.vgg_19, - 'inception_v1': inception.inception_v1, - 'inception_v2': inception.inception_v2, - 'inception_v3': inception.inception_v3, - 'inception_v4': inception.inception_v4, - 'inception_resnet_v2': inception.inception_resnet_v2, - 'lenet': lenet.lenet, - 'resnet_v1_50': resnet_v1.resnet_v1_50, - 'resnet_v1_101': resnet_v1.resnet_v1_101, - 'resnet_v1_152': resnet_v1.resnet_v1_152, - 'resnet_v1_200': resnet_v1.resnet_v1_200, - 'resnet_v2_50': resnet_v2.resnet_v2_50, - 'resnet_v2_101': resnet_v2.resnet_v2_101, - 'resnet_v2_152': resnet_v2.resnet_v2_152, - 'resnet_v2_200': resnet_v2.resnet_v2_200, - 'mobilenet_v1': mobilenet_v1.mobilenet_v1, - } - -arg_scopes_map = {'alexnet_v2': alexnet.alexnet_v2_arg_scope, - 'cifarnet': cifarnet.cifarnet_arg_scope, - 'overfeat': overfeat.overfeat_arg_scope, - 'vgg_a': vgg.vgg_arg_scope, - 'vgg_16': vgg.vgg_arg_scope, - 'vgg_19': vgg.vgg_arg_scope, - 'inception_v1': inception.inception_v3_arg_scope, - 'inception_v2': inception.inception_v3_arg_scope, - 'inception_v3': inception.inception_v3_arg_scope, - 'inception_v4': inception.inception_v4_arg_scope, - 'inception_resnet_v2': - inception.inception_resnet_v2_arg_scope, - 'lenet': lenet.lenet_arg_scope, - 'resnet_v1_50': resnet_v1.resnet_arg_scope, - 'resnet_v1_101': resnet_v1.resnet_arg_scope, - 'resnet_v1_152': resnet_v1.resnet_arg_scope, - 'resnet_v1_200': resnet_v1.resnet_arg_scope, - 'resnet_v2_50': resnet_v2.resnet_arg_scope, - 'resnet_v2_101': resnet_v2.resnet_arg_scope, - 'resnet_v2_152': resnet_v2.resnet_arg_scope, - 'resnet_v2_200': resnet_v2.resnet_arg_scope, - 'mobilenet_v1': mobilenet_v1.mobilenet_v1_arg_scope, - } - - -def get_network_fn(name, num_classes, weight_decay=0.0, is_training=False): - """Returns a network_fn such as `logits, end_points = network_fn(images)`. - - Args: - name: The name of the network. - num_classes: The number of classes to use for classification. - weight_decay: The l2 coefficient for the model weights. - is_training: `True` if the model is being used for training and `False` - otherwise. - - Returns: - network_fn: A function that applies the model to a batch of images. It has - the following signature: - logits, end_points = network_fn(images) - Raises: - ValueError: If network `name` is not recognized. - """ - if name not in networks_map: - raise ValueError('Name of network unknown %s' % name) - arg_scope = arg_scopes_map[name](weight_decay=weight_decay) - func = networks_map[name] - @functools.wraps(func) - def network_fn(images): - with slim.arg_scope(arg_scope): - return func(images, num_classes, is_training=is_training) - if hasattr(func, 'default_image_size'): - network_fn.default_image_size = func.default_image_size - - return network_fn diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/nets_factory_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/nets_factory_test.py deleted file mode 100755 index b4ab1f8..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/nets_factory_test.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright 2016 Google Inc. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -"""Tests for slim.inception.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from nets import nets_factory - -slim = tf.contrib.slim - - -class NetworksTest(tf.test.TestCase): - - def testGetNetworkFn(self): - batch_size = 5 - num_classes = 1000 - for net in nets_factory.networks_map: - with self.test_session(): - net_fn = nets_factory.get_network_fn(net, num_classes) - # Most networks use 224 as their default_image_size - image_size = getattr(net_fn, 'default_image_size', 224) - inputs = tf.random_uniform((batch_size, image_size, image_size, 3)) - logits, end_points = net_fn(inputs) - self.assertTrue(isinstance(logits, tf.Tensor)) - self.assertTrue(isinstance(end_points, dict)) - self.assertEqual(logits.get_shape().as_list()[0], batch_size) - self.assertEqual(logits.get_shape().as_list()[-1], num_classes) - - def testGetNetworkFnArgScope(self): - batch_size = 5 - num_classes = 10 - net = 'cifarnet' - with self.test_session(use_gpu=True): - net_fn = nets_factory.get_network_fn(net, num_classes) - image_size = getattr(net_fn, 'default_image_size', 224) - with slim.arg_scope([slim.model_variable, slim.variable], - device='/CPU:0'): - inputs = tf.random_uniform((batch_size, image_size, image_size, 3)) - net_fn(inputs) - weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, 'CifarNet/conv1')[0] - self.assertDeviceEqual('/CPU:0', weights.device) - -if __name__ == '__main__': - tf.test.main() diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat.py deleted file mode 100755 index 64a5425..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat.py +++ /dev/null @@ -1,118 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains the model definition for the OverFeat network. - -The definition for the network was obtained from: - OverFeat: Integrated Recognition, Localization and Detection using - Convolutional Networks - Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and - Yann LeCun, 2014 - http://arxiv.org/abs/1312.6229 - -Usage: - with slim.arg_scope(overfeat.overfeat_arg_scope()): - outputs, end_points = overfeat.overfeat(inputs) - -@@overfeat -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -slim = tf.contrib.slim -trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) - - -def overfeat_arg_scope(weight_decay=0.0005): - with slim.arg_scope([slim.conv2d, slim.fully_connected], - activation_fn=tf.nn.relu, - weights_regularizer=slim.l2_regularizer(weight_decay), - biases_initializer=tf.zeros_initializer()): - with slim.arg_scope([slim.conv2d], padding='SAME'): - with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc: - return arg_sc - - -def overfeat(inputs, - num_classes=1000, - is_training=True, - dropout_keep_prob=0.5, - spatial_squeeze=True, - scope='overfeat'): - """Contains the model definition for the OverFeat network. - - The definition for the network was obtained from: - OverFeat: Integrated Recognition, Localization and Detection using - Convolutional Networks - Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and - Yann LeCun, 2014 - http://arxiv.org/abs/1312.6229 - - Note: All the fully_connected layers have been transformed to conv2d layers. - To use in classification mode, resize input to 231x231. To use in fully - convolutional mode, set spatial_squeeze to false. - - Args: - inputs: a tensor of size [batch_size, height, width, channels]. - num_classes: number of predicted classes. - is_training: whether or not the model is being trained. - dropout_keep_prob: the probability that activations are kept in the dropout - layers during training. - spatial_squeeze: whether or not should squeeze the spatial dimensions of the - outputs. Useful to remove unnecessary dimensions for classification. - scope: Optional scope for the variables. - - Returns: - the last op containing the log predictions and end_points dict. - - """ - with tf.variable_scope(scope, 'overfeat', [inputs]) as sc: - end_points_collection = sc.name + '_end_points' - # Collect outputs for conv2d, fully_connected and max_pool2d - with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], - outputs_collections=end_points_collection): - net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID', - scope='conv1') - net = slim.max_pool2d(net, [2, 2], scope='pool1') - net = slim.conv2d(net, 256, [5, 5], padding='VALID', scope='conv2') - net = slim.max_pool2d(net, [2, 2], scope='pool2') - net = slim.conv2d(net, 512, [3, 3], scope='conv3') - net = slim.conv2d(net, 1024, [3, 3], scope='conv4') - net = slim.conv2d(net, 1024, [3, 3], scope='conv5') - net = slim.max_pool2d(net, [2, 2], scope='pool5') - with slim.arg_scope([slim.conv2d], - weights_initializer=trunc_normal(0.005), - biases_initializer=tf.constant_initializer(0.1)): - # Use conv2d instead of fully_connected layers. - net = slim.conv2d(net, 3072, [6, 6], padding='VALID', scope='fc6') - net = slim.dropout(net, dropout_keep_prob, is_training=is_training, - scope='dropout6') - net = slim.conv2d(net, 4096, [1, 1], scope='fc7') - net = slim.dropout(net, dropout_keep_prob, is_training=is_training, - scope='dropout7') - net = slim.conv2d(net, num_classes, [1, 1], - activation_fn=None, - normalizer_fn=None, - biases_initializer=tf.zeros_initializer(), - scope='fc8') - # Convert end_points_collection into a end_point dict. - end_points = slim.utils.convert_collection_to_dict(end_points_collection) - if spatial_squeeze: - net = tf.squeeze(net, [1, 2], name='fc8/squeezed') - end_points[sc.name + '/fc8'] = net - return net, end_points -overfeat.default_image_size = 231 diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat_test.py deleted file mode 100755 index 446f9ac..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat_test.py +++ /dev/null @@ -1,145 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for slim.slim_nets.overfeat.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from nets import overfeat - -slim = tf.contrib.slim - - -class OverFeatTest(tf.test.TestCase): - - def testBuild(self): - batch_size = 5 - height, width = 231, 231 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = overfeat.overfeat(inputs, num_classes) - self.assertEquals(logits.op.name, 'overfeat/fc8/squeezed') - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - - def testFullyConvolutional(self): - batch_size = 1 - height, width = 281, 281 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = overfeat.overfeat(inputs, num_classes, spatial_squeeze=False) - self.assertEquals(logits.op.name, 'overfeat/fc8/BiasAdd') - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, 2, 2, num_classes]) - - def testEndPoints(self): - batch_size = 5 - height, width = 231, 231 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = overfeat.overfeat(inputs, num_classes) - expected_names = ['overfeat/conv1', - 'overfeat/pool1', - 'overfeat/conv2', - 'overfeat/pool2', - 'overfeat/conv3', - 'overfeat/conv4', - 'overfeat/conv5', - 'overfeat/pool5', - 'overfeat/fc6', - 'overfeat/fc7', - 'overfeat/fc8' - ] - self.assertSetEqual(set(end_points.keys()), set(expected_names)) - - def testModelVariables(self): - batch_size = 5 - height, width = 231, 231 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - overfeat.overfeat(inputs, num_classes) - expected_names = ['overfeat/conv1/weights', - 'overfeat/conv1/biases', - 'overfeat/conv2/weights', - 'overfeat/conv2/biases', - 'overfeat/conv3/weights', - 'overfeat/conv3/biases', - 'overfeat/conv4/weights', - 'overfeat/conv4/biases', - 'overfeat/conv5/weights', - 'overfeat/conv5/biases', - 'overfeat/fc6/weights', - 'overfeat/fc6/biases', - 'overfeat/fc7/weights', - 'overfeat/fc7/biases', - 'overfeat/fc8/weights', - 'overfeat/fc8/biases', - ] - model_variables = [v.op.name for v in slim.get_model_variables()] - self.assertSetEqual(set(model_variables), set(expected_names)) - - def testEvaluation(self): - batch_size = 2 - height, width = 231, 231 - num_classes = 1000 - with self.test_session(): - eval_inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = overfeat.overfeat(eval_inputs, is_training=False) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - predictions = tf.argmax(logits, 1) - self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) - - def testTrainEvalWithReuse(self): - train_batch_size = 2 - eval_batch_size = 1 - train_height, train_width = 231, 231 - eval_height, eval_width = 281, 281 - num_classes = 1000 - with self.test_session(): - train_inputs = tf.random_uniform( - (train_batch_size, train_height, train_width, 3)) - logits, _ = overfeat.overfeat(train_inputs) - self.assertListEqual(logits.get_shape().as_list(), - [train_batch_size, num_classes]) - tf.get_variable_scope().reuse_variables() - eval_inputs = tf.random_uniform( - (eval_batch_size, eval_height, eval_width, 3)) - logits, _ = overfeat.overfeat(eval_inputs, is_training=False, - spatial_squeeze=False) - self.assertListEqual(logits.get_shape().as_list(), - [eval_batch_size, 2, 2, num_classes]) - logits = tf.reduce_mean(logits, [1, 2]) - predictions = tf.argmax(logits, 1) - self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) - - def testForward(self): - batch_size = 1 - height, width = 231, 231 - with self.test_session() as sess: - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = overfeat.overfeat(inputs) - sess.run(tf.global_variables_initializer()) - output = sess.run(logits) - self.assertTrue(output.any()) - -if __name__ == '__main__': - tf.test.main() diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_utils.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_utils.py deleted file mode 100755 index bb674ea..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_utils.py +++ /dev/null @@ -1,244 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains building blocks for various versions of Residual Networks. - -Residual networks (ResNets) were proposed in: - Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - Deep Residual Learning for Image Recognition. arXiv:1512.03385, 2015 - -More variants were introduced in: - Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - Identity Mappings in Deep Residual Networks. arXiv: 1603.05027, 2016 - -We can obtain different ResNet variants by changing the network depth, width, -and form of residual unit. This module implements the infrastructure for -building them. Concrete ResNet units and full ResNet networks are implemented in -the accompanying resnet_v1.py and resnet_v2.py modules. - -Compared to https://github.com/KaimingHe/deep-residual-networks, in the current -implementation we subsample the output activations in the last residual unit of -each block, instead of subsampling the input activations in the first residual -unit of each block. The two implementations give identical results but our -implementation is more memory efficient. -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import collections -import tensorflow as tf - -slim = tf.contrib.slim - - -class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])): - """A named tuple describing a ResNet block. - - Its parts are: - scope: The scope of the `Block`. - unit_fn: The ResNet unit function which takes as input a `Tensor` and - returns another `Tensor` with the output of the ResNet unit. - args: A list of length equal to the number of units in the `Block`. The list - contains one (depth, depth_bottleneck, stride) tuple for each unit in the - block to serve as argument to unit_fn. - """ - - -def subsample(inputs, factor, scope=None): - """Subsamples the input along the spatial dimensions. - - Args: - inputs: A `Tensor` of size [batch, height_in, width_in, channels]. - factor: The subsampling factor. - scope: Optional variable_scope. - - Returns: - output: A `Tensor` of size [batch, height_out, width_out, channels] with the - input, either intact (if factor == 1) or subsampled (if factor > 1). - """ - if factor == 1: - return inputs - else: - return slim.max_pool2d(inputs, [1, 1], stride=factor, scope=scope) - - -def conv2d_same(inputs, num_outputs, kernel_size, stride, rate=1, scope=None): - """Strided 2-D convolution with 'SAME' padding. - - When stride > 1, then we do explicit zero-padding, followed by conv2d with - 'VALID' padding. - - Note that - - net = conv2d_same(inputs, num_outputs, 3, stride=stride) - - is equivalent to - - net = slim.conv2d(inputs, num_outputs, 3, stride=1, padding='SAME') - net = subsample(net, factor=stride) - - whereas - - net = slim.conv2d(inputs, num_outputs, 3, stride=stride, padding='SAME') - - is different when the input's height or width is even, which is why we add the - current function. For more details, see ResnetUtilsTest.testConv2DSameEven(). - - Args: - inputs: A 4-D tensor of size [batch, height_in, width_in, channels]. - num_outputs: An integer, the number of output filters. - kernel_size: An int with the kernel_size of the filters. - stride: An integer, the output stride. - rate: An integer, rate for atrous convolution. - scope: Scope. - - Returns: - output: A 4-D tensor of size [batch, height_out, width_out, channels] with - the convolution output. - """ - if stride == 1: - return slim.conv2d(inputs, num_outputs, kernel_size, stride=1, rate=rate, - padding='SAME', scope=scope) - else: - kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1) - pad_total = kernel_size_effective - 1 - pad_beg = pad_total // 2 - pad_end = pad_total - pad_beg - inputs = tf.pad(inputs, - [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]]) - return slim.conv2d(inputs, num_outputs, kernel_size, stride=stride, - rate=rate, padding='VALID', scope=scope) - - -@slim.add_arg_scope -def stack_blocks_dense(net, blocks, output_stride=None, - outputs_collections=None): - """Stacks ResNet `Blocks` and controls output feature density. - - First, this function creates scopes for the ResNet in the form of - 'block_name/unit_1', 'block_name/unit_2', etc. - - Second, this function allows the user to explicitly control the ResNet - output_stride, which is the ratio of the input to output spatial resolution. - This is useful for dense prediction tasks such as semantic segmentation or - object detection. - - Most ResNets consist of 4 ResNet blocks and subsample the activations by a - factor of 2 when transitioning between consecutive ResNet blocks. This results - to a nominal ResNet output_stride equal to 8. If we set the output_stride to - half the nominal network stride (e.g., output_stride=4), then we compute - responses twice. - - Control of the output feature density is implemented by atrous convolution. - - Args: - net: A `Tensor` of size [batch, height, width, channels]. - blocks: A list of length equal to the number of ResNet `Blocks`. Each - element is a ResNet `Block` object describing the units in the `Block`. - output_stride: If `None`, then the output will be computed at the nominal - network stride. If output_stride is not `None`, it specifies the requested - ratio of input to output spatial resolution, which needs to be equal to - the product of unit strides from the start up to some level of the ResNet. - For example, if the ResNet employs units with strides 1, 2, 1, 3, 4, 1, - then valid values for the output_stride are 1, 2, 6, 24 or None (which - is equivalent to output_stride=24). - outputs_collections: Collection to add the ResNet block outputs. - - Returns: - net: Output tensor with stride equal to the specified output_stride. - - Raises: - ValueError: If the target output_stride is not valid. - """ - # The current_stride variable keeps track of the effective stride of the - # activations. This allows us to invoke atrous convolution whenever applying - # the next residual unit would result in the activations having stride larger - # than the target output_stride. - current_stride = 1 - - # The atrous convolution rate parameter. - rate = 1 - - for block in blocks: - with tf.variable_scope(block.scope, 'block', [net]) as sc: - for i, unit in enumerate(block.args): - if output_stride is not None and current_stride > output_stride: - raise ValueError('The target output_stride cannot be reached.') - - with tf.variable_scope('unit_%d' % (i + 1), values=[net]): - # If we have reached the target output_stride, then we need to employ - # atrous convolution with stride=1 and multiply the atrous rate by the - # current unit's stride for use in subsequent layers. - if output_stride is not None and current_stride == output_stride: - net = block.unit_fn(net, rate=rate, **dict(unit, stride=1)) - rate *= unit.get('stride', 1) - - else: - net = block.unit_fn(net, rate=1, **unit) - current_stride *= unit.get('stride', 1) - net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net) - - if output_stride is not None and current_stride != output_stride: - raise ValueError('The target output_stride cannot be reached.') - - return net - - -def resnet_arg_scope(weight_decay=0.0001, - batch_norm_decay=0.997, #0.997 - batch_norm_epsilon=1e-5, - batch_norm_scale=True): - """Defines the default ResNet arg scope. - - TODO(gpapan): The batch-normalization related default values above are - appropriate for use in conjunction with the reference ResNet models - released at https://github.com/KaimingHe/deep-residual-networks. When - training ResNets from scratch, they might need to be tuned. - - Args: - weight_decay: The weight decay to use for regularizing the model. - batch_norm_decay: The moving average decay when estimating layer activation - statistics in batch normalization. - batch_norm_epsilon: Small constant to prevent division by zero when - normalizing activations by their variance in batch normalization. - batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the - activations in the batch normalization layer. - - Returns: - An `arg_scope` to use for the resnet models. - """ - batch_norm_params = { - 'decay': batch_norm_decay, - 'epsilon': batch_norm_epsilon, - 'scale': batch_norm_scale, - 'updates_collections': tf.GraphKeys.UPDATE_OPS, - } - - with slim.arg_scope( - [slim.conv2d], - weights_regularizer=slim.l2_regularizer(weight_decay), - weights_initializer=slim.variance_scaling_initializer(), - activation_fn=tf.nn.relu, - normalizer_fn=slim.batch_norm, - normalizer_params=batch_norm_params): - with slim.arg_scope([slim.batch_norm], **batch_norm_params): - # The following implies padding='SAME' for pool1, which makes feature - # alignment easier for dense prediction tasks. This is also used in - # https://github.com/facebook/fb.resnet.torch. However the accompanying - # code of 'Deep Residual Learning for Image Recognition' uses - # padding='VALID' for pool1. You can switch to that choice by setting - # slim.arg_scope([slim.max_pool2d], padding='VALID'). - with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc: - return arg_sc diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_v1.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_v1.py deleted file mode 100755 index 81f2a83..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_v1.py +++ /dev/null @@ -1,329 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains definitions for the original form of Residual Networks. - -The 'v1' residual networks (ResNets) implemented in this module were proposed -by: -[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - Deep Residual Learning for Image Recognition. arXiv:1512.03385 - -Other variants were introduced in: -[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - Identity Mappings in Deep Residual Networks. arXiv: 1603.05027 - -The networks defined in this module utilize the bottleneck building block of -[1] with projection shortcuts only for increasing depths. They employ batch -normalization *after* every weight layer. This is the architecture used by -MSRA in the Imagenet and MSCOCO 2016 competition models ResNet-101 and -ResNet-152. See [2; Fig. 1a] for a comparison between the current 'v1' -architecture and the alternative 'v2' architecture of [2] which uses batch -normalization *before* every weight layer in the so-called full pre-activation -units. - -Typical use: - - from tensorflow.contrib.slim.slim_nets import resnet_v1 - -ResNet-101 for image classification into 1000 classes: - - # inputs has shape [batch, 224, 224, 3] - with slim.arg_scope(resnet_v1.resnet_arg_scope()): - net, end_points = resnet_v1.resnet_v1_101(inputs, 1000, is_training=False) - -ResNet-101 for semantic segmentation into 21 classes: - - # inputs has shape [batch, 513, 513, 3] - with slim.arg_scope(resnet_v1.resnet_arg_scope()): - net, end_points = resnet_v1.resnet_v1_101(inputs, - 21, - is_training=False, - global_pool=False, - output_stride=16) -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from libs.networks.slim_nets import resnet_utils - - -resnet_arg_scope = resnet_utils.resnet_arg_scope -slim = tf.contrib.slim - - -@slim.add_arg_scope -def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1, - outputs_collections=None, scope=None): - """Bottleneck residual unit variant with BN after convolutions. - - This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for - its definition. Note that we use here the bottleneck variant which has an - extra bottleneck layer. - - When putting together two consecutive ResNet blocks that use this unit, one - should use stride = 2 in the last unit of the first block. - - Args: - inputs: A tensor of size [batch, height, width, channels]. - depth: The depth of the ResNet unit output. - depth_bottleneck: The depth of the bottleneck layers. - stride: The ResNet unit's stride. Determines the amount of downsampling of - the units output compared to its input. - rate: An integer, rate for atrous convolution. - outputs_collections: Collection to add the ResNet unit output. - scope: Optional variable_scope. - - Returns: - The ResNet unit's output. - """ - with tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc: - depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4) - if depth == depth_in: - shortcut = resnet_utils.subsample(inputs, stride, 'shortcut') - else: - shortcut = slim.conv2d(inputs, depth, [1, 1], stride=stride, - activation_fn=None, scope='shortcut') - - residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1, - scope='conv1') - residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride, - rate=rate, scope='conv2') - residual = slim.conv2d(residual, depth, [1, 1], stride=1, - activation_fn=None, scope='conv3') - - output = tf.nn.relu(shortcut + residual) - - return slim.utils.collect_named_outputs(outputs_collections, - sc.original_name_scope, - output) - - -def resnet_v1(inputs, - blocks, - num_classes=None, - is_training=True, - global_pool=True, - output_stride=None, - include_root_block=True, - spatial_squeeze=False, - reuse=None, - scope=None): - """Generator for v1 ResNet models. - - This function generates a family of ResNet v1 models. See the resnet_v1_*() - methods for specific model instantiations, obtained by selecting different - block instantiations that produce ResNets of various depths. - - Training for image classification on Imagenet is usually done with [224, 224] - inputs, resulting in [7, 7] feature maps at the output of the last ResNet - block for the ResNets defined in [1] that have nominal stride equal to 32. - However, for dense prediction tasks we advise that one uses inputs with - spatial dimensions that are multiples of 32 plus 1, e.g., [321, 321]. In - this case the feature maps at the ResNet output will have spatial shape - [(height - 1) / output_stride + 1, (width - 1) / output_stride + 1] - and corners exactly aligned with the input image corners, which greatly - facilitates alignment of the features to the image. Using as input [225, 225] - images results in [8, 8] feature maps at the output of the last ResNet block. - - For dense prediction tasks, the ResNet needs to run in fully-convolutional - (FCN) mode and global_pool needs to be set to False. The ResNets in [1, 2] all - have nominal stride equal to 32 and a good choice in FCN mode is to use - output_stride=16 in order to increase the density of the computed features at - small computational and memory overhead, cf. http://arxiv.org/abs/1606.00915. - - Args: - inputs: A tensor of size [batch, height_in, width_in, channels]. - blocks: A list of length equal to the number of ResNet blocks. Each element - is a resnet_utils.Block object describing the units in the block. - num_classes: Number of predicted classes for classification tasks. If None - we return the features before the logit layer. - is_training: whether is training or not. - global_pool: If True, we perform global average pooling before computing the - logits. Set to True for image classification, False for dense prediction. - output_stride: If None, then the output will be computed at the nominal - network stride. If output_stride is not None, it specifies the requested - ratio of input to output spatial resolution. - include_root_block: If True, include the initial convolution followed by - max-pooling, if False excludes it. - spatial_squeeze: if True, logits is of shape [B, C], if false logits is - of shape [B, 1, 1, C], where B is batch_size and C is number of classes. - reuse: whether or not the network and its variables should be reused. To be - able to reuse 'scope' must be given. - scope: Optional variable_scope. - - Returns: - net: A rank-4 tensor of size [batch, height_out, width_out, channels_out]. - If global_pool is False, then height_out and width_out are reduced by a - factor of output_stride compared to the respective height_in and width_in, - else both height_out and width_out equal one. If num_classes is None, then - net is the output of the last ResNet block, potentially after global - average pooling. If num_classes is not None, net contains the pre-softmax - activations. - end_points: A dictionary from components of the network to the corresponding - activation. - - Raises: - ValueError: If the target output_stride is not valid. - """ - with tf.variable_scope(scope, 'resnet_v1', [inputs], reuse=reuse) as sc: - end_points_collection = sc.name + '_end_points' - with slim.arg_scope([slim.conv2d, bottleneck, - resnet_utils.stack_blocks_dense], - outputs_collections=end_points_collection): - with slim.arg_scope([slim.batch_norm], is_training=is_training): - net = inputs - if include_root_block: - if output_stride is not None: - if output_stride % 4 != 0: - raise ValueError('The output_stride needs to be a multiple of 4.') - output_stride /= 4 - net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1') - net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1') - net = resnet_utils.stack_blocks_dense(net, blocks, output_stride) - if global_pool: - # Global average pooling. - net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True) - # yjr_feature = tf.squeeze(net, [0, 1, 2]) - if num_classes is not None: - net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, - normalizer_fn=None, scope='logits') - if spatial_squeeze: - logits = tf.squeeze(net, [1, 2], name='SpatialSqueeze') - else: - logits = net - # Convert end_points_collection into a dictionary of end_points. - end_points = slim.utils.convert_collection_to_dict( - end_points_collection) - if num_classes is not None: - end_points['predictions'] = slim.softmax(logits, scope='predictions') - - ### - # end_points['yjr_feature'] = yjr_feature - return logits, end_points -resnet_v1.default_image_size = 224 - - -def resnet_v1_block(scope, base_depth, num_units, stride): - """Helper function for creating a resnet_v1 bottleneck block. - - Args: - scope: The scope of the block. - base_depth: The depth of the bottleneck layer for each unit. - num_units: The number of units in the block. - stride: The stride of the block, implemented as a stride in the last unit. - All other units have stride=1. - - Returns: - A resnet_v1 bottleneck block. - """ - return resnet_utils.Block(scope, bottleneck, [{ - 'depth': base_depth * 4, - 'depth_bottleneck': base_depth, - 'stride': 1 - }] * (num_units - 1) + [{ - 'depth': base_depth * 4, - 'depth_bottleneck': base_depth, - 'stride': stride - }]) - - -def resnet_v1_50(inputs, - num_classes=None, - is_training=True, - global_pool=True, - output_stride=None, - spatial_squeeze=True, - reuse=None, - scope='resnet_v1_50'): - """ResNet-50 model of [1]. See resnet_v1() for arg and return description.""" - blocks = [ - resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), - resnet_v1_block('block2', base_depth=128, num_units=4, stride=2), - resnet_v1_block('block3', base_depth=256, num_units=6, stride=2), - resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), - ] - return resnet_v1(inputs, blocks, num_classes, is_training, - global_pool=global_pool, output_stride=output_stride, - include_root_block=True, spatial_squeeze=spatial_squeeze, - reuse=reuse, scope=scope) -resnet_v1_50.default_image_size = resnet_v1.default_image_size - - -def resnet_v1_101(inputs, - num_classes=None, - is_training=True, - global_pool=True, - output_stride=None, - spatial_squeeze=True, - reuse=None, - scope='resnet_v1_101'): - """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" - blocks = [ - resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), - resnet_v1_block('block2', base_depth=128, num_units=4, stride=2), - resnet_v1_block('block3', base_depth=256, num_units=23, stride=2), - resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), - ] - return resnet_v1(inputs, blocks, num_classes, is_training, - global_pool=global_pool, output_stride=output_stride, - include_root_block=True, spatial_squeeze=spatial_squeeze, - reuse=reuse, scope=scope) -resnet_v1_101.default_image_size = resnet_v1.default_image_size - - -def resnet_v1_152(inputs, - num_classes=None, - is_training=True, - global_pool=True, - output_stride=None, - spatial_squeeze=True, - reuse=None, - scope='resnet_v1_152'): - """ResNet-152 model of [1]. See resnet_v1() for arg and return description.""" - blocks = [ - resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), - resnet_v1_block('block2', base_depth=128, num_units=8, stride=2), - resnet_v1_block('block3', base_depth=256, num_units=36, stride=2), - resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), - ] - return resnet_v1(inputs, blocks, num_classes, is_training, - global_pool=global_pool, output_stride=output_stride, - include_root_block=True, spatial_squeeze=spatial_squeeze, - reuse=reuse, scope=scope) -resnet_v1_152.default_image_size = resnet_v1.default_image_size - - -def resnet_v1_200(inputs, - num_classes=None, - is_training=True, - global_pool=True, - output_stride=None, - spatial_squeeze=True, - reuse=None, - scope='resnet_v1_200'): - """ResNet-200 model of [2]. See resnet_v1() for arg and return description.""" - blocks = [ - resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), - resnet_v1_block('block2', base_depth=128, num_units=24, stride=2), - resnet_v1_block('block3', base_depth=256, num_units=36, stride=2), - resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), - ] - return resnet_v1(inputs, blocks, num_classes, is_training, - global_pool=global_pool, output_stride=output_stride, - include_root_block=True, spatial_squeeze=spatial_squeeze, - reuse=reuse, scope=scope) -resnet_v1_200.default_image_size = resnet_v1.default_image_size diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_v1_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_v1_test.py deleted file mode 100755 index 5b27a18..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_v1_test.py +++ /dev/null @@ -1,440 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for slim.slim_nets.resnet_v1.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np -import tensorflow as tf - -from nets import resnet_utils -from nets import resnet_v1 - -slim = tf.contrib.slim - - -def create_test_input(batch_size, height, width, channels): - """Create test input tensor. - - Args: - batch_size: The number of images per batch or `None` if unknown. - height: The height of each image or `None` if unknown. - width: The width of each image or `None` if unknown. - channels: The number of channels per image or `None` if unknown. - - Returns: - Either a placeholder `Tensor` of dimension - [batch_size, height, width, channels] if any of the inputs are `None` or a - constant `Tensor` with the mesh grid values along the spatial dimensions. - """ - if None in [batch_size, height, width, channels]: - return tf.placeholder(tf.float32, (batch_size, height, width, channels)) - else: - return tf.to_float( - np.tile( - np.reshape( - np.reshape(np.arange(height), [height, 1]) + - np.reshape(np.arange(width), [1, width]), - [1, height, width, 1]), - [batch_size, 1, 1, channels])) - - -class ResnetUtilsTest(tf.test.TestCase): - - def testSubsampleThreeByThree(self): - x = tf.reshape(tf.to_float(tf.range(9)), [1, 3, 3, 1]) - x = resnet_utils.subsample(x, 2) - expected = tf.reshape(tf.constant([0, 2, 6, 8]), [1, 2, 2, 1]) - with self.test_session(): - self.assertAllClose(x.eval(), expected.eval()) - - def testSubsampleFourByFour(self): - x = tf.reshape(tf.to_float(tf.range(16)), [1, 4, 4, 1]) - x = resnet_utils.subsample(x, 2) - expected = tf.reshape(tf.constant([0, 2, 8, 10]), [1, 2, 2, 1]) - with self.test_session(): - self.assertAllClose(x.eval(), expected.eval()) - - def testConv2DSameEven(self): - n, n2 = 4, 2 - - # Input image. - x = create_test_input(1, n, n, 1) - - # Convolution kernel. - w = create_test_input(1, 3, 3, 1) - w = tf.reshape(w, [3, 3, 1, 1]) - - tf.get_variable('Conv/weights', initializer=w) - tf.get_variable('Conv/biases', initializer=tf.zeros([1])) - tf.get_variable_scope().reuse_variables() - - y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv') - y1_expected = tf.to_float([[14, 28, 43, 26], - [28, 48, 66, 37], - [43, 66, 84, 46], - [26, 37, 46, 22]]) - y1_expected = tf.reshape(y1_expected, [1, n, n, 1]) - - y2 = resnet_utils.subsample(y1, 2) - y2_expected = tf.to_float([[14, 43], - [43, 84]]) - y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1]) - - y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv') - y3_expected = y2_expected - - y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv') - y4_expected = tf.to_float([[48, 37], - [37, 22]]) - y4_expected = tf.reshape(y4_expected, [1, n2, n2, 1]) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - self.assertAllClose(y1.eval(), y1_expected.eval()) - self.assertAllClose(y2.eval(), y2_expected.eval()) - self.assertAllClose(y3.eval(), y3_expected.eval()) - self.assertAllClose(y4.eval(), y4_expected.eval()) - - def testConv2DSameOdd(self): - n, n2 = 5, 3 - - # Input image. - x = create_test_input(1, n, n, 1) - - # Convolution kernel. - w = create_test_input(1, 3, 3, 1) - w = tf.reshape(w, [3, 3, 1, 1]) - - tf.get_variable('Conv/weights', initializer=w) - tf.get_variable('Conv/biases', initializer=tf.zeros([1])) - tf.get_variable_scope().reuse_variables() - - y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv') - y1_expected = tf.to_float([[14, 28, 43, 58, 34], - [28, 48, 66, 84, 46], - [43, 66, 84, 102, 55], - [58, 84, 102, 120, 64], - [34, 46, 55, 64, 30]]) - y1_expected = tf.reshape(y1_expected, [1, n, n, 1]) - - y2 = resnet_utils.subsample(y1, 2) - y2_expected = tf.to_float([[14, 43, 34], - [43, 84, 55], - [34, 55, 30]]) - y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1]) - - y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv') - y3_expected = y2_expected - - y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv') - y4_expected = y2_expected - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - self.assertAllClose(y1.eval(), y1_expected.eval()) - self.assertAllClose(y2.eval(), y2_expected.eval()) - self.assertAllClose(y3.eval(), y3_expected.eval()) - self.assertAllClose(y4.eval(), y4_expected.eval()) - - def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None): - """A plain ResNet without extra layers before or after the ResNet blocks.""" - with tf.variable_scope(scope, values=[inputs]): - with slim.arg_scope([slim.conv2d], outputs_collections='end_points'): - net = resnet_utils.stack_blocks_dense(inputs, blocks, output_stride) - end_points = slim.utils.convert_collection_to_dict('end_points') - return net, end_points - - def testEndPointsV1(self): - """Test the end points of a tiny v1 bottleneck network.""" - blocks = [ - resnet_v1.resnet_v1_block( - 'block1', base_depth=1, num_units=2, stride=2), - resnet_v1.resnet_v1_block( - 'block2', base_depth=2, num_units=2, stride=1), - ] - inputs = create_test_input(2, 32, 16, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - _, end_points = self._resnet_plain(inputs, blocks, scope='tiny') - expected = [ - 'tiny/block1/unit_1/bottleneck_v1/shortcut', - 'tiny/block1/unit_1/bottleneck_v1/conv1', - 'tiny/block1/unit_1/bottleneck_v1/conv2', - 'tiny/block1/unit_1/bottleneck_v1/conv3', - 'tiny/block1/unit_2/bottleneck_v1/conv1', - 'tiny/block1/unit_2/bottleneck_v1/conv2', - 'tiny/block1/unit_2/bottleneck_v1/conv3', - 'tiny/block2/unit_1/bottleneck_v1/shortcut', - 'tiny/block2/unit_1/bottleneck_v1/conv1', - 'tiny/block2/unit_1/bottleneck_v1/conv2', - 'tiny/block2/unit_1/bottleneck_v1/conv3', - 'tiny/block2/unit_2/bottleneck_v1/conv1', - 'tiny/block2/unit_2/bottleneck_v1/conv2', - 'tiny/block2/unit_2/bottleneck_v1/conv3'] - self.assertItemsEqual(expected, end_points) - - def _stack_blocks_nondense(self, net, blocks): - """A simplified ResNet Block stacker without output stride control.""" - for block in blocks: - with tf.variable_scope(block.scope, 'block', [net]): - for i, unit in enumerate(block.args): - with tf.variable_scope('unit_%d' % (i + 1), values=[net]): - net = block.unit_fn(net, rate=1, **unit) - return net - - def testAtrousValuesBottleneck(self): - """Verify the values of dense feature extraction by atrous convolution. - - Make sure that dense feature extraction by stack_blocks_dense() followed by - subsampling gives identical results to feature extraction at the nominal - network output stride using the simple self._stack_blocks_nondense() above. - """ - block = resnet_v1.resnet_v1_block - blocks = [ - block('block1', base_depth=1, num_units=2, stride=2), - block('block2', base_depth=2, num_units=2, stride=2), - block('block3', base_depth=4, num_units=2, stride=2), - block('block4', base_depth=8, num_units=2, stride=1), - ] - nominal_stride = 8 - - # Test both odd and even input dimensions. - height = 30 - width = 31 - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - with slim.arg_scope([slim.batch_norm], is_training=False): - for output_stride in [1, 2, 4, 8, None]: - with tf.Graph().as_default(): - with self.test_session() as sess: - tf.set_random_seed(0) - inputs = create_test_input(1, height, width, 3) - # Dense feature extraction followed by subsampling. - output = resnet_utils.stack_blocks_dense(inputs, - blocks, - output_stride) - if output_stride is None: - factor = 1 - else: - factor = nominal_stride // output_stride - - output = resnet_utils.subsample(output, factor) - # Make the two networks use the same weights. - tf.get_variable_scope().reuse_variables() - # Feature extraction at the nominal network rate. - expected = self._stack_blocks_nondense(inputs, blocks) - sess.run(tf.global_variables_initializer()) - output, expected = sess.run([output, expected]) - self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4) - - -class ResnetCompleteNetworkTest(tf.test.TestCase): - """Tests with complete small ResNet v1 networks.""" - - def _resnet_small(self, - inputs, - num_classes=None, - is_training=True, - global_pool=True, - output_stride=None, - include_root_block=True, - reuse=None, - scope='resnet_v1_small'): - """A shallow and thin ResNet v1 for faster tests.""" - block = resnet_v1.resnet_v1_block - blocks = [ - block('block1', base_depth=1, num_units=3, stride=2), - block('block2', base_depth=2, num_units=3, stride=2), - block('block3', base_depth=4, num_units=3, stride=2), - block('block4', base_depth=8, num_units=2, stride=1), - ] - return resnet_v1.resnet_v1(inputs, blocks, num_classes, - is_training=is_training, - global_pool=global_pool, - output_stride=output_stride, - include_root_block=include_root_block, - reuse=reuse, - scope=scope) - - def testClassificationEndPoints(self): - global_pool = True - num_classes = 10 - inputs = create_test_input(2, 224, 224, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - logits, end_points = self._resnet_small(inputs, num_classes, - global_pool=global_pool, - scope='resnet') - self.assertTrue(logits.op.name.startswith('resnet/logits')) - self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes]) - self.assertTrue('predictions' in end_points) - self.assertListEqual(end_points['predictions'].get_shape().as_list(), - [2, 1, 1, num_classes]) - - def testClassificationShapes(self): - global_pool = True - num_classes = 10 - inputs = create_test_input(2, 224, 224, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - _, end_points = self._resnet_small(inputs, num_classes, - global_pool=global_pool, - scope='resnet') - endpoint_to_shape = { - 'resnet/block1': [2, 28, 28, 4], - 'resnet/block2': [2, 14, 14, 8], - 'resnet/block3': [2, 7, 7, 16], - 'resnet/block4': [2, 7, 7, 32]} - for endpoint in endpoint_to_shape: - shape = endpoint_to_shape[endpoint] - self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) - - def testFullyConvolutionalEndpointShapes(self): - global_pool = False - num_classes = 10 - inputs = create_test_input(2, 321, 321, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - _, end_points = self._resnet_small(inputs, num_classes, - global_pool=global_pool, - scope='resnet') - endpoint_to_shape = { - 'resnet/block1': [2, 41, 41, 4], - 'resnet/block2': [2, 21, 21, 8], - 'resnet/block3': [2, 11, 11, 16], - 'resnet/block4': [2, 11, 11, 32]} - for endpoint in endpoint_to_shape: - shape = endpoint_to_shape[endpoint] - self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) - - def testRootlessFullyConvolutionalEndpointShapes(self): - global_pool = False - num_classes = 10 - inputs = create_test_input(2, 128, 128, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - _, end_points = self._resnet_small(inputs, num_classes, - global_pool=global_pool, - include_root_block=False, - scope='resnet') - endpoint_to_shape = { - 'resnet/block1': [2, 64, 64, 4], - 'resnet/block2': [2, 32, 32, 8], - 'resnet/block3': [2, 16, 16, 16], - 'resnet/block4': [2, 16, 16, 32]} - for endpoint in endpoint_to_shape: - shape = endpoint_to_shape[endpoint] - self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) - - def testAtrousFullyConvolutionalEndpointShapes(self): - global_pool = False - num_classes = 10 - output_stride = 8 - inputs = create_test_input(2, 321, 321, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - _, end_points = self._resnet_small(inputs, - num_classes, - global_pool=global_pool, - output_stride=output_stride, - scope='resnet') - endpoint_to_shape = { - 'resnet/block1': [2, 41, 41, 4], - 'resnet/block2': [2, 41, 41, 8], - 'resnet/block3': [2, 41, 41, 16], - 'resnet/block4': [2, 41, 41, 32]} - for endpoint in endpoint_to_shape: - shape = endpoint_to_shape[endpoint] - self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) - - def testAtrousFullyConvolutionalValues(self): - """Verify dense feature extraction with atrous convolution.""" - nominal_stride = 32 - for output_stride in [4, 8, 16, 32, None]: - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - with tf.Graph().as_default(): - with self.test_session() as sess: - tf.set_random_seed(0) - inputs = create_test_input(2, 81, 81, 3) - # Dense feature extraction followed by subsampling. - output, _ = self._resnet_small(inputs, None, is_training=False, - global_pool=False, - output_stride=output_stride) - if output_stride is None: - factor = 1 - else: - factor = nominal_stride // output_stride - output = resnet_utils.subsample(output, factor) - # Make the two networks use the same weights. - tf.get_variable_scope().reuse_variables() - # Feature extraction at the nominal network rate. - expected, _ = self._resnet_small(inputs, None, is_training=False, - global_pool=False) - sess.run(tf.global_variables_initializer()) - self.assertAllClose(output.eval(), expected.eval(), - atol=1e-4, rtol=1e-4) - - def testUnknownBatchSize(self): - batch = 2 - height, width = 65, 65 - global_pool = True - num_classes = 10 - inputs = create_test_input(None, height, width, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - logits, _ = self._resnet_small(inputs, num_classes, - global_pool=global_pool, - scope='resnet') - self.assertTrue(logits.op.name.startswith('resnet/logits')) - self.assertListEqual(logits.get_shape().as_list(), - [None, 1, 1, num_classes]) - images = create_test_input(batch, height, width, 3) - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(logits, {inputs: images.eval()}) - self.assertEqual(output.shape, (batch, 1, 1, num_classes)) - - def testFullyConvolutionalUnknownHeightWidth(self): - batch = 2 - height, width = 65, 65 - global_pool = False - inputs = create_test_input(batch, None, None, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - output, _ = self._resnet_small(inputs, None, global_pool=global_pool) - self.assertListEqual(output.get_shape().as_list(), - [batch, None, None, 32]) - images = create_test_input(batch, height, width, 3) - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(output, {inputs: images.eval()}) - self.assertEqual(output.shape, (batch, 3, 3, 32)) - - def testAtrousFullyConvolutionalUnknownHeightWidth(self): - batch = 2 - height, width = 65, 65 - global_pool = False - output_stride = 8 - inputs = create_test_input(batch, None, None, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - output, _ = self._resnet_small(inputs, - None, - global_pool=global_pool, - output_stride=output_stride) - self.assertListEqual(output.get_shape().as_list(), - [batch, None, None, 32]) - images = create_test_input(batch, height, width, 3) - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(output, {inputs: images.eval()}) - self.assertEqual(output.shape, (batch, 9, 9, 32)) - - -if __name__ == '__main__': - tf.test.main() diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_v2.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_v2.py deleted file mode 100755 index 7617701..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_v2.py +++ /dev/null @@ -1,333 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains definitions for the preactivation form of Residual Networks. - -Residual networks (ResNets) were originally proposed in: -[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - Deep Residual Learning for Image Recognition. arXiv:1512.03385 - -The full preactivation 'v2' ResNet variant implemented in this module was -introduced by: -[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - Identity Mappings in Deep Residual Networks. arXiv: 1603.05027 - -The key difference of the full preactivation 'v2' variant compared to the -'v1' variant in [1] is the use of batch normalization before every weight layer. - -Typical use: - - from tensorflow.contrib.slim.slim_nets import resnet_v2 - -ResNet-101 for image classification into 1000 classes: - - # inputs has shape [batch, 224, 224, 3] - with slim.arg_scope(resnet_v2.resnet_arg_scope()): - net, end_points = resnet_v2.resnet_v2_101(inputs, 1000, is_training=False) - -ResNet-101 for semantic segmentation into 21 classes: - - # inputs has shape [batch, 513, 513, 3] - with slim.arg_scope(resnet_v2.resnet_arg_scope(is_training)): - net, end_points = resnet_v2.resnet_v2_101(inputs, - 21, - is_training=False, - global_pool=False, - output_stride=16) -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from nets import resnet_utils - -slim = tf.contrib.slim -resnet_arg_scope = resnet_utils.resnet_arg_scope - - -@slim.add_arg_scope -def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1, - outputs_collections=None, scope=None): - """Bottleneck residual unit variant with BN before convolutions. - - This is the full preactivation residual unit variant proposed in [2]. See - Fig. 1(b) of [2] for its definition. Note that we use here the bottleneck - variant which has an extra bottleneck layer. - - When putting together two consecutive ResNet blocks that use this unit, one - should use stride = 2 in the last unit of the first block. - - Args: - inputs: A tensor of size [batch, height, width, channels]. - depth: The depth of the ResNet unit output. - depth_bottleneck: The depth of the bottleneck layers. - stride: The ResNet unit's stride. Determines the amount of downsampling of - the units output compared to its input. - rate: An integer, rate for atrous convolution. - outputs_collections: Collection to add the ResNet unit output. - scope: Optional variable_scope. - - Returns: - The ResNet unit's output. - """ - with tf.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc: - depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4) - preact = slim.batch_norm(inputs, activation_fn=tf.nn.relu, scope='preact') - if depth == depth_in: - shortcut = resnet_utils.subsample(inputs, stride, 'shortcut') - else: - shortcut = slim.conv2d(preact, depth, [1, 1], stride=stride, - normalizer_fn=None, activation_fn=None, - scope='shortcut') - - residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1, - scope='conv1') - residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride, - rate=rate, scope='conv2') - residual = slim.conv2d(residual, depth, [1, 1], stride=1, - normalizer_fn=None, activation_fn=None, - scope='conv3') - - output = shortcut + residual - - return slim.utils.collect_named_outputs(outputs_collections, - sc.original_name_scope, - output) - - -def resnet_v2(inputs, - blocks, - num_classes=None, - is_training=True, - global_pool=True, - output_stride=None, - include_root_block=True, - spatial_squeeze=False, - reuse=None, - scope=None): - """Generator for v2 (preactivation) ResNet models. - - This function generates a family of ResNet v2 models. See the resnet_v2_*() - methods for specific model instantiations, obtained by selecting different - block instantiations that produce ResNets of various depths. - - Training for image classification on Imagenet is usually done with [224, 224] - inputs, resulting in [7, 7] feature maps at the output of the last ResNet - block for the ResNets defined in [1] that have nominal stride equal to 32. - However, for dense prediction tasks we advise that one uses inputs with - spatial dimensions that are multiples of 32 plus 1, e.g., [321, 321]. In - this case the feature maps at the ResNet output will have spatial shape - [(height - 1) / output_stride + 1, (width - 1) / output_stride + 1] - and corners exactly aligned with the input image corners, which greatly - facilitates alignment of the features to the image. Using as input [225, 225] - images results in [8, 8] feature maps at the output of the last ResNet block. - - For dense prediction tasks, the ResNet needs to run in fully-convolutional - (FCN) mode and global_pool needs to be set to False. The ResNets in [1, 2] all - have nominal stride equal to 32 and a good choice in FCN mode is to use - output_stride=16 in order to increase the density of the computed features at - small computational and memory overhead, cf. http://arxiv.org/abs/1606.00915. - - Args: - inputs: A tensor of size [batch, height_in, width_in, channels]. - blocks: A list of length equal to the number of ResNet blocks. Each element - is a resnet_utils.Block object describing the units in the block. - num_classes: Number of predicted classes for classification tasks. If None - we return the features before the logit layer. - is_training: whether is training or not. - global_pool: If True, we perform global average pooling before computing the - logits. Set to True for image classification, False for dense prediction. - output_stride: If None, then the output will be computed at the nominal - network stride. If output_stride is not None, it specifies the requested - ratio of input to output spatial resolution. - include_root_block: If True, include the initial convolution followed by - max-pooling, if False excludes it. If excluded, `inputs` should be the - results of an activation-less convolution. - spatial_squeeze: if True, logits is of shape [B, C], if false logits is - of shape [B, 1, 1, C], where B is batch_size and C is number of classes. - reuse: whether or not the network and its variables should be reused. To be - able to reuse 'scope' must be given. - scope: Optional variable_scope. - - - Returns: - net: A rank-4 tensor of size [batch, height_out, width_out, channels_out]. - If global_pool is False, then height_out and width_out are reduced by a - factor of output_stride compared to the respective height_in and width_in, - else both height_out and width_out equal one. If num_classes is None, then - net is the output of the last ResNet block, potentially after global - average pooling. If num_classes is not None, net contains the pre-softmax - activations. - end_points: A dictionary from components of the network to the corresponding - activation. - - Raises: - ValueError: If the target output_stride is not valid. - """ - with tf.variable_scope(scope, 'resnet_v2', [inputs], reuse=reuse) as sc: - end_points_collection = sc.name + '_end_points' - with slim.arg_scope([slim.conv2d, bottleneck, - resnet_utils.stack_blocks_dense], - outputs_collections=end_points_collection): - with slim.arg_scope([slim.batch_norm], is_training=is_training): - net = inputs - if include_root_block: - if output_stride is not None: - if output_stride % 4 != 0: - raise ValueError('The output_stride needs to be a multiple of 4.') - output_stride /= 4 - # We do not include batch normalization or activation functions in - # conv1 because the first ResNet unit will perform these. Cf. - # Appendix of [2]. - with slim.arg_scope([slim.conv2d], - activation_fn=None, normalizer_fn=None): - net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1') - net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1') - net = resnet_utils.stack_blocks_dense(net, blocks, output_stride) - # This is needed because the pre-activation variant does not have batch - # normalization or activation functions in the residual unit output. See - # Appendix of [2]. - net = slim.batch_norm(net, activation_fn=tf.nn.relu, scope='postnorm') - if global_pool: - # Global average pooling. - net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True) - if num_classes is not None: - net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, - normalizer_fn=None, scope='logits') - if spatial_squeeze: - logits = tf.squeeze(net, [1, 2], name='SpatialSqueeze') - else: - logits = net - # Convert end_points_collection into a dictionary of end_points. - end_points = slim.utils.convert_collection_to_dict( - end_points_collection) - if num_classes is not None: - end_points['predictions'] = slim.softmax(logits, scope='predictions') - return logits, end_points -resnet_v2.default_image_size = 224 - - -def resnet_v2_block(scope, base_depth, num_units, stride): - """Helper function for creating a resnet_v2 bottleneck block. - - Args: - scope: The scope of the block. - base_depth: The depth of the bottleneck layer for each unit. - num_units: The number of units in the block. - stride: The stride of the block, implemented as a stride in the last unit. - All other units have stride=1. - - Returns: - A resnet_v2 bottleneck block. - """ - return resnet_utils.Block(scope, bottleneck, [{ - 'depth': base_depth * 4, - 'depth_bottleneck': base_depth, - 'stride': 1 - }] * (num_units - 1) + [{ - 'depth': base_depth * 4, - 'depth_bottleneck': base_depth, - 'stride': stride - }]) -resnet_v2.default_image_size = 224 - - -def resnet_v2_50(inputs, - num_classes=None, - is_training=True, - global_pool=True, - output_stride=None, - spatial_squeeze=False, - reuse=None, - scope='resnet_v2_50'): - """ResNet-50 model of [1]. See resnet_v2() for arg and return description.""" - blocks = [ - resnet_v2_block('block1', base_depth=64, num_units=3, stride=2), - resnet_v2_block('block2', base_depth=128, num_units=4, stride=2), - resnet_v2_block('block3', base_depth=256, num_units=6, stride=2), - resnet_v2_block('block4', base_depth=512, num_units=3, stride=1), - ] - return resnet_v2(inputs, blocks, num_classes, is_training=is_training, - global_pool=global_pool, output_stride=output_stride, - include_root_block=True, spatial_squeeze=spatial_squeeze, - reuse=reuse, scope=scope) -resnet_v2_50.default_image_size = resnet_v2.default_image_size - - -def resnet_v2_101(inputs, - num_classes=None, - is_training=True, - global_pool=True, - output_stride=None, - spatial_squeeze=False, - reuse=None, - scope='resnet_v2_101'): - """ResNet-101 model of [1]. See resnet_v2() for arg and return description.""" - blocks = [ - resnet_v2_block('block1', base_depth=64, num_units=3, stride=2), - resnet_v2_block('block2', base_depth=128, num_units=4, stride=2), - resnet_v2_block('block3', base_depth=256, num_units=23, stride=2), - resnet_v2_block('block4', base_depth=512, num_units=3, stride=1), - ] - return resnet_v2(inputs, blocks, num_classes, is_training=is_training, - global_pool=global_pool, output_stride=output_stride, - include_root_block=True, spatial_squeeze=spatial_squeeze, - reuse=reuse, scope=scope) -resnet_v2_101.default_image_size = resnet_v2.default_image_size - - -def resnet_v2_152(inputs, - num_classes=None, - is_training=True, - global_pool=True, - output_stride=None, - spatial_squeeze=False, - reuse=None, - scope='resnet_v2_152'): - """ResNet-152 model of [1]. See resnet_v2() for arg and return description.""" - blocks = [ - resnet_v2_block('block1', base_depth=64, num_units=3, stride=2), - resnet_v2_block('block2', base_depth=128, num_units=8, stride=2), - resnet_v2_block('block3', base_depth=256, num_units=36, stride=2), - resnet_v2_block('block4', base_depth=512, num_units=3, stride=1), - ] - return resnet_v2(inputs, blocks, num_classes, is_training=is_training, - global_pool=global_pool, output_stride=output_stride, - include_root_block=True, spatial_squeeze=spatial_squeeze, - reuse=reuse, scope=scope) -resnet_v2_152.default_image_size = resnet_v2.default_image_size - - -def resnet_v2_200(inputs, - num_classes=None, - is_training=True, - global_pool=True, - output_stride=None, - spatial_squeeze=False, - reuse=None, - scope='resnet_v2_200'): - """ResNet-200 model of [2]. See resnet_v2() for arg and return description.""" - blocks = [ - resnet_v2_block('block1', base_depth=64, num_units=3, stride=2), - resnet_v2_block('block2', base_depth=128, num_units=24, stride=2), - resnet_v2_block('block3', base_depth=256, num_units=36, stride=2), - resnet_v2_block('block4', base_depth=512, num_units=3, stride=1), - ] - return resnet_v2(inputs, blocks, num_classes, is_training=is_training, - global_pool=global_pool, output_stride=output_stride, - include_root_block=True, spatial_squeeze=spatial_squeeze, - reuse=reuse, scope=scope) -resnet_v2_200.default_image_size = resnet_v2.default_image_size diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_v2_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_v2_test.py deleted file mode 100755 index c181664..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/resnet_v2_test.py +++ /dev/null @@ -1,443 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for slim.slim_nets.resnet_v2.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np -import tensorflow as tf - -from nets import resnet_utils -from nets import resnet_v2 - -slim = tf.contrib.slim - - -def create_test_input(batch_size, height, width, channels): - """Create test input tensor. - - Args: - batch_size: The number of images per batch or `None` if unknown. - height: The height of each image or `None` if unknown. - width: The width of each image or `None` if unknown. - channels: The number of channels per image or `None` if unknown. - - Returns: - Either a placeholder `Tensor` of dimension - [batch_size, height, width, channels] if any of the inputs are `None` or a - constant `Tensor` with the mesh grid values along the spatial dimensions. - """ - if None in [batch_size, height, width, channels]: - return tf.placeholder(tf.float32, (batch_size, height, width, channels)) - else: - return tf.to_float( - np.tile( - np.reshape( - np.reshape(np.arange(height), [height, 1]) + - np.reshape(np.arange(width), [1, width]), - [1, height, width, 1]), - [batch_size, 1, 1, channels])) - - -class ResnetUtilsTest(tf.test.TestCase): - - def testSubsampleThreeByThree(self): - x = tf.reshape(tf.to_float(tf.range(9)), [1, 3, 3, 1]) - x = resnet_utils.subsample(x, 2) - expected = tf.reshape(tf.constant([0, 2, 6, 8]), [1, 2, 2, 1]) - with self.test_session(): - self.assertAllClose(x.eval(), expected.eval()) - - def testSubsampleFourByFour(self): - x = tf.reshape(tf.to_float(tf.range(16)), [1, 4, 4, 1]) - x = resnet_utils.subsample(x, 2) - expected = tf.reshape(tf.constant([0, 2, 8, 10]), [1, 2, 2, 1]) - with self.test_session(): - self.assertAllClose(x.eval(), expected.eval()) - - def testConv2DSameEven(self): - n, n2 = 4, 2 - - # Input image. - x = create_test_input(1, n, n, 1) - - # Convolution kernel. - w = create_test_input(1, 3, 3, 1) - w = tf.reshape(w, [3, 3, 1, 1]) - - tf.get_variable('Conv/weights', initializer=w) - tf.get_variable('Conv/biases', initializer=tf.zeros([1])) - tf.get_variable_scope().reuse_variables() - - y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv') - y1_expected = tf.to_float([[14, 28, 43, 26], - [28, 48, 66, 37], - [43, 66, 84, 46], - [26, 37, 46, 22]]) - y1_expected = tf.reshape(y1_expected, [1, n, n, 1]) - - y2 = resnet_utils.subsample(y1, 2) - y2_expected = tf.to_float([[14, 43], - [43, 84]]) - y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1]) - - y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv') - y3_expected = y2_expected - - y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv') - y4_expected = tf.to_float([[48, 37], - [37, 22]]) - y4_expected = tf.reshape(y4_expected, [1, n2, n2, 1]) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - self.assertAllClose(y1.eval(), y1_expected.eval()) - self.assertAllClose(y2.eval(), y2_expected.eval()) - self.assertAllClose(y3.eval(), y3_expected.eval()) - self.assertAllClose(y4.eval(), y4_expected.eval()) - - def testConv2DSameOdd(self): - n, n2 = 5, 3 - - # Input image. - x = create_test_input(1, n, n, 1) - - # Convolution kernel. - w = create_test_input(1, 3, 3, 1) - w = tf.reshape(w, [3, 3, 1, 1]) - - tf.get_variable('Conv/weights', initializer=w) - tf.get_variable('Conv/biases', initializer=tf.zeros([1])) - tf.get_variable_scope().reuse_variables() - - y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv') - y1_expected = tf.to_float([[14, 28, 43, 58, 34], - [28, 48, 66, 84, 46], - [43, 66, 84, 102, 55], - [58, 84, 102, 120, 64], - [34, 46, 55, 64, 30]]) - y1_expected = tf.reshape(y1_expected, [1, n, n, 1]) - - y2 = resnet_utils.subsample(y1, 2) - y2_expected = tf.to_float([[14, 43, 34], - [43, 84, 55], - [34, 55, 30]]) - y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1]) - - y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv') - y3_expected = y2_expected - - y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv') - y4_expected = y2_expected - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - self.assertAllClose(y1.eval(), y1_expected.eval()) - self.assertAllClose(y2.eval(), y2_expected.eval()) - self.assertAllClose(y3.eval(), y3_expected.eval()) - self.assertAllClose(y4.eval(), y4_expected.eval()) - - def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None): - """A plain ResNet without extra layers before or after the ResNet blocks.""" - with tf.variable_scope(scope, values=[inputs]): - with slim.arg_scope([slim.conv2d], outputs_collections='end_points'): - net = resnet_utils.stack_blocks_dense(inputs, blocks, output_stride) - end_points = slim.utils.convert_collection_to_dict('end_points') - return net, end_points - - def testEndPointsV2(self): - """Test the end points of a tiny v2 bottleneck network.""" - blocks = [ - resnet_v2.resnet_v2_block( - 'block1', base_depth=1, num_units=2, stride=2), - resnet_v2.resnet_v2_block( - 'block2', base_depth=2, num_units=2, stride=1), - ] - inputs = create_test_input(2, 32, 16, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - _, end_points = self._resnet_plain(inputs, blocks, scope='tiny') - expected = [ - 'tiny/block1/unit_1/bottleneck_v2/shortcut', - 'tiny/block1/unit_1/bottleneck_v2/conv1', - 'tiny/block1/unit_1/bottleneck_v2/conv2', - 'tiny/block1/unit_1/bottleneck_v2/conv3', - 'tiny/block1/unit_2/bottleneck_v2/conv1', - 'tiny/block1/unit_2/bottleneck_v2/conv2', - 'tiny/block1/unit_2/bottleneck_v2/conv3', - 'tiny/block2/unit_1/bottleneck_v2/shortcut', - 'tiny/block2/unit_1/bottleneck_v2/conv1', - 'tiny/block2/unit_1/bottleneck_v2/conv2', - 'tiny/block2/unit_1/bottleneck_v2/conv3', - 'tiny/block2/unit_2/bottleneck_v2/conv1', - 'tiny/block2/unit_2/bottleneck_v2/conv2', - 'tiny/block2/unit_2/bottleneck_v2/conv3'] - self.assertItemsEqual(expected, end_points) - - def _stack_blocks_nondense(self, net, blocks): - """A simplified ResNet Block stacker without output stride control.""" - for block in blocks: - with tf.variable_scope(block.scope, 'block', [net]): - for i, unit in enumerate(block.args): - with tf.variable_scope('unit_%d' % (i + 1), values=[net]): - net = block.unit_fn(net, rate=1, **unit) - return net - - def testAtrousValuesBottleneck(self): - """Verify the values of dense feature extraction by atrous convolution. - - Make sure that dense feature extraction by stack_blocks_dense() followed by - subsampling gives identical results to feature extraction at the nominal - network output stride using the simple self._stack_blocks_nondense() above. - """ - block = resnet_v2.resnet_v2_block - blocks = [ - block('block1', base_depth=1, num_units=2, stride=2), - block('block2', base_depth=2, num_units=2, stride=2), - block('block3', base_depth=4, num_units=2, stride=2), - block('block4', base_depth=8, num_units=2, stride=1), - ] - nominal_stride = 8 - - # Test both odd and even input dimensions. - height = 30 - width = 31 - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - with slim.arg_scope([slim.batch_norm], is_training=False): - for output_stride in [1, 2, 4, 8, None]: - with tf.Graph().as_default(): - with self.test_session() as sess: - tf.set_random_seed(0) - inputs = create_test_input(1, height, width, 3) - # Dense feature extraction followed by subsampling. - output = resnet_utils.stack_blocks_dense(inputs, - blocks, - output_stride) - if output_stride is None: - factor = 1 - else: - factor = nominal_stride // output_stride - - output = resnet_utils.subsample(output, factor) - # Make the two networks use the same weights. - tf.get_variable_scope().reuse_variables() - # Feature extraction at the nominal network rate. - expected = self._stack_blocks_nondense(inputs, blocks) - sess.run(tf.global_variables_initializer()) - output, expected = sess.run([output, expected]) - self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4) - - -class ResnetCompleteNetworkTest(tf.test.TestCase): - """Tests with complete small ResNet v2 networks.""" - - def _resnet_small(self, - inputs, - num_classes=None, - is_training=True, - global_pool=True, - output_stride=None, - include_root_block=True, - reuse=None, - scope='resnet_v2_small'): - """A shallow and thin ResNet v2 for faster tests.""" - block = resnet_v2.resnet_v2_block - blocks = [ - block('block1', base_depth=1, num_units=3, stride=2), - block('block2', base_depth=2, num_units=3, stride=2), - block('block3', base_depth=4, num_units=3, stride=2), - block('block4', base_depth=8, num_units=2, stride=1), - ] - return resnet_v2.resnet_v2(inputs, blocks, num_classes, - is_training=is_training, - global_pool=global_pool, - output_stride=output_stride, - include_root_block=include_root_block, - reuse=reuse, - scope=scope) - - def testClassificationEndPoints(self): - global_pool = True - num_classes = 10 - inputs = create_test_input(2, 224, 224, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - logits, end_points = self._resnet_small(inputs, num_classes, - global_pool=global_pool, - scope='resnet') - self.assertTrue(logits.op.name.startswith('resnet/logits')) - self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes]) - self.assertTrue('predictions' in end_points) - self.assertListEqual(end_points['predictions'].get_shape().as_list(), - [2, 1, 1, num_classes]) - - def testClassificationShapes(self): - global_pool = True - num_classes = 10 - inputs = create_test_input(2, 224, 224, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - _, end_points = self._resnet_small(inputs, num_classes, - global_pool=global_pool, - scope='resnet') - endpoint_to_shape = { - 'resnet/block1': [2, 28, 28, 4], - 'resnet/block2': [2, 14, 14, 8], - 'resnet/block3': [2, 7, 7, 16], - 'resnet/block4': [2, 7, 7, 32]} - for endpoint in endpoint_to_shape: - shape = endpoint_to_shape[endpoint] - self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) - - def testFullyConvolutionalEndpointShapes(self): - global_pool = False - num_classes = 10 - inputs = create_test_input(2, 321, 321, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - _, end_points = self._resnet_small(inputs, num_classes, - global_pool=global_pool, - scope='resnet') - endpoint_to_shape = { - 'resnet/block1': [2, 41, 41, 4], - 'resnet/block2': [2, 21, 21, 8], - 'resnet/block3': [2, 11, 11, 16], - 'resnet/block4': [2, 11, 11, 32]} - for endpoint in endpoint_to_shape: - shape = endpoint_to_shape[endpoint] - self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) - - def testRootlessFullyConvolutionalEndpointShapes(self): - global_pool = False - num_classes = 10 - inputs = create_test_input(2, 128, 128, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - _, end_points = self._resnet_small(inputs, num_classes, - global_pool=global_pool, - include_root_block=False, - scope='resnet') - endpoint_to_shape = { - 'resnet/block1': [2, 64, 64, 4], - 'resnet/block2': [2, 32, 32, 8], - 'resnet/block3': [2, 16, 16, 16], - 'resnet/block4': [2, 16, 16, 32]} - for endpoint in endpoint_to_shape: - shape = endpoint_to_shape[endpoint] - self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) - - def testAtrousFullyConvolutionalEndpointShapes(self): - global_pool = False - num_classes = 10 - output_stride = 8 - inputs = create_test_input(2, 321, 321, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - _, end_points = self._resnet_small(inputs, - num_classes, - global_pool=global_pool, - output_stride=output_stride, - scope='resnet') - endpoint_to_shape = { - 'resnet/block1': [2, 41, 41, 4], - 'resnet/block2': [2, 41, 41, 8], - 'resnet/block3': [2, 41, 41, 16], - 'resnet/block4': [2, 41, 41, 32]} - for endpoint in endpoint_to_shape: - shape = endpoint_to_shape[endpoint] - self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) - - def testAtrousFullyConvolutionalValues(self): - """Verify dense feature extraction with atrous convolution.""" - nominal_stride = 32 - for output_stride in [4, 8, 16, 32, None]: - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - with tf.Graph().as_default(): - with self.test_session() as sess: - tf.set_random_seed(0) - inputs = create_test_input(2, 81, 81, 3) - # Dense feature extraction followed by subsampling. - output, _ = self._resnet_small(inputs, None, - is_training=False, - global_pool=False, - output_stride=output_stride) - if output_stride is None: - factor = 1 - else: - factor = nominal_stride // output_stride - output = resnet_utils.subsample(output, factor) - # Make the two networks use the same weights. - tf.get_variable_scope().reuse_variables() - # Feature extraction at the nominal network rate. - expected, _ = self._resnet_small(inputs, None, - is_training=False, - global_pool=False) - sess.run(tf.global_variables_initializer()) - self.assertAllClose(output.eval(), expected.eval(), - atol=1e-4, rtol=1e-4) - - def testUnknownBatchSize(self): - batch = 2 - height, width = 65, 65 - global_pool = True - num_classes = 10 - inputs = create_test_input(None, height, width, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - logits, _ = self._resnet_small(inputs, num_classes, - global_pool=global_pool, - scope='resnet') - self.assertTrue(logits.op.name.startswith('resnet/logits')) - self.assertListEqual(logits.get_shape().as_list(), - [None, 1, 1, num_classes]) - images = create_test_input(batch, height, width, 3) - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(logits, {inputs: images.eval()}) - self.assertEqual(output.shape, (batch, 1, 1, num_classes)) - - def testFullyConvolutionalUnknownHeightWidth(self): - batch = 2 - height, width = 65, 65 - global_pool = False - inputs = create_test_input(batch, None, None, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - output, _ = self._resnet_small(inputs, None, - global_pool=global_pool) - self.assertListEqual(output.get_shape().as_list(), - [batch, None, None, 32]) - images = create_test_input(batch, height, width, 3) - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(output, {inputs: images.eval()}) - self.assertEqual(output.shape, (batch, 3, 3, 32)) - - def testAtrousFullyConvolutionalUnknownHeightWidth(self): - batch = 2 - height, width = 65, 65 - global_pool = False - output_stride = 8 - inputs = create_test_input(batch, None, None, 3) - with slim.arg_scope(resnet_utils.resnet_arg_scope()): - output, _ = self._resnet_small(inputs, - None, - global_pool=global_pool, - output_stride=output_stride) - self.assertListEqual(output.get_shape().as_list(), - [batch, None, None, 32]) - images = create_test_input(batch, height, width, 3) - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(output, {inputs: images.eval()}) - self.assertEqual(output.shape, (batch, 9, 9, 32)) - - -if __name__ == '__main__': - tf.test.main() diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/vgg.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/vgg.py deleted file mode 100755 index 4c74c5a..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/vgg.py +++ /dev/null @@ -1,265 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains model definitions for versions of the Oxford VGG network. - -These model definitions were introduced in the following technical report: - - Very Deep Convolutional Networks For Large-Scale Image Recognition - Karen Simonyan and Andrew Zisserman - arXiv technical report, 2015 - PDF: http://arxiv.org/pdf/1409.1556.pdf - ILSVRC 2014 Slides: http://www.robots.ox.ac.uk/~karen/pdf/ILSVRC_2014.pdf - CC-BY-4.0 - -More information can be obtained from the VGG website: -www.robots.ox.ac.uk/~vgg/research/very_deep/ - -Usage: - with slim.arg_scope(vgg.vgg_arg_scope()): - outputs, end_points = vgg.vgg_a(inputs) - - with slim.arg_scope(vgg.vgg_arg_scope()): - outputs, end_points = vgg.vgg_16(inputs) - -@@vgg_a -@@vgg_16 -@@vgg_19 -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -slim = tf.contrib.slim - - -def vgg_arg_scope(weight_decay=0.0005): - """Defines the VGG arg scope. - - Args: - weight_decay: The l2 regularization coefficient. - - Returns: - An arg_scope. - """ - with slim.arg_scope([slim.conv2d, slim.fully_connected], - activation_fn=tf.nn.relu, - weights_regularizer=slim.l2_regularizer(weight_decay), - biases_initializer=tf.zeros_initializer()): - with slim.arg_scope([slim.conv2d], padding='SAME') as arg_sc: - return arg_sc - - -def vgg_a(inputs, - num_classes=1000, - is_training=True, - dropout_keep_prob=0.5, - spatial_squeeze=True, - scope='vgg_a', - fc_conv_padding='VALID'): - """Oxford Net VGG 11-Layers version A Example. - - Note: All the fully_connected layers have been transformed to conv2d layers. - To use in classification mode, resize input to 224x224. - - Args: - inputs: a tensor of size [batch_size, height, width, channels]. - num_classes: number of predicted classes. - is_training: whether or not the model is being trained. - dropout_keep_prob: the probability that activations are kept in the dropout - layers during training. - spatial_squeeze: whether or not should squeeze the spatial dimensions of the - outputs. Useful to remove unnecessary dimensions for classification. - scope: Optional scope for the variables. - fc_conv_padding: the type of padding to use for the fully connected layer - that is implemented as a convolutional layer. Use 'SAME' padding if you - are applying the network in a fully convolutional manner and want to - get a prediction map downsampled by a factor of 32 as an output. Otherwise, - the output prediction map will be (input / 32) - 6 in case of 'VALID' padding. - - Returns: - the last op containing the log predictions and end_points dict. - """ - with tf.variable_scope(scope, 'vgg_a', [inputs]) as sc: - end_points_collection = sc.name + '_end_points' - # Collect outputs for conv2d, fully_connected and max_pool2d. - with slim.arg_scope([slim.conv2d, slim.max_pool2d], - outputs_collections=end_points_collection): - net = slim.repeat(inputs, 1, slim.conv2d, 64, [3, 3], scope='conv1') - net = slim.max_pool2d(net, [2, 2], scope='pool1') - net = slim.repeat(net, 1, slim.conv2d, 128, [3, 3], scope='conv2') - net = slim.max_pool2d(net, [2, 2], scope='pool2') - net = slim.repeat(net, 2, slim.conv2d, 256, [3, 3], scope='conv3') - net = slim.max_pool2d(net, [2, 2], scope='pool3') - net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv4') - net = slim.max_pool2d(net, [2, 2], scope='pool4') - net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv5') - net = slim.max_pool2d(net, [2, 2], scope='pool5') - # Use conv2d instead of fully_connected layers. - net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6') - net = slim.dropout(net, dropout_keep_prob, is_training=is_training, - scope='dropout6') - net = slim.conv2d(net, 4096, [1, 1], scope='fc7') - net = slim.dropout(net, dropout_keep_prob, is_training=is_training, - scope='dropout7') - net = slim.conv2d(net, num_classes, [1, 1], - activation_fn=None, - normalizer_fn=None, - scope='fc8') - # Convert end_points_collection into a end_point dict. - end_points = slim.utils.convert_collection_to_dict(end_points_collection) - if spatial_squeeze: - net = tf.squeeze(net, [1, 2], name='fc8/squeezed') - end_points[sc.name + '/fc8'] = net - return net, end_points -vgg_a.default_image_size = 224 - - -def vgg_16(inputs, - num_classes=1000, - is_training=True, - dropout_keep_prob=0.5, - spatial_squeeze=True, - scope='vgg_16', - fc_conv_padding='VALID'): - """Oxford Net VGG 16-Layers version D Example. - - Note: All the fully_connected layers have been transformed to conv2d layers. - To use in classification mode, resize input to 224x224. - - Args: - inputs: a tensor of size [batch_size, height, width, channels]. - num_classes: number of predicted classes. - is_training: whether or not the model is being trained. - dropout_keep_prob: the probability that activations are kept in the dropout - layers during training. - spatial_squeeze: whether or not should squeeze the spatial dimensions of the - outputs. Useful to remove unnecessary dimensions for classification. - scope: Optional scope for the variables. - fc_conv_padding: the type of padding to use for the fully connected layer - that is implemented as a convolutional layer. Use 'SAME' padding if you - are applying the network in a fully convolutional manner and want to - get a prediction map downsampled by a factor of 32 as an output. Otherwise, - the output prediction map will be (input / 32) - 6 in case of 'VALID' padding. - - Returns: - the last op containing the log predictions and end_points dict. - """ - with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc: - end_points_collection = sc.name + '_end_points' - # Collect outputs for conv2d, fully_connected and max_pool2d. - with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], - outputs_collections=end_points_collection): - net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1') - net = slim.max_pool2d(net, [2, 2], scope='pool1') - net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2') - net = slim.max_pool2d(net, [2, 2], scope='pool2') - net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3') - net = slim.max_pool2d(net, [2, 2], scope='pool3') - net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4') - net = slim.max_pool2d(net, [2, 2], scope='pool4') - net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5') - net = slim.max_pool2d(net, [2, 2], scope='pool5') - # Use conv2d instead of fully_connected layers. - net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6') - net = slim.dropout(net, dropout_keep_prob, is_training=is_training, - scope='dropout6') - net = slim.conv2d(net, 4096, [1, 1], scope='fc7') - net = slim.dropout(net, dropout_keep_prob, is_training=is_training, - scope='dropout7') - # yjr_feature = tf.squeeze(net) - net = slim.conv2d(net, num_classes, [1, 1], - activation_fn=None, - normalizer_fn=None, - scope='fc8') - # Convert end_points_collection into a end_point dict. - end_points = slim.utils.convert_collection_to_dict(end_points_collection) - if spatial_squeeze: - net = tf.squeeze(net, [1, 2], name='fc8/squeezed') - end_points[sc.name + '/fc8'] = net - # end_points['yjr_feature'] = yjr_feature - end_points['predictions'] = slim.softmax(net, scope='predictions') - return net, end_points -vgg_16.default_image_size = 224 - - -def vgg_19(inputs, - num_classes=1000, - is_training=True, - dropout_keep_prob=0.5, - spatial_squeeze=True, - scope='vgg_19', - fc_conv_padding='VALID'): - """Oxford Net VGG 19-Layers version E Example. - - Note: All the fully_connected layers have been transformed to conv2d layers. - To use in classification mode, resize input to 224x224. - - Args: - inputs: a tensor of size [batch_size, height, width, channels]. - num_classes: number of predicted classes. - is_training: whether or not the model is being trained. - dropout_keep_prob: the probability that activations are kept in the dropout - layers during training. - spatial_squeeze: whether or not should squeeze the spatial dimensions of the - outputs. Useful to remove unnecessary dimensions for classification. - scope: Optional scope for the variables. - fc_conv_padding: the type of padding to use for the fully connected layer - that is implemented as a convolutional layer. Use 'SAME' padding if you - are applying the network in a fully convolutional manner and want to - get a prediction map downsampled by a factor of 32 as an output. Otherwise, - the output prediction map will be (input / 32) - 6 in case of 'VALID' padding. - - Returns: - the last op containing the log predictions and end_points dict. - """ - with tf.variable_scope(scope, 'vgg_19', [inputs]) as sc: - end_points_collection = sc.name + '_end_points' - # Collect outputs for conv2d, fully_connected and max_pool2d. - with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], - outputs_collections=end_points_collection): - net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1') - net = slim.max_pool2d(net, [2, 2], scope='pool1') - net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2') - net = slim.max_pool2d(net, [2, 2], scope='pool2') - net = slim.repeat(net, 4, slim.conv2d, 256, [3, 3], scope='conv3') - net = slim.max_pool2d(net, [2, 2], scope='pool3') - net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv4') - net = slim.max_pool2d(net, [2, 2], scope='pool4') - net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv5') - net = slim.max_pool2d(net, [2, 2], scope='pool5') - # Use conv2d instead of fully_connected layers. - net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6') - net = slim.dropout(net, dropout_keep_prob, is_training=is_training, - scope='dropout6') - net = slim.conv2d(net, 4096, [1, 1], scope='fc7') - net = slim.dropout(net, dropout_keep_prob, is_training=is_training, - scope='dropout7') - net = slim.conv2d(net, num_classes, [1, 1], - activation_fn=None, - normalizer_fn=None, - scope='fc8') - # Convert end_points_collection into a end_point dict. - end_points = slim.utils.convert_collection_to_dict(end_points_collection) - if spatial_squeeze: - net = tf.squeeze(net, [1, 2], name='fc8/squeezed') - end_points[sc.name + '/fc8'] = net - return net, end_points -vgg_19.default_image_size = 224 - -# Alias -vgg_d = vgg_16 -vgg_e = vgg_19 diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/vgg_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/vgg_test.py deleted file mode 100755 index 8e383b3..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/vgg_test.py +++ /dev/null @@ -1,455 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for slim.slim_nets.vgg.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from nets import vgg - -slim = tf.contrib.slim - - -class VGGATest(tf.test.TestCase): - - def testBuild(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = vgg.vgg_a(inputs, num_classes) - self.assertEquals(logits.op.name, 'vgg_a/fc8/squeezed') - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - - def testFullyConvolutional(self): - batch_size = 1 - height, width = 256, 256 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = vgg.vgg_a(inputs, num_classes, spatial_squeeze=False) - self.assertEquals(logits.op.name, 'vgg_a/fc8/BiasAdd') - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, 2, 2, num_classes]) - - def testEndPoints(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = vgg.vgg_a(inputs, num_classes) - expected_names = ['vgg_a/conv1/conv1_1', - 'vgg_a/pool1', - 'vgg_a/conv2/conv2_1', - 'vgg_a/pool2', - 'vgg_a/conv3/conv3_1', - 'vgg_a/conv3/conv3_2', - 'vgg_a/pool3', - 'vgg_a/conv4/conv4_1', - 'vgg_a/conv4/conv4_2', - 'vgg_a/pool4', - 'vgg_a/conv5/conv5_1', - 'vgg_a/conv5/conv5_2', - 'vgg_a/pool5', - 'vgg_a/fc6', - 'vgg_a/fc7', - 'vgg_a/fc8' - ] - self.assertSetEqual(set(end_points.keys()), set(expected_names)) - - def testModelVariables(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - vgg.vgg_a(inputs, num_classes) - expected_names = ['vgg_a/conv1/conv1_1/weights', - 'vgg_a/conv1/conv1_1/biases', - 'vgg_a/conv2/conv2_1/weights', - 'vgg_a/conv2/conv2_1/biases', - 'vgg_a/conv3/conv3_1/weights', - 'vgg_a/conv3/conv3_1/biases', - 'vgg_a/conv3/conv3_2/weights', - 'vgg_a/conv3/conv3_2/biases', - 'vgg_a/conv4/conv4_1/weights', - 'vgg_a/conv4/conv4_1/biases', - 'vgg_a/conv4/conv4_2/weights', - 'vgg_a/conv4/conv4_2/biases', - 'vgg_a/conv5/conv5_1/weights', - 'vgg_a/conv5/conv5_1/biases', - 'vgg_a/conv5/conv5_2/weights', - 'vgg_a/conv5/conv5_2/biases', - 'vgg_a/fc6/weights', - 'vgg_a/fc6/biases', - 'vgg_a/fc7/weights', - 'vgg_a/fc7/biases', - 'vgg_a/fc8/weights', - 'vgg_a/fc8/biases', - ] - model_variables = [v.op.name for v in slim.get_model_variables()] - self.assertSetEqual(set(model_variables), set(expected_names)) - - def testEvaluation(self): - batch_size = 2 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - eval_inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = vgg.vgg_a(eval_inputs, is_training=False) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - predictions = tf.argmax(logits, 1) - self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) - - def testTrainEvalWithReuse(self): - train_batch_size = 2 - eval_batch_size = 1 - train_height, train_width = 224, 224 - eval_height, eval_width = 256, 256 - num_classes = 1000 - with self.test_session(): - train_inputs = tf.random_uniform( - (train_batch_size, train_height, train_width, 3)) - logits, _ = vgg.vgg_a(train_inputs) - self.assertListEqual(logits.get_shape().as_list(), - [train_batch_size, num_classes]) - tf.get_variable_scope().reuse_variables() - eval_inputs = tf.random_uniform( - (eval_batch_size, eval_height, eval_width, 3)) - logits, _ = vgg.vgg_a(eval_inputs, is_training=False, - spatial_squeeze=False) - self.assertListEqual(logits.get_shape().as_list(), - [eval_batch_size, 2, 2, num_classes]) - logits = tf.reduce_mean(logits, [1, 2]) - predictions = tf.argmax(logits, 1) - self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) - - def testForward(self): - batch_size = 1 - height, width = 224, 224 - with self.test_session() as sess: - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = vgg.vgg_a(inputs) - sess.run(tf.global_variables_initializer()) - output = sess.run(logits) - self.assertTrue(output.any()) - - -class VGG16Test(tf.test.TestCase): - - def testBuild(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = vgg.vgg_16(inputs, num_classes) - self.assertEquals(logits.op.name, 'vgg_16/fc8/squeezed') - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - - def testFullyConvolutional(self): - batch_size = 1 - height, width = 256, 256 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False) - self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd') - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, 2, 2, num_classes]) - - def testEndPoints(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = vgg.vgg_16(inputs, num_classes) - expected_names = ['vgg_16/conv1/conv1_1', - 'vgg_16/conv1/conv1_2', - 'vgg_16/pool1', - 'vgg_16/conv2/conv2_1', - 'vgg_16/conv2/conv2_2', - 'vgg_16/pool2', - 'vgg_16/conv3/conv3_1', - 'vgg_16/conv3/conv3_2', - 'vgg_16/conv3/conv3_3', - 'vgg_16/pool3', - 'vgg_16/conv4/conv4_1', - 'vgg_16/conv4/conv4_2', - 'vgg_16/conv4/conv4_3', - 'vgg_16/pool4', - 'vgg_16/conv5/conv5_1', - 'vgg_16/conv5/conv5_2', - 'vgg_16/conv5/conv5_3', - 'vgg_16/pool5', - 'vgg_16/fc6', - 'vgg_16/fc7', - 'vgg_16/fc8' - ] - self.assertSetEqual(set(end_points.keys()), set(expected_names)) - - def testModelVariables(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - vgg.vgg_16(inputs, num_classes) - expected_names = ['vgg_16/conv1/conv1_1/weights', - 'vgg_16/conv1/conv1_1/biases', - 'vgg_16/conv1/conv1_2/weights', - 'vgg_16/conv1/conv1_2/biases', - 'vgg_16/conv2/conv2_1/weights', - 'vgg_16/conv2/conv2_1/biases', - 'vgg_16/conv2/conv2_2/weights', - 'vgg_16/conv2/conv2_2/biases', - 'vgg_16/conv3/conv3_1/weights', - 'vgg_16/conv3/conv3_1/biases', - 'vgg_16/conv3/conv3_2/weights', - 'vgg_16/conv3/conv3_2/biases', - 'vgg_16/conv3/conv3_3/weights', - 'vgg_16/conv3/conv3_3/biases', - 'vgg_16/conv4/conv4_1/weights', - 'vgg_16/conv4/conv4_1/biases', - 'vgg_16/conv4/conv4_2/weights', - 'vgg_16/conv4/conv4_2/biases', - 'vgg_16/conv4/conv4_3/weights', - 'vgg_16/conv4/conv4_3/biases', - 'vgg_16/conv5/conv5_1/weights', - 'vgg_16/conv5/conv5_1/biases', - 'vgg_16/conv5/conv5_2/weights', - 'vgg_16/conv5/conv5_2/biases', - 'vgg_16/conv5/conv5_3/weights', - 'vgg_16/conv5/conv5_3/biases', - 'vgg_16/fc6/weights', - 'vgg_16/fc6/biases', - 'vgg_16/fc7/weights', - 'vgg_16/fc7/biases', - 'vgg_16/fc8/weights', - 'vgg_16/fc8/biases', - ] - model_variables = [v.op.name for v in slim.get_model_variables()] - self.assertSetEqual(set(model_variables), set(expected_names)) - - def testEvaluation(self): - batch_size = 2 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - eval_inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = vgg.vgg_16(eval_inputs, is_training=False) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - predictions = tf.argmax(logits, 1) - self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) - - def testTrainEvalWithReuse(self): - train_batch_size = 2 - eval_batch_size = 1 - train_height, train_width = 224, 224 - eval_height, eval_width = 256, 256 - num_classes = 1000 - with self.test_session(): - train_inputs = tf.random_uniform( - (train_batch_size, train_height, train_width, 3)) - logits, _ = vgg.vgg_16(train_inputs) - self.assertListEqual(logits.get_shape().as_list(), - [train_batch_size, num_classes]) - tf.get_variable_scope().reuse_variables() - eval_inputs = tf.random_uniform( - (eval_batch_size, eval_height, eval_width, 3)) - logits, _ = vgg.vgg_16(eval_inputs, is_training=False, - spatial_squeeze=False) - self.assertListEqual(logits.get_shape().as_list(), - [eval_batch_size, 2, 2, num_classes]) - logits = tf.reduce_mean(logits, [1, 2]) - predictions = tf.argmax(logits, 1) - self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) - - def testForward(self): - batch_size = 1 - height, width = 224, 224 - with self.test_session() as sess: - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = vgg.vgg_16(inputs) - sess.run(tf.global_variables_initializer()) - output = sess.run(logits) - self.assertTrue(output.any()) - - -class VGG19Test(tf.test.TestCase): - - def testBuild(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = vgg.vgg_19(inputs, num_classes) - self.assertEquals(logits.op.name, 'vgg_19/fc8/squeezed') - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - - def testFullyConvolutional(self): - batch_size = 1 - height, width = 256, 256 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = vgg.vgg_19(inputs, num_classes, spatial_squeeze=False) - self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd') - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, 2, 2, num_classes]) - - def testEndPoints(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = vgg.vgg_19(inputs, num_classes) - expected_names = [ - 'vgg_19/conv1/conv1_1', - 'vgg_19/conv1/conv1_2', - 'vgg_19/pool1', - 'vgg_19/conv2/conv2_1', - 'vgg_19/conv2/conv2_2', - 'vgg_19/pool2', - 'vgg_19/conv3/conv3_1', - 'vgg_19/conv3/conv3_2', - 'vgg_19/conv3/conv3_3', - 'vgg_19/conv3/conv3_4', - 'vgg_19/pool3', - 'vgg_19/conv4/conv4_1', - 'vgg_19/conv4/conv4_2', - 'vgg_19/conv4/conv4_3', - 'vgg_19/conv4/conv4_4', - 'vgg_19/pool4', - 'vgg_19/conv5/conv5_1', - 'vgg_19/conv5/conv5_2', - 'vgg_19/conv5/conv5_3', - 'vgg_19/conv5/conv5_4', - 'vgg_19/pool5', - 'vgg_19/fc6', - 'vgg_19/fc7', - 'vgg_19/fc8' - ] - self.assertSetEqual(set(end_points.keys()), set(expected_names)) - - def testModelVariables(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - vgg.vgg_19(inputs, num_classes) - expected_names = [ - 'vgg_19/conv1/conv1_1/weights', - 'vgg_19/conv1/conv1_1/biases', - 'vgg_19/conv1/conv1_2/weights', - 'vgg_19/conv1/conv1_2/biases', - 'vgg_19/conv2/conv2_1/weights', - 'vgg_19/conv2/conv2_1/biases', - 'vgg_19/conv2/conv2_2/weights', - 'vgg_19/conv2/conv2_2/biases', - 'vgg_19/conv3/conv3_1/weights', - 'vgg_19/conv3/conv3_1/biases', - 'vgg_19/conv3/conv3_2/weights', - 'vgg_19/conv3/conv3_2/biases', - 'vgg_19/conv3/conv3_3/weights', - 'vgg_19/conv3/conv3_3/biases', - 'vgg_19/conv3/conv3_4/weights', - 'vgg_19/conv3/conv3_4/biases', - 'vgg_19/conv4/conv4_1/weights', - 'vgg_19/conv4/conv4_1/biases', - 'vgg_19/conv4/conv4_2/weights', - 'vgg_19/conv4/conv4_2/biases', - 'vgg_19/conv4/conv4_3/weights', - 'vgg_19/conv4/conv4_3/biases', - 'vgg_19/conv4/conv4_4/weights', - 'vgg_19/conv4/conv4_4/biases', - 'vgg_19/conv5/conv5_1/weights', - 'vgg_19/conv5/conv5_1/biases', - 'vgg_19/conv5/conv5_2/weights', - 'vgg_19/conv5/conv5_2/biases', - 'vgg_19/conv5/conv5_3/weights', - 'vgg_19/conv5/conv5_3/biases', - 'vgg_19/conv5/conv5_4/weights', - 'vgg_19/conv5/conv5_4/biases', - 'vgg_19/fc6/weights', - 'vgg_19/fc6/biases', - 'vgg_19/fc7/weights', - 'vgg_19/fc7/biases', - 'vgg_19/fc8/weights', - 'vgg_19/fc8/biases', - ] - model_variables = [v.op.name for v in slim.get_model_variables()] - self.assertSetEqual(set(model_variables), set(expected_names)) - - def testEvaluation(self): - batch_size = 2 - height, width = 224, 224 - num_classes = 1000 - with self.test_session(): - eval_inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = vgg.vgg_19(eval_inputs, is_training=False) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - predictions = tf.argmax(logits, 1) - self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) - - def testTrainEvalWithReuse(self): - train_batch_size = 2 - eval_batch_size = 1 - train_height, train_width = 224, 224 - eval_height, eval_width = 256, 256 - num_classes = 1000 - with self.test_session(): - train_inputs = tf.random_uniform( - (train_batch_size, train_height, train_width, 3)) - logits, _ = vgg.vgg_19(train_inputs) - self.assertListEqual(logits.get_shape().as_list(), - [train_batch_size, num_classes]) - tf.get_variable_scope().reuse_variables() - eval_inputs = tf.random_uniform( - (eval_batch_size, eval_height, eval_width, 3)) - logits, _ = vgg.vgg_19(eval_inputs, is_training=False, - spatial_squeeze=False) - self.assertListEqual(logits.get_shape().as_list(), - [eval_batch_size, 2, 2, num_classes]) - logits = tf.reduce_mean(logits, [1, 2]) - predictions = tf.argmax(logits, 1) - self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) - - def testForward(self): - batch_size = 1 - height, width = 224, 224 - with self.test_session() as sess: - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = vgg.vgg_19(inputs) - sess.run(tf.global_variables_initializer()) - output = sess.run(logits) - self.assertTrue(output.any()) - -if __name__ == '__main__': - tf.test.main() diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/some_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/some_test.py deleted file mode 100755 index 5c71438..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/some_test.py +++ /dev/null @@ -1,20 +0,0 @@ -# -*- coding: utf-8 -*- - -import tensorflow.contrib.slim as slim -import os - -os.environ["CUDA_VISIBLE_DEVICES"] = "" - - -@slim.add_arg_scope -def fn(a, c=100): - - return a+c - - -with slim.arg_scope([fn], a=2): - with slim.arg_scope([fn], a=1): - - print(fn()) - - diff --git a/example-apps/PDD/pcb-defect-detection/libs/setup.py b/example-apps/PDD/pcb-defect-detection/libs/setup.py deleted file mode 100755 index e7e4b4e..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/setup.py +++ /dev/null @@ -1,225 +0,0 @@ -# -------------------------------------------------------- -# Fast R-CNN -# Copyright (c) 2015 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Ross Girshick -# -------------------------------------------------------- - -import os -from os.path import join as pjoin -from setuptools import setup -from distutils.extension import Extension -from Cython.Distutils import build_ext -import subprocess -import numpy as np - - -def find_in_path(name, path): - "Find a file in a search path" - # Adapted fom - # http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/ - for dir in path.split(os.pathsep): - binpath = pjoin(dir, name) - if os.path.exists(binpath): - return os.path.abspath(binpath) - return None - - -def locate_cuda(): - """Locate the CUDA environment on the system - - Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64' - and values giving the absolute path to each directory. - - Starts by looking for the CUDAHOME env variable. If not found, everything - is based on finding 'nvcc' in the PATH. - """ - - # first check if the CUDAHOME env variable is in use - if 'CUDAHOME' in os.environ: - home = os.environ['CUDAHOME'] - nvcc = pjoin(home, 'bin', 'nvcc') - else: - # otherwise, search the PATH for NVCC - default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin') - nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path) - if nvcc is None: - raise EnvironmentError('The nvcc binary could not be ' - 'located in your $PATH. Either add it to your path, or set $CUDAHOME') - home = os.path.dirname(os.path.dirname(nvcc)) - - cudaconfig = {'home':home, 'nvcc':nvcc, - 'include': pjoin(home, 'include'), - 'lib64': pjoin(home, 'lib64')} - for k, v in cudaconfig.iteritems(): - if not os.path.exists(v): - raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v)) - - return cudaconfig -CUDA = locate_cuda() - - -# Obtain the numpy include directory. This logic works across numpy versions. -try: - numpy_include = np.get_include() -except AttributeError: - numpy_include = np.get_numpy_include() - - -def customize_compiler_for_nvcc(self): - """inject deep into distutils to customize how the dispatch - to gcc/nvcc works. - - If you subclass UnixCCompiler, it's not trivial to get your subclass - injected in, and still have the right customizations (i.e. - distutils.sysconfig.customize_compiler) run on it. So instead of going - the OO route, I have this. Note, it's kindof like a wierd functional - subclassing going on.""" - - # tell the compiler it can processes .cu - self.src_extensions.append('.cu') - - # save references to the default compiler_so and _comple methods - default_compiler_so = self.compiler_so - super = self._compile - - # now redefine the _compile method. This gets executed for each - # object but distutils doesn't have the ability to change compilers - # based on source extension: we add it. - def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts): - if os.path.splitext(src)[1] == '.cu': - # use the cuda for .cu files - self.set_executable('compiler_so', CUDA['nvcc']) - # use only a subset of the extra_postargs, which are 1-1 translated - # from the extra_compile_args in the Extension class - postargs = extra_postargs['nvcc'] - else: - postargs = extra_postargs['gcc'] - - super(obj, src, ext, cc_args, postargs, pp_opts) - # reset the default compiler_so, which we might have changed for cuda - self.compiler_so = default_compiler_so - - # inject our redefined _compile method into the class - self._compile = _compile - - -# run the customize_compiler -class custom_build_ext(build_ext): - def build_extensions(self): - customize_compiler_for_nvcc(self.compiler) - build_ext.build_extensions(self) - - -ext_modules = [ - # Extension( - # "utils.cython_bbox", - # ["utils/bbox.pyx"], - # extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, - # include_dirs = [numpy_include] - # ), - # Extension( - # "nms.cpu_nms", - # ["nms/cpu_nms.pyx"], - # extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, - # include_dirs = [numpy_include] - # ), - # - # Extension( - # "rotation.rotate_cython_nms", - # ["rotation/rotate_cython_nms.pyx"], - # extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, - # include_dirs = [numpy_include] - # ), - # - # Extension( - # "rotation.rotate_circle_nms", - # ["rotation/rotate_circle_nms.pyx"], - # extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, - # include_dirs = [numpy_include] - # ), - # - # Extension('nms.gpu_nms', - # ['nms/nms_kernel.cu', 'nms/gpu_nms.pyx'], - # library_dirs=[CUDA['lib64']], - # libraries=['cudart'], - # language='c++', - # runtime_library_dirs=[CUDA['lib64']], - # # this syntax is specific to this build system - # # we're only going to use certain compiler args with nvcc and not with - # # gcc the implementation of this trick is in customize_compiler() below - # extra_compile_args={'gcc': ["-Wno-unused-function"], - # 'nvcc': ['-arch=sm_35', - # '--ptxas-options=-v', - # '-c', - # '--compiler-options', - # "'-fPIC'"]}, - # include_dirs = [numpy_include, CUDA['include']] - # ), - # Extension('rotation.rotate_gpu_nms', - # ['rotation/rotate_nms_kernel.cu', 'rotation/rotate_gpu_nms.pyx'], - # library_dirs=[CUDA['lib64']], - # libraries=['cudart'], - # language='c++', - # runtime_library_dirs=[CUDA['lib64']], - # # this syntax is specific to this build system - # # we're only going to use certain compiler args with nvcc anrbd not with - # # gcc the implementation of this trick is in customize_compiler() below - # extra_compile_args={'gcc': ["-Wno-unused-function"], - # 'nvcc': ['-arch=sm_35', - # '--ptxas-options=-v', - # '-c', - # '--compiler-options', - # "'-fPIC'"]}, - # include_dirs = [numpy_include, CUDA['include']] - # ), - Extension('rotation.rbbox_overlaps', - ['rotation/rbbox_overlaps_kernel.cu', 'rotation/rbbox_overlaps.pyx'], - library_dirs=[CUDA['lib64']], - libraries=['cudart'], - language='c++', - runtime_library_dirs=[CUDA['lib64']], - # this syntax is specific to this build system - # we're only going to use certain compiler args with nvcc and not with - # gcc the implementation of this trick is in customize_compiler() below - extra_compile_args={'gcc': ["-Wno-unused-function"], - 'nvcc': ['-arch=sm_35', - '--ptxas-options=-v', - '-c', - '--compiler-options', - "'-fPIC'"]}, - include_dirs = [numpy_include, CUDA['include']] - ), - # Extension('rotation.rotate_polygon_nms', - # ['rotation/rotate_polygon_nms_kernel.cu', 'rotation/rotate_polygon_nms.pyx'], - # library_dirs=[CUDA['lib64']], - # libraries=['cudart'], - # language='c++', - # runtime_library_dirs=[CUDA['lib64']], - # # this syntax is specific to this build system - # # we're only going to use certain compiler args with nvcc and not with - # # gcc the implementation of this trick is in customize_compiler() below - # extra_compile_args={'gcc': ["-Wno-unused-function"], - # 'nvcc': ['-arch=sm_35', - # '--ptxas-options=-v', - # '-c', - # '--compiler-options', - # "'-fPIC'"]}, - # include_dirs = [numpy_include, CUDA['include']] - # ), - # - # Extension( - # 'pycocotools._mask', - # sources=['pycocotools/maskApi.c', 'pycocotools/_mask.pyx'], - # include_dirs = [numpy_include, 'pycocotools'], - # extra_compile_args={ - # 'gcc': ['-Wno-cpp', '-Wno-unused-function', '-std=c99']}, - # ), -] - -setup( - name='fast_rcnn', - ext_modules=ext_modules, - # inject our custom trigger - cmdclass={'build_ext': custom_build_ext}, -) diff --git a/example-apps/PDD/pcb-defect-detection/libs/val_libs/__init__.py b/example-apps/PDD/pcb-defect-detection/libs/val_libs/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/libs/val_libs/voc_eval.py b/example-apps/PDD/pcb-defect-detection/libs/val_libs/voc_eval.py deleted file mode 100755 index fc2bde1..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/val_libs/voc_eval.py +++ /dev/null @@ -1,274 +0,0 @@ -# -------------------------------------------------------- -# Fast/er R-CNN -# Licensed under The MIT License [see LICENSE for details] -# Written by Bharath Hariharan -# -------------------------------------------------------- -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import xml.etree.ElementTree as ET -import os -import pickle -import numpy as np - -import matplotlib.pyplot as plt -import pylab as pl -from sklearn.metrics import precision_recall_curve -from itertools import cycle - -from libs.label_name_dict.label_dict import NAME_LABEL_MAP -from libs.configs import cfgs -from help_utils.tools import * - -def write_voc_results_file(all_boxes, test_imgid_list, det_save_dir): - ''' - - :param all_boxes: is a list. each item reprensent the detections of a img. - the detections is a array. shape is [-1, 6]. [category, score, xmin, ymin, xmax, ymax] - Note that: if none detections in this img. that the detetions is : [] - - :param test_imgid_list: - :param det_save_path: - :return: - ''' - for cls, cls_id in NAME_LABEL_MAP.items(): - if cls == 'back_ground': - continue - print("Writing {} VOC resutls file".format(cls)) - - mkdir(det_save_dir) - det_save_path = os.path.join(det_save_dir, "det_"+cls+".txt") - with open(det_save_path, 'wt') as f: - for index, img_name in enumerate(test_imgid_list): - this_img_detections = all_boxes[index] - - this_cls_detections = this_img_detections[this_img_detections[:, 0]==cls_id] - if this_cls_detections.shape[0] == 0: - continue # this cls has none detections in this img - for a_det in this_cls_detections: - f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'. - format(img_name, a_det[1], - a_det[2], a_det[3], - a_det[4], a_det[5])) # that is [img_name, score, xmin, ymin, xmax, ymax] - - -def parse_rec(filename): - """ Parse a PASCAL VOC xml file """ - tree = ET.parse(filename) - objects = [] - for obj in tree.findall('object'): - obj_struct = {} - obj_struct['name'] = obj.find('name').text - obj_struct['pose'] = obj.find('pose').text - obj_struct['truncated'] = int(obj.find('truncated').text) - obj_struct['difficult'] = int(obj.find('difficult').text) - bbox = obj.find('bndbox') - obj_struct['bbox'] = [int(bbox.find('xmin').text), - int(bbox.find('ymin').text), - int(bbox.find('xmax').text), - int(bbox.find('ymax').text)] - objects.append(obj_struct) - - return objects - - -def voc_ap(rec, prec, use_07_metric=False): - """ ap = voc_ap(rec, prec, [use_07_metric]) - Compute VOC AP given precision and recall. - If use_07_metric is true, uses the - VOC 07 11 point method (default:False). - """ - if use_07_metric: - # 11 point metric - ap = 0. - for t in np.arange(0., 1.1, 0.1): - if np.sum(rec >= t) == 0: - p = 0 - else: - p = np.max(prec[rec >= t]) - ap = ap + p / 11. - else: - # correct AP calculation - # first append sentinel values at the end - mrec = np.concatenate(([0.], rec, [1.])) - mpre = np.concatenate(([0.], prec, [0.])) - - # compute the precision envelope - for i in range(mpre.size - 1, 0, -1): - mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) - - # to calculate area under PR curve, look for points - # where X axis (recall) changes value - i = np.where(mrec[1:] != mrec[:-1])[0] - - # and sum (\Delta recall) * prec - ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) - return ap - - -def voc_eval(detpath, annopath, test_imgid_list, cls_name, ovthresh=0.5, - use_07_metric=False, use_diff=False): - ''' - - :param detpath: - :param annopath: - :param test_imgid_list: it 's a list that contains the img_name of test_imgs - :param cls_name: - :param ovthresh: - :param use_07_metric: - :param use_diff: - :return: - ''' - # 1. parse xml to get gtboxes - - # read list of images - imagenames = test_imgid_list - - recs = {} - for i, imagename in enumerate(imagenames): - recs[imagename] = parse_rec(os.path.join(annopath, imagename+'.xml')) - # if i % 100 == 0: - # print('Reading annotation for {:d}/{:d}'.format( - # i + 1, len(imagenames))) - - # 2. get gtboxes for this class. - class_recs = {} - num_pos = 0 - # if cls_name == 'person': - # print ("aaa") - for imagename in imagenames: - R = [obj for obj in recs[imagename] if obj['name'] == cls_name] - bbox = np.array([x['bbox'] for x in R]) - if use_diff: - difficult = np.array([False for x in R]).astype(np.bool) - else: - difficult = np.array([x['difficult'] for x in R]).astype(np.bool) - det = [False] * len(R) - num_pos = num_pos + sum(~difficult) # ignored the diffcult boxes - class_recs[imagename] = {'bbox': bbox, - 'difficult': difficult, - 'det': det} # det means that gtboxes has already been detected - - # 3. read the detection file - detfile = os.path.join(detpath, "det_"+cls_name+".txt") - with open(detfile, 'r') as f: - lines = f.readlines() - - # for a line. that is [img_name, confidence, xmin, ymin, xmax, ymax] - splitlines = [x.strip().split(' ') for x in lines] # a list that include a list - image_ids = [x[0] for x in splitlines] # img_id is img_name - confidence = np.array([float(x[1]) for x in splitlines]) - BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) - - nd = len(image_ids) # num of detections. That, a line is a det_box. - tp = np.zeros(nd) - fp = np.zeros(nd) - - if BB.shape[0] > 0: - # sort by confidence - sorted_ind = np.argsort(-confidence) - sorted_scores = np.sort(-confidence) - BB = BB[sorted_ind, :] - image_ids = [image_ids[x] for x in sorted_ind] #reorder the img_name - - # go down dets and mark TPs and FPs - for d in range(nd): - R = class_recs[image_ids[d]] # img_id is img_name - bb = BB[d, :].astype(float) - ovmax = -np.inf - BBGT = R['bbox'].astype(float) - - if BBGT.size > 0: - # compute overlaps - # intersection - ixmin = np.maximum(BBGT[:, 0], bb[0]) - iymin = np.maximum(BBGT[:, 1], bb[1]) - ixmax = np.minimum(BBGT[:, 2], bb[2]) - iymax = np.minimum(BBGT[:, 3], bb[3]) - iw = np.maximum(ixmax - ixmin + 1., 0.) - ih = np.maximum(iymax - iymin + 1., 0.) - inters = iw * ih - - # union - uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) + - (BBGT[:, 2] - BBGT[:, 0] + 1.) * - (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters) - - overlaps = inters / uni - ovmax = np.max(overlaps) - jmax = np.argmax(overlaps) - - if ovmax > ovthresh: - if not R['difficult'][jmax]: - if not R['det'][jmax]: - tp[d] = 1. - R['det'][jmax] = 1 - else: - fp[d] = 1. - else: - fp[d] = 1. - - # 4. get recall, precison and AP - fp = np.cumsum(fp) - tp = np.cumsum(tp) - rec = tp / float(num_pos) - # avoid divide by zero in case the first detection matches a difficult - # ground truth - prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) - ap = voc_ap(rec, prec, use_07_metric=cfgs.USE_07_METRIC) - - return rec, prec, ap - - -def do_python_eval(test_imgid_list, test_annotation_path): - AP_list = [] - #import matplotlib.pyplot as plt - #import matplotlib.colors as colors - #color_list = colors.cnames.keys()[::6] - - for cls, index in NAME_LABEL_MAP.items(): - if cls == 'back_ground': - continue - recall, precision, AP = voc_eval(detpath=os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION), - test_imgid_list=test_imgid_list, - cls_name=cls, - annopath=test_annotation_path) - AP_list += [AP] - pl.plot(recall, precision, lw=2, label='{} (AP = {:.4f})'''.format(cls, AP)) - print(10*"__") - pl.xlabel('Recall') - pl.ylabel('Precision') - pl.grid(True) - pl.ylim([0.0, 1.05]) - pl.xlim([0.0, 1.0]) - pl.title('Precision-Recall') - pl.legend(loc="lower left") - pl.show() - pl.savefig(cfgs.VERSION+'_eval.jpg') - print("hello") - print("mAP is : {}".format(np.mean(AP_list))) - - -def voc_evaluate_detections(all_boxes, test_annotation_path, test_imgid_list): - ''' - - :param all_boxes: is a list. each item reprensent the detections of a img. - - The detections is a array. shape is [-1, 6]. [category, score, xmin, ymin, xmax, ymax] - Note that: if none detections in this img. that the detetions is : [] - :return: - ''' - test_imgid_list = [item.split('.')[0] for item in test_imgid_list] - - write_voc_results_file(all_boxes, test_imgid_list=test_imgid_list, - det_save_dir=os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION)) - do_python_eval(test_imgid_list, test_annotation_path=test_annotation_path) - - - - - - - - diff --git a/example-apps/PDD/pcb-defect-detection/output/.gitignore b/example-apps/PDD/pcb-defect-detection/output/.gitignore deleted file mode 100755 index 71cea80..0000000 --- a/example-apps/PDD/pcb-defect-detection/output/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -# ignore all except .gitignore file -* -!.gitignore \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/pcb-be-k8s.yaml b/example-apps/PDD/pcb-defect-detection/pcb-be-k8s.yaml deleted file mode 100644 index b4411a0..0000000 --- a/example-apps/PDD/pcb-defect-detection/pcb-be-k8s.yaml +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. ---- -apiVersion: apps/v1 -kind: Deployment -metadata: - name: pcbdefdet-be -spec: - replicas: 1 - selector: - matchLabels: - app: pcbdefdet - template: - metadata: - labels: - app: pcbdefdet - spec: - containers: - - name: pcbdefdet-be - image: ealtedge/pcbdefdet-be - ports: - - containerPort: 9991 - volumeMounts: - - name: host-path-storage - mountPath: "/usr/app/data/pretrained_weights" - - name: host-path-storage - mountPath: "/usr/app/output/trained_weights/FPN_Res101_0117_OHEM" - volumes: - - name: host-path-storage - persistentVolumeClaim: - claimName: host-path-claim - ---- -apiVersion: v1 -kind: PersistentVolume -metadata: - name: host-path-pv - labels: - type: local -spec: - storageClassName: manual - capacity: - storage: 2Gi - accessModes: - - ReadWriteOnce - hostPath: - path: "/root/pcb-def-det/model/" - ---- -apiVersion: v1 -kind: PersistentVolumeClaim -metadata: - name: host-path-claim -spec: - storageClassName: manual - accessModes: - - ReadWriteOnce - resources: - requests: - storage: 2Gi - -# backup-be serice ---- -apiVersion: v1 -kind: Service -metadata: - name: pcbdefdet-be -spec: - selector: - app: pcbdefdet - type: NodePort - ports: - - port: 9991 - name: service-port - nodePort: 30281 diff --git a/example-apps/PDD/pcb-defect-detection/requirements.txt b/example-apps/PDD/pcb-defect-detection/requirements.txt deleted file mode 100644 index 9d2ca76..0000000 --- a/example-apps/PDD/pcb-defect-detection/requirements.txt +++ /dev/null @@ -1,26 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -flask~=1.1.2 -requests~=2.18.4 -flask_sslify -opencv-python~=3.3.1.11 -flask_cors -numpy -Wand -tensorflow-plot -tensorflow~=1.12 -Pillow \ No newline at end of file diff --git a/example-apps/PDD/pcb-defect-detection/run.py b/example-apps/PDD/pcb-defect-detection/run.py deleted file mode 100644 index 54508b3..0000000 --- a/example-apps/PDD/pcb-defect-detection/run.py +++ /dev/null @@ -1,25 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -import logging -from logging.handlers import RotatingFileHandler -from tools.application import start_server - -if __name__ == '__main__': - handler = RotatingFileHandler('/usr/app/log/defect_detection.log', - maxBytes=10000000, backupCount=10) - handler.setLevel(logging.INFO) - start_server(handler) diff --git a/example-apps/PDD/pcb-defect-detection/tools/__init__.py b/example-apps/PDD/pcb-defect-detection/tools/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/example-apps/PDD/pcb-defect-detection/tools/__init__.pyc b/example-apps/PDD/pcb-defect-detection/tools/__init__.pyc deleted file mode 100644 index 0c03069..0000000 Binary files a/example-apps/PDD/pcb-defect-detection/tools/__init__.pyc and /dev/null differ diff --git a/example-apps/PDD/pcb-defect-detection/tools/application.py b/example-apps/PDD/pcb-defect-detection/tools/application.py deleted file mode 100644 index b1da1a2..0000000 --- a/example-apps/PDD/pcb-defect-detection/tools/application.py +++ /dev/null @@ -1,179 +0,0 @@ - -import config -from flask_sslify import SSLify -from flask import Flask, request, jsonify, Response -from flask_cors import CORS - -import json -import requests -import os -import os.path -from os import path -import base64 -import sys - -app = Flask(__name__) -CORS(app) -sslify = SSLify(app) -app.config['JSON_AS_ASCII'] = False -app.config['INPUT_IMAGE_PATH'] = '/usr/app/input_image/' -app.config['OUTPUT_IMAGE_PATH'] = '/usr/app/output_image/' - -app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024 -ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg']) -count = 0 -listOfPath = [] -listOfImages = [] -mock_func = 0 -# result_image_path = app.config['OUTPUT_IMAGE_PATH'] - -listOfPath = ["beijinglab/camera1", "beijinglab/camera2", - "beijinglab/camera3", - "shenzhenlab/camera1", "shenzhenlab/camera2", - "shenzhenlab/camera3", - "shanghailab/camera1", "shanghailab/camera2", - "shanghailab/camera3"] - -def path_exist(image_path): - print('image path:',image_path) - - pathexist = 0 - for imagepath in listOfPath: - print('image path in list:', imagepath) - if (image_path == imagepath): - print('image path match') - pathexist = 1 - break - - return pathexist - -def get_images(abs_image_path): - """ - preview images - - :return: result image - """ - del listOfImages[:] - - arr = os.listdir(abs_image_path) - for file in arr: - print('image name:',abs_image_path + file) - - for image in arr: - ## as base64 string - with open(abs_image_path + image, "rb") as img_file: - jpeg_bin = base64.b64encode(img_file.read()) - listOfImages.append(jpeg_bin) - - response = {'image01': listOfImages[0], - 'image02': listOfImages[1], - 'image03': listOfImages[2],} - #'image04': listOfImages[3]} - # 'image05': listOfImages[4],} - - return response - -def detection(input_image_path, output_image_path): - """ - detection - - :return: detection status - """ - cmd = 'cd tools' + ' && python inference.py --data_dir=' + \ - input_image_path + ' --save_dir=' + output_image_path + ' --GPU=0' - print(cmd) - - os.system(cmd) - return 1 - - -@app.route('/v1/pcb/preview//', methods=['GET']) -def preview_image(EdgeLoc, cameraNum): - """ - preview images - - :return: result image - """ - app.logger.info("Received message from ClientIP [" + request.remote_addr - + "] Operation [" + request.method + "]" + - " Resource [" + request.url + "]") - - image_path = EdgeLoc + '/' + cameraNum - pathexist = path_exist(image_path) - if pathexist == 0: - response = {'image' : 'null'} - print ('image path not exist:',image_path) - return jsonify(response) - - abs_image_path = app.config['INPUT_IMAGE_PATH'] + image_path + '/' - print('abs image path:',abs_image_path) - response = get_images(abs_image_path) - - return jsonify(response) - - -@app.route('/v1/pcb/resultimage', methods=['GET']) -def result_image(): - """ - preview images - - :return: result image - """ - app.logger.info("Received message from ClientIP [" + request.remote_addr - + "] Operation [" + request.method + "]" + - " Resource [" + request.url + "]") - - print('result image path global:',result_image_path) - response = get_images(result_image_path) - - return jsonify(response) - - -@app.route('/v1/pcb/detection//', methods=['GET']) -def detect_image(EdgeLoc, cameraNum): - """ - detect images - - :return: success or failure - """ - app.logger.info("Received message from ClientIP [" + request.remote_addr - + "] Operation [" + request.method + "]" + - " Resource [" + request.url + "]") - global result_image_path - - image_path = EdgeLoc + '/' + cameraNum - pathexist = path_exist(image_path) - if pathexist == 0: - response = {'responce': 'failure'} - print ('image path not exist:',image_path) - return jsonify(response) - - input_image_path = app.config['INPUT_IMAGE_PATH'] + image_path + '/' - print('input image path:', input_image_path) - - output_image_path = app.config['OUTPUT_IMAGE_PATH'] + image_path + '/' - print('out image path:', output_image_path) - - ret = detection(input_image_path, output_image_path) - if (ret == 0): - response = {'responce': 'failure'} - print('detectio algo failed ') - return jsonify(response) - - result_image_path = output_image_path - print('result image path:', result_image_path) - - response = {'responce': 'success'} - print('detection sucess') - return jsonify(response) - - -def start_server(handler): - app.logger.addHandler(handler) - if config.ssl_enabled: - context = (config.ssl_certfilepath, config.ssl_keyfilepath) - app.run(host=config.server_address, debug=True, ssl_context=context, - threaded=True, port=config.server_port) - else: - app.run(host=config.server_address, debug=True, threaded=True, - port=config.server_port) diff --git a/example-apps/PDD/pcb-defect-detection/tools/eval.py b/example-apps/PDD/pcb-defect-detection/tools/eval.py deleted file mode 100755 index e496e30..0000000 --- a/example-apps/PDD/pcb-defect-detection/tools/eval.py +++ /dev/null @@ -1,201 +0,0 @@ -# -*- coding:utf-8 -*- - -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import os, sys -import tensorflow as tf -import time -import cv2 -import pickle -import numpy as np -sys.path.append("../") - -from data.io.image_preprocess import short_side_resize_for_inference_data -from libs.configs import cfgs -from libs.networks import build_whole_network -from libs.val_libs import voc_eval -from libs.box_utils import draw_box_in_img -import argparse -from help_utils import tools - - -def eval_with_plac(det_net, real_test_imgname_list, img_root, draw_imgs=False): - - # 1. preprocess img - img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not BGR - img_batch = tf.cast(img_plac, tf.float32) - - img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, - target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, - length_limitation=cfgs.IMG_MAX_LENGTH) - img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) - img_batch = tf.expand_dims(img_batch, axis=0) - - detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( - input_img_batch=img_batch, - gtboxes_batch=None) - - init_op = tf.group( - tf.global_variables_initializer(), - tf.local_variables_initializer() - ) - - restorer, restore_ckpt = det_net.get_restorer() - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - - compute_time = 0 - compute_imgnum = 0 - - with tf.Session(config=config) as sess: - sess.run(init_op) - if not restorer is None: - restorer.restore(sess, restore_ckpt) - print('restore model') - - all_boxes = [] - for i, a_img_name in enumerate(real_test_imgname_list): - - raw_img = cv2.imread(os.path.join(img_root, a_img_name)) - raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] - - start = time.time() - resized_img, detected_boxes, detected_scores, detected_categories = \ - sess.run( - [img_batch, detection_boxes, detection_scores, detection_category], - feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB - ) - end = time.time() - compute_time = compute_time + (end - start) - compute_imgnum = compute_imgnum + 1 - # print("{} cost time : {} ".format(img_name, (end - start))) - if draw_imgs: - show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD - show_scores = detected_scores[show_indices] - show_boxes = detected_boxes[show_indices] - show_categories = detected_categories[show_indices] - final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(np.squeeze(resized_img, 0), - boxes=show_boxes, - labels=show_categories, - scores=show_scores) - if not os.path.exists(cfgs.TEST_SAVE_PATH): - os.makedirs(cfgs.TEST_SAVE_PATH) - - cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + a_img_name + '.jpg', - final_detections[:, :, ::-1]) - - xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \ - detected_boxes[:, 2], detected_boxes[:, 3] - - resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] - - xmin = xmin * raw_w / resized_w - xmax = xmax * raw_w / resized_w - - ymin = ymin * raw_h / resized_h - ymax = ymax * raw_h / resized_h - - boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax])) - dets = np.hstack((detected_categories.reshape(-1, 1), - detected_scores.reshape(-1, 1), - boxes)) - all_boxes.append(dets) - - tools.view_bar('{} image cost {}s'.format(a_img_name, (end - start)), i + 1, len(real_test_imgname_list)) - - # save_dir = os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION) - # if not os.path.exists(save_dir): - # os.makedirs(save_dir) - # fw1 = open(os.path.join(save_dir, 'detections.pkl'), 'wb') - # pickle.dump(all_boxes, fw1) - print('\n average_training_time_per_image is' + str(compute_time / compute_imgnum)) - return all_boxes - - -def eval(num_imgs, eval_dir, annotation_dir, showbox): - - # with open('/home/yjr/DataSet/VOC/VOC_test/VOC2007/ImageSets/Main/aeroplane_test.txt') as f: - # all_lines = f.readlines() - # test_imgname_list = [a_line.split()[0].strip() for a_line in all_lines] - - test_imgname_list = [item for item in os.listdir(eval_dir) - if item.endswith(('.jpg', 'jpeg', '.png', '.tif', '.tiff'))] - if num_imgs == np.inf: - real_test_imgname_list = test_imgname_list - else: - real_test_imgname_list = test_imgname_list[: num_imgs] - - faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, - is_training=False) - all_boxes = eval_with_plac(det_net=faster_rcnn, real_test_imgname_list=real_test_imgname_list, - img_root=eval_dir, - draw_imgs=showbox) - - # save_dir = os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION) - # if not os.path.exists(save_dir): - # os.makedirs(save_dir) - # with open(os.path.join(save_dir, 'detections.pkl'), 'rb') as f: - # all_boxes = pickle.load(f) - # - # print(len(all_boxes)) - - voc_eval.voc_evaluate_detections(all_boxes=all_boxes, - test_annotation_path=annotation_dir, - test_imgid_list=real_test_imgname_list) - -def parse_args(): - - parser = argparse.ArgumentParser('evaluate the result with Pascal2007 stdand') - - parser.add_argument('--eval_imgs', dest='eval_imgs', - help='evaluate imgs dir ', - default='../data/pcb_test/JPEGImages', type=str) - parser.add_argument('--annotation_dir', dest='test_annotation_dir', - help='the dir save annotations', - default='../data/pcb_test/Annotations', type=str) - parser.add_argument('--showbox', dest='showbox', - help='whether show detecion results when evaluation', - default=False, type=bool) - parser.add_argument('--GPU', dest='GPU', - help='gpu id', - default='2', type=str) - #parser.add_argument('--eval_num', dest='eval_num', - # help='the num of eval imgs', - # default=np.inf, type=int) - parser.add_argument('--eval_num', dest='eval_num', - help='the num of eval imgs', - default=100, type=int) - args = parser.parse_args() - return args - - -if __name__ == '__main__': - - args = parse_args() - print(20*"--") - print(args) - print(20*"--") - os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU - eval(np.inf, # use np.inf to test all the imgs. use 10 to test 10 imgs. - eval_dir=args.eval_imgs, - annotation_dir=args.test_annotation_dir, - showbox=args.showbox) - - - - - - - - - - - - - - - - diff --git a/example-apps/PDD/pcb-defect-detection/tools/inference.py b/example-apps/PDD/pcb-defect-detection/tools/inference.py deleted file mode 100755 index e525d46..0000000 --- a/example-apps/PDD/pcb-defect-detection/tools/inference.py +++ /dev/null @@ -1,139 +0,0 @@ -# -*- coding:utf-8 -*- - -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import os, sys -import tensorflow as tf -import time -import cv2 -import argparse -import numpy as np -sys.path.append("../") - -from data.io.image_preprocess import short_side_resize_for_inference_data -from libs.configs import cfgs -from libs.networks import build_whole_network -from libs.box_utils import draw_box_in_img -from help_utils import tools - - -def detect(det_net, inference_save_path, real_test_imgname_list): - - # 1. preprocess img - img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not GBR - img_batch = tf.cast(img_plac, tf.float32) - img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, - target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, - length_limitation=cfgs.IMG_MAX_LENGTH) - img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) - img_batch = tf.expand_dims(img_batch, axis=0) # [1, None, None, 3] - - detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( - input_img_batch=img_batch, - gtboxes_batch=None) - - init_op = tf.group( - tf.global_variables_initializer(), - tf.local_variables_initializer() - ) - - restorer, restore_ckpt = det_net.get_restorer() - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - - with tf.Session(config=config) as sess: - sess.run(init_op) - if not restorer is None: - restorer.restore(sess, restore_ckpt) - print('restore model') - - for i, a_img_name in enumerate(real_test_imgname_list): - - raw_img = cv2.imread(a_img_name) - start = time.time() - resized_img, detected_boxes, detected_scores, detected_categories = \ - sess.run( - [img_batch, detection_boxes, detection_scores, detection_category], - feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB - ) - end = time.time() - # print("{} cost time : {} ".format(img_name, (end - start))) - - show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD - show_scores = detected_scores[show_indices] - show_boxes = detected_boxes[show_indices] - show_categories = detected_categories[show_indices] - final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(np.squeeze(resized_img, 0), - boxes=show_boxes, - labels=show_categories, - scores=show_scores) - nake_name = a_img_name.split('/')[-1] - # print (inference_save_path + '/' + nake_name) - cv2.imwrite(inference_save_path + '/' + nake_name, - final_detections[:, :, ::-1]) - - tools.view_bar('{} image cost {}s'.format(a_img_name, (end - start)), i + 1, len(real_test_imgname_list)) - - -def inference(test_dir, inference_save_path): - - test_imgname_list = [os.path.join(test_dir, img_name) for img_name in os.listdir(test_dir) - if img_name.endswith(('.jpg', '.png', '.jpeg', '.tif', '.tiff'))] - assert len(test_imgname_list) != 0, 'test_dir has no imgs there.' \ - ' Note that, we only support img format of (.jpg, .png, and .tiff) ' - - faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, - is_training=False) - detect(det_net=faster_rcnn, inference_save_path=inference_save_path, real_test_imgname_list=test_imgname_list) - - -def parse_args(): - """ - Parse input arguments - """ - parser = argparse.ArgumentParser(description='TestImgs...U need provide the test dir') - - parser.add_argument('--data_dir', dest='data_dir', - help='data path', - default='demos', type=str) - parser.add_argument('--save_dir', dest='save_dir', - help='demo imgs to save', - default='inference_results', type=str) - parser.add_argument('--GPU', dest='GPU', - help='gpu id ', - default='0', type=str) - - if len(sys.argv) == 1: - parser.print_help() - sys.exit(1) - - args = parser.parse_args() - - return args -if __name__ == '__main__': - - args = parse_args() - print('Called with args:') - print(args) - os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU - inference(args.data_dir, - inference_save_path=args.save_dir) - - - - - - - - - - - - - - - - diff --git a/example-apps/PDD/pcb-defect-detection/tools/inference_for_coco.py b/example-apps/PDD/pcb-defect-detection/tools/inference_for_coco.py deleted file mode 100755 index 835f251..0000000 --- a/example-apps/PDD/pcb-defect-detection/tools/inference_for_coco.py +++ /dev/null @@ -1,179 +0,0 @@ -# -*- coding:utf-8 -*- - -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import os, sys -import tensorflow as tf -import time -import cv2 -import pickle -import numpy as np -sys.path.append("../") -sys.path.insert(0, '/home/yjr/PycharmProjects/Faster-RCNN_TF/data/lib_coco/PythonAPI') -from data.io.image_preprocess import short_side_resize_for_inference_data -from libs.configs import cfgs -from libs.networks import build_whole_network -from libs.val_libs import voc_eval -from libs.box_utils import draw_box_in_img -from libs.label_name_dict.coco_dict import LABEL_NAME_MAP, classes_originID -from help_utils import tools -from data.lib_coco.PythonAPI.pycocotools.coco import COCO -import json - -os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP - - -def eval_with_plac(det_net, imgId_list, coco, out_json_root, draw_imgs=False): - - # 1. preprocess img - img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not GBR - img_batch = tf.cast(img_plac, tf.float32) - - img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, - target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, - length_limitation=cfgs.IMG_MAX_LENGTH) - img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) - img_batch = tf.expand_dims(img_batch, axis=0) - - detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( - input_img_batch=img_batch, - gtboxes_batch=None) - - init_op = tf.group( - tf.global_variables_initializer(), - tf.local_variables_initializer() - ) - - restorer, restore_ckpt = det_net.get_restorer() - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - - # coco_test_results = [] - - with tf.Session(config=config) as sess: - sess.run(init_op) - if not restorer is None: - restorer.restore(sess, restore_ckpt) - print('restore model') - - for i, imgid in enumerate(imgId_list): - imgname = coco.loadImgs(ids=[imgid])[0]['file_name'] - raw_img = cv2.imread(os.path.join("/home/yjr/DataSet/COCO/2017/test2017", imgname)) - - raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] - start = time.time() - resized_img, detected_boxes, detected_scores, detected_categories = \ - sess.run( - [img_batch, detection_boxes, detection_scores, detection_category], - feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB - ) - end = time.time() - - if draw_imgs: - show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD - show_scores = detected_scores[show_indices] - show_boxes = detected_boxes[show_indices] - show_categories = detected_categories[show_indices] - final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(np.squeeze(resized_img, 0), - boxes=show_boxes, - labels=show_categories, - scores=show_scores) - cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + str(imgid) + '.jpg', - final_detections[:, :, ::-1]) - - xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \ - detected_boxes[:, 2], detected_boxes[:, 3] - - resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] - - xmin = xmin * raw_w / resized_w - xmax = xmax * raw_w / resized_w - - ymin = ymin * raw_h / resized_h - ymax = ymax * raw_h / resized_h - - boxes = np.transpose(np.stack([xmin, ymin, xmax-xmin, ymax-ymin])) - - dets = np.hstack((detected_categories.reshape(-1, 1), - detected_scores.reshape(-1, 1), - boxes)) - - a_img_detect_result = [] - for a_det in dets: - label, score, bbox = a_det[0], a_det[1], a_det[2:] - cat_id = classes_originID[LABEL_NAME_MAP[label]] - if score<0.00001: - continue - det_object = {"image_id": imgid, - "category_id": cat_id, - "bbox": bbox.tolist(), - "score": float(score)} - # print (det_object) - a_img_detect_result.append(det_object) - f = open(os.path.join(out_json_root, 'each_img', str(imgid)+'.json'), 'w') - json.dump(a_img_detect_result, f) # , indent=4 - f.close() - del a_img_detect_result - del dets - del boxes - del resized_img - del raw_img - tools.view_bar('{} image cost {}s'.format(imgid, (end - start)), i + 1, len(imgId_list)) - - -def eval(num_imgs): - - - # annotation_path = '/home/yjr/DataSet/COCO/2017/test_annotations/image_info_test2017.json' - annotation_path = '/home/yjr/DataSet/COCO/2017/test_annotations/image_info_test-dev2017.json' - # annotation_path = '/home/yjr/DataSet/COCO/2017/annotations/instances_train2017.json' - print("load coco .... it will cost about 17s..") - coco = COCO(annotation_path) - - imgId_list = coco.getImgIds() - - if num_imgs !=np.inf: - imgId_list = imgId_list[: num_imgs] - - faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, - is_training=False) - save_dir = os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION) - eval_with_plac(det_net=faster_rcnn, coco=coco, imgId_list=imgId_list, out_json_root=save_dir, - draw_imgs=True) - print("each img over**************") - - final_detections = [] - with open(os.path.join(save_dir, 'coco2017test_results.json'), 'w') as wf: - for imgid in imgId_list: - f = open(os.path.join(save_dir, 'each_img', str(imgid)+'.json')) - tmp_list = json.load(f) - # print (type(tmp_list)) - final_detections.extend(tmp_list) - del tmp_list - f.close() - json.dump(final_detections, wf) - - -if __name__ == '__main__': - - eval(np.inf) - - - - - - - - - - - - - - - - - diff --git a/example-apps/PDD/pcb-defect-detection/tools/test.py b/example-apps/PDD/pcb-defect-detection/tools/test.py deleted file mode 100755 index 86984d9..0000000 --- a/example-apps/PDD/pcb-defect-detection/tools/test.py +++ /dev/null @@ -1,153 +0,0 @@ -# -*- coding:utf-8 -*- - -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import os, sys -import tensorflow as tf -import time -import cv2 -import argparse -import numpy as np -sys.path.append("../") - -from data.io.image_preprocess import short_side_resize_for_inference_data -from libs.configs import cfgs -from libs.networks import build_whole_network -from libs.box_utils import draw_box_in_img -from help_utils import tools - - -def detect(det_net, inference_save_path, real_test_imgname_list): - - # 1. preprocess img - img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not GBR - img_batch = tf.cast(img_plac, tf.float32) - img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, - target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, - length_limitation=cfgs.IMG_MAX_LENGTH) - img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) - img_batch = tf.expand_dims(img_batch, axis=0) # [1, None, None, 3] - - detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( - input_img_batch=img_batch, - gtboxes_batch=None) - - init_op = tf.group( - tf.global_variables_initializer(), - tf.local_variables_initializer() - ) - - restorer, restore_ckpt = det_net.get_restorer() - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - - with tf.Session(config=config) as sess: - sess.run(init_op) - if not restorer is None: - restorer.restore(sess, restore_ckpt) - print('restore model') - - for i, a_img_name in enumerate(real_test_imgname_list): - - raw_img = cv2.imread(a_img_name) - start = time.time() - resized_img, detected_boxes, detected_scores, detected_categories = \ - sess.run( - [img_batch, detection_boxes, detection_scores, detection_category], - feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB - ) - end = time.time() - # print("{} cost time : {} ".format(img_name, (end - start))) - - raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] - - xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \ - detected_boxes[:, 2], detected_boxes[:, 3] - - resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] - - xmin = xmin * raw_w / resized_w - xmax = xmax * raw_w / resized_w - - ymin = ymin * raw_h / resized_h - ymax = ymax * raw_h / resized_h - - detected_boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax])) - - show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD - show_scores = detected_scores[show_indices] - show_boxes = detected_boxes[show_indices] - show_categories = detected_categories[show_indices] - final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(raw_img - np.array(cfgs.PIXEL_MEAN), - boxes=show_boxes, - labels=show_categories, - scores=show_scores) - nake_name = a_img_name.split('/')[-1] - # print (inference_save_path + '/' + nake_name) - cv2.imwrite(inference_save_path + '/' + nake_name, - final_detections[:, :, ::-1]) - - tools.view_bar('{} image cost {}s'.format(a_img_name, (end - start)), i + 1, len(real_test_imgname_list)) - - -def test(test_dir, inference_save_path): - - test_imgname_list = [os.path.join(test_dir, img_name) for img_name in os.listdir(test_dir) - if img_name.endswith(('.jpg', '.png', '.jpeg', '.tif', '.tiff'))] - assert len(test_imgname_list) != 0, 'test_dir has no imgs there.' \ - ' Note that, we only support img format of (.jpg, .png, and .tiff) ' - - faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, - is_training=False) - detect(det_net=faster_rcnn, inference_save_path=inference_save_path, real_test_imgname_list=test_imgname_list) - - -def parse_args(): - """ - Parse input arguments - """ - parser = argparse.ArgumentParser(description='TestImgs...U need provide the test dir') - parser.add_argument('--data_dir', dest='data_dir', - help='data path', - default='demos', type=str) - parser.add_argument('--save_dir', dest='save_dir', - help='demo imgs to save', - default='inference_results', type=str) - parser.add_argument('--GPU', dest='GPU', - help='gpu id ', - default='0', type=str) - - if len(sys.argv) == 1: - parser.print_help() - sys.exit(1) - - args = parser.parse_args() - - return args -if __name__ == '__main__': - - args = parse_args() - print('Called with args:') - print(args) - os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU - test(args.data_dir, - inference_save_path=args.save_dir) - - - - - - - - - - - - - - - - diff --git a/example-apps/PDD/pcb-defect-detection/tools/test_annotation/01_missing_hole_01.xml b/example-apps/PDD/pcb-defect-detection/tools/test_annotation/01_missing_hole_01.xml deleted file mode 100755 index 90cc96f..0000000 --- a/example-apps/PDD/pcb-defect-detection/tools/test_annotation/01_missing_hole_01.xml +++ /dev/null @@ -1,50 +0,0 @@ - - Missing_hole - 01_missing_hole_01.jpg - /home/weapon/Desktop/PCB_DATASET/Missing_hole/01_missing_hole_01.jpg - - Unknown - - - 3034 - 1586 - 3 - - 0 - - missing_hole - Unspecified - 0 - 0 - - 2459 - 1274 - 2530 - 1329 - - - - missing_hole - Unspecified - 0 - 0 - - 1613 - 334 - 1679 - 396 - - - - missing_hole - Unspecified - 0 - 0 - - 1726 - 794 - 1797 - 854 - - - diff --git a/example-apps/PDD/pcb-defect-detection/tools/test_annotation/01_missing_hole_12.xml b/example-apps/PDD/pcb-defect-detection/tools/test_annotation/01_missing_hole_12.xml deleted file mode 100755 index b8f4aba..0000000 --- a/example-apps/PDD/pcb-defect-detection/tools/test_annotation/01_missing_hole_12.xml +++ /dev/null @@ -1,50 +0,0 @@ - - Missing_hole - 01_missing_hole_12.jpg - /home/weapon/Desktop/PCB_DATASET/Missing_hole/01_missing_hole_12.jpg - - Unknown - - - 3034 - 1586 - 3 - - 0 - - missing_hole - Unspecified - 0 - 0 - - 1062 - 1047 - 1117 - 1112 - - - - missing_hole - Unspecified - 0 - 0 - - 2633 - 932 - 2702 - 989 - - - - missing_hole - Unspecified - 0 - 0 - - 2460 - 1220 - 2528 - 1276 - - - diff --git a/example-apps/PDD/pcb-defect-detection/tools/train.py b/example-apps/PDD/pcb-defect-detection/tools/train.py deleted file mode 100755 index c5ea76c..0000000 --- a/example-apps/PDD/pcb-defect-detection/tools/train.py +++ /dev/null @@ -1,204 +0,0 @@ -# -*- coding:utf-8 -*- - -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import tensorflow as tf -import tensorflow.contrib.slim as slim -import os, sys -import numpy as np -import time -sys.path.append("../") - -from libs.configs import cfgs -# from libs.networks import build_whole_network2 -from libs.networks import build_whole_network -from data.io.read_tfrecord import next_batch -from libs.box_utils import show_box_in_tensor -from help_utils import tools - -os.environ["CUDA_VISIBLE_DEVICES"] = "2" - - -def train(): - - faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, - is_training=True) - - with tf.name_scope('get_batch'): - img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch = \ - next_batch(dataset_name=cfgs.DATASET_NAME, # 'pascal', 'coco' - batch_size=cfgs.BATCH_SIZE, - shortside_len=cfgs.IMG_SHORT_SIDE_LEN, - is_training=True) - gtboxes_and_label = tf.reshape(gtboxes_and_label_batch, [-1, 5]) - - biases_regularizer = tf.no_regularizer - weights_regularizer = tf.contrib.layers.l2_regularizer(cfgs.WEIGHT_DECAY) - - # list as many types of layers as possible, even if they are not used now - with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane, \ - slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], - weights_regularizer=weights_regularizer, - biases_regularizer=biases_regularizer, - biases_initializer=tf.constant_initializer(0.0)): - final_bbox, final_scores, final_category, loss_dict = faster_rcnn.build_whole_detection_network( - input_img_batch=img_batch, - gtboxes_batch=gtboxes_and_label) - - # ----------------------------------------------------------------------------------------------------build loss - weight_decay_loss = tf.add_n(slim.losses.get_regularization_losses()) - rpn_location_loss = loss_dict['rpn_loc_loss'] - rpn_cls_loss = loss_dict['rpn_cls_loss'] - rpn_total_loss = rpn_location_loss + rpn_cls_loss - - fastrcnn_cls_loss = loss_dict['fastrcnn_cls_loss'] - fastrcnn_loc_loss = loss_dict['fastrcnn_loc_loss'] - fastrcnn_total_loss = fastrcnn_cls_loss + fastrcnn_loc_loss - - total_loss = rpn_total_loss + fastrcnn_total_loss + weight_decay_loss - # ____________________________________________________________________________________________________build loss - - - # ---------------------------------------------------------------------------------------------------add summary - - tf.summary.scalar('RPN_LOSS/cls_loss', rpn_cls_loss) - tf.summary.scalar('RPN_LOSS/location_loss', rpn_location_loss) - tf.summary.scalar('RPN_LOSS/rpn_total_loss', rpn_total_loss) - - tf.summary.scalar('FAST_LOSS/fastrcnn_cls_loss', fastrcnn_cls_loss) - tf.summary.scalar('FAST_LOSS/fastrcnn_location_loss', fastrcnn_loc_loss) - tf.summary.scalar('FAST_LOSS/fastrcnn_total_loss', fastrcnn_total_loss) - - tf.summary.scalar('LOSS/total_loss', total_loss) - tf.summary.scalar('LOSS/regular_weights', weight_decay_loss) - - gtboxes_in_img = show_box_in_tensor.draw_boxes_with_categories(img_batch=img_batch, - boxes=gtboxes_and_label[:, :-1], - labels=gtboxes_and_label[:, -1]) - if cfgs.ADD_BOX_IN_TENSORBOARD: - detections_in_img = show_box_in_tensor.draw_boxes_with_categories_and_scores(img_batch=img_batch, - boxes=final_bbox, - labels=final_category, - scores=final_scores) - tf.summary.image('Compare/final_detection', detections_in_img) - tf.summary.image('Compare/gtboxes', gtboxes_in_img) - - # ___________________________________________________________________________________________________add summary - - global_step = slim.get_or_create_global_step() - lr = tf.train.piecewise_constant(global_step, - boundaries=[np.int64(cfgs.DECAY_STEP[0]), np.int64(cfgs.DECAY_STEP[1])], - values=[cfgs.LR, cfgs.LR / 10., cfgs.LR / 100.]) - tf.summary.scalar('lr', lr) - optimizer = tf.train.MomentumOptimizer(lr, momentum=cfgs.MOMENTUM) - # optimizer = tf.train.AdamOptimizer(lr) - - # ---------------------------------------------------------------------------------------------compute gradients - gradients = faster_rcnn.get_gradients(optimizer, total_loss) - - # enlarge_gradients for bias - if cfgs.MUTILPY_BIAS_GRADIENT: - gradients = faster_rcnn.enlarge_gradients_for_bias(gradients) - - if cfgs.GRADIENT_CLIPPING_BY_NORM: - with tf.name_scope('clip_gradients_YJR'): - gradients = slim.learning.clip_gradient_norms(gradients, - cfgs.GRADIENT_CLIPPING_BY_NORM) - # _____________________________________________________________________________________________compute gradients - - - - # train_op - train_op = optimizer.apply_gradients(grads_and_vars=gradients, - global_step=global_step) - summary_op = tf.summary.merge_all() - init_op = tf.group( - tf.global_variables_initializer(), - tf.local_variables_initializer() - ) - - restorer, restore_ckpt = faster_rcnn.get_restorer() - saver = tf.train.Saver(max_to_keep=30) - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - compute_time = 0 - compute_imgnum = 0 - - with tf.Session(config=config) as sess: - sess.run(init_op) - if not restorer is None: - restorer.restore(sess, restore_ckpt) - print('restore model') - coord = tf.train.Coordinator() - threads = tf.train.start_queue_runners(sess, coord) - - summary_path = os.path.join(cfgs.SUMMARY_PATH, cfgs.VERSION) - tools.mkdir(summary_path) - summary_writer = tf.summary.FileWriter(summary_path, graph=sess.graph) - training_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) - - for step in range(cfgs.MAX_ITERATION): - training_time1 = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) - if step % cfgs.SHOW_TRAIN_INFO_INTE != 0 and step % cfgs.SMRY_ITER != 0: - _, global_stepnp = sess.run([train_op, global_step]) - - else: - if step % cfgs.SHOW_TRAIN_INFO_INTE == 0 and step % cfgs.SMRY_ITER != 0: - start = time.time() - - _, global_stepnp, img_name, rpnLocLoss, rpnClsLoss, rpnTotalLoss, \ - fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, totalLoss = \ - sess.run( - [train_op, global_step, img_name_batch, rpn_location_loss, rpn_cls_loss, rpn_total_loss, - fastrcnn_loc_loss, fastrcnn_cls_loss, fastrcnn_total_loss, total_loss]) - - end = time.time() - compute_time = compute_time + (end - start) - compute_imgnum = compute_imgnum + 1 - print(""" {}: step{} image_name:{} |\t - rpn_loc_loss:{} |\t rpn_cla_loss:{} |\t rpn_total_loss:{} | - fast_rcnn_loc_loss:{} |\t fast_rcnn_cla_loss:{} |\t fast_rcnn_total_loss:{} | - total_loss:{} |\t per_cost_time:{}s""" \ - .format(training_time1, global_stepnp, str(img_name[0]), rpnLocLoss, rpnClsLoss, - rpnTotalLoss, fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, totalLoss, - (end - start))) - else: - if step % cfgs.SMRY_ITER == 0: - _, global_stepnp, summary_str = sess.run([train_op, global_step, summary_op]) - summary_writer.add_summary(summary_str, global_stepnp) - summary_writer.flush() - - if (step > 0 and step % cfgs.SAVE_WEIGHTS_INTE == 0) or (step == cfgs.MAX_ITERATION - 1): - - save_dir = os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION) - if not os.path.exists(save_dir): - os.mkdir(save_dir) - - #save_ckpt = os.path.join(save_dir, 'voc_' + str(global_stepnp) + 'model.ckpt') - save_ckpt = os.path.join(save_dir, 'pcb_' + str(global_stepnp) + 'model.ckpt') - saver.save(sess, save_ckpt) - print(' weights had been saved') - print('average_training_time_per_image is' + str(compute_time / compute_imgnum)) - print('traning start time is ' + training_time) - end_training_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) - print('traning end time is ' + end_training_time) - coord.request_stop() - coord.join(threads) - - -if __name__ == '__main__': - - train() - - - - - - - - - - diff --git a/example-apps/PDD/pcb-defect-detection/tools/train_with_placeholder.py b/example-apps/PDD/pcb-defect-detection/tools/train_with_placeholder.py deleted file mode 100755 index b329715..0000000 --- a/example-apps/PDD/pcb-defect-detection/tools/train_with_placeholder.py +++ /dev/null @@ -1,233 +0,0 @@ -# -*- coding:utf-8 -*- - -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import tensorflow as tf -import tensorflow.contrib.slim as slim -import os, sys -sys.path.append("../") -sys.path.append("../data/lib_coco") -sys.path.append('../data/lib_coco/PythonAPI/') - -import numpy as np -import time - -from libs.configs import cfgs -from libs.networks import build_whole_network -from data.io import image_preprocess -from libs.box_utils import show_box_in_tensor -from help_utils import tools -from data.lib_coco.get_coco_next_batch import next_img - - -os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP - - -def preprocess_img(img_plac, gtbox_plac): - ''' - - :param img_plac: [H, W, 3] uint 8 img. In RGB. - :param gtbox_plac: shape of [-1, 5]. [xmin, ymin, xmax, ymax, label] - :return: - ''' - - img = tf.cast(img_plac, tf.float32) - - # gtboxes_and_label = tf.cast(gtbox_plac, tf.float32) - img, gtboxes_and_label = image_preprocess.short_side_resize(img_tensor=img, - gtboxes_and_label=gtbox_plac, - target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, - length_limitation=cfgs.IMG_MAX_LENGTH) - img, gtboxes_and_label = image_preprocess.random_flip_left_right(img_tensor=img, - gtboxes_and_label=gtboxes_and_label) - img = img - tf.constant([[cfgs.PIXEL_MEAN]]) - img_batch = tf.expand_dims(img, axis=0) - - # gtboxes_and_label = tf.Print(gtboxes_and_label, [tf.shape(gtboxes_and_label)], message='gtbox shape') - return img_batch, gtboxes_and_label - -def train(): - - faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, - is_training=True) - - with tf.name_scope('get_batch'): - img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) - gtbox_plac = tf.placeholder(dtype=tf.int32, shape=[None, 5]) - - img_batch, gtboxes_and_label = preprocess_img(img_plac, gtbox_plac) - # gtboxes_and_label = tf.reshape(gtboxes_and_label_batch, [-1, 5]) - - biases_regularizer = tf.no_regularizer - weights_regularizer = tf.contrib.layers.l2_regularizer(cfgs.WEIGHT_DECAY) - - # list as many types of layers as possible, even if they are not used now - with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane, \ - slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], - weights_regularizer=weights_regularizer, - biases_regularizer=biases_regularizer, - biases_initializer=tf.constant_initializer(0.0)): - final_bbox, final_scores, final_category, loss_dict = faster_rcnn.build_whole_detection_network( - input_img_batch=img_batch, - gtboxes_batch=gtboxes_and_label) - - # ----------------------------------------------------------------------------------------------------build loss - weight_decay_loss = 0 # tf.add_n(slim.losses.get_regularization_losses()) - rpn_location_loss = loss_dict['rpn_loc_loss'] - rpn_cls_loss = loss_dict['rpn_cls_loss'] - rpn_total_loss = rpn_location_loss + rpn_cls_loss - - fastrcnn_cls_loss = loss_dict['fastrcnn_cls_loss'] - fastrcnn_loc_loss = loss_dict['fastrcnn_loc_loss'] - fastrcnn_total_loss = fastrcnn_cls_loss + fastrcnn_loc_loss - - total_loss = rpn_total_loss + fastrcnn_total_loss + weight_decay_loss - # ____________________________________________________________________________________________________build loss - - - - # ---------------------------------------------------------------------------------------------------add summary - tf.summary.scalar('RPN_LOSS/cls_loss', rpn_cls_loss) - tf.summary.scalar('RPN_LOSS/location_loss', rpn_location_loss) - tf.summary.scalar('RPN_LOSS/rpn_total_loss', rpn_total_loss) - - tf.summary.scalar('FAST_LOSS/fastrcnn_cls_loss', fastrcnn_cls_loss) - tf.summary.scalar('FAST_LOSS/fastrcnn_location_loss', fastrcnn_loc_loss) - tf.summary.scalar('FAST_LOSS/fastrcnn_total_loss', fastrcnn_total_loss) - - tf.summary.scalar('LOSS/total_loss', total_loss) - tf.summary.scalar('LOSS/regular_weights', weight_decay_loss) - - gtboxes_in_img = show_box_in_tensor.draw_boxes_with_categories(img_batch=img_batch, - boxes=gtboxes_and_label[:, :-1], - labels=gtboxes_and_label[:, -1]) - if cfgs.ADD_BOX_IN_TENSORBOARD: - detections_in_img = show_box_in_tensor.draw_boxes_with_categories_and_scores(img_batch=img_batch, - boxes=final_bbox, - labels=final_category, - scores=final_scores) - tf.summary.image('Compare/final_detection', detections_in_img) - tf.summary.image('Compare/gtboxes', gtboxes_in_img) - - # ___________________________________________________________________________________________________add summary - - global_step = slim.get_or_create_global_step() - lr = tf.train.piecewise_constant(global_step, - boundaries=[np.int64(cfgs.DECAY_STEP[0]), np.int64(cfgs.DECAY_STEP[1])], - values=[cfgs.LR, cfgs.LR / 10., cfgs.LR / 100.]) - tf.summary.scalar('lr', lr) - optimizer = tf.train.MomentumOptimizer(lr, momentum=cfgs.MOMENTUM) - - # ---------------------------------------------------------------------------------------------compute gradients - gradients = faster_rcnn.get_gradients(optimizer, total_loss) - - # enlarge_gradients for bias - if cfgs.MUTILPY_BIAS_GRADIENT: - gradients = faster_rcnn.enlarge_gradients_for_bias(gradients) - - if cfgs.GRADIENT_CLIPPING_BY_NORM: - with tf.name_scope('clip_gradients_YJR'): - gradients = slim.learning.clip_gradient_norms(gradients, - cfgs.GRADIENT_CLIPPING_BY_NORM) - # _____________________________________________________________________________________________compute gradients - - - - # train_op - train_op = optimizer.apply_gradients(grads_and_vars=gradients, - global_step=global_step) - summary_op = tf.summary.merge_all() - init_op = tf.group( - tf.global_variables_initializer(), - tf.local_variables_initializer() - ) - - restorer, restore_ckpt = faster_rcnn.get_restorer() - saver = tf.train.Saver(max_to_keep=30) - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - - with tf.Session(config=config) as sess: - sess.run(init_op) - if not restorer is None: - restorer.restore(sess, restore_ckpt) - print('restore model') - - summary_path = os.path.join(cfgs.SUMMARY_PATH, cfgs.VERSION) - tools.mkdir(summary_path) - summary_writer = tf.summary.FileWriter(summary_path, graph=sess.graph) - - for step in range(cfgs.MAX_ITERATION): - - img_id, img, gt_info = next_img(step=step) - training_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) - - if step % cfgs.SHOW_TRAIN_INFO_INTE != 0 and step % cfgs.SMRY_ITER != 0: - _, global_stepnp = sess.run([train_op, global_step], - feed_dict={img_plac: img, - gtbox_plac: gt_info} - ) - - else: - if step % cfgs.SHOW_TRAIN_INFO_INTE == 0 and step % cfgs.SMRY_ITER != 0: - start = time.time() - - _, global_stepnp, rpnLocLoss, rpnClsLoss, rpnTotalLoss, \ - fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, totalLoss = \ - sess.run( - [train_op, global_step, rpn_location_loss, rpn_cls_loss, rpn_total_loss, - fastrcnn_loc_loss, fastrcnn_cls_loss, fastrcnn_total_loss, total_loss], - feed_dict={img_plac: img, - gtbox_plac: gt_info}) - end = time.time() - print(""" {}: step{} image_name:{} |\t - rpn_loc_loss:{} |\t rpn_cla_loss:{} |\t rpn_total_loss:{} | - fast_rcnn_loc_loss:{} |\t fast_rcnn_cla_loss:{} |\t fast_rcnn_total_loss:{} | - total_loss:{} |\t per_cost_time:{}s""" \ - .format(training_time, global_stepnp, str(img_id), rpnLocLoss, rpnClsLoss, - rpnTotalLoss, fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, totalLoss, - (end - start))) - else: - if step % cfgs.SMRY_ITER == 0: - _, global_stepnp, summary_str = sess.run([train_op, global_step, summary_op], - feed_dict={img_plac: img, - gtbox_plac: gt_info} - ) - summary_writer.add_summary(summary_str, global_stepnp) - summary_writer.flush() - - if (step > 0 and step % cfgs.SAVE_WEIGHTS_INTE == 0) or (step == cfgs.MAX_ITERATION - 1): - - save_dir = os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION) - if not os.path.exists(save_dir): - os.mkdir(save_dir) - - save_ckpt = os.path.join(save_dir, 'voc_' + str(global_stepnp) + 'model.ckpt') - saver.save(sess, save_ckpt) - print(' weights had been saved') - - -if __name__ == '__main__': - - train() - -# - - - - - - - - - - - - - - - - diff --git a/example-apps/README.md b/example-apps/README.md deleted file mode 100644 index ebb2ede..0000000 --- a/example-apps/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# Example applications over ealt-edge -This folder contains example applications for application developers to refer while carrying out their development. \ No newline at end of file diff --git a/example-apps/ROBO/README.md b/example-apps/ROBO/README.md deleted file mode 100644 index 04d7c83..0000000 --- a/example-apps/ROBO/README.md +++ /dev/null @@ -1,31 +0,0 @@ -# Smart Shelf Application - -This application is an entry level application to demonstrate Remote Office Branch Office usecase (ROBO) for Retail Enterprise for scenario wherein there is a retail chain comprising of multiple retail stores and a retail headquater. -The application provides Smart Shelf solution to monitor shelf's for a retail chain. - -Why Smart Shelf solution: -Smart Shelf is a technological breakthrough that enhances the overall shopping experience and improves a store’s operational efficiency in terms of stock misplacement and notify empty shelves. - -Smart shelf benefits: -- Smart Shelves can notify empty shelf for items when needed without requiring humans, making it easier to prevent - shortages of popular items at peak times. -- Smart Shelves allow retailers to make more informed decisions about stocking and inventory, notify the back-end - system about the existing quantity of items on the shelves, alert when product levels are running low and when items are misplaced. -- Provides access to the store or shelf information remotely, enables visibility of stock position, cross and up - selling of products. -- Better customer experience with proper inventory for each shelf. - -Available Solutions: -- Camera based with Video analytics solution -- weight sensors/optical sensors based solutions -This application is based on 1st solution - -Why Edge: -This use case demands for low latency, jitter for scenarios like theft detection, stock misplacement. -Currently available in-retail enterprise solutions, wherein in store deployment of compute/storage resources - are done, consumes store spaces and are not cost effective. 5G Telco edge based solution is a need here and this - application demonstrates the same. - -This application is developed as part of Akraino EALTEdge BP using EdgeGallery as an upstream project. Kindly refer - below link to get more insights about this application: -https://wiki.akraino.org/display/AK/EALT-EDGE+Landing+Application \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/Dockerfile b/example-apps/ROBO/RoboUI/Dockerfile deleted file mode 100644 index 223b916..0000000 --- a/example-apps/ROBO/RoboUI/Dockerfile +++ /dev/null @@ -1,23 +0,0 @@ -# Stage 1 - -#FROM node:10-alpine as build-step - -FROM node:12.9 as build-step - -RUN mkdir -p /app - -WORKDIR /app - -COPY package.json /app - -RUN npm install - -COPY . /app - -RUN npm run build --prod - -# Stage 2 - -FROM nginx:1.17.1-alpine - -COPY --from=build-step /app/dist/RoboUI /usr/share/nginx/html diff --git a/example-apps/ROBO/RoboUI/README.md b/example-apps/ROBO/RoboUI/README.md deleted file mode 100644 index 23c4cad..0000000 --- a/example-apps/ROBO/RoboUI/README.md +++ /dev/null @@ -1,27 +0,0 @@ -# RoboUI - -This project was generated with [Angular CLI](https://github.com/angular/angular-cli) version 8.2.2. - -## Development server - -Run `ng serve` for a dev server. Navigate to `http://localhost:4200/`. The app will automatically reload if you change any of the source files. - -## Code scaffolding - -Run `ng generate component component-name` to generate a new component. You can also use `ng generate directive|pipe|service|class|guard|interface|enum|module`. - -## Build - -Run `ng build` to build the project. The build artifacts will be stored in the `dist/` directory. Use the `--prod` flag for a production build. - -## Running unit tests - -Run `ng test` to execute the unit tests via [Karma](https://karma-runner.github.io). - -## Running end-to-end tests - -Run `ng e2e` to execute the end-to-end tests via [Protractor](http://www.protractortest.org/). - -## Further help - -To get more help on the Angular CLI use `ng help` or go check out the [Angular CLI README](https://github.com/angular/angular-cli/blob/master/README.md). diff --git a/example-apps/ROBO/RoboUI/angular.json b/example-apps/ROBO/RoboUI/angular.json deleted file mode 100644 index 81dd9af..0000000 --- a/example-apps/ROBO/RoboUI/angular.json +++ /dev/null @@ -1,131 +0,0 @@ -{ - "$schema": "./node_modules/@angular/cli/lib/config/schema.json", - "version": 1, - "newProjectRoot": "projects", - "projects": { - "RoboUI": { - "projectType": "application", - "schematics": {}, - "root": "", - "sourceRoot": "src", - "prefix": "app", - "architect": { - "build": { - "builder": "@angular-devkit/build-angular:browser", - "options": { - "outputPath": "dist/RoboUI", - "index": "src/index.html", - "main": "src/main.ts", - "polyfills": "src/polyfills.ts", - "tsConfig": "tsconfig.app.json", - "aot": true, - "assets": [ - "src/favicon.ico", - "src/assets" - ], - "styles": [ - "src/styles.scss", - "node_modules/ngx-toastr/toastr.css" - ], - "scripts": [] - }, - "configurations": { - "production": { - "fileReplacements": [ - { - "replace": "src/environments/environment.ts", - "with": "src/environments/environment.prod.ts" - } - ], - "optimization": true, - "outputHashing": "all", - "namedChunks": false, - "aot": true, - "extractLicenses": true, - "vendorChunk": false, - "buildOptimizer": true, - "budgets": [ - { - "type": "initial", - "maximumWarning": "2mb", - "maximumError": "5mb" - }, - { - "type": "anyComponentStyle", - "maximumWarning": "6kb", - "maximumError": "10kb" - } - ] - } - } - }, - "serve": { - "builder": "@angular-devkit/build-angular:dev-server", - "options": { - "browserTarget": "RoboUI:build", - "proxyConfig": "src/proxy.conf.json" - }, - "configurations": { - "production": { - "browserTarget": "RoboUI:build:production" - } - } - }, - "extract-i18n": { - "builder": "@angular-devkit/build-angular:extract-i18n", - "options": { - "browserTarget": "RoboUI:build" - } - }, - "test": { - "builder": "@angular-devkit/build-angular:karma", - "options": { - "main": "src/test.ts", - "polyfills": "src/polyfills.ts", - "tsConfig": "tsconfig.spec.json", - "karmaConfig": "karma.conf.js", - "assets": [ - "src/favicon.ico", - "src/assets" - ], - "styles": [ - "src/styles.scss" - ], - "scripts": [] - } - }, - "lint": { - "builder": "@angular-devkit/build-angular:tslint", - "options": { - "tsConfig": [ - "tsconfig.app.json", - "tsconfig.spec.json", - "e2e/tsconfig.json" - ], - "exclude": [ - "**/node_modules/**" - ] - } - }, - "e2e": { - "builder": "@angular-devkit/build-angular:protractor", - "options": { - "protractorConfig": "e2e/protractor.conf.js", - "devServerTarget": "RoboUI:serve" - }, - "configurations": { - "production": { - "devServerTarget": "RoboUI:serve:production" - } - } - } - } - } - }, - "defaultProject": "RoboUI", - "schematics": { - "@schematics/angular:component": { - "styleext": "scss" - } - } -} \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/e2e/protractor.conf.js b/example-apps/ROBO/RoboUI/e2e/protractor.conf.js deleted file mode 100644 index 73e4e68..0000000 --- a/example-apps/ROBO/RoboUI/e2e/protractor.conf.js +++ /dev/null @@ -1,32 +0,0 @@ -// @ts-check -// Protractor configuration file, see link for more information -// https://github.com/angular/protractor/blob/master/lib/config.ts - -const { SpecReporter } = require('jasmine-spec-reporter'); - -/** - * @type { import("protractor").Config } - */ -exports.config = { - allScriptsTimeout: 11000, - specs: [ - './src/**/*.e2e-spec.ts' - ], - capabilities: { - 'browserName': 'chrome' - }, - directConnect: true, - baseUrl: 'http://localhost:4200/', - framework: 'jasmine', - jasmineNodeOpts: { - showColors: true, - defaultTimeoutInterval: 30000, - print: function() {} - }, - onPrepare() { - require('ts-node').register({ - project: require('path').join(__dirname, './tsconfig.json') - }); - jasmine.getEnv().addReporter(new SpecReporter({ spec: { displayStacktrace: true } })); - } -}; \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/e2e/src/app.e2e-spec.ts b/example-apps/ROBO/RoboUI/e2e/src/app.e2e-spec.ts deleted file mode 100644 index ddf23bd..0000000 --- a/example-apps/ROBO/RoboUI/e2e/src/app.e2e-spec.ts +++ /dev/null @@ -1,23 +0,0 @@ -import { AppPage } from './app.po'; -import { browser, logging } from 'protractor'; - -describe('workspace-project App', () => { - let page: AppPage; - - beforeEach(() => { - page = new AppPage(); - }); - - it('should display welcome message', () => { - page.navigateTo(); - expect(page.getTitleText()).toEqual('Welcome to RoboUI!'); - }); - - afterEach(async () => { - // Assert that there are no errors emitted from the browser - const logs = await browser.manage().logs().get(logging.Type.BROWSER); - expect(logs).not.toContain(jasmine.objectContaining({ - level: logging.Level.SEVERE, - } as logging.Entry)); - }); -}); diff --git a/example-apps/ROBO/RoboUI/e2e/src/app.po.ts b/example-apps/ROBO/RoboUI/e2e/src/app.po.ts deleted file mode 100644 index 5776aa9..0000000 --- a/example-apps/ROBO/RoboUI/e2e/src/app.po.ts +++ /dev/null @@ -1,11 +0,0 @@ -import { browser, by, element } from 'protractor'; - -export class AppPage { - navigateTo() { - return browser.get(browser.baseUrl) as Promise; - } - - getTitleText() { - return element(by.css('app-root h1')).getText() as Promise; - } -} diff --git a/example-apps/ROBO/RoboUI/e2e/tsconfig.json b/example-apps/ROBO/RoboUI/e2e/tsconfig.json deleted file mode 100644 index c92199c..0000000 --- a/example-apps/ROBO/RoboUI/e2e/tsconfig.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "extends": "../tsconfig.json", - "compilerOptions": { - "outDir": "../out-tsc/e2e", - "module": "commonjs", - "target": "es2018", - "types": [ - "jasmine", - "jasminewd2", - "node" - ] - } -} diff --git a/example-apps/ROBO/RoboUI/karma.conf.js b/example-apps/ROBO/RoboUI/karma.conf.js deleted file mode 100644 index 3431969..0000000 --- a/example-apps/ROBO/RoboUI/karma.conf.js +++ /dev/null @@ -1,32 +0,0 @@ -// Karma configuration file, see link for more information -// https://karma-runner.github.io/1.0/config/configuration-file.html - -module.exports = function (config) { - config.set({ - basePath: '', - frameworks: ['jasmine', '@angular-devkit/build-angular'], - plugins: [ - require('karma-jasmine'), - require('karma-chrome-launcher'), - require('karma-jasmine-html-reporter'), - require('karma-coverage-istanbul-reporter'), - require('@angular-devkit/build-angular/plugins/karma') - ], - client: { - clearContext: false // leave Jasmine Spec Runner output visible in browser - }, - coverageIstanbulReporter: { - dir: require('path').join(__dirname, './coverage/RoboUI'), - reports: ['html', 'lcovonly', 'text-summary'], - fixWebpackSourcePaths: true - }, - reporters: ['progress', 'kjhtml'], - port: 9876, - colors: true, - logLevel: config.LOG_INFO, - autoWatch: true, - browsers: ['Chrome'], - singleRun: false, - restartOnFileChange: true - }); -}; diff --git a/example-apps/ROBO/RoboUI/package-lock.json b/example-apps/ROBO/RoboUI/package-lock.json deleted file mode 100644 index 2fee7e1..0000000 --- a/example-apps/ROBO/RoboUI/package-lock.json +++ /dev/null @@ -1,14412 +0,0 @@ -{ - "name": "robo-ui", - "version": "0.0.0", - "lockfileVersion": 1, - "requires": true, - "dependencies": { - "@angular-devkit/architect": { - "version": "0.1100.5", - "resolved": "https://registry.npmjs.org/@angular-devkit/architect/-/architect-0.1100.5.tgz", - "integrity": "sha512-yOYfucNouc1doTbcGbCNMXGMSc36+j97XpdNoeGyzFQ7GwezLAro0a9gxc5PdOxndfelkND7J1JuOjxdW5O17A==", - "dev": true, - "requires": { - "@angular-devkit/core": "11.0.5", - "rxjs": "6.6.3" - } - }, - "@angular-devkit/build-angular": { - "version": "0.1100.5", - "resolved": "https://registry.npmjs.org/@angular-devkit/build-angular/-/build-angular-0.1100.5.tgz", - 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} - } -} diff --git a/example-apps/ROBO/RoboUI/package.json b/example-apps/ROBO/RoboUI/package.json deleted file mode 100644 index 393dd73..0000000 --- a/example-apps/ROBO/RoboUI/package.json +++ /dev/null @@ -1,53 +0,0 @@ -{ - "name": "robo-ui", - "version": "0.0.0", - "scripts": { - "ng": "ng", - "start": "ng serve", - "build": "ng build", - "test": "ng test", - "lint": "ng lint", - "e2e": "ng e2e" - }, - "private": true, - "dependencies": { - "@angular/animations": "^11.0.5", - "@angular/cdk": "^11.0.3", - "@angular/common": "~11.0.5", - "@angular/compiler": "~11.0.5", - "@angular/core": "~11.0.5", - "@angular/forms": "~11.0.5", - "@angular/material": "^11.0.3", - "@angular/platform-browser": "~11.0.5", - "@angular/platform-browser-dynamic": "~11.0.5", - "@angular/router": "~11.0.5", - "@ng-bootstrap/ng-bootstrap": "^8.0.0", - "bootstrap": "^4.5.3", - "image-to-base64": "^2.1.1", - "ngx-toastr": "^13.2.0", - "rxjs": "~6.6.3", - "tslib": "^2.0.0", - "zone.js": "~0.10.2" - }, - "devDependencies": { - "@angular-devkit/build-angular": "~0.1100.5", - "@angular/cli": "~11.0.5", - "@angular/compiler-cli": "~11.0.5", - "@angular/language-service": "~11.0.5", - "@types/jasmine": "~3.6.0", - "@types/jasminewd2": "~2.0.3", - "@types/node": "^12.11.1", - "codelyzer": "^6.0.0", - "jasmine-core": "~3.6.0", - "jasmine-spec-reporter": "~5.0.0", - "karma": "~5.0.0", - "karma-chrome-launcher": "~3.1.0", - "karma-coverage-istanbul-reporter": "~3.0.2", - "karma-jasmine": "~4.0.0", - "karma-jasmine-html-reporter": "^1.5.0", - "protractor": "~7.0.0", - "ts-node": "~7.0.0", - "tslint": "~6.1.0", - "typescript": "^4.0.5" - } -} diff --git a/example-apps/ROBO/RoboUI/src/app/app-routing.module.ts b/example-apps/ROBO/RoboUI/src/app/app-routing.module.ts deleted file mode 100644 index 67db521..0000000 --- a/example-apps/ROBO/RoboUI/src/app/app-routing.module.ts +++ /dev/null @@ -1,33 +0,0 @@ -import { NgModule } from '@angular/core'; -import { Routes, RouterModule } from '@angular/router'; -import { DataFetchComponent } from './data-fetch/data-fetch.component'; -import { DataMonitorComponent } from './data-monitor/data-monitor.component'; -import { BackuprestoreComponent } from './backuprestore/backuprestore.component'; - -const routes: Routes = [ - { - path: 'dataupload', - component: DataFetchComponent - }, - { - path: 'datamonitor', - component: DataMonitorComponent - }, - - { - path: 'backuprestore', - component: BackuprestoreComponent - } - // { - // path: '**', - // redirectTo: '' - // } -]; - -@NgModule({ - imports: [RouterModule.forRoot(routes)], - exports: [RouterModule] -}) -export class AppRoutingModule { - -} diff --git a/example-apps/ROBO/RoboUI/src/app/app.component.html b/example-apps/ROBO/RoboUI/src/app/app.component.html deleted file mode 100644 index 66c780b..0000000 --- a/example-apps/ROBO/RoboUI/src/app/app.component.html +++ /dev/null @@ -1,37 +0,0 @@ - - - -

    ROBO Usecase

    - - -     - - -     - - - - - - Shelf Data Upload - Shelf Management - Backup and Restore - - - - -
    - - -
    -     -     \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/app/app.component.scss b/example-apps/ROBO/RoboUI/src/app/app.component.scss deleted file mode 100644 index e69de29..0000000 diff --git a/example-apps/ROBO/RoboUI/src/app/app.component.spec.ts b/example-apps/ROBO/RoboUI/src/app/app.component.spec.ts deleted file mode 100644 index 8563757..0000000 --- a/example-apps/ROBO/RoboUI/src/app/app.component.spec.ts +++ /dev/null @@ -1,35 +0,0 @@ -import { TestBed, async } from '@angular/core/testing'; -import { RouterTestingModule } from '@angular/router/testing'; -import { AppComponent } from './app.component'; - -describe('AppComponent', () => { - beforeEach(async(() => { - TestBed.configureTestingModule({ - imports: [ - RouterTestingModule - ], - declarations: [ - AppComponent - ], - }).compileComponents(); - })); - - it('should create the app', () => { - const fixture = TestBed.createComponent(AppComponent); - const app = fixture.debugElement.componentInstance; - expect(app).toBeTruthy(); - }); - - it(`should have as title 'RoboUI'`, () => { - const fixture = TestBed.createComponent(AppComponent); - const app = fixture.debugElement.componentInstance; - expect(app.title).toEqual('RoboUI'); - }); - - it('should render title in a h1 tag', () => { - const fixture = TestBed.createComponent(AppComponent); - fixture.detectChanges(); - const compiled = fixture.debugElement.nativeElement; - expect(compiled.querySelector('h1').textContent).toContain('Welcome to RoboUI!'); - }); -}); diff --git a/example-apps/ROBO/RoboUI/src/app/app.component.ts b/example-apps/ROBO/RoboUI/src/app/app.component.ts deleted file mode 100644 index 3b85575..0000000 --- a/example-apps/ROBO/RoboUI/src/app/app.component.ts +++ /dev/null @@ -1,14 +0,0 @@ -import { Component } from '@angular/core'; - -@Component({ - selector: 'app-root', - templateUrl: './app.component.html', - styleUrls: ['./app.component.scss'] -}) -export class AppComponent { - title = 'RoboUI'; - - - ngOnInit() { - } -} diff --git a/example-apps/ROBO/RoboUI/src/app/app.module.ts b/example-apps/ROBO/RoboUI/src/app/app.module.ts deleted file mode 100644 index d7c1bfd..0000000 --- a/example-apps/ROBO/RoboUI/src/app/app.module.ts +++ /dev/null @@ -1,102 +0,0 @@ -import { BrowserModule } from '@angular/platform-browser'; -import { NgModule } from '@angular/core'; - -import { AppRoutingModule } from './app-routing.module'; -import { AppComponent } from './app.component'; -import { DataFetchComponent } from './data-fetch/data-fetch.component'; - -// Angular Material components -import {BrowserAnimationsModule} from '@angular/platform-browser/animations'; -import {MatCheckboxModule} from '@angular/material/checkbox'; -import {MatButtonModule} from '@angular/material/button'; -import {MatInputModule} from '@angular/material/input'; -import {MatAutocompleteModule} from '@angular/material/autocomplete'; -import {MatDatepickerModule} from '@angular/material/datepicker'; -import {MatFormFieldModule} from '@angular/material/form-field'; -import {MatRadioModule} from '@angular/material/radio'; -import {MatSelectModule} from '@angular/material/select'; -import {MatSliderModule} from '@angular/material/slider'; -import {MatSlideToggleModule} from '@angular/material/slide-toggle'; -import {MatMenuModule} from '@angular/material/menu'; -import {MatSidenavModule} from '@angular/material/sidenav'; -import {MatToolbarModule} from '@angular/material/toolbar'; -import {MatListModule} from '@angular/material/list'; -import {MatGridListModule} from '@angular/material/grid-list'; -import {MatCardModule} from '@angular/material/card'; -import {MatStepperModule} from '@angular/material/stepper'; -import {MatTabsModule} from '@angular/material/tabs'; -import {MatExpansionModule} from '@angular/material/expansion'; -import {MatButtonToggleModule} from '@angular/material/button-toggle'; -import {MatChipsModule} from '@angular/material/chips'; -import {MatIconModule} from '@angular/material/icon'; -import {MatProgressSpinnerModule} from '@angular/material/progress-spinner'; -import {MatProgressBarModule} from '@angular/material/progress-bar'; -import {MatDialogModule} from '@angular/material/dialog'; -import {MatTooltipModule} from '@angular/material/tooltip'; -import {MatSnackBarModule} from '@angular/material/snack-bar'; -import {MatTableModule} from '@angular/material/table'; -import {MatSortModule} from '@angular/material/sort'; -import {MatPaginatorModule} from '@angular/material/paginator'; - -import { HttpClientModule } from '@angular/common/http'; - - -import { FormsModule, ReactiveFormsModule } from '@angular/forms'; - -import { ToastrModule } from 'ngx-toastr'; -import { DataMonitorComponent } from './data-monitor/data-monitor.component'; -import { BackuprestoreComponent } from './backuprestore/backuprestore.component'; - -@NgModule({ - declarations: [ - AppComponent, - DataFetchComponent, - DataMonitorComponent, - BackuprestoreComponent - ], - imports: [ - BrowserModule, - AppRoutingModule, - BrowserAnimationsModule, - MatCheckboxModule, - MatButtonModule, - MatInputModule, - MatAutocompleteModule, - MatDatepickerModule, - MatFormFieldModule, - MatRadioModule, - MatSelectModule, - MatSliderModule, - MatSlideToggleModule, - MatMenuModule, - MatSidenavModule, - MatToolbarModule, - MatListModule, - MatGridListModule, - MatCardModule, - MatStepperModule, - MatTabsModule, - MatExpansionModule, - MatButtonToggleModule, - MatChipsModule, - MatIconModule, - MatProgressSpinnerModule, - MatProgressBarModule, - MatDialogModule, - MatTooltipModule, - MatSnackBarModule, - MatTableModule, - MatSortModule, - MatPaginatorModule, - - - HttpClientModule, - ReactiveFormsModule, - FormsModule, - ToastrModule.forRoot() - - ], - providers: [], - bootstrap: [AppComponent] -}) -export class AppModule { } diff --git a/example-apps/ROBO/RoboUI/src/app/backuprestore/backuprestore.component.html b/example-apps/ROBO/RoboUI/src/app/backuprestore/backuprestore.component.html deleted file mode 100644 index 0ffb5a0..0000000 --- a/example-apps/ROBO/RoboUI/src/app/backuprestore/backuprestore.component.html +++ /dev/null @@ -1,254 +0,0 @@ -
    -
    - -
    -
    - - - - Apps -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    NAMESPACE {{apps.namespace}} NAME {{apps.name}} STATUS {{apps.status}} IP {{apps.ip}} NODE {{apps.node}}
    - - - -
    -     - - - - PVCs -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    NAMESPACE {{pvs.namespace}} NAME {{pvs.name}} STATUS {{pvs.status}} VOLUME {{pvs.volume}} STORAGE CLASS {{pvs.storageclass}} VOLUMEMODE {{pvs.volumemode}} READINESS GATES {{pvs.readinessgates}}
    - - - -
    -
    - -
    -     - - Backups -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    NAME {{backups.name}} STATUS {{backups.status}} ERRORS {{backups.errors}} WARNINGS {{backups.warnings}} CREATED {{backups.created}}
    - - - -
    -
    - -
    - Backup Name - - Backup Name - - -
    -
    - Namespace.. - - Namespace - - - - -
    -
    - -
    -
    - -     - - Restores -
    - - - - - - - - - - - - - - - - - - - - -
    NAME {{restores.name}} BACKUP {{restores.backup}} STATUS {{restores.status}}
    - - - -
    -
    - Restore Name - - Restore Name - - -
    -
    - Backup Name - - Backup Name - - -
    -
    - -
    -
    -
    -     -     - -
    -
    \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/app/backuprestore/backuprestore.component.scss b/example-apps/ROBO/RoboUI/src/app/backuprestore/backuprestore.component.scss deleted file mode 100644 index b4e714f..0000000 --- a/example-apps/ROBO/RoboUI/src/app/backuprestore/backuprestore.component.scss +++ /dev/null @@ -1,43 +0,0 @@ -.main { - .center{ - padding-top: 20px; - display: flex; - justify-content: center; - align-items: center; - } - .tabs{ - padding-top: 10px; - .appstable { - - } - .pvstable { - - } - .center{ - padding-top: 20px; - display: flex; - justify-content: center; - align-items: center; - } - .backup-det { - .center{ - padding-top: 20px; - display: flex; - justify-content: center; - align-items: center; - } - } - .restore-det { - .center{ - padding-top: 20px; - display: flex; - justify-content: center; - align-items: center; - } - } - } -} - -table{ - width: 100%; -} \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/app/backuprestore/backuprestore.component.spec.ts b/example-apps/ROBO/RoboUI/src/app/backuprestore/backuprestore.component.spec.ts deleted file mode 100644 index 0164415..0000000 --- a/example-apps/ROBO/RoboUI/src/app/backuprestore/backuprestore.component.spec.ts +++ /dev/null @@ -1,25 +0,0 @@ -import { ComponentFixture, TestBed } from '@angular/core/testing'; - -import { BackuprestoreComponent } from './backuprestore.component'; - -describe('BackuprestoreComponent', () => { - let component: BackuprestoreComponent; - let fixture: ComponentFixture; - - beforeEach(async () => { - await TestBed.configureTestingModule({ - declarations: [ BackuprestoreComponent ] - }) - .compileComponents(); - }); - - beforeEach(() => { - fixture = TestBed.createComponent(BackuprestoreComponent); - component = fixture.componentInstance; - fixture.detectChanges(); - }); - - it('should create', () => { - expect(component).toBeTruthy(); - }); -}); diff --git a/example-apps/ROBO/RoboUI/src/app/backuprestore/backuprestore.component.ts b/example-apps/ROBO/RoboUI/src/app/backuprestore/backuprestore.component.ts deleted file mode 100644 index 0d51276..0000000 --- a/example-apps/ROBO/RoboUI/src/app/backuprestore/backuprestore.component.ts +++ /dev/null @@ -1,181 +0,0 @@ -import { Component, OnInit, ViewChild } from '@angular/core'; - -import {MatTableDataSource} from '@angular/material/table'; -import { MatPaginator } from '@angular/material/paginator'; -import { RoboService } from './../../app/robo.service'; - -import { appsinfo,pvpvsinfo,backupsinfo,restoresinfo,backupData,restoreData } from './../../app/datainterface' - -import { ToastrService } from 'ngx-toastr'; - -@Component({ - selector: 'app-backuprestore', - templateUrl: './backuprestore.component.html', - styleUrls: ['./backuprestore.component.scss'] -}) -export class BackuprestoreComponent implements OnInit { - - appsColumns: string [] = ['namespace','name','status','ip','node'] - appsDataSource = new MatTableDataSource(APPS_INFO_LIST); - - appsArrayList: appsinfo []; - - pvsColumns: string [] = ['namespace','name','status','volume','storageclass','volumemode'] - pvsDataSource = new MatTableDataSource(PVS_INFO_LIST); - - pvsArrayList : pvpvsinfo []; - - backupsColumns: string [] = ['name','status','errors','warnings','created'] - backupsDataSource = new MatTableDataSource(BACKUPS_INFO_LIST); - - backupsArrayList = []; - - restoresColumns: string [] = ['name','backup','status'] - restoresDataSource = new MatTableDataSource(RESTORES_INFO_LIST); - - restoresArrayList = []; - - selectedNamespace : string - selectedBackupName : string - - selectedRestoreName: string - selectedBackupname : string - - appsinfo = {} - - backupData = {} as backupData - - restoreData = {} as restoreData - - @ViewChild(MatPaginator, {static: true}) paginator: MatPaginator; - - constructor( - private roboService: RoboService, - private toastService: ToastrService - ) {} - - ngOnInit(): void { - this.selectedBackupName = "backup01" - this.selectedRestoreName = "restore01" - this.selectedNamespace = "default" - this.selectedBackupname = "backup01" - - this.getAppsPvcs(); - - this.getBackupsRestores(); - } - - getAppsPvcs() { - this.roboService.getAppsPvcsInfo() - .subscribe(data => { - debugger; - console.log(data); - this.appsinfo = data; - this.appsArrayList = data.appsData; - debugger; - this.appsDataSource = new MatTableDataSource(this.appsArrayList); - this.appsDataSource.paginator = this.paginator; - - this.pvsArrayList = data.pvcData; - this.pvsDataSource = new MatTableDataSource(this.pvsArrayList); - this.pvsDataSource.paginator = this.paginator; - }, - error => console.log(error)); - } - - getBackupsRestores() { - this.roboService.getBackupRestoreInfo() - .subscribe(data => { - debugger; - console.log(data); - this.appsinfo = data; - this.backupsArrayList = data.backupsData; - this.backupsDataSource = new MatTableDataSource(this.backupsArrayList); - this.backupsDataSource.paginator = this.paginator; - - this.restoresArrayList = data.restoresData; - this.restoresDataSource = new MatTableDataSource(this.restoresArrayList); - this.restoresDataSource.paginator = this.paginator; - }, - error => console.log(error)); - } - - refreshPage() { - debugger; - this.getBackupsRestores(); - this.getAppsPvcs(); - } - - postBackup() { - console.log("Inside postBackup.....") - this.backupData.backupName = this.selectedBackupName; - this.backupData.namespace = this.selectedNamespace; - this.showBackupSuccess() - this.roboService.postBackup(this.backupData) - .subscribe(data => { - debugger; - if(data.responce == "success"){ - this.showBackupSuccess(); - } - console.log(data); - } - ,error => console.log(error) - ); - } - - restore() { - console.log("Inside postBackup.....") - - this.restoreData.restoreName = this.selectedRestoreName; - this.restoreData.backupName = this.selectedBackupname; - this.showRestoreSuccess() - this.roboService.postRestore(this.restoreData) - .subscribe(data => { - debugger; - if(data.responce == "success"){ - this.showRestoreSuccess(); - } - console.log(data); - } - ,error => console.log(error) - ); - } - - showBackupSuccess() { - this.toastService.success('Backup Successful..','Backup Data'); - } - - showRestoreSuccess() { - this.toastService.success('Restore Successful..','Restore Data'); - } - - simulateDisaster() { - console.log("Inside simulateDisaster....") - this.roboService.disturbCluster() - .subscribe(data => { - debugger; - console.log(data); - }, - error => console.log(error)); - } - -} - -const APPS_INFO_LIST: appsinfo[] = [ - { namespace: '',name: '', status: '', ip: '', node: ''} -]; - -const PVS_INFO_LIST: pvpvsinfo[] = [ - { namespace: '',name: '', status: '', volume: '', storageclass: '', volumemode: '' } -]; - -const BACKUPS_INFO_LIST: backupsinfo[] = [ - { name: '', status: '', errors: '', warnings: '', created: ''} -]; - -const RESTORES_INFO_LIST: restoresinfo[] = [ - { name: '', backup: '', status: ''} -]; - - -// "zone.js": "~0.10.2" \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/app/data-fetch/data-fetch.component.html b/example-apps/ROBO/RoboUI/src/app/data-fetch/data-fetch.component.html deleted file mode 100644 index 9934163..0000000 --- a/example-apps/ROBO/RoboUI/src/app/data-fetch/data-fetch.component.html +++ /dev/null @@ -1,153 +0,0 @@ -
    - -
    - -
    - - - - - Camera Video upload - - - Please upload the required details - - -

    Camera Upload Video

    - -
    -
    -
    -
    -
    - -
    -
    - -
    -
    - -
    -     -     -
    - -
    - - - - - Input Stream and location - - - Please upload the required details - - - -
    - -
    -
    - Provide Video Details..... - -
    -
    - Camera Input Stream..... - - Select Camera Input - - - - -
    -
    - Select Camera Location - - Camera Location - - - - -
    - -
    - Provide RTSP URL....... - - RTSP URL - - -
    -
    - -
    - -
    -
    -     -     -     - -
    - -
    -
    - - - - - Check - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    CAMERA ID {{camera.cameraID}} CAMERA LOCATION {{camera.cameraLocation}} CAMERA NUMBER {{camera.cameraNumber}} RTSP URL {{camera.rtspUrl}}
    - - -
    - - - {{ camera }} - - -
    -
    -
    -     -
    - -
    -
    - -
    -
    - -
    \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/app/data-fetch/data-fetch.component.scss b/example-apps/ROBO/RoboUI/src/app/data-fetch/data-fetch.component.scss deleted file mode 100644 index 2f9dcc8..0000000 --- a/example-apps/ROBO/RoboUI/src/app/data-fetch/data-fetch.component.scss +++ /dev/null @@ -1,43 +0,0 @@ -.maindiv { - position: center; - position: relative; - padding-top: 20px; - - .centerdiv { - - width: 100%; - margin: 0; - position: absolute; - padding-top: 20px; - padding-right: 20px; - padding-left: 20px; - - .mp4-upload { - - .center-button { - display:flex; justify-content: center - } - - } - - mat-accordion { - width: 80%; - } - - .tablepage { - - position: relative; - .filter { - width: 70%; - padding-left: 20px; - } - .cameratable { - table { - width: 100%; - } - } - - } - } - } - \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/app/data-fetch/data-fetch.component.spec.ts b/example-apps/ROBO/RoboUI/src/app/data-fetch/data-fetch.component.spec.ts deleted file mode 100644 index ac7a3e8..0000000 --- a/example-apps/ROBO/RoboUI/src/app/data-fetch/data-fetch.component.spec.ts +++ /dev/null @@ -1,25 +0,0 @@ -import { async, ComponentFixture, TestBed } from '@angular/core/testing'; - -import { DataFetchComponent } from './data-fetch.component'; - -describe('DataFetchComponent', () => { - let component: DataFetchComponent; - let fixture: ComponentFixture; - - beforeEach(async(() => { - TestBed.configureTestingModule({ - declarations: [ DataFetchComponent ] - }) - .compileComponents(); - })); - - beforeEach(() => { - fixture = TestBed.createComponent(DataFetchComponent); - component = fixture.componentInstance; - fixture.detectChanges(); - }); - - it('should create', () => { - expect(component).toBeTruthy(); - }); -}); diff --git a/example-apps/ROBO/RoboUI/src/app/data-fetch/data-fetch.component.ts b/example-apps/ROBO/RoboUI/src/app/data-fetch/data-fetch.component.ts deleted file mode 100644 index 58260f7..0000000 --- a/example-apps/ROBO/RoboUI/src/app/data-fetch/data-fetch.component.ts +++ /dev/null @@ -1,228 +0,0 @@ -import { Component, OnInit, TemplateRef, ViewChild } from '@angular/core'; -import { FormBuilder, FormGroup } from '@angular/forms'; -import { HttpClient } from '@angular/common/http'; - -import { RoboService } from './../../app/robo.service'; - -import { cameraData,camerainfo } from './../datainterface' - -import { cameraDetails } from './../datainterface'; - -// import { ToastService } from './toast.service'; - -import { ToastrService } from 'ngx-toastr'; -import {MatTableDataSource} from '@angular/material/table'; - -import { MatPaginator } from '@angular/material/paginator'; - -@Component({ - selector: 'app-data-fetch', - templateUrl: './data-fetch.component.html', - styleUrls: ['./data-fetch.component.scss'] -}) -export class DataFetchComponent implements OnInit { - - cameraColumns: string [] = ['cameraID','cameraLocation','cameraNumber','rtspUrl']; - cameraDataSource = new MatTableDataSource(CAMERA_INFO_LIST); - - SERVER_URL = "http://localhost:30092/v1/monitor/video"; - videoUploadForm: FormGroup; - cameraDetailsForm: FormGroup; - - cameraData = {} as cameraData; - camerasArray = []; - location = []; - - selectedCamera: string; - selectedLocation: string; - - selectedRTSP: string; - - cameradetconcat: string - - url; - format; - - - cameraInfo = {} as cameraDetails; - cameraArrayList = []; - - selectedCameraId = [] - selectedCameraID: string - - selectedValues = [] - - @ViewChild(MatPaginator, {static: true}) paginator: MatPaginator; - - constructor( - private formBuilder: FormBuilder, - private httpClient: HttpClient, - private roboService: RoboService, - private toastService: ToastrService - ) { } - - ngOnInit() { - this.fetchCameraDetails(); - this.videoUploadForm = this.formBuilder.group({ - video: [''] - }); - - this.cameraDetailsForm = this.formBuilder.group({ - cameraLocation: [''], - cameraNumber: [''] - }); - - this.camerasArray = - [ - { - "value": "camera01", - "viewValue": "camera01" - }, - { - "value": "camera02", - "viewValue": "camera02" - }, - { - "value": "camera03", - "viewValue": "camera03" - } - ]; - - this.location = - [ - { - "value": "Bangalore", - "viewValue": "Bangalore" - } - ]; - - this.selectedCamera = "Camera" - this.selectedLocation = "Bangalore" - // this.fetchCameraDetails(); - } - - onFileSelect(event) { - - - if (event.target.files.length > 0) { - const file = event.target.files[0]; - this.videoUploadForm.get('video').setValue(file); - } - - const file = event.target.files && event.target.files[0]; - if (file) { - var reader = new FileReader(); - reader.readAsDataURL(file); - if (file.type.indexOf('image') > -1) { - this.format = 'image'; - } else if (file.type.indexOf('video') > -1) { - this.format = 'video'; - } - reader.onload = (event) => { - this.url = (event.target).result; - } - } - } - - onSubmit() { - const formData = new FormData(); - formData.append('file', this.videoUploadForm.get('video').value); - debugger; - this.showFileSuccess(); - this.httpClient.post(this.SERVER_URL, formData).subscribe - ( - (res) => console.log(res), - (err) => console.log(err) - ); - } - cameraDetailsSubmit() { - const formData = new FormData(); - - this.cameraData.cameraNumber = this.selectedCamera; - this.cameraData.cameraLocation = this.selectedLocation; - this.cameraData.rtspUrl = this.selectedRTSP; - - this.roboService.postCameraDetails(this.cameraData) - .subscribe(data => { - debugger; - if(data.responce == "success"){ - this.showSuccess(); - } - console.log(data); - } - ,error => console.log(error) - ); - - } - - onCameraSelection() { - console.log("Inside onCameraSelection.....") - } - - onCameraIDSelection() { - console.log("Spotted...") - var index: number - console.log("Inside onCameraIDSelection.......") - debugger; - // this.roboService.postCameraID(this.selectedCameraID) - index = this.selectedCameraId.indexOf(this.selectedCameraID) - debugger; - this.roboService.triggerDetection(this.selectedValues[index]) - .subscribe(data => { - debugger; - console.log(data) - }, - error => console.log(error)); - index = null; - } - - - showSuccess() { - console.log("Inside showSuccess.... Method") - this.toastService.success('Uploaded Succesfully!','Camera Data'); - } - - showFileSuccess() { - console.log("Inside showSuccess.... Method") - this.toastService.success('Uploaded Succesfully!','Video File'); - } - - fetchCameraDetails() { - - debugger; - this.roboService.getCameraInfo() - .subscribe(data => { - debugger; - console.log(data); - this.cameraInfo = data; - - this.cameraArrayList = data.roboCamera; - this.cameraDataSource = new MatTableDataSource(this.cameraArrayList); - this.cameraDataSource.paginator = this.paginator; - - console.log("For loop started.....") - for (var val of this.cameraArrayList) { - debugger; - - console.log(val); - this.cameradetconcat = val.cameraNumber + '/'+ val.rtspUrl + '/' +val.cameraLocation - // this.selectedCameraId.push(val.camera); - this.selectedCameraId.push(this.cameradetconcat) - this.selectedValues.push(val.cameraID) - - } - debugger; - console.log("SelectedCameraID") - console.log(this.selectedCameraId) - }, - error => console.log(error)); - } - - refreshPage() { - this.fetchCameraDetails(); - } -} - -const CAMERA_INFO_LIST: camerainfo[] = [ - { cameraID: '',cameraLocation: '', cameraNumber: '', rtspUrl: '' } -]; diff --git a/example-apps/ROBO/RoboUI/src/app/data-monitor/data-monitor.component.html b/example-apps/ROBO/RoboUI/src/app/data-monitor/data-monitor.component.html deleted file mode 100644 index 36cda5e..0000000 --- a/example-apps/ROBO/RoboUI/src/app/data-monitor/data-monitor.component.html +++ /dev/null @@ -1,58 +0,0 @@ -
    -
    - -
    -
    -

    SHELF STATUS

    -
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Shelf Name {{monitor.shelfName}} Object Type {{monitor.ObjType}} Current Count {{monitor.currentCount}} Max Count {{monitor.maxCount}} Status {{monitor.status}} - Time {{monitor.time}}
    - - - -
    -
    -
    - - - -
    - -
    \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/app/data-monitor/data-monitor.component.scss b/example-apps/ROBO/RoboUI/src/app/data-monitor/data-monitor.component.scss deleted file mode 100644 index 62edbf7..0000000 --- a/example-apps/ROBO/RoboUI/src/app/data-monitor/data-monitor.component.scss +++ /dev/null @@ -1,39 +0,0 @@ - -.maindiv{ - - position: center; - position: relative; - .refresh { - padding-top: 20px; - display:flex; - // justify-content: center; - float: right; - padding-bottom: 10px; - } - .tablepage { - // padding-top:10px; - position: relative; - .filter { - width: 70%; - padding-left: 20px; - } - .monitortable { - table { - width: 100%; - } - } - - } - - .image-card { - padding: 20px; - } -} - -// .warn{ -// color:orange; -// } - -// .success{ -// color:green; -// } \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/app/data-monitor/data-monitor.component.spec.ts b/example-apps/ROBO/RoboUI/src/app/data-monitor/data-monitor.component.spec.ts deleted file mode 100644 index 71389ff..0000000 --- a/example-apps/ROBO/RoboUI/src/app/data-monitor/data-monitor.component.spec.ts +++ /dev/null @@ -1,25 +0,0 @@ -import { ComponentFixture, TestBed } from '@angular/core/testing'; - -import { DataMonitorComponent } from './data-monitor.component'; - -describe('DataMonitorComponent', () => { - let component: DataMonitorComponent; - let fixture: ComponentFixture; - - beforeEach(async () => { - await TestBed.configureTestingModule({ - declarations: [ DataMonitorComponent ] - }) - .compileComponents(); - }); - - beforeEach(() => { - fixture = TestBed.createComponent(DataMonitorComponent); - component = fixture.componentInstance; - fixture.detectChanges(); - }); - - it('should create', () => { - expect(component).toBeTruthy(); - }); -}); diff --git a/example-apps/ROBO/RoboUI/src/app/data-monitor/data-monitor.component.ts b/example-apps/ROBO/RoboUI/src/app/data-monitor/data-monitor.component.ts deleted file mode 100644 index e17ce7d..0000000 --- a/example-apps/ROBO/RoboUI/src/app/data-monitor/data-monitor.component.ts +++ /dev/null @@ -1,135 +0,0 @@ -import { Component, OnInit, ViewChild } from '@angular/core'; - -import {MatTableDataSource} from '@angular/material/table'; - -import { MatPaginator } from '@angular/material/paginator'; - -import { cameraData,monitorinfo, monitorDetails } from './../datainterface' -import { RoboService } from './../../app/robo.service'; - -import { DomSanitizer, SafeUrl } from "@angular/platform-browser"; - -import { timer } from "rxjs" - -@Component({ - selector: 'app-data-monitor', - templateUrl: './data-monitor.component.html', - styleUrls: ['./data-monitor.component.scss'] -}) -export class DataMonitorComponent implements OnInit { - - monitorColumns: string [] = ['shelfName','objType','currentCount','maxCount','status','time']; - monitorDataSource = new MatTableDataSource(MONITOR_INFO_LIST); - - monitorArrayList = []; - - imageBlobUrl : any; - image: any; - thumbnail: any; - myvar: any - - monitorInfo = {} as monitorDetails; - - @ViewChild(MatPaginator, {static: true}) paginator: MatPaginator; - - constructor( - private roboService: RoboService, - private sanitizer: DomSanitizer - ) { } - - ngOnInit(): void { - this.monitorDetails(); - this.inventoryDetails(); - } - - timerFunc() { - debugger; - setInterval(this.refreshPage, 2000); - // setTimeout(this.refreshPage , 2000); - } - - refreshPage() { - console.log("Inside refreshPage....") - this.inventoryDetails(); - this.monitorDetails(); - } - - monitorDetails() { - debugger; - // setInterval(function(){ - // console.log("Oooo Yeaaa!"); - // this.roboService.getMonitorImage() - // .subscribe( (data:any) => { - - // debugger; - // console.log(data); - // // referred https://stackoverflow.com/questions/55591871/view-blob-response-as-image-in-angular - - // let objectURL = 'data:image/jpeg;base64,' + data.image; - // this.thumbnail = this.sanitizer.bypassSecurityTrustUrl(objectURL); - - // }, - // error => console.log(error)); - - - // }, 2000); - this.roboService.getMonitorImage() - .subscribe( (data:any) => { - - debugger; - - let objectURL = 'data:image/jpeg;base64,' + data.image; - this.thumbnail = this.sanitizer.bypassSecurityTrustUrl(objectURL); - debugger; - - }, - error => console.log(error)); - - } - - inventoryDetails() { - console.log("inventoryDetails started....") - - // setInterval(function(){ - // console.log("Oooo!"); - // this.roboService.getMonitorInfo() - // .subscribe(data => { - // console.log(data); - // this.monitorInfo = data; - - // this.monitorArrayList = data.InventryData; - // this.monitorDataSource = new MatTableDataSource(this.monitorArrayList); - // this.monitorDataSource.paginator = this.paginator; - // }, - // error => console.log(error)); - // }, 2000); - this.roboService.getMonitorInfo() - .subscribe(data => { - console.log(data); - this.monitorInfo = data; - - this.monitorArrayList = data.InventryData; - this.monitorDataSource = new MatTableDataSource(this.monitorArrayList); - this.monitorDataSource.paginator = this.paginator; - }, - error => console.log(error)); - console.log("inventoryDetails finished...") - } - - getColor(clr) { - if(clr == "Mostly Filled"){ - return "green" - } - if(clr == "Partially Filled"){ - return "orange" - } - if(clr == "Needs Filling"){ - return "red" - } - } - -} - -const MONITOR_INFO_LIST: monitorinfo[] = [ - { shelfName: '', ObjType: '', currentCount: '', maxCount: '', status: '', time: '' } -]; diff --git a/example-apps/ROBO/RoboUI/src/app/datainterface.ts b/example-apps/ROBO/RoboUI/src/app/datainterface.ts deleted file mode 100644 index e244712..0000000 --- a/example-apps/ROBO/RoboUI/src/app/datainterface.ts +++ /dev/null @@ -1,100 +0,0 @@ -export interface cameraData { - cameraLocation: string; - cameraNumber: string; - rtspUrl: string; - // videoName: string; -} - -export interface cameraDetails { - roboCamera: camerainfo[]; -} - -export interface camerainfo { - cameraID: any; - cameraLocation: string; - cameraNumber: string; - rtspUrl: string; -} - -export interface monitorDetails { - InventryData: monitorinfo[]; -} - -export interface monitorImage { - image: Blob; -} - - -export interface monitorinfo { - shelfName: string; - ObjType: string; - currentCount: string; - maxCount: string; - status: string; - time: string; -} - - -export interface cameraID { - cameraID: string; -} - - -export interface appsinfo { - namespace: string; - name: string; - status: string; - ip: string; - node: string; -} - -export interface appsPvcs { - appsData: appsinfo[]; - pvcData: pvpvsinfo[]; -} - -export interface pvpvsinfo { - namespace: string; - name: string; - status: string; - volume: string; - storageclass: string; - volumemode: string; -} - -export interface backupRestore { - backupsData: backupsinfo[]; - restoresData: restoresinfo[]; -} - -export interface backupsinfo { - name: string; - status: string; - errors: string; - warnings: string; - created: string; -} - -// export interface backups { -// backupsData: backupsinfo[]; -// } - -export interface restoresinfo { - name: string; - backup: string; - status: string; -} - -export interface backupData { - backupName: string; - namespace: string; -} - -export interface restoreData { - restoreName: string; - backupName: string; -} - -// export interface restores { -// restoresData: restoresinfo[]; -// } \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/app/robo.service.spec.ts b/example-apps/ROBO/RoboUI/src/app/robo.service.spec.ts deleted file mode 100644 index 6a11559..0000000 --- a/example-apps/ROBO/RoboUI/src/app/robo.service.spec.ts +++ /dev/null @@ -1,16 +0,0 @@ -import { TestBed } from '@angular/core/testing'; - -import { RoboService } from './robo.service'; - -describe('RoboService', () => { - let service: RoboService; - - beforeEach(() => { - TestBed.configureTestingModule({}); - service = TestBed.inject(RoboService); - }); - - it('should be created', () => { - expect(service).toBeTruthy(); - }); -}); diff --git a/example-apps/ROBO/RoboUI/src/app/robo.service.ts b/example-apps/ROBO/RoboUI/src/app/robo.service.ts deleted file mode 100644 index 6546fe7..0000000 --- a/example-apps/ROBO/RoboUI/src/app/robo.service.ts +++ /dev/null @@ -1,125 +0,0 @@ -import { Injectable } from '@angular/core'; - -import { HttpClient, HttpHeaders, HttpParams, HttpResponse } from '@angular/common/http'; -import { Observable,throwError } from 'rxjs' -import { timer, Subscription, pipe } from 'rxjs'; -import { switchMap } from 'rxjs/operators'; - -import { cameraData, camerainfo, cameraDetails, monitorDetails, monitorinfo, cameraID, appsPvcs, backupRestore, monitorImage } from './datainterface' - - -@Injectable({ - providedIn: 'root' -}) -export class RoboService { - - private baseUrl = 'http://localhost:30091'; - - private inventoryBaseUrl = 'http://localhost:30092'; - - private postCameraDetailsUrl = this.inventoryBaseUrl + '/v1/monitor/cameras' - private cameraDetailsUrl = this.inventoryBaseUrl + '/v1/monitor/cameras' - private cameraDetails_url = './../assets/data/sample.json' - - private monitorDetails_url = './../assets/data/inventory.json' - private monitorDetailsUrl = this.inventoryBaseUrl + '/v1/inventry/table' - - private monitorImageUrl = this.inventoryBaseUrl + '/v1/inventry/image' - - private triggerObjUrl = this.inventoryBaseUrl + '/v1/monitor/cameras/' - - private appsPvcsDetailsUrl = this.baseUrl + '/v1/robo/apps-pvcs' - private appsPvcsDetails_url = './../assets/data/appspvc.json' - - private backupRestoreDetailsUrl = this.baseUrl + '/v1/robo/backup-restore' - - private backupRestoreDetails_url = './../assets/data/backuprestore.json' - - private postBackupDetailsUrl = this.baseUrl + '/v1/robo/backup' - - private postRestoreDetailsUrl = this.baseUrl + '/v1/robo/restore' - - private disasterUrl = this.baseUrl + '/v1/robo/disaster' - - constructor(private http:HttpClient) { } - - httpOptions = { - headers: new HttpHeaders({ - 'Content-Type':'application/json' - }) - } - - httpOptionss = { - headers: new HttpHeaders({ - 'Content-Type':'application/json' - }) - } - - postCameraDetails(data): Observable { - console.log(data); - debugger; - return this.http.post(this.postCameraDetailsUrl, data) - } - - getCameraInfo(): Observable { - debugger; - return this.http.get(this.cameraDetailsUrl); - } - - getMonitorInfo(): Observable { - debugger; - return this.http.get(this.monitorDetailsUrl); - } - - - - getMonitorImage(): Observable { - debugger; - // return timer(0, 2000) - // .pipe( - - // switchMap(_ => this.http.get(this.monitorImageUrl)), - // ); - return this.http.get(this.monitorImageUrl); - } - - - triggerDetection(data): Observable { - console.log(data); - debugger; - this.triggerObjUrl = this.inventoryBaseUrl + '/v1/monitor/cameras/'; - this.triggerObjUrl = this.triggerObjUrl + data; - - debugger; - console.log(this.triggerObjUrl); - return this.http.get(this.triggerObjUrl) - } - - getAppsPvcsInfo(): Observable { - return this.http.get(this.appsPvcsDetailsUrl); - } - - // getBackupRestoreInfo(): Observable { - // } - // return this.http.get(this.backupRestoreDetails_url); - - getBackupRestoreInfo(): Observable { - return this.http.get(this.backupRestoreDetailsUrl); - } - - disturbCluster(): Observable { - return this.http.get(this.disasterUrl); - } - - postBackup(data): Observable { - console.log(data); - debugger; - return this.http.post(this.postBackupDetailsUrl, data) - } - - postRestore(data): Observable { - console.log(data); - debugger; - return this.http.post(this.postRestoreDetailsUrl, data) - } -} diff --git a/example-apps/ROBO/RoboUI/src/assets/.gitkeep b/example-apps/ROBO/RoboUI/src/assets/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/example-apps/ROBO/RoboUI/src/assets/data/appspvc.json b/example-apps/ROBO/RoboUI/src/assets/data/appspvc.json deleted file mode 100644 index 0106666..0000000 --- a/example-apps/ROBO/RoboUI/src/assets/data/appspvc.json +++ /dev/null @@ -1,224 +0,0 @@ -{ - "appsData": [ - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - }, - { - "namespace": "default01", - "name": "appname", - "status": "running", - "ip": "10.10.0.1", - "node": "ealtcenter" - } - ], - - "pvcData": [ - { - "namespace": "default", - "name": "pvcname01", - "status": "ready", - "volume": "samplevolume", - "storageclass" : "samplestorage", - "volumemode" : "samplevolumemode" - }, - { - "namespace": "default", - "name": "pvcname02", - "status": "ready", - "volume": "samplevolume", - "storageclass" : "samplestorage", - "volumemode" : "samplevolumemode" - }, - { - "namespace": "default", - "name": "pvcname03", - "status": "ready", - "volume": "samplevolume", - "storageclass" : "samplestorage", - "volumemode" : "samplevolumemode" - }, - { - "namespace": "default", - "name": "pvcname04", - "status": "ready", - "volume": "samplevolume", - "storageclass" : "samplestorage", - "volumemode" : "samplevolumemode" - }, - { - "namespace": "default", - "name": "pvcname05", - "status": "ready", - "volume": "samplevolume", - "storageclass" : "samplestorage", - "volumemode" : "samplevolumemode" - }, - { - "namespace": "default", - "name": "pvcname06", - "status": "ready", - "volume": "samplevolume", - "storageclass" : "samplestorage", - "volumemode" : "samplevolumemode" - } - - ] -} \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/assets/data/backuprestore.json b/example-apps/ROBO/RoboUI/src/assets/data/backuprestore.json deleted file mode 100644 index f68d9c7..0000000 --- a/example-apps/ROBO/RoboUI/src/assets/data/backuprestore.json +++ /dev/null @@ -1,50 +0,0 @@ -{ - "backupsData": [ - { - "name": "backupname01", - "status": "ready", - "errors": "sampleerrors", - "warnings": "warning", - "created": "created", - "expires": "expires", - "storage": "storage", - "location": "samplelocation", - "selector": "selector" - }, - { - "name": "backupname02", - "status": "ready", - "errors": "sampleerrors", - "warnings": "warning", - "created": "created", - "expires": "expires", - "storage": "storage", - "location": "samplelocation", - "selector": "selector" - } - ], - "restoresData": [ - { - "name": "backupname01", - "backup": "nback03", - "status": "ready", - "started": "starts", - "completed": "completed", - "errors": "err", - "warnings": "warn02", - "created": "createdsample", - "selector": "selector" - }, - { - "name": "backupname01", - "backup": "nback03", - "status": "ready", - "started": "starts", - "completed": "completed", - "errors": "err", - "warnings": "warn02", - "created": "createdsample", - "selector": "selector" - } - ] -} \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/assets/data/camera.json b/example-apps/ROBO/RoboUI/src/assets/data/camera.json deleted file mode 100644 index 518a957..0000000 --- a/example-apps/ROBO/RoboUI/src/assets/data/camera.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "roboCamera": [ - { - "cameraID": "12313213", - "cameraLocation": "Shanghaiii", - "cameraNumber": "camera0001", - "rtspUrl": "rtspUrldata" - - }, - { - "cameraID": "43243243", - "cameraLocation": "Beijing", - "cameraNumber": "camera0002", - "rtspUrl": "rtspUrlbeijing" - }, - { - "cameraID": "24342342", - "cameraLocation": "Bangalore", - "cameraNumber": "camera0003", - "rtspUrl": "rtspUrlBangalore" - } - ] -} \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/assets/data/inventory.json b/example-apps/ROBO/RoboUI/src/assets/data/inventory.json deleted file mode 100644 index 6806487..0000000 --- a/example-apps/ROBO/RoboUI/src/assets/data/inventory.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "InventryData": [ - { - "shelfName": "Shelf1", - "ObjType": "Bottles", - "status": "Mostly Filled", - "time": "Tue Feb 26 12:11:59 2020" - }, - { - "shelfName": "Shelf2", - "ObjType": "Bottles", - "time": "Tue Feb 26 12:15:00 2020", - "status": "Needs Filling" - - }, - { - "shelfName": "Shelf2", - "ObjType": "Bottles", - "status": "Partially Filled", - "time": "Tue Feb 26 12:15:00 2020" - } - ] -} \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/assets/data/sample.json b/example-apps/ROBO/RoboUI/src/assets/data/sample.json deleted file mode 100644 index b4f76d6..0000000 --- a/example-apps/ROBO/RoboUI/src/assets/data/sample.json +++ /dev/null @@ -1,88 +0,0 @@ -{ - "roboCamera": [ - { - "cameraID": 1960388362306041504, - "cameraLocation": "Bangalore", - "cameraNumber": "Camera1", - "rtspUrl": "shelf1.mp4" - }, - { - "cameraID": "4914975009606440017", - "cameraLocation": "bangalore", - "cameraNumber": "camshelf7", - "rtspUrl": "randomname.mp4" - }, - { - "cameraID": "1570908640724175325", - "cameraLocation": "Beijing", - "cameraNumber": "Camera", - "rtspUrl": "shelf" - }, - { - "cameraID": "2010229651372697543", - "cameraLocation": "Beijing", - "cameraNumber": "Camera", - "rtspUrl": "shelf1.mp4" - }, - { - "cameraID": "826993028663777933", - "cameraLocation": "Shangai", - "cameraNumber": "Camera01", - "rtspUrl": "shelf1.mp4" - }, - { - "cameraID": "2926498843313055982", - "cameraLocation": "Mumbai", - "cameraNumber": "Camera-mumbai", - "rtspUrl": "bottleshelf.mp4" - }, - { - "cameraID": "3813790383850240300", - "cameraLocation": "chennai", - "cameraNumber": "Camera-chen", - "rtspUrl": "shelf1.mp4" - }, - { - "cameraID": "4819542429618771549", - "cameraLocation": "Mumbai", - "cameraNumber": "Camera05", - "rtspUrl": "bottleshelf01.mp4" - }, - { - "cameraID": "5028594284584149292", - "cameraLocation": "Bangalore1", - "cameraNumber": "Camera001", - "rtspUrl": "shelf1.mp4" - }, - { - "cameraID": "527201027173642822", - "cameraLocation": "Beijing", - "cameraNumber": "Camera08", - "rtspUrl": "shelf1.mp4" - }, - { - "cameraID": "2297729249451603771", - "cameraLocation": "Beijing", - "cameraNumber": "Cam11", - "rtspUrl": "demoshelf.mp4" - }, - { - "cameraID": "943016121424881133", - "cameraLocation": "Bangalore", - "cameraNumber": "Cam12", - "rtspUrl": "demoshelf.mp4" - }, - { - "cameraID": "7697628706616486018", - "cameraLocation": "Bengaluru", - "cameraNumber": "Cam55", - "rtspUrl": "demoshelf.mp4" - }, - { - "cameraID": "1234241466428109927", - "cameraLocation": "bangalore", - "cameraNumber": "camshelf8", - "rtspUrl": "randomname.mp4" - } - ] -} diff --git a/example-apps/ROBO/RoboUI/src/assets/images/common-appgridvue.png b/example-apps/ROBO/RoboUI/src/assets/images/common-appgridvue.png deleted file mode 100644 index e23b89e..0000000 Binary files a/example-apps/ROBO/RoboUI/src/assets/images/common-appgridvue.png and /dev/null differ diff --git a/example-apps/ROBO/RoboUI/src/assets/images/sample.jpg b/example-apps/ROBO/RoboUI/src/assets/images/sample.jpg deleted file mode 100644 index 0571429..0000000 Binary files a/example-apps/ROBO/RoboUI/src/assets/images/sample.jpg and /dev/null differ diff --git a/example-apps/ROBO/RoboUI/src/environments/environment.prod.ts b/example-apps/ROBO/RoboUI/src/environments/environment.prod.ts deleted file mode 100644 index 3612073..0000000 --- a/example-apps/ROBO/RoboUI/src/environments/environment.prod.ts +++ /dev/null @@ -1,3 +0,0 @@ -export const environment = { - production: true -}; diff --git a/example-apps/ROBO/RoboUI/src/environments/environment.ts b/example-apps/ROBO/RoboUI/src/environments/environment.ts deleted file mode 100644 index 7b4f817..0000000 --- a/example-apps/ROBO/RoboUI/src/environments/environment.ts +++ /dev/null @@ -1,16 +0,0 @@ -// This file can be replaced during build by using the `fileReplacements` array. -// `ng build --prod` replaces `environment.ts` with `environment.prod.ts`. -// The list of file replacements can be found in `angular.json`. - -export const environment = { - production: false -}; - -/* - * For easier debugging in development mode, you can import the following file - * to ignore zone related error stack frames such as `zone.run`, `zoneDelegate.invokeTask`. - * - * This import should be commented out in production mode because it will have a negative impact - * on performance if an error is thrown. - */ -// import 'zone.js/dist/zone-error'; // Included with Angular CLI. diff --git a/example-apps/ROBO/RoboUI/src/favicon.ico b/example-apps/ROBO/RoboUI/src/favicon.ico deleted file mode 100644 index 8081c7c..0000000 Binary files a/example-apps/ROBO/RoboUI/src/favicon.ico and /dev/null differ diff --git a/example-apps/ROBO/RoboUI/src/index.html b/example-apps/ROBO/RoboUI/src/index.html deleted file mode 100644 index d54b372..0000000 --- a/example-apps/ROBO/RoboUI/src/index.html +++ /dev/null @@ -1,14 +0,0 @@ - - - - - RoboUI - - - - - - - - - diff --git a/example-apps/ROBO/RoboUI/src/main.ts b/example-apps/ROBO/RoboUI/src/main.ts deleted file mode 100644 index c7b673c..0000000 --- a/example-apps/ROBO/RoboUI/src/main.ts +++ /dev/null @@ -1,12 +0,0 @@ -import { enableProdMode } from '@angular/core'; -import { platformBrowserDynamic } from '@angular/platform-browser-dynamic'; - -import { AppModule } from './app/app.module'; -import { environment } from './environments/environment'; - -if (environment.production) { - enableProdMode(); -} - -platformBrowserDynamic().bootstrapModule(AppModule) - .catch(err => console.error(err)); diff --git a/example-apps/ROBO/RoboUI/src/polyfills.ts b/example-apps/ROBO/RoboUI/src/polyfills.ts deleted file mode 100644 index aa665d6..0000000 --- a/example-apps/ROBO/RoboUI/src/polyfills.ts +++ /dev/null @@ -1,63 +0,0 @@ -/** - * This file includes polyfills needed by Angular and is loaded before the app. - * You can add your own extra polyfills to this file. - * - * This file is divided into 2 sections: - * 1. Browser polyfills. These are applied before loading ZoneJS and are sorted by browsers. - * 2. Application imports. Files imported after ZoneJS that should be loaded before your main - * file. - * - * The current setup is for so-called "evergreen" browsers; the last versions of browsers that - * automatically update themselves. This includes Safari >= 10, Chrome >= 55 (including Opera), - * Edge >= 13 on the desktop, and iOS 10 and Chrome on mobile. - * - * Learn more in https://angular.io/guide/browser-support - */ - -/*************************************************************************************************** - * BROWSER POLYFILLS - */ - -/** IE10 and IE11 requires the following for NgClass support on SVG elements */ -// import 'classlist.js'; // Run `npm install --save classlist.js`. - -/** - * Web Animations `@angular/platform-browser/animations` - * Only required if AnimationBuilder is used within the application and using IE/Edge or Safari. - * Standard animation support in Angular DOES NOT require any polyfills (as of Angular 6.0). - */ -// import 'web-animations-js'; // Run `npm install --save web-animations-js`. - -/** - * By default, zone.js will patch all possible macroTask and DomEvents - * user can disable parts of macroTask/DomEvents patch by setting following flags - * because those flags need to be set before `zone.js` being loaded, and webpack - * will put import in the top of bundle, so user need to create a separate file - * in this directory (for example: zone-flags.ts), and put the following flags - * into that file, and then add the following code before importing zone.js. - * import './zone-flags.ts'; - * - * The flags allowed in zone-flags.ts are listed here. - * - * The following flags will work for all browsers. - * - * (window as any).__Zone_disable_requestAnimationFrame = true; // disable patch requestAnimationFrame - * (window as any).__Zone_disable_on_property = true; // disable patch onProperty such as onclick - * (window as any).__zone_symbol__UNPATCHED_EVENTS = ['scroll', 'mousemove']; // disable patch specified eventNames - * - * in IE/Edge developer tools, the addEventListener will also be wrapped by zone.js - * with the following flag, it will bypass `zone.js` patch for IE/Edge - * - * (window as any).__Zone_enable_cross_context_check = true; - * - */ - -/*************************************************************************************************** - * Zone JS is required by default for Angular itself. - */ -import 'zone.js/dist/zone'; // Included with Angular CLI. - - -/*************************************************************************************************** - * APPLICATION IMPORTS - */ diff --git a/example-apps/ROBO/RoboUI/src/proxy.conf.json b/example-apps/ROBO/RoboUI/src/proxy.conf.json deleted file mode 100644 index 386d42c..0000000 --- a/example-apps/ROBO/RoboUI/src/proxy.conf.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "/v1/*": { - "target": "http://192.168.29.203:3000", - "secure": false, - "logLevel": "debug" - } -} \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/styles.scss b/example-apps/ROBO/RoboUI/src/styles.scss deleted file mode 100644 index 3ba7c74..0000000 --- a/example-apps/ROBO/RoboUI/src/styles.scss +++ /dev/null @@ -1,20 +0,0 @@ -/* You can add global styles to this file, and also import other style files */ -@import "~@angular/material/prebuilt-themes/indigo-pink.css"; -/* @import '@angular/material/prebuilt-themes/deeppurple-amber.css'; */ -/* @import '@angular/material/prebuilt-themes/pink-bluegrey.css'; */ - -@import url("https://fonts.googleapis.com/icon?family=Material+Icons"); -/* @import "~bootstrap/dist/css/bootstrap.css"; */ - -@import url("https://use.fontawesome.com/releases/v5.7.0/css/all.css"); - -/* @import './styles/material-theme.scss'; */ -/* @import './app/' */ - -@import '~ngx-toastr/toastr'; - - - -body { - margin: 0 auto; -} \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/styles/material-theme.scss b/example-apps/ROBO/RoboUI/src/styles/material-theme.scss deleted file mode 100644 index 3a7dce0..0000000 --- a/example-apps/ROBO/RoboUI/src/styles/material-theme.scss +++ /dev/null @@ -1,10 +0,0 @@ -@import '~@angular/material/theming'; - -@include mat-core(); - -$primary: mat-palette($mat-grey, 900); -$accent: mat-palette($mat-pink, A200, A100, A400); -$warn: mat-palette($mat-red); -$theme: mat-light-theme($primary, $accent, $warn); - -@include angular-material-theme($theme); \ No newline at end of file diff --git a/example-apps/ROBO/RoboUI/src/test.ts b/example-apps/ROBO/RoboUI/src/test.ts deleted file mode 100644 index 1631789..0000000 --- a/example-apps/ROBO/RoboUI/src/test.ts +++ /dev/null @@ -1,20 +0,0 @@ -// This file is required by karma.conf.js and loads recursively all the .spec and framework files - -import 'zone.js/dist/zone-testing'; -import { getTestBed } from '@angular/core/testing'; -import { - BrowserDynamicTestingModule, - platformBrowserDynamicTesting -} from '@angular/platform-browser-dynamic/testing'; - -declare const require: any; - -// First, initialize the Angular testing environment. -getTestBed().initTestEnvironment( - BrowserDynamicTestingModule, - platformBrowserDynamicTesting() -); -// Then we find all the tests. -const context = require.context('./', true, /\.spec\.ts$/); -// And load the modules. -context.keys().map(context); diff --git a/example-apps/ROBO/RoboUI/tsconfig.app.json b/example-apps/ROBO/RoboUI/tsconfig.app.json deleted file mode 100644 index df4d05e..0000000 --- a/example-apps/ROBO/RoboUI/tsconfig.app.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "extends": "./tsconfig.json", - "compilerOptions": { - "outDir": "./out-tsc/app", - "types": [] - }, - "files": [ - "src/main.ts", - "src/polyfills.ts" - ], - "include": [ - "src/**/*.d.ts" - ], - "exclude": [ - "src/test.ts", - "src/**/*.spec.ts" - ] -} diff --git a/example-apps/ROBO/RoboUI/tsconfig.json b/example-apps/ROBO/RoboUI/tsconfig.json deleted file mode 100644 index dadb0a9..0000000 --- a/example-apps/ROBO/RoboUI/tsconfig.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "compileOnSave": false, - "compilerOptions": { - "skipLibCheck": true, - "baseUrl": "./", - "outDir": "./dist/out-tsc", - "sourceMap": true, - "declaration": false, - "downlevelIteration": true, - "experimentalDecorators": true, - "module": "es2020", - "moduleResolution": "node", - "importHelpers": true, - "target": "es2015", - "typeRoots": [ - "node_modules/@types" - ], - "lib": [ - "es2018", - "dom" - ] - }, - "angularCompilerOptions": { - "fullTemplateTypeCheck": true, - "strictInjectionParameters": true - } -} diff --git a/example-apps/ROBO/RoboUI/tsconfig.spec.json b/example-apps/ROBO/RoboUI/tsconfig.spec.json deleted file mode 100644 index 6400fde..0000000 --- a/example-apps/ROBO/RoboUI/tsconfig.spec.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "extends": "./tsconfig.json", - "compilerOptions": { - "outDir": "./out-tsc/spec", - "types": [ - "jasmine", - "node" - ] - }, - "files": [ - "src/test.ts", - "src/polyfills.ts" - ], - "include": [ - "src/**/*.spec.ts", - "src/**/*.d.ts" - ] -} diff --git a/example-apps/ROBO/RoboUI/tslint.json b/example-apps/ROBO/RoboUI/tslint.json deleted file mode 100644 index 6ccb655..0000000 --- a/example-apps/ROBO/RoboUI/tslint.json +++ /dev/null @@ -1,155 +0,0 @@ -{ - "extends": "tslint:recommended", - "rules": { - "align": { - "options": [ - "parameters", - "statements" - ] - }, - "array-type": false, - "arrow-parens": false, - "arrow-return-shorthand": true, - "deprecation": { - "severity": "warning" - }, - "component-class-suffix": true, - "contextual-lifecycle": true, - "curly": true, - "directive-class-suffix": true, - "directive-selector": [ - true, - "attribute", - "app", - "camelCase" - ], - "component-selector": [ - true, - "element", - "app", - "kebab-case" - ], - "eofline": true, - "import-blacklist": [ - true, - "rxjs/Rx" - ], - "import-spacing": true, - "indent": { - "options": [ - "spaces" - ] - }, - "interface-name": false, - "max-classes-per-file": false, - "max-line-length": [ - true, - 140 - ], - "member-access": false, - "member-ordering": [ - true, - { - "order": [ - "static-field", - "instance-field", - "static-method", - "instance-method" - ] - } - ], - "no-consecutive-blank-lines": false, - "no-console": [ - true, - "debug", - "info", - "time", - "timeEnd", - "trace" - ], - "no-empty": false, - "no-inferrable-types": [ - true, - "ignore-params" - ], - "no-non-null-assertion": true, - "no-redundant-jsdoc": true, - "no-switch-case-fall-through": true, - "no-var-requires": false, - "object-literal-key-quotes": [ - true, - "as-needed" - ], - "object-literal-sort-keys": false, - "ordered-imports": false, - "quotemark": [ - true, - "single" - ], - "trailing-comma": false, - "no-conflicting-lifecycle": true, - "no-host-metadata-property": true, - "no-input-rename": true, - "no-inputs-metadata-property": true, - "no-output-native": true, - "no-output-on-prefix": true, - "no-output-rename": true, - "semicolon": { - "options": [ - "always" - ] - }, - "space-before-function-paren": { - "options": { - "anonymous": "never", - "asyncArrow": "always", - "constructor": "never", - "method": "never", - "named": "never" - } - }, - "no-outputs-metadata-property": true, - "template-banana-in-box": true, - "template-no-negated-async": true, - "typedef-whitespace": { - "options": [ - { - "call-signature": "nospace", - "index-signature": "nospace", - "parameter": "nospace", - "property-declaration": "nospace", - "variable-declaration": "nospace" - }, - { - "call-signature": "onespace", - "index-signature": "onespace", - "parameter": "onespace", - "property-declaration": "onespace", - "variable-declaration": "onespace" - } - ] - }, - "use-lifecycle-interface": true, - "use-pipe-transform-interface": true, - "variable-name": { - "options": [ - "ban-keywords", - "check-format", - "allow-pascal-case" - ] - }, - "whitespace": { - "options": [ - "check-branch", - "check-decl", - "check-operator", - "check-separator", - "check-type", - "check-typecast" - ] - } - }, - "rulesDirectory": [ - "codelyzer" - ] -} \ No newline at end of file diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/.gitignore b/example-apps/ROBO/aPaaS/Obj_Detection_service/.gitignore deleted file mode 100644 index 329ec83..0000000 --- a/example-apps/ROBO/aPaaS/Obj_Detection_service/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -venv -__pycache__ -.idea diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/Dockerfile b/example-apps/ROBO/aPaaS/Obj_Detection_service/Dockerfile deleted file mode 100644 index c84560a..0000000 --- a/example-apps/ROBO/aPaaS/Obj_Detection_service/Dockerfile +++ /dev/null @@ -1,92 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# Prepare stage for multistage image build -## START OF STAGE0 ## -FROM python:3.6-slim-stretch - -# # CREATE APP USER ## -# Set umask -RUN sed -i "s|umask 022|umask 027|g" /etc/profile - -# Create the home directory for the new app user. -RUN mkdir -p /usr/app -RUN mkdir -p /usr/app/bin -RUN mkdir -p /usr/app/detection -RUN mkdir -p /usr/app/resources - -# Set the home directory to our app user's home. -ENV APP_HOME=/usr/app -ENV UID=166 -ENV GID=166 -ENV USER_NAME=eguser -ENV GROUP_NAME=eggroup -ENV ENV="/etc/profile" - -# Create an app user so our program doesn't run as root. -RUN apt-get -y update &&\ - groupadd -r -g $GID $GROUP_NAME &&\ - useradd -r -u $UID -g $GID -d $APP_HOME -s /sbin/nologin -c "Docker image user" $USER_NAME - -RUN apt-get install -y --fix-missing \ - build-essential \ - cmake \ - gfortran \ - git \ - wget \ - curl \ - graphicsmagick \ - libgraphicsmagick1-dev \ - libatlas-dev \ - libavcodec-dev \ - libavformat-dev \ - libgtk2.0-dev \ - libjpeg-dev \ - liblapack-dev \ - libswscale-dev \ - pkg-config \ - python3-dev \ - python3-numpy \ - software-properties-common \ - zip \ - && apt-get clean && rm -rf /tmp/* /var/tmp/* - -# Set the working directory. -WORKDIR $APP_HOME - -# Copy the application & scripts -COPY config.py requirements.txt run.py $APP_HOME/ -COPY detection $APP_HOME/monitoring/ -COPY resources $APP_HOME/resources/ -COPY configs/*.sh $APP_HOME/bin - -RUN chmod 750 $APP_HOME &&\ - chmod -R 550 $APP_HOME/bin &&\ - mkdir -p -m 750 $APP_HOME/log &&\ - mkdir -p -m 700 $APP_HOME/ssl &&\ - chown -R $USER_NAME:$GROUP_NAME $APP_HOME - -# Exposed port -EXPOSE 9997 - -# Change to the app user. -USER $USER_NAME - -# Install requirements -RUN pip install -r requirements.txt - -# Execute script & application -ENTRYPOINT ["sh", "./bin/start.sh"] diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/MobileNetSSD_deploy.prototxt b/example-apps/ROBO/aPaaS/Obj_Detection_service/MobileNetSSD_deploy.prototxt deleted file mode 100755 index fdc8126..0000000 --- a/example-apps/ROBO/aPaaS/Obj_Detection_service/MobileNetSSD_deploy.prototxt +++ /dev/null @@ -1,1912 +0,0 @@ -name: "MobileNet-SSD" -input: "data" -input_shape { - dim: 1 - dim: 3 - dim: 300 - dim: 300 -} -layer { - name: "conv0" - type: "Convolution" - bottom: "data" - top: "conv0" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 32 - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv0/relu" - type: "ReLU" - bottom: "conv0" - top: "conv0" -} -layer { - name: "conv1/dw" - type: "Convolution" - bottom: "conv0" - top: "conv1/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 32 - pad: 1 - kernel_size: 3 - group: 32 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv1/dw/relu" - type: "ReLU" - bottom: "conv1/dw" - top: "conv1/dw" -} -layer { - name: "conv1" - type: "Convolution" - bottom: "conv1/dw" - top: "conv1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 64 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv1/relu" - type: "ReLU" - bottom: "conv1" - top: "conv1" -} -layer { - name: "conv2/dw" - type: "Convolution" - bottom: "conv1" - top: "conv2/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 64 - pad: 1 - kernel_size: 3 - stride: 2 - group: 64 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv2/dw/relu" - type: "ReLU" - bottom: "conv2/dw" - top: "conv2/dw" -} -layer { - name: "conv2" - type: "Convolution" - bottom: "conv2/dw" - top: "conv2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv2/relu" - type: "ReLU" - bottom: "conv2" - top: "conv2" -} -layer { - name: "conv3/dw" - type: "Convolution" - bottom: "conv2" - top: "conv3/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 128 - pad: 1 - kernel_size: 3 - group: 128 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv3/dw/relu" - type: "ReLU" - bottom: "conv3/dw" - top: "conv3/dw" -} -layer { - name: "conv3" - type: "Convolution" - bottom: "conv3/dw" - top: "conv3" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv3/relu" - type: "ReLU" - bottom: "conv3" - top: "conv3" -} -layer { - name: "conv4/dw" - type: "Convolution" - bottom: "conv3" - top: "conv4/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 128 - pad: 1 - kernel_size: 3 - stride: 2 - group: 128 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv4/dw/relu" - type: "ReLU" - bottom: "conv4/dw" - top: "conv4/dw" -} -layer { - name: "conv4" - type: "Convolution" - bottom: "conv4/dw" - top: "conv4" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 256 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv4/relu" - type: "ReLU" - bottom: "conv4" - top: "conv4" -} -layer { - name: "conv5/dw" - type: "Convolution" - bottom: "conv4" - top: "conv5/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - group: 256 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv5/dw/relu" - type: "ReLU" - bottom: "conv5/dw" - top: "conv5/dw" -} -layer { - name: "conv5" - type: "Convolution" - bottom: "conv5/dw" - top: "conv5" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 256 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv5/relu" - type: "ReLU" - bottom: "conv5" - top: "conv5" -} -layer { - name: "conv6/dw" - type: "Convolution" - bottom: "conv5" - top: "conv6/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - stride: 2 - group: 256 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv6/dw/relu" - type: "ReLU" - bottom: "conv6/dw" - top: "conv6/dw" -} -layer { - name: "conv6" - type: "Convolution" - bottom: "conv6/dw" - top: "conv6" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv6/relu" - type: "ReLU" - bottom: "conv6" - top: "conv6" -} -layer { - name: "conv7/dw" - type: "Convolution" - bottom: "conv6" - top: "conv7/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv7/dw/relu" - type: "ReLU" - bottom: "conv7/dw" - top: "conv7/dw" -} -layer { - name: "conv7" - type: "Convolution" - bottom: "conv7/dw" - top: "conv7" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv7/relu" - type: "ReLU" - bottom: "conv7" - top: "conv7" -} -layer { - name: "conv8/dw" - type: "Convolution" - bottom: "conv7" - top: "conv8/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv8/dw/relu" - type: "ReLU" - bottom: "conv8/dw" - top: "conv8/dw" -} -layer { - name: "conv8" - type: "Convolution" - bottom: "conv8/dw" - top: "conv8" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv8/relu" - type: "ReLU" - bottom: "conv8" - top: "conv8" -} -layer { - name: "conv9/dw" - type: "Convolution" - bottom: "conv8" - top: "conv9/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv9/dw/relu" - type: "ReLU" - bottom: "conv9/dw" - top: "conv9/dw" -} -layer { - name: "conv9" - type: "Convolution" - bottom: "conv9/dw" - top: "conv9" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv9/relu" - type: "ReLU" - bottom: "conv9" - top: "conv9" -} -layer { - name: "conv10/dw" - type: "Convolution" - bottom: "conv9" - top: "conv10/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv10/dw/relu" - type: "ReLU" - bottom: "conv10/dw" - top: "conv10/dw" -} -layer { - name: "conv10" - type: "Convolution" - bottom: "conv10/dw" - top: "conv10" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv10/relu" - type: "ReLU" - bottom: "conv10" - top: "conv10" -} -layer { - name: "conv11/dw" - type: "Convolution" - bottom: "conv10" - top: "conv11/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv11/dw/relu" - type: "ReLU" - bottom: "conv11/dw" - top: "conv11/dw" -} -layer { - name: "conv11" - type: "Convolution" - bottom: "conv11/dw" - top: "conv11" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv11/relu" - type: "ReLU" - bottom: "conv11" - top: "conv11" -} -layer { - name: "conv12/dw" - type: "Convolution" - bottom: "conv11" - top: "conv12/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 2 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv12/dw/relu" - type: "ReLU" - bottom: "conv12/dw" - top: "conv12/dw" -} -layer { - name: "conv12" - type: "Convolution" - bottom: "conv12/dw" - top: "conv12" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 1024 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv12/relu" - type: "ReLU" - bottom: "conv12" - top: "conv12" -} -layer { - name: "conv13/dw" - type: "Convolution" - bottom: "conv12" - top: "conv13/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 1024 - pad: 1 - kernel_size: 3 - group: 1024 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv13/dw/relu" - type: "ReLU" - bottom: "conv13/dw" - top: "conv13/dw" -} -layer { - name: "conv13" - type: "Convolution" - bottom: "conv13/dw" - top: "conv13" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 1024 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv13/relu" - type: "ReLU" - bottom: "conv13" - top: "conv13" -} -layer { - name: "conv14_1" - type: "Convolution" - bottom: "conv13" - top: "conv14_1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 256 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv14_1/relu" - type: "ReLU" - bottom: "conv14_1" - top: "conv14_1" -} -layer { - name: "conv14_2" - type: "Convolution" - bottom: "conv14_1" - top: "conv14_2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv14_2/relu" - type: "ReLU" - bottom: "conv14_2" - top: "conv14_2" -} -layer { - name: "conv15_1" - type: "Convolution" - bottom: "conv14_2" - top: "conv15_1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv15_1/relu" - type: "ReLU" - bottom: "conv15_1" - top: "conv15_1" -} -layer { - name: "conv15_2" - type: "Convolution" - bottom: "conv15_1" - top: "conv15_2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv15_2/relu" - type: "ReLU" - bottom: "conv15_2" - top: "conv15_2" -} -layer { - name: "conv16_1" - type: "Convolution" - bottom: "conv15_2" - top: "conv16_1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv16_1/relu" - type: "ReLU" - bottom: "conv16_1" - top: "conv16_1" -} -layer { - name: "conv16_2" - type: "Convolution" - bottom: "conv16_1" - top: "conv16_2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv16_2/relu" - type: "ReLU" - bottom: "conv16_2" - top: "conv16_2" -} -layer { - name: "conv17_1" - type: "Convolution" - bottom: "conv16_2" - top: "conv17_1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 64 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv17_1/relu" - type: "ReLU" - bottom: "conv17_1" - top: "conv17_1" -} -layer { - name: "conv17_2" - type: "Convolution" - bottom: "conv17_1" - top: "conv17_2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 128 - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv17_2/relu" - type: "ReLU" - bottom: "conv17_2" - top: "conv17_2" -} -layer { - name: "conv11_mbox_loc" - type: "Convolution" - bottom: "conv11" - top: "conv11_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 12 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv11_mbox_loc_perm" - type: "Permute" - bottom: "conv11_mbox_loc" - top: "conv11_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv11_mbox_loc_flat" - type: "Flatten" - bottom: "conv11_mbox_loc_perm" - top: "conv11_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv11_mbox_conf" - type: "Convolution" - bottom: "conv11" - top: "conv11_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 63 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv11_mbox_conf_perm" - type: "Permute" - bottom: "conv11_mbox_conf" - top: "conv11_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv11_mbox_conf_flat" - type: "Flatten" - bottom: "conv11_mbox_conf_perm" - top: "conv11_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv11_mbox_priorbox" - type: "PriorBox" - bottom: "conv11" - bottom: "data" - top: "conv11_mbox_priorbox" - prior_box_param { - min_size: 60.0 - aspect_ratio: 2.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv13_mbox_loc" - type: "Convolution" - bottom: "conv13" - top: "conv13_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv13_mbox_loc_perm" - type: "Permute" - bottom: "conv13_mbox_loc" - top: "conv13_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv13_mbox_loc_flat" - type: "Flatten" - bottom: "conv13_mbox_loc_perm" - top: "conv13_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv13_mbox_conf" - type: "Convolution" - bottom: "conv13" - top: "conv13_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv13_mbox_conf_perm" - type: "Permute" - bottom: "conv13_mbox_conf" - top: "conv13_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv13_mbox_conf_flat" - type: "Flatten" - bottom: "conv13_mbox_conf_perm" - top: "conv13_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv13_mbox_priorbox" - type: "PriorBox" - bottom: "conv13" - bottom: "data" - top: "conv13_mbox_priorbox" - prior_box_param { - min_size: 105.0 - max_size: 150.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv14_2_mbox_loc" - type: "Convolution" - bottom: "conv14_2" - top: "conv14_2_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv14_2_mbox_loc_perm" - type: "Permute" - bottom: "conv14_2_mbox_loc" - top: "conv14_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv14_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv14_2_mbox_loc_perm" - top: "conv14_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv14_2_mbox_conf" - type: "Convolution" - bottom: "conv14_2" - top: "conv14_2_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv14_2_mbox_conf_perm" - type: "Permute" - bottom: "conv14_2_mbox_conf" - top: "conv14_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv14_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv14_2_mbox_conf_perm" - top: "conv14_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv14_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv14_2" - bottom: "data" - top: "conv14_2_mbox_priorbox" - prior_box_param { - min_size: 150.0 - max_size: 195.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv15_2_mbox_loc" - type: "Convolution" - bottom: "conv15_2" - top: "conv15_2_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv15_2_mbox_loc_perm" - type: "Permute" - bottom: "conv15_2_mbox_loc" - top: "conv15_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv15_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv15_2_mbox_loc_perm" - top: "conv15_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv15_2_mbox_conf" - type: "Convolution" - bottom: "conv15_2" - top: "conv15_2_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv15_2_mbox_conf_perm" - type: "Permute" - bottom: "conv15_2_mbox_conf" - top: "conv15_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv15_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv15_2_mbox_conf_perm" - top: "conv15_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv15_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv15_2" - bottom: "data" - top: "conv15_2_mbox_priorbox" - prior_box_param { - min_size: 195.0 - max_size: 240.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv16_2_mbox_loc" - type: "Convolution" - bottom: "conv16_2" - top: "conv16_2_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv16_2_mbox_loc_perm" - type: "Permute" - bottom: "conv16_2_mbox_loc" - top: "conv16_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv16_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv16_2_mbox_loc_perm" - top: "conv16_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv16_2_mbox_conf" - type: "Convolution" - bottom: "conv16_2" - top: "conv16_2_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv16_2_mbox_conf_perm" - type: "Permute" - bottom: "conv16_2_mbox_conf" - top: "conv16_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv16_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv16_2_mbox_conf_perm" - top: "conv16_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv16_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv16_2" - bottom: "data" - top: "conv16_2_mbox_priorbox" - prior_box_param { - min_size: 240.0 - max_size: 285.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv17_2_mbox_loc" - type: "Convolution" - bottom: "conv17_2" - top: "conv17_2_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv17_2_mbox_loc_perm" - type: "Permute" - bottom: "conv17_2_mbox_loc" - top: "conv17_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv17_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv17_2_mbox_loc_perm" - top: "conv17_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv17_2_mbox_conf" - type: "Convolution" - bottom: "conv17_2" - top: "conv17_2_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv17_2_mbox_conf_perm" - type: "Permute" - bottom: "conv17_2_mbox_conf" - top: "conv17_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv17_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv17_2_mbox_conf_perm" - top: "conv17_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv17_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv17_2" - bottom: "data" - top: "conv17_2_mbox_priorbox" - prior_box_param { - min_size: 285.0 - max_size: 300.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "mbox_loc" - type: "Concat" - bottom: "conv11_mbox_loc_flat" - bottom: "conv13_mbox_loc_flat" - bottom: "conv14_2_mbox_loc_flat" - bottom: "conv15_2_mbox_loc_flat" - bottom: "conv16_2_mbox_loc_flat" - bottom: "conv17_2_mbox_loc_flat" - top: "mbox_loc" - concat_param { - axis: 1 - } -} -layer { - name: "mbox_conf" - type: "Concat" - bottom: "conv11_mbox_conf_flat" - bottom: "conv13_mbox_conf_flat" - bottom: "conv14_2_mbox_conf_flat" - bottom: "conv15_2_mbox_conf_flat" - bottom: "conv16_2_mbox_conf_flat" - bottom: "conv17_2_mbox_conf_flat" - top: "mbox_conf" - concat_param { - axis: 1 - } -} -layer { - name: "mbox_priorbox" - type: "Concat" - bottom: "conv11_mbox_priorbox" - bottom: "conv13_mbox_priorbox" - bottom: "conv14_2_mbox_priorbox" - bottom: "conv15_2_mbox_priorbox" - bottom: "conv16_2_mbox_priorbox" - bottom: "conv17_2_mbox_priorbox" - top: "mbox_priorbox" - concat_param { - axis: 2 - } -} -layer { - name: "mbox_conf_reshape" - type: "Reshape" - bottom: "mbox_conf" - top: "mbox_conf_reshape" - reshape_param { - shape { - dim: 0 - dim: -1 - dim: 21 - } - } -} -layer { - name: "mbox_conf_softmax" - type: "Softmax" - bottom: "mbox_conf_reshape" - top: "mbox_conf_softmax" - softmax_param { - axis: 2 - } -} -layer { - name: "mbox_conf_flatten" - type: "Flatten" - bottom: "mbox_conf_softmax" - top: "mbox_conf_flatten" - flatten_param { - axis: 1 - } -} -layer { - name: "detection_out" - type: "DetectionOutput" - bottom: "mbox_loc" - bottom: "mbox_conf_flatten" - bottom: "mbox_priorbox" - top: "detection_out" - include { - phase: TEST - } - detection_output_param { - num_classes: 21 - share_location: true - background_label_id: 0 - nms_param { - nms_threshold: 0.45 - top_k: 100 - } - code_type: CENTER_SIZE - keep_top_k: 100 - confidence_threshold: 0.25 - } -} diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/config.py b/example-apps/ROBO/aPaaS/Obj_Detection_service/config.py deleted file mode 100644 index 31e91be..0000000 --- a/example-apps/ROBO/aPaaS/Obj_Detection_service/config.py +++ /dev/null @@ -1,40 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -import os - -# [Server Configurations] -server_port = 9998 -server_address = os.environ.get('LISTEN_IP') - -# [SSL Configurations] -ssl_enabled = False -ssl_protocol = "TLSv1.2" -ssl_ciphers = ["TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256", - "TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256", - "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384", - "TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384"] -ssl_certfilepath = "/usr/app/ssl/server_tls.crt" -ssl_keyfilepath = "/usr/app/ssl/server_tls.key" -ssl_cacertpath = "/usr/app/ssl/ca.crt" -ssl_server_name = os.environ.get('SERVER_NAME', "ealtedge") - -# [Service Configurations] -api_gateway = os.environ.get("API_GATEWAY", "apigw.mep.org") -obj_det = os.environ.get("OBJ_DETECTION", "objdetection") - -# [Constants] -recognition_url = "http://" + api_gateway + "/" + obj_det diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/config.pyc b/example-apps/ROBO/aPaaS/Obj_Detection_service/config.pyc deleted file mode 100644 index 6df092d..0000000 Binary files a/example-apps/ROBO/aPaaS/Obj_Detection_service/config.pyc and /dev/null differ diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/configs/start.sh b/example-apps/ROBO/aPaaS/Obj_Detection_service/configs/start.sh deleted file mode 100644 index de45014..0000000 --- a/example-apps/ROBO/aPaaS/Obj_Detection_service/configs/start.sh +++ /dev/null @@ -1,106 +0,0 @@ -#!/bin/bash -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Validates if ip is valid -validate_ip() -{ - ip_var="$1" - # checks if variable is unset - if [ -z "$ip_var" ] ; then - echo "ip is not set" - return 1 - fi - - if ! echo "$ip_var" | grep -qE '^(([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])\.)' ; then - return 1 - fi - return 0 -} - -validate_name() { - hostname="$1" - len="${#hostname}" - if [ "${len}" -gt "64" ]; then - return 1 - fi - if ! echo "$hostname" | grep -qE '^[a-zA-Z0-9]*$|^[a-zA-Z0-9][a-zA-Z0-9_\-]*[a-zA-Z0-9]$'; then - return 1 - fi - return 0 -} - -# validates whether file exist -validate_file_exists() { - file_path="$1" - - # checks variable is unset - if [ -z "$file_path" ]; then - echo "file path variable is not set" - return 1 - fi - - # checks if file exists - if [ ! -f "$file_path" ]; then - echo "file does not exist" - return 1 - fi - - return 0 -} - -validate_ip "$LISTEN_IP" -valid_listen_ip="$?" -if [ ! "$valid_listen_ip" -eq "0" ]; then - echo "invalid ip address for listen ip" - exit 1 -fi - -if [ ! -z "$SERVER_NAME" ]; then - validate_name "$SERVER_NAME" - valid_name="$?" - if [ ! "$valid_name" -eq "0" ]; then - echo "invalid ssl server name" - exit 1 - fi -fi - -# ssl parameters validation -validate_file_exists "/usr/app/ssl/server_tls.crt" -valid_ssl_server_cert="$?" -if [ ! "$valid_ssl_server_cert" -eq "0" ]; then - echo "invalid ssl server certificate" - exit 1 -fi - -# ssl parameters validation -validate_file_exists "/usr/app/ssl/server_tls.key" -valid_ssl_server_key="$?" -if [ ! "$valid_ssl_server_key" -eq "0" ]; then - echo "invalid ssl server key" - exit 1 -fi - -# ssl parameters validation -validate_file_exists "/usr/app/ssl/ca.crt" -valid_ssl_ca_crt="$?" -if [ ! "$valid_ssl_ca_crt" -eq "0" ]; then - echo "invalid ssl ca cert" - exit 1 -fi - -echo "Running Monitoring Service" -umask 0027 -cd /usr/app || exit -python run.py \ No newline at end of file diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/detection/__init__.py b/example-apps/ROBO/aPaaS/Obj_Detection_service/detection/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/detection/__init__.pyc b/example-apps/ROBO/aPaaS/Obj_Detection_service/detection/__init__.pyc deleted file mode 100644 index 4a779cf..0000000 Binary files a/example-apps/ROBO/aPaaS/Obj_Detection_service/detection/__init__.pyc and /dev/null differ diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/detection/obj_detection_service.py b/example-apps/ROBO/aPaaS/Obj_Detection_service/detection/obj_detection_service.py deleted file mode 100644 index b35bd91..0000000 --- a/example-apps/ROBO/aPaaS/Obj_Detection_service/detection/obj_detection_service.py +++ /dev/null @@ -1,290 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import os -import cv2 -import config -from flask_sslify import SSLify -from flask import Flask, request, jsonify, Response, send_file -from flask_cors import CORS -from werkzeug import secure_filename - -app = Flask(__name__) -CORS(app) -sslify = SSLify(app) -app.config['JSON_AS_ASCII'] = False -app.config['UPLOAD_PATH'] = '/usr/app/images/input/' -app.config['supports_credentials'] = True -app.config['CORS_SUPPORTS_CREDENTIALS'] = True -app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024 -ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg']) -MODEL_PATH = '/usr/app/model/' -IMAGE_PATH = '/usr/app/images/result/' -count = 0 -listOfMsgs = [] - - -class model_info(): - def __init__(self, model_name): - self.model_name = 'MobileNetSSD_deploy.caffemodel' - self.prototxt = 'MobileNetSSD_deploy.prototxt' - self.confidenceLevel = 0.2 - - def get_prototxt(self): - return self.prototxt - - def get_model_name(self): - return self.model_name - - def set_confidence_level(self, confidenceLevel): - self.confidenceLevel = confidenceLevel - - def get_confidence_level(self): - return self.confidenceLevel - - def update_model(self, model_loc, prototxt, model_name): - self.prototxt = prototxt - self.model_name = model_name - - -# Labels of Network. -classNames = {0: 'background', - 1: 'aeroplane', 2: 'bicycle', 3: 'bird', 4: 'boat', - 5: 'bottle', 6: 'bus', 7: 'car', 8: 'cat', 9: 'chair', - 10: 'cow', 11: 'diningtable', 12: 'dog', 13: 'horse', - 14: 'motorbike', 15: 'person', 16: 'pottedplant', - 17: 'sheep', 18: 'sofa', 19: 'train', 20: 'tvmonitor'} - - -def allowed_file(filename): - return '.' in filename and filename.rsplit('.', 1)[1].lower() \ - in ALLOWED_EXTENSIONS - - -# Obj-detection from input frame -def Detection(img): - print ('inside detection func') - modelInfo = model_info("caffe") - ConfPercent = modelInfo.get_confidence_level() - model_name = modelInfo.get_model_name() - prototxt_name = modelInfo.get_prototxt() - - model = MODEL_PATH + model_name - prototxt = MODEL_PATH + prototxt_name - image = app.config['UPLOAD_PATH'] + img - label = 'bottels' - print(ConfPercent) - print(model) - print(prototxt) - print("image path is" + image) - - # Load the Caffe model - net = cv2.dnn.readNetFromCaffe(prototxt, model) - # Load image fro - frame = cv2.imread(image) - - frame_resized = cv2.resize(frame, (300, 300)) # resize frame for - # prediction - heightFactor = frame.shape[0]/300.0 - widthFactor = frame.shape[1]/300.0 - - # MobileNet requires fixed dimensions for input image(s) - # so we have to ensure that it is resized to 300x300 pixels. - # set a scale factor to image because network the objects has - # differents size. - # We perform a mean subtraction (127.5, 127.5, 127.5) - # to normalize the input; - # after executing this command our "blob" now has the shape: - # (1, 3, 300, 300) - blob = cv2.dnn.blobFromImage(frame_resized, 0.007843, (300, 300), - (127.5, 127.5, 127.5), False) - # Set to network the input blob - net.setInput(blob) - # Prediction of network - detections = net.forward() - - frame_copy = frame.copy() - # Size of frame resize (300x300) - cols = frame_resized.shape[1] - rows = frame_resized.shape[0] - - # For get the class and location of object detected, - # There is a fix index for class, location and confidence - # value in @detections array . - for i in range(detections.shape[2]): - confidence = detections[0, 0, i, 2] # Confidence of prediction - if confidence > ConfPercent: # Filter prediction - class_id = int(detections[0, 0, i, 1]) # Class label - - # Object location - xLeftBottom = int(detections[0, 0, i, 3] * cols) - yLeftBottom = int(detections[0, 0, i, 4] * rows) - xRightTop = int(detections[0, 0, i, 5] * cols) - yRightTop = int(detections[0, 0, i, 6] * rows) - - xLeftBottom_ = int(widthFactor * xLeftBottom) - yLeftBottom_ = int(heightFactor * yLeftBottom) - xRightTop_ = int(widthFactor * xRightTop) - yRightTop_ = int(heightFactor * yRightTop) - # Draw location of object - cv2.rectangle(frame_resized, (xLeftBottom, yLeftBottom), - (xRightTop, yRightTop), - (0, 255, 0)) - - cv2.rectangle(frame_copy, (xLeftBottom_, yLeftBottom_), - (xRightTop_, yRightTop_), - (0, 255, 0), -1) - opacity = 0.3 - cv2.addWeighted(frame_copy, opacity, frame, 1 - opacity, 0, frame) - - count = 0 - for i in range(detections.shape[2]): - confidence = detections[0, 0, i, 2] # Confidence of prediction - if confidence > ConfPercent: # Filter prediction - class_id = int(detections[0, 0, i, 1]) # Class label - - # Object location - xLeftBottom = int(detections[0, 0, i, 3] * cols) - yLeftBottom = int(detections[0, 0, i, 4] * rows) - xRightTop = int(detections[0, 0, i, 5] * cols) - yRightTop = int(detections[0, 0, i, 6] * rows) - - xLeftBottom_ = int(widthFactor * xLeftBottom) - yLeftBottom_ = int(heightFactor * yLeftBottom) - xRightTop_ = int(widthFactor * xRightTop) - yRightTop_ = int(heightFactor * yRightTop) - cv2.rectangle(frame, (xLeftBottom_, yLeftBottom_), - (xRightTop_, yRightTop_), - (0, 0, 0), 2) - - # Draw label and confidence of prediction in frame resized - if class_id in classNames: - label = classNames[class_id] + ": " + str(confidence) - labelSize, baseLine = cv2.getTextSize(label, - cv2.FONT_HERSHEY_TRIPLEX, - 0.8, 1) - - yLeftBottom_ = max(yLeftBottom_, labelSize[1]) - cv2.rectangle( - frame, - (xLeftBottom_, yLeftBottom_ - labelSize[1]), - (xLeftBottom_ + labelSize[0], yLeftBottom_ + baseLine), - (255, 255, 255), cv2.FILLED) - cv2.putText(frame, label, (xLeftBottom_, yLeftBottom_), - cv2.FONT_HERSHEY_TRIPLEX, 0.8, (0, 0, 0)) - print(label) - count = count + 1 - - print("total item count", count) - # cv2.namedWindow("frame", cv2.WINDOW_NORMAL) - print("before im write") - cv2.imwrite(IMAGE_PATH + "result.jpeg", frame) - # cv2.imshow("frame", frame) - # cv2.waitKey(0) - print("before im before destroy window") - # cv2.destroyAllWindows() - # Detect_result = {'ImposedImage': 'frame', 'ObjCount': count, - # 'ObjType': type, 'Time': time} - Detect_result = {'ObjCount': count} - print(Detect_result) - return Detect_result - - -@app.route('/mep/v1/obj_detection/uploadModel', methods=['POST']) -def uploadModel(): - """ - upload model - :return: html file - """ - app.logger.info("Received message from ClientIP [" + request.remote_addr - + "] Operation [" + request.method + "]" + - " Resource [" + request.url + "]") - - modelInfo = model_info("caffe") - modelInfo.update_model("caffe", "mobilenet_ssd", "prototext") - return Response("success") - - -@app.route('/mep/v1/obj_detection/confidencelevel', methods=['POST']) -def setConfidenceLevel(): - """ - Trigger the video_feed() function on opening "0.0.0.0:5000/video_feed" URL - :return: - """ - app.logger.info("Received message from ClientIP [" + request.remote_addr - + "] Operation [" + request.method + "]" + - " Resource [" + request.url + "]") - - confidenceLevel = 0.2 - modelInfo = model_info("caffe") - modelInfo.set_confidence_level(confidenceLevel) - return Response("success") - - -@app.route('/mep/v1/obj_detection/detect', methods=['POST']) -def Obj_Detection(): - """ - Trigger the Obj detection on input frame/image - Input: frame/image - :return: imposed frame, count, Obj type, time taken by detection - """ - app.logger.info("Received message from ClientIP [" + request.remote_addr - + "] Operation [" + request.method + "]" + - " Resource [" + request.url + "]") - - if 'file' not in request.files: - raise IOError('No file') - - file = request.files['file'] - if file.filename == '': - app.logger.info('No file selected for uploading') - raise IOError('No file') - if file and allowed_file(file.filename): - filename = secure_filename(file.filename) - print('file name', filename) - file.save(os.path.join(app.config['UPLOAD_PATH'], filename)) - app.logger.info('File successfully uploaded') - print('file path', app.config['UPLOAD_PATH'] + filename) - Detect_result = Detection(filename) - else: - app.logger.info('Allowed file types are txt, pdf, png, jpg, jpeg, gif') - return Response("failure") - return jsonify(Detect_result) - - -@app.route('/mep/v1/obj_detection/image', methods=['GET']) -def image_download(): - """ - Trigger the Obj detection on input frame/image - Input: frame/image - :return: imposed frame, count, Obj type, time taken by detection - """ - app.logger.info("Received message from ClientIP [" + request.remote_addr - + "] Operation [" + request.method + "]" + - " Resource [" + request.url + "]") - - return send_file(IMAGE_PATH + "result.jpeg", - attachment_filename='result.jpeg') - - -def start_server(handler): - app.logger.addHandler(handler) - if config.ssl_enabled: - context = (config.ssl_certfilepath, config.ssl_keyfilepath) - app.run(host=config.server_address, debug=True, ssl_context=context, - threaded=True, port=config.server_port) - else: - app.run(host=config.server_address, debug=True, threaded=True, - port=config.server_port) diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/detection/obj_detection_service.pyc b/example-apps/ROBO/aPaaS/Obj_Detection_service/detection/obj_detection_service.pyc deleted file mode 100644 index 4f69f83..0000000 Binary files a/example-apps/ROBO/aPaaS/Obj_Detection_service/detection/obj_detection_service.pyc and /dev/null differ diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/images/img.jpeg b/example-apps/ROBO/aPaaS/Obj_Detection_service/images/img.jpeg deleted file mode 100644 index df1ebc1..0000000 Binary files a/example-apps/ROBO/aPaaS/Obj_Detection_service/images/img.jpeg and /dev/null differ diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/model_info/MobileNetSSD_deploy.prototxt b/example-apps/ROBO/aPaaS/Obj_Detection_service/model_info/MobileNetSSD_deploy.prototxt deleted file mode 100755 index fdc8126..0000000 --- a/example-apps/ROBO/aPaaS/Obj_Detection_service/model_info/MobileNetSSD_deploy.prototxt +++ /dev/null @@ -1,1912 +0,0 @@ -name: "MobileNet-SSD" -input: "data" -input_shape { - dim: 1 - dim: 3 - dim: 300 - dim: 300 -} -layer { - name: "conv0" - type: "Convolution" - bottom: "data" - top: "conv0" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 32 - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv0/relu" - type: "ReLU" - bottom: "conv0" - top: "conv0" -} -layer { - name: "conv1/dw" - type: "Convolution" - bottom: "conv0" - top: "conv1/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 32 - pad: 1 - kernel_size: 3 - group: 32 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv1/dw/relu" - type: "ReLU" - bottom: "conv1/dw" - top: "conv1/dw" -} -layer { - name: "conv1" - type: "Convolution" - bottom: "conv1/dw" - top: "conv1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 64 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv1/relu" - type: "ReLU" - bottom: "conv1" - top: "conv1" -} -layer { - name: "conv2/dw" - type: "Convolution" - bottom: "conv1" - top: "conv2/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 64 - pad: 1 - kernel_size: 3 - stride: 2 - group: 64 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv2/dw/relu" - type: "ReLU" - bottom: "conv2/dw" - top: "conv2/dw" -} -layer { - name: "conv2" - type: "Convolution" - bottom: "conv2/dw" - top: "conv2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv2/relu" - type: "ReLU" - bottom: "conv2" - top: "conv2" -} -layer { - name: "conv3/dw" - type: "Convolution" - bottom: "conv2" - top: "conv3/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 128 - pad: 1 - kernel_size: 3 - group: 128 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv3/dw/relu" - type: "ReLU" - bottom: "conv3/dw" - top: "conv3/dw" -} -layer { - name: "conv3" - type: "Convolution" - bottom: "conv3/dw" - top: "conv3" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv3/relu" - type: "ReLU" - bottom: "conv3" - top: "conv3" -} -layer { - name: "conv4/dw" - type: "Convolution" - bottom: "conv3" - top: "conv4/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 128 - pad: 1 - kernel_size: 3 - stride: 2 - group: 128 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv4/dw/relu" - type: "ReLU" - bottom: "conv4/dw" - top: "conv4/dw" -} -layer { - name: "conv4" - type: "Convolution" - bottom: "conv4/dw" - top: "conv4" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 256 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv4/relu" - type: "ReLU" - bottom: "conv4" - top: "conv4" -} -layer { - name: "conv5/dw" - type: "Convolution" - bottom: "conv4" - top: "conv5/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - group: 256 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv5/dw/relu" - type: "ReLU" - bottom: "conv5/dw" - top: "conv5/dw" -} -layer { - name: "conv5" - type: "Convolution" - bottom: "conv5/dw" - top: "conv5" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 256 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv5/relu" - type: "ReLU" - bottom: "conv5" - top: "conv5" -} -layer { - name: "conv6/dw" - type: "Convolution" - bottom: "conv5" - top: "conv6/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - stride: 2 - group: 256 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv6/dw/relu" - type: "ReLU" - bottom: "conv6/dw" - top: "conv6/dw" -} -layer { - name: "conv6" - type: "Convolution" - bottom: "conv6/dw" - top: "conv6" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv6/relu" - type: "ReLU" - bottom: "conv6" - top: "conv6" -} -layer { - name: "conv7/dw" - type: "Convolution" - bottom: "conv6" - top: "conv7/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv7/dw/relu" - type: "ReLU" - bottom: "conv7/dw" - top: "conv7/dw" -} -layer { - name: "conv7" - type: "Convolution" - bottom: "conv7/dw" - top: "conv7" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv7/relu" - type: "ReLU" - bottom: "conv7" - top: "conv7" -} -layer { - name: "conv8/dw" - type: "Convolution" - bottom: "conv7" - top: "conv8/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv8/dw/relu" - type: "ReLU" - bottom: "conv8/dw" - top: "conv8/dw" -} -layer { - name: "conv8" - type: "Convolution" - bottom: "conv8/dw" - top: "conv8" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv8/relu" - type: "ReLU" - bottom: "conv8" - top: "conv8" -} -layer { - name: "conv9/dw" - type: "Convolution" - bottom: "conv8" - top: "conv9/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv9/dw/relu" - type: "ReLU" - bottom: "conv9/dw" - top: "conv9/dw" -} -layer { - name: "conv9" - type: "Convolution" - bottom: "conv9/dw" - top: "conv9" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv9/relu" - type: "ReLU" - bottom: "conv9" - top: "conv9" -} -layer { - name: "conv10/dw" - type: "Convolution" - bottom: "conv9" - top: "conv10/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv10/dw/relu" - type: "ReLU" - bottom: "conv10/dw" - top: "conv10/dw" -} -layer { - name: "conv10" - type: "Convolution" - bottom: "conv10/dw" - top: "conv10" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv10/relu" - type: "ReLU" - bottom: "conv10" - top: "conv10" -} -layer { - name: "conv11/dw" - type: "Convolution" - bottom: "conv10" - top: "conv11/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv11/dw/relu" - type: "ReLU" - bottom: "conv11/dw" - top: "conv11/dw" -} -layer { - name: "conv11" - type: "Convolution" - bottom: "conv11/dw" - top: "conv11" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv11/relu" - type: "ReLU" - bottom: "conv11" - top: "conv11" -} -layer { - name: "conv12/dw" - type: "Convolution" - bottom: "conv11" - top: "conv12/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 2 - group: 512 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv12/dw/relu" - type: "ReLU" - bottom: "conv12/dw" - top: "conv12/dw" -} -layer { - name: "conv12" - type: "Convolution" - bottom: "conv12/dw" - top: "conv12" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 1024 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv12/relu" - type: "ReLU" - bottom: "conv12" - top: "conv12" -} -layer { - name: "conv13/dw" - type: "Convolution" - bottom: "conv12" - top: "conv13/dw" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 1024 - pad: 1 - kernel_size: 3 - group: 1024 - engine: CAFFE - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv13/dw/relu" - type: "ReLU" - bottom: "conv13/dw" - top: "conv13/dw" -} -layer { - name: "conv13" - type: "Convolution" - bottom: "conv13/dw" - top: "conv13" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 1024 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv13/relu" - type: "ReLU" - bottom: "conv13" - top: "conv13" -} -layer { - name: "conv14_1" - type: "Convolution" - bottom: "conv13" - top: "conv14_1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 256 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv14_1/relu" - type: "ReLU" - bottom: "conv14_1" - top: "conv14_1" -} -layer { - name: "conv14_2" - type: "Convolution" - bottom: "conv14_1" - top: "conv14_2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 512 - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv14_2/relu" - type: "ReLU" - bottom: "conv14_2" - top: "conv14_2" -} -layer { - name: "conv15_1" - type: "Convolution" - bottom: "conv14_2" - top: "conv15_1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv15_1/relu" - type: "ReLU" - bottom: "conv15_1" - top: "conv15_1" -} -layer { - name: "conv15_2" - type: "Convolution" - bottom: "conv15_1" - top: "conv15_2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv15_2/relu" - type: "ReLU" - bottom: "conv15_2" - top: "conv15_2" -} -layer { - name: "conv16_1" - type: "Convolution" - bottom: "conv15_2" - top: "conv16_1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 128 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv16_1/relu" - type: "ReLU" - bottom: "conv16_1" - top: "conv16_1" -} -layer { - name: "conv16_2" - type: "Convolution" - bottom: "conv16_1" - top: "conv16_2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 256 - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv16_2/relu" - type: "ReLU" - bottom: "conv16_2" - top: "conv16_2" -} -layer { - name: "conv17_1" - type: "Convolution" - bottom: "conv16_2" - top: "conv17_1" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 64 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv17_1/relu" - type: "ReLU" - bottom: "conv17_1" - top: "conv17_1" -} -layer { - name: "conv17_2" - type: "Convolution" - bottom: "conv17_1" - top: "conv17_2" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 128 - pad: 1 - kernel_size: 3 - stride: 2 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv17_2/relu" - type: "ReLU" - bottom: "conv17_2" - top: "conv17_2" -} -layer { - name: "conv11_mbox_loc" - type: "Convolution" - bottom: "conv11" - top: "conv11_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 12 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv11_mbox_loc_perm" - type: "Permute" - bottom: "conv11_mbox_loc" - top: "conv11_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv11_mbox_loc_flat" - type: "Flatten" - bottom: "conv11_mbox_loc_perm" - top: "conv11_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv11_mbox_conf" - type: "Convolution" - bottom: "conv11" - top: "conv11_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 63 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv11_mbox_conf_perm" - type: "Permute" - bottom: "conv11_mbox_conf" - top: "conv11_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv11_mbox_conf_flat" - type: "Flatten" - bottom: "conv11_mbox_conf_perm" - top: "conv11_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv11_mbox_priorbox" - type: "PriorBox" - bottom: "conv11" - bottom: "data" - top: "conv11_mbox_priorbox" - prior_box_param { - min_size: 60.0 - aspect_ratio: 2.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv13_mbox_loc" - type: "Convolution" - bottom: "conv13" - top: "conv13_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv13_mbox_loc_perm" - type: "Permute" - bottom: "conv13_mbox_loc" - top: "conv13_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv13_mbox_loc_flat" - type: "Flatten" - bottom: "conv13_mbox_loc_perm" - top: "conv13_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv13_mbox_conf" - type: "Convolution" - bottom: "conv13" - top: "conv13_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv13_mbox_conf_perm" - type: "Permute" - bottom: "conv13_mbox_conf" - top: "conv13_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv13_mbox_conf_flat" - type: "Flatten" - bottom: "conv13_mbox_conf_perm" - top: "conv13_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv13_mbox_priorbox" - type: "PriorBox" - bottom: "conv13" - bottom: "data" - top: "conv13_mbox_priorbox" - prior_box_param { - min_size: 105.0 - max_size: 150.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv14_2_mbox_loc" - type: "Convolution" - bottom: "conv14_2" - top: "conv14_2_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv14_2_mbox_loc_perm" - type: "Permute" - bottom: "conv14_2_mbox_loc" - top: "conv14_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv14_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv14_2_mbox_loc_perm" - top: "conv14_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv14_2_mbox_conf" - type: "Convolution" - bottom: "conv14_2" - top: "conv14_2_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv14_2_mbox_conf_perm" - type: "Permute" - bottom: "conv14_2_mbox_conf" - top: "conv14_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv14_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv14_2_mbox_conf_perm" - top: "conv14_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv14_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv14_2" - bottom: "data" - top: "conv14_2_mbox_priorbox" - prior_box_param { - min_size: 150.0 - max_size: 195.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv15_2_mbox_loc" - type: "Convolution" - bottom: "conv15_2" - top: "conv15_2_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv15_2_mbox_loc_perm" - type: "Permute" - bottom: "conv15_2_mbox_loc" - top: "conv15_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv15_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv15_2_mbox_loc_perm" - top: "conv15_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv15_2_mbox_conf" - type: "Convolution" - bottom: "conv15_2" - top: "conv15_2_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv15_2_mbox_conf_perm" - type: "Permute" - bottom: "conv15_2_mbox_conf" - top: "conv15_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv15_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv15_2_mbox_conf_perm" - top: "conv15_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv15_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv15_2" - bottom: "data" - top: "conv15_2_mbox_priorbox" - prior_box_param { - min_size: 195.0 - max_size: 240.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv16_2_mbox_loc" - type: "Convolution" - bottom: "conv16_2" - top: "conv16_2_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv16_2_mbox_loc_perm" - type: "Permute" - bottom: "conv16_2_mbox_loc" - top: "conv16_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv16_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv16_2_mbox_loc_perm" - top: "conv16_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv16_2_mbox_conf" - type: "Convolution" - bottom: "conv16_2" - top: "conv16_2_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv16_2_mbox_conf_perm" - type: "Permute" - bottom: "conv16_2_mbox_conf" - top: "conv16_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv16_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv16_2_mbox_conf_perm" - top: "conv16_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv16_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv16_2" - bottom: "data" - top: "conv16_2_mbox_priorbox" - prior_box_param { - min_size: 240.0 - max_size: 285.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "conv17_2_mbox_loc" - type: "Convolution" - bottom: "conv17_2" - top: "conv17_2_mbox_loc" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 24 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv17_2_mbox_loc_perm" - type: "Permute" - bottom: "conv17_2_mbox_loc" - top: "conv17_2_mbox_loc_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv17_2_mbox_loc_flat" - type: "Flatten" - bottom: "conv17_2_mbox_loc_perm" - top: "conv17_2_mbox_loc_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv17_2_mbox_conf" - type: "Convolution" - bottom: "conv17_2" - top: "conv17_2_mbox_conf" - param { - lr_mult: 1.0 - decay_mult: 1.0 - } - param { - lr_mult: 2.0 - decay_mult: 0.0 - } - convolution_param { - num_output: 126 - kernel_size: 1 - weight_filler { - type: "msra" - } - bias_filler { - type: "constant" - value: 0.0 - } - } -} -layer { - name: "conv17_2_mbox_conf_perm" - type: "Permute" - bottom: "conv17_2_mbox_conf" - top: "conv17_2_mbox_conf_perm" - permute_param { - order: 0 - order: 2 - order: 3 - order: 1 - } -} -layer { - name: "conv17_2_mbox_conf_flat" - type: "Flatten" - bottom: "conv17_2_mbox_conf_perm" - top: "conv17_2_mbox_conf_flat" - flatten_param { - axis: 1 - } -} -layer { - name: "conv17_2_mbox_priorbox" - type: "PriorBox" - bottom: "conv17_2" - bottom: "data" - top: "conv17_2_mbox_priorbox" - prior_box_param { - min_size: 285.0 - max_size: 300.0 - aspect_ratio: 2.0 - aspect_ratio: 3.0 - flip: true - clip: false - variance: 0.1 - variance: 0.1 - variance: 0.2 - variance: 0.2 - offset: 0.5 - } -} -layer { - name: "mbox_loc" - type: "Concat" - bottom: "conv11_mbox_loc_flat" - bottom: "conv13_mbox_loc_flat" - bottom: "conv14_2_mbox_loc_flat" - bottom: "conv15_2_mbox_loc_flat" - bottom: "conv16_2_mbox_loc_flat" - bottom: "conv17_2_mbox_loc_flat" - top: "mbox_loc" - concat_param { - axis: 1 - } -} -layer { - name: "mbox_conf" - type: "Concat" - bottom: "conv11_mbox_conf_flat" - bottom: "conv13_mbox_conf_flat" - bottom: "conv14_2_mbox_conf_flat" - bottom: "conv15_2_mbox_conf_flat" - bottom: "conv16_2_mbox_conf_flat" - bottom: "conv17_2_mbox_conf_flat" - top: "mbox_conf" - concat_param { - axis: 1 - } -} -layer { - name: "mbox_priorbox" - type: "Concat" - bottom: "conv11_mbox_priorbox" - bottom: "conv13_mbox_priorbox" - bottom: "conv14_2_mbox_priorbox" - bottom: "conv15_2_mbox_priorbox" - bottom: "conv16_2_mbox_priorbox" - bottom: "conv17_2_mbox_priorbox" - top: "mbox_priorbox" - concat_param { - axis: 2 - } -} -layer { - name: "mbox_conf_reshape" - type: "Reshape" - bottom: "mbox_conf" - top: "mbox_conf_reshape" - reshape_param { - shape { - dim: 0 - dim: -1 - dim: 21 - } - } -} -layer { - name: "mbox_conf_softmax" - type: "Softmax" - bottom: "mbox_conf_reshape" - top: "mbox_conf_softmax" - softmax_param { - axis: 2 - } -} -layer { - name: "mbox_conf_flatten" - type: "Flatten" - bottom: "mbox_conf_softmax" - top: "mbox_conf_flatten" - flatten_param { - axis: 1 - } -} -layer { - name: "detection_out" - type: "DetectionOutput" - bottom: "mbox_loc" - bottom: "mbox_conf_flatten" - bottom: "mbox_priorbox" - top: "detection_out" - include { - phase: TEST - } - detection_output_param { - num_classes: 21 - share_location: true - background_label_id: 0 - nms_param { - nms_threshold: 0.45 - top_k: 100 - } - code_type: CENTER_SIZE - keep_top_k: 100 - confidence_threshold: 0.25 - } -} diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/requirements.txt b/example-apps/ROBO/aPaaS/Obj_Detection_service/requirements.txt deleted file mode 100644 index 9e7cae3..0000000 --- a/example-apps/ROBO/aPaaS/Obj_Detection_service/requirements.txt +++ /dev/null @@ -1,21 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -flask -requests -flask_sslify -opencv-python -flask_cors diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/run.py b/example-apps/ROBO/aPaaS/Obj_Detection_service/run.py deleted file mode 100644 index 1f14088..0000000 --- a/example-apps/ROBO/aPaaS/Obj_Detection_service/run.py +++ /dev/null @@ -1,25 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -import logging -from logging.handlers import RotatingFileHandler -from detection.obj_detection_service import start_server - -if __name__ == '__main__': - handler = RotatingFileHandler('/usr/app/log/obj_detection.log', - maxBytes=10000000, backupCount=10) - handler.setLevel(logging.INFO) - start_server(handler) diff --git a/example-apps/ROBO/aPaaS/Obj_Detection_service/test/resources/img.jpeg b/example-apps/ROBO/aPaaS/Obj_Detection_service/test/resources/img.jpeg deleted file mode 100755 index 483e586..0000000 Binary files a/example-apps/ROBO/aPaaS/Obj_Detection_service/test/resources/img.jpeg and /dev/null differ diff --git a/example-apps/ROBO/aPaaS/README.md b/example-apps/ROBO/aPaaS/README.md deleted file mode 100644 index c8d9838..0000000 --- a/example-apps/ROBO/aPaaS/README.md +++ /dev/null @@ -1,6 +0,0 @@ -# example-apps aPaaS for obj-detection - -#### 介绍 -aPaaS based on EdgeGallery - - diff --git a/example-apps/ROBO/backup_BE/backup-k8s.yaml b/example-apps/ROBO/backup_BE/backup-k8s.yaml deleted file mode 100644 index 782ac75..0000000 --- a/example-apps/ROBO/backup_BE/backup-k8s.yaml +++ /dev/null @@ -1,92 +0,0 @@ -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. ---- -apiVersion: apps/v1 -kind: Deployment -metadata: - name: backup-be -spec: - replicas: 1 - selector: - matchLabels: - app: backup - template: - metadata: - labels: - app: backup - spec: - volumes: - - name: task-pv-storage - persistentVolumeClaim: - claimName: task-pv-claim - containers: - - name: backup-be - image: ealtedge/robo-be - env: - - name: KUBE_CONFIG - value: "/usr/app/.kube/config" - - name: HOSTIP - value: "10.10.0.54" - - ports: - - containerPort: 8091 - volumeMounts: - - name: task-pv-storage - mountPath: "/usr/app/.kube/" - -# PV with host path of kubeconfig in VM ---- -apiVersion: v1 -kind: PersistentVolume -metadata: - name: task-pv-volume - labels: - type: local -spec: - storageClassName: manual - capacity: - storage: 1Gi - accessModes: - - ReadWriteOnce - hostPath: - path: "/root/.kube/" - -# pvc ---- -apiVersion: v1 -kind: PersistentVolumeClaim -metadata: - name: task-pv-claim -spec: - storageClassName: manual - accessModes: - - ReadWriteOnce - resources: - requests: - storage: 1Gi - -# backup-be serice ---- -apiVersion: v1 -kind: Service -metadata: - name: backup-be -spec: - selector: - app: backup - type: NodePort - ports: - - port: 8091 - name: service-port - nodePort: 30091 diff --git a/example-apps/ROBO/backup_BE/backup_be.iml b/example-apps/ROBO/backup_BE/backup_be.iml deleted file mode 100644 index 78b2cc5..0000000 --- a/example-apps/ROBO/backup_BE/backup_be.iml +++ /dev/null @@ -1,2 +0,0 @@ - - \ No newline at end of file diff --git a/example-apps/ROBO/backup_BE/docker/Dockerfile b/example-apps/ROBO/backup_BE/docker/Dockerfile deleted file mode 100644 index 75d01ba..0000000 --- a/example-apps/ROBO/backup_BE/docker/Dockerfile +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# FROM swr.ap-southeast-1.myhuaweicloud.com/edgegallery/openjdk:8u201-jdk-alpine - -# currently this pod is running as root user due to this need to communicate with host shell.config -# in future this can be further checked with adding some level restrictions to user -FROM openjdk:11-jre-slim - -ENV JAVA_HOME /usr/lib/jvm/java-11-openjdk-amd64/bin/java - -ENV APP_FILE backup.jar -ENV APP_HOME /usr/app - -# Create the home directory for the new app user. -RUN mkdir -p /usr/app -RUN mkdir -p /usr/app/bin - -# Create an app user so our program doesn't run as root. -RUN apt-get update && apt-get install -y sshpass - -WORKDIR $APP_HOME - -RUN chmod 750 $APP_HOME &&\ - chmod -R 550 $APP_HOME/bin &&\ - mkdir -p -m 750 $APP_HOME/config &&\ - mkdir -p -m 750 $APP_HOME/log &&\ - mkdir -p -m 750 $APP_HOME/packages &&\ - mkdir -p -m 700 $APP_HOME/ssl &&\ - mkdir -p -m 700 $APP_HOME/.kube - -COPY /target/*.jar $APP_HOME/bin - -EXPOSE 8091 - -# Change to the app user. - -# Execute script & application -ENTRYPOINT ["sh", "-c"] -CMD ["exec java -jar ./bin/backup.jar"] \ No newline at end of file diff --git a/example-apps/ROBO/backup_BE/pom.xml b/example-apps/ROBO/backup_BE/pom.xml deleted file mode 100644 index a6a1958..0000000 --- a/example-apps/ROBO/backup_BE/pom.xml +++ /dev/null @@ -1,140 +0,0 @@ - - - 4.0.0 - - org.example - backup_be - 1.0-SNAPSHOT - - 1.8 - 1.8 - - jar - - org.springframework.boot - spring-boot-starter-parent - 2.3.0.RELEASE - - - - - org.springframework.boot - spring-boot-starter-web - - - org.springframework - spring-core - 5.2.12.RELEASE - - - org.apache.logging.log4j - log4j-core - 2.13.2 - - - org.apache.logging.log4j - log4j-api - 2.13.2 - - - commons-logging - commons-logging - 1.1.1 - - - - io.kubernetes - client-java-api - 5.0.0 - - - io.kubernetes - client-java - 5.0.0 - compile - - - org.springframework.boot - spring-boot - 2.3.8.RELEASE - - - org.springframework.boot - spring-boot-autoconfigure - 2.3.8.RELEASE - - - org.springframework - spring-web - 5.2.12.RELEASE - - - javax.validation - validation-api - 2.0.1.Final - - - javax.ws.rs - javax.ws.rs-api - 2.1.1 - - - org.hibernate.validator - hibernate-validator - 6.1.5.Final - - - org.slf4j - slf4j-api - 1.7.5 - - - org.slf4j - slf4j-log4j12 - 1.7.5 - - - org.projectlombok - lombok - 1.18.10 - - - - - backup - - - org.springframework.boot - spring-boot-maven-plugin - 2.3.8.RELEASE - - - org.apache.maven.plugins - maven-compiler-plugin - 3.8.1 - - - - org.apache.maven.plugins - maven-jar-plugin - 3.2.0 - - - - true - lib/ - org.edgegallery.example_app.backupApplication - - - - - - - - \ No newline at end of file diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/backupApplication.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/backupApplication.java deleted file mode 100644 index 33b79aa..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/backupApplication.java +++ /dev/null @@ -1,42 +0,0 @@ -package org.edgegallery.example_app; - - -import io.kubernetes.client.ApiException; -import io.kubernetes.client.Configuration; -import io.kubernetes.client.apis.CoreV1Api; -import io.kubernetes.client.models.V1PersistentVolumeClaim; -import io.kubernetes.client.models.V1PersistentVolumeClaimList; -import io.kubernetes.client.util.ClientBuilder; -import io.kubernetes.client.util.KubeConfig; -import java.util.ArrayList; -import java.util.List; -import org.edgegallery.example_app.model.Pvcs; -import org.springframework.boot.SpringApplication; -import org.springframework.boot.autoconfigure.SpringBootApplication; -import io.kubernetes.client.ApiClient; -import org.springframework.context.annotation.Bean; -import java.io.FileReader; -import java.io.IOException; - -@SpringBootApplication -public class backupApplication { - - @Bean - public static void apiclient() throws IOException { - // file path to your KubeConfig - //String homePath = System.getenv("HOME"); - //String kubeConfigPath = homePath + "/.kube/config"; - String kubeConfigPath = System.getenv("KUBE_CONFIG"); - - // loading the out-of-cluster config, a kubeconfig from file-system - ApiClient client = - ClientBuilder.kubeconfig(KubeConfig.loadKubeConfig(new FileReader(kubeConfigPath))).build(); - - // set the global default api-client to the in-cluster one from above - Configuration.setDefaultApiClient(client); - } - - public static void main(String[] args) throws IOException { - SpringApplication.run(backupApplication.class, args); - } -} \ No newline at end of file diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/common/Constants.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/common/Constants.java deleted file mode 100644 index 6564e99..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/common/Constants.java +++ /dev/null @@ -1,5 +0,0 @@ -package org.edgegallery.example_app.common; - -public class Constants { - public static final String NAME_REGEX = "^[a-zA-Z0-9]*$|^[a-zA-Z0-9][a-zA-Z0-9_\\-]*[a-zA-Z0-9]$"; -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/controller/backupController.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/controller/backupController.java deleted file mode 100644 index 7d7a263..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/controller/backupController.java +++ /dev/null @@ -1,88 +0,0 @@ -package org.edgegallery.example_app.controller; - -import io.swagger.annotations.ApiOperation; -import io.swagger.annotations.ApiParam; -import io.swagger.annotations.ApiResponse; -import io.swagger.annotations.ApiResponses; -import java.util.List; -import javax.validation.Valid; -import javax.validation.constraints.NotNull; -import javax.ws.rs.core.MediaType; -import org.hibernate.validator.constraints.Length; -import org.edgegallery.example_app.model.EALTEdgeBackupRestore; -import org.edgegallery.example_app.service.backupServiceHandler; -import org.edgegallery.example_app.service.createParam; -import org.edgegallery.example_app.service.createParamRestore; -import org.springframework.beans.factory.annotation.Autowired; -import org.springframework.http.ResponseEntity; -import org.springframework.stereotype.Controller; -import org.springframework.validation.annotation.Validated; -import org.springframework.web.bind.annotation.CrossOrigin; -import org.springframework.web.bind.annotation.GetMapping; -import org.springframework.web.bind.annotation.PostMapping; -import org.springframework.web.bind.annotation.RequestBody; -import org.springframework.web.bind.annotation.RequestMapping; -import org.springframework.web.bind.annotation.RequestPart; -import org.springframework.web.bind.annotation.RestController; - -@CrossOrigin -@Controller -@RequestMapping("/v1/robo") -@Validated -public class backupController { - private static final int MAX_COMMON_STRING_LENGTH = 255; - - private static final int MAX_DETAILS_STRING_LENGTH = 1024; - - @Autowired - private backupServiceHandler BackupServiceHandler; - - @GetMapping(value = "/backup-restore", produces = MediaType.APPLICATION_JSON) - @ApiOperation(value = "get backup and restore tables.", response = EALTEdgeBackupRestore.class, - responseContainer = "List") - @ApiResponses(value = { - @ApiResponse(code = 404, message = "microservice not found", response = String.class), - @ApiResponse(code = 415, message = "Unprocessable " + "MicroServiceInfo Entity ", - response = String.class), - @ApiResponse(code = 500, message = "resource grant " + "error", response = String.class) - }) - public ResponseEntity getBackupRestoreDetails() { - return BackupServiceHandler.getBackupRestoreDetails(); - } - - @PostMapping(value = "/backup", produces = MediaType.APPLICATION_JSON) - @ApiOperation(value = "create backup.", response = String.class) - @ApiResponses(value = { - @ApiResponse(code = 404, message = "microservice not found", response = String.class), - @ApiResponse(code = 415, message = "Unprocessable " + "MicroServiceInfo Entity ", - response = String.class), - @ApiResponse(code = 500, message = "resource grant " + "error", response = String.class) - }) - public ResponseEntity getBackupRestoreDetails(@ApiParam(value = "create backup instance") - @Valid @RequestBody createParam CreateParam) { - BackupServiceHandler.createBackup(CreateParam.getBackupName(), CreateParam.getNamespace()); - return ResponseEntity.ok("create backup success."); - } - - -/* @PostMapping(value = "/v1/robo/backup", produces = MediaType.APPLICATION_JSON) - public ResponseEntity getBackupRestoreDetails( @ApiParam(value = "create backup instance") - @Valid @RequestBody createParam CreateParam) { - BackupServiceHandler.createBackup(CreateParam.getBackupName(), CreateParam.getNamespace()); - return ResponseEntity.ok("create backup success."); - }*/ - - @PostMapping(value = "/restore", produces = MediaType.APPLICATION_JSON) - @ApiOperation(value = "create restore.", response = String.class) - @ApiResponses(value = { - @ApiResponse(code = 404, message = "microservice not found", response = String.class), - @ApiResponse(code = 415, message = "Unprocessable " + "MicroServiceInfo Entity ", - response = String.class), - @ApiResponse(code = 500, message = "resource grant " + "error", response = String.class) - }) - public ResponseEntity createRestore(@ApiParam(value = "create restore instance") - @Valid @RequestBody createParamRestore CreateParam) { - BackupServiceHandler.createRestore(CreateParam.getRestoreName(), CreateParam.getBackupName()); - return ResponseEntity.ok("create restore success."); - } -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/controller/k8sController.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/controller/k8sController.java deleted file mode 100644 index cbe169f..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/controller/k8sController.java +++ /dev/null @@ -1,93 +0,0 @@ -package org.edgegallery.example_app.controller; - -import io.swagger.annotations.ApiOperation; -import io.swagger.annotations.ApiResponse; -import io.swagger.annotations.ApiResponses; -import javax.ws.rs.core.MediaType; -import org.edgegallery.example_app.model.*; -import io.kubernetes.client.ApiException; -import io.kubernetes.client.apis.CoreV1Api; -import io.kubernetes.client.models.*; -import org.checkerframework.common.reflection.qual.GetMethod; -import org.springframework.beans.factory.annotation.Autowired; -import org.springframework.http.HttpStatus; -import org.springframework.http.ResponseEntity; -import org.springframework.stereotype.Controller; -import org.springframework.validation.annotation.Validated; -import org.springframework.web.bind.annotation.*; -import org.edgegallery.example_app.service.podService; -import org.edgegallery.example_app.service.pvcService; -import org.edgegallery.example_app.service.deleteNamespace; - - -import java.util.ArrayList; -import java.util.List; - -@CrossOrigin -@Controller -@RequestMapping("/v1/robo") -@Validated -public class k8sController { - - @Autowired - EALTEdgePodsPvcs eALTEdgePodsPvcs; - - @Autowired - pvcService PvcService; - - @Autowired - podService PodService; - - private final deleteNamespace DeleteNamespace; - - @Autowired - public k8sController(deleteNamespace DeleteNamespace) { - this.DeleteNamespace = DeleteNamespace; - } - - @GetMapping(path = "/apps-pvcs") - @ApiOperation(value = "get pod and pvcs tables.", response = EALTEdgePodsPvcs.class, - responseContainer = "List") - @ApiResponses(value = { - @ApiResponse(code = 404, message = "microservice not found", response = String.class), - @ApiResponse(code = 415, message = "Unprocessable " + "MicroServiceInfo Entity ", - response = String.class), - @ApiResponse(code = 500, message = "resource grant " + "error", response = String.class) - }) - public ResponseEntity getAllPodPvcList() throws ApiException { - - List podlistElement; - podlistElement = PodService.getPodsList(); - if (podlistElement.isEmpty()) { - System.out.println("Pod list is null"); - } - - List pvcslistElement; - pvcslistElement = PvcService.getPvcsList(); - if (pvcslistElement.isEmpty()) { - System.out.println("Pvcs list is null"); - } - - eALTEdgePodsPvcs.setAppsData(podlistElement); - eALTEdgePodsPvcs.setPvcData(pvcslistElement); - - return new ResponseEntity(eALTEdgePodsPvcs, HttpStatus.OK); - } - - @GetMapping(path = "/disaster") - @ApiOperation(value = "delete namespace. in k8s", response = String.class, - responseContainer = "List") - @ApiResponses(value = { - @ApiResponse(code = 404, message = "microservice not found", response = String.class), - @ApiResponse(code = 415, message = "Unprocessable " + "MicroServiceInfo Entity ", - response = String.class), - @ApiResponse(code = 500, message = "resource grant " + "error", response = String.class) - }) - public ResponseEntity createDisaster() throws ApiException { - - DeleteNamespace.deleteNS(); - System.out.println("k8s 1 api is ok"); - - return ResponseEntity.ok("success"); - } -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/EALTEdgeBackup.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/EALTEdgeBackup.java deleted file mode 100644 index 6ec2dc0..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/EALTEdgeBackup.java +++ /dev/null @@ -1,20 +0,0 @@ -package org.edgegallery.example_app.model; - - -import lombok.AllArgsConstructor; -import lombok.Getter; -import lombok.NoArgsConstructor; -import lombok.Setter; -import lombok.ToString; - -@Getter -@Setter -@NoArgsConstructor -@AllArgsConstructor -public class EALTEdgeBackup { - private String name; - private String status; - private String errors; - private String warnings; - private String created; -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/EALTEdgeBackupRestore.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/EALTEdgeBackupRestore.java deleted file mode 100644 index b1b6ae1..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/EALTEdgeBackupRestore.java +++ /dev/null @@ -1,21 +0,0 @@ -package org.edgegallery.example_app.model; - -import java.util.List; -import lombok.AllArgsConstructor; -import lombok.Getter; -import lombok.NoArgsConstructor; -import lombok.Setter; -import lombok.ToString; -import org.springframework.stereotype.Service; - -@Getter -@Setter -@NoArgsConstructor -@AllArgsConstructor -@Service -public class EALTEdgeBackupRestore { - - private List backupsData; - private List restoresData; - -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/EALTEdgePodsPvcs.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/EALTEdgePodsPvcs.java deleted file mode 100644 index 0d3a2c2..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/EALTEdgePodsPvcs.java +++ /dev/null @@ -1,21 +0,0 @@ -package org.edgegallery.example_app.model; - -import java.util.ArrayList; -import java.util.List; -import lombok.AllArgsConstructor; -import lombok.Getter; -import lombok.NoArgsConstructor; -import lombok.Setter; -import lombok.ToString; -import org.springframework.stereotype.Service; - -@Getter -@Setter -@NoArgsConstructor -@AllArgsConstructor -@Service -public class EALTEdgePodsPvcs { - - private List appsData; - private List pvcData; -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/EALTEdgeRestore.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/EALTEdgeRestore.java deleted file mode 100644 index fa321e8..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/EALTEdgeRestore.java +++ /dev/null @@ -1,16 +0,0 @@ -package org.edgegallery.example_app.model; - -import lombok.AllArgsConstructor; -import lombok.Getter; -import lombok.NoArgsConstructor; -import lombok.Setter; - -@Getter -@Setter -@NoArgsConstructor -@AllArgsConstructor -public class EALTEdgeRestore { - private String name; - private String backup; - private String status; -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/Pod.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/Pod.java deleted file mode 100644 index 0aa454f..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/Pod.java +++ /dev/null @@ -1,96 +0,0 @@ -package org.edgegallery.example_app.model; - -public class Pod { - private String namespace; - private String name; - private String ready; - private String status; - private String restarts; - private String age; - private String ip; - private String node; - private String nominated; - private String readiness; - - - public String getNamespace() { - return namespace; - } - - public void setNamespace(String namespace) { - this.namespace = namespace; - } - - public String getName() { - return name; - } - - public void setName(String name) { - this.name = name; - } - - public String getReady() { - return ready; - } - - public void setReady(String ready) { - this.ready = ready; - } - - public String getStatus() { - return status; - } - - public void setStatus(String status) { - this.status = status; - } - - public String getRestarts() { - return restarts; - } - - public void setRestarts(String restarts) { - this.restarts = restarts; - } - - public String getAge() { - return age; - } - - public void setAge(String age) { - this.age = age; - } - - public String getIp() { - return ip; - } - - public void setIp(String ip) { - this.ip = ip; - } - - public String getNode() { - return node; - } - - public void setNode(String node) { - this.node = node; - } - - public String getNominated() { - return nominated; - } - - public void setNominated(String nominated) { - this.nominated = nominated; - } - - public String getReadiness() { - return readiness; - } - - public void setReadiness(String readiness) { - this.readiness = readiness; - } - -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/Pvcs.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/Pvcs.java deleted file mode 100644 index 72b9855..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/model/Pvcs.java +++ /dev/null @@ -1,22 +0,0 @@ -package org.edgegallery.example_app.model; - -import lombok.AllArgsConstructor; -import lombok.Getter; -import lombok.NoArgsConstructor; -import lombok.Setter; - -@Getter -@Setter -@NoArgsConstructor -@AllArgsConstructor -public class Pvcs { - private String namespace; - private String name; - private String status; - private String volume; - private String capacity; - private String accessmodes; - private String storageclass; - private String age; - private String volumemode; -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/backupService.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/backupService.java deleted file mode 100644 index 877835c..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/backupService.java +++ /dev/null @@ -1,42 +0,0 @@ -package org.edgegallery.example_app.service; - -import java.util.ArrayList; -import java.util.Arrays; -import java.util.LinkedList; -import java.util.List; -import java.util.StringTokenizer; - -import org.apache.commons.lang.StringUtils; -import org.edgegallery.example_app.model.EALTEdgeBackup; -import org.springframework.beans.factory.annotation.Autowired; -import org.edgegallery.example_app.util.ShellCommand; -import org.springframework.stereotype.Service; -import org.edgegallery.example_app.common.*; - -@Service -public class backupService { - - @Autowired - private ShellCommand ShellCommands; - - public String create_backup(String backupname, String namespace) { - String ip = System.getenv("HOSTIP"); - String command = - "sshpass ssh root@" + ip + " velero backup create " + backupname + " --include-namespaces " + namespace; - - String output = ShellCommands.executeCommand(command); - - System.out.println(output); - return "success"; - } - - public List getBackupTables() { - String ip = System.getenv("HOSTIP"); - String command = "sshpass ssh root@" + ip + " velero get backups"; - - List backupsList = new ArrayList(); - backupsList = ShellCommands.executeBackupCommand(command); - - return backupsList; - } -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/backupServiceHandler.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/backupServiceHandler.java deleted file mode 100644 index 5e01c02..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/backupServiceHandler.java +++ /dev/null @@ -1,55 +0,0 @@ -package org.edgegallery.example_app.service; - -import java.util.List; -import org.edgegallery.example_app.model.EALTEdgeBackup; -import org.edgegallery.example_app.model.EALTEdgeBackupRestore; -import org.edgegallery.example_app.model.EALTEdgeRestore; -import org.springframework.beans.factory.annotation.Autowired; -import org.springframework.http.ResponseEntity; -import org.springframework.stereotype.Service; - -@Service -public class backupServiceHandler { - - @Autowired - private backupService BackupService; - - @Autowired - private restoreService RestoreService; - - /** - * get back/restore tables. - * @return - */ - public ResponseEntity getBackupRestoreDetails() { - - EALTEdgeBackupRestore ealtEdgeBackupRestore = new EALTEdgeBackupRestore(); - - List backupsList = BackupService.getBackupTables(); - List restoresList = RestoreService.getRestoreTables(); - - ealtEdgeBackupRestore.setBackupsData(backupsList); - ealtEdgeBackupRestore.setRestoresData(restoresList); - return ResponseEntity.ok(ealtEdgeBackupRestore); - } - - /** - * create restore tables. - * @param restoreName restore name. - * @param backupName backup name. - * @return - */ - public String createRestore(String restoreName, String backupName){ - return RestoreService.create_restore(restoreName, backupName); - } - - /** - * create backup tables. - * @param backupName restore name. - * @param namespaces backup name. - * @return - */ - public String createBackup(String backupName, String namespaces){ - return BackupService.create_backup(backupName, namespaces); - } -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/createParam.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/createParam.java deleted file mode 100644 index 2bf8244..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/createParam.java +++ /dev/null @@ -1,49 +0,0 @@ -package org.edgegallery.example_app.service; - -import javax.validation.constraints.NotEmpty; -import javax.validation.constraints.Pattern; -import javax.validation.constraints.Size; -import org.springframework.validation.annotation.Validated; - -import static org.edgegallery.example_app.common.Constants.NAME_REGEX; - -import javax.validation.constraints.NotEmpty; -import javax.validation.constraints.Pattern; -import javax.validation.constraints.Size; -import lombok.AllArgsConstructor; -import lombok.Getter; -import lombok.NoArgsConstructor; -import lombok.Setter; -import lombok.ToString; -import org.springframework.validation.annotation.Validated; - -/** - * Create instance input schema. - */ -@Validated -@Getter -@Setter -@ToString -@NoArgsConstructor -@AllArgsConstructor -public class createParam { - - @NotEmpty(message = "backup name is mandatory") - @Size(max = 64) - @Pattern(regexp = NAME_REGEX) - private String backupName; - - @NotEmpty(message = "namespace is mandatory") - @Size(max = 64) - @Pattern(regexp = NAME_REGEX) - private String namespace; - -/* public String getBackupName() { - return backupName; - } - - public void setBackupName(String backupName) { - this.backupName = backupName; - }*/ -} - diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/createParamRestore.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/createParamRestore.java deleted file mode 100644 index 19a52d2..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/createParamRestore.java +++ /dev/null @@ -1,40 +0,0 @@ -package org.edgegallery.example_app.service; - - -import javax.validation.constraints.NotEmpty; -import javax.validation.constraints.Pattern; -import javax.validation.constraints.Size; -import org.springframework.validation.annotation.Validated; - -import static org.edgegallery.example_app.common.Constants.NAME_REGEX; - -import javax.validation.constraints.NotEmpty; -import javax.validation.constraints.Pattern; -import javax.validation.constraints.Size; -import lombok.AllArgsConstructor; -import lombok.Getter; -import lombok.NoArgsConstructor; -import lombok.Setter; -import lombok.ToString; -import org.springframework.validation.annotation.Validated; - -/** - * Create instance input schema. - */ -@Validated -@Getter -@Setter -@ToString -@NoArgsConstructor -@AllArgsConstructor -public class createParamRestore { - @NotEmpty(message = "namespace name is mandatory") - @Size(max = 64) - @Pattern(regexp = NAME_REGEX) - private String restoreName; - - @NotEmpty(message = "backup name is mandatory") - @Size(max = 64) - @Pattern(regexp = NAME_REGEX) - private String backupName; -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/deleteNamespace.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/deleteNamespace.java deleted file mode 100644 index 313484e..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/deleteNamespace.java +++ /dev/null @@ -1,8 +0,0 @@ -package org.edgegallery.example_app.service; - -import io.kubernetes.client.ApiException; - -public interface deleteNamespace { - - String deleteNS() throws ApiException; -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/deleteNamespaceImp.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/deleteNamespaceImp.java deleted file mode 100644 index ac70683..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/deleteNamespaceImp.java +++ /dev/null @@ -1,27 +0,0 @@ -package org.edgegallery.example_app.service; - -import io.kubernetes.client.ApiException; -import io.kubernetes.client.apis.CoreV1Api; -import io.kubernetes.client.models.V1PodList; -import io.kubernetes.client.models.V1Status; -import org.springframework.stereotype.Service; -import io.kubernetes.client.models.V1DeleteOptions; - -@Service -public class deleteNamespaceImp implements deleteNamespace { - - @Override - public String deleteNS() throws ApiException { - String namespace = "test"; - CoreV1Api api = new CoreV1Api(); - - // invokes the CoreV1Api client - //TODO: this API delete the namespace but it crash after execute. need to check some example for this API to - // cnfirm the para - api.deleteNamespace(namespace, null, new V1DeleteOptions(),null,null,null,null); - - System.out.println("k8s api is ok"); - - return "success"; - } -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/podService.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/podService.java deleted file mode 100644 index 1f28fdd..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/podService.java +++ /dev/null @@ -1,42 +0,0 @@ -package org.edgegallery.example_app.service; - -import io.kubernetes.client.ApiException; -import io.kubernetes.client.apis.CoreV1Api; -import io.kubernetes.client.models.V1Pod; -import io.kubernetes.client.models.V1PodList; -import java.util.ArrayList; -import java.util.List; -import org.edgegallery.example_app.model.Pod; -import org.springframework.stereotype.Service; - -@Service -public class podService { - - public List getPodsList() throws ApiException { - - Pod podDetail; - - // the CoreV1Api loads default api-client from global configuration. - CoreV1Api api = new CoreV1Api(); - - // invokes the CoreV1Api client - - V1PodList eliotPods = api.listPodForAllNamespaces(null, null, - null, null, null, null, null, null, null); - - List podlistElement = new ArrayList(); - - for (V1Pod item : eliotPods.getItems()) { - podDetail = new Pod(); - podDetail.setNamespace(item.getMetadata().getNamespace()); - podDetail.setName(item.getMetadata().getName()); - podDetail.setStatus(item.getStatus().getPhase()); - podDetail.setIp(item.getStatus().getPodIP()); - podDetail.setNode(item.getSpec().getNodeName()); - podDetail.setReadiness("null"); - podlistElement.add(podDetail); - } - - return podlistElement; - } -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/pvcService.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/pvcService.java deleted file mode 100644 index 061abde..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/pvcService.java +++ /dev/null @@ -1,60 +0,0 @@ -package org.edgegallery.example_app.service; - -import io.kubernetes.client.ApiException; -import io.kubernetes.client.apis.CoreV1Api; -import io.kubernetes.client.models.V1PersistentVolumeClaim; -import io.kubernetes.client.models.V1PersistentVolumeClaimList; - -import java.util.ArrayList; -import java.util.List; -import org.edgegallery.example_app.model.Pvcs; -import org.springframework.stereotype.Service; - -@Service -public class pvcService { - private Pvcs pvcsDetail; - - public List getPvcsList() { - String namespace = "default"; - CoreV1Api api = new CoreV1Api(); - V1PersistentVolumeClaimList list = null; - try { - /* list = api.listNamespacedPersistentVolumeClaim(namespace, null, null, null, - null, null, null, null, null, - null);*/ - list = api.listPersistentVolumeClaimForAllNamespaces(null, null, null, - null,null,null,null,null,null); - } catch (ApiException apie) { - System.err.println("Exception when calling CoreV1Api#listNamespacedPersistentVolumeClaim"); - apie.printStackTrace(); - System.exit(1); - } - - if (list == null) { - System.out.println("Inside- pvcs obj is null"); - } - List pvcslistElement = new ArrayList(); - - for (V1PersistentVolumeClaim item : list.getItems()) { - pvcsDetail = new Pvcs(); - pvcsDetail.setNamespace(item.getMetadata().getNamespace()); - pvcsDetail.setName(item.getMetadata().getName()); - pvcsDetail.setStatus(item.getStatus().getPhase()); - - pvcsDetail.setVolume(item.getSpec().getVolumeName()); - pvcsDetail.setVolumemode(item.getSpec().getVolumeMode()); - //TODO: getAccessModes return list of string, need to check - // pvcsDetail.setAccessmodes(item.getSpec().getAccessModes()); - pvcsDetail.setStorageclass(item.getSpec().getStorageClassName()); - - // pvcsDetail.setAge(item.getStatus().getConditions().getLastTransitionTime()); - //TODO: getCapacity is a map. need to get quantity from map and fill - //pvcsDetail.setCapacity(item.getStatus().getCapacity()); - pvcsDetail.setAge("null"); - pvcslistElement.add(pvcsDetail); - } - - return pvcslistElement; - } - -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/restoreService.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/restoreService.java deleted file mode 100644 index b981e9f..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/service/restoreService.java +++ /dev/null @@ -1,47 +0,0 @@ -package org.edgegallery.example_app.service; - -import java.util.ArrayList; -import java.util.Arrays; -import java.util.LinkedList; -import java.util.List; -import java.util.StringTokenizer; - -import org.apache.commons.lang.StringUtils; -import org.edgegallery.example_app.common.Constants; -import org.edgegallery.example_app.model.EALTEdgeBackup; -import org.edgegallery.example_app.model.EALTEdgeRestore; -import org.edgegallery.example_app.util.ShellCommand; -import org.springframework.beans.factory.annotation.Autowired; -import org.springframework.stereotype.Service; - -@Service -public class restoreService { - - @Autowired - ShellCommand shellCommand; - - public String create_restore(String restorename, String backupname) { - String ip = System.getenv("HOSTIP"); - String command = "sshpass ssh root@" + ip + " velero restore create " + restorename + " --from-backup " + - backupname; - - String output = shellCommand.executeCommand(command); - - System.out.println(output); - return "success"; - } - - /** - * get restore table and parse - * @return - */ - public List getRestoreTables() { - String ip = System.getenv("HOSTIP"); - String command = "sshpass ssh root@" + ip + " velero get restores"; - - List restoresList = new ArrayList(); - restoresList = shellCommand.executeRestoreCommand(command); - - return restoresList; - } -} diff --git a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/util/ShellCommand.java b/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/util/ShellCommand.java deleted file mode 100644 index 716e528..0000000 --- a/example-apps/ROBO/backup_BE/src/main/java/org/edgegallery/example_app/util/ShellCommand.java +++ /dev/null @@ -1,172 +0,0 @@ -package org.edgegallery.example_app.util; - -import java.io.BufferedReader; -import java.io.IOException; -import java.io.InputStream; -import java.io.InputStreamReader; -import java.util.ArrayList; -import java.util.Arrays; -import java.util.LinkedList; -import java.util.List; -import java.util.StringTokenizer; - -import org.apache.commons.lang.StringUtils; -import org.edgegallery.example_app.model.EALTEdgeBackup; -import org.edgegallery.example_app.model.EALTEdgeRestore; -import org.springframework.stereotype.Service; - - -@Service -public class ShellCommand { - - public String executeCommand(String command) { - - StringBuffer output = new StringBuffer(); - - Process p; - try { - - p = Runtime.getRuntime().exec(command); - p.waitFor(); - BufferedReader reader = new BufferedReader(new InputStreamReader(p.getInputStream())); - - String line = ""; - while ((line = reader.readLine())!= null) { - output.append(line + "\n"); - } - } - catch (Exception e) { - e.printStackTrace(); - } - return output.toString(); - - } - - public List executeBackupCommand(String command) { - - EALTEdgeBackup backup = new EALTEdgeBackup(); - List backupsList = new ArrayList(); - - try { - Process p; - p = Runtime.getRuntime().exec(command); - p.waitFor(); - BufferedReader reader = - new BufferedReader(new InputStreamReader(p.getInputStream())); - - String line = ""; - while ((line = reader.readLine())!= null) { - if(line.startsWith("NAME")) { - continue; - } - else { - backup = parseBackupResult(line); - backupsList.add(backup); - } - } - - } catch (Exception e) { - e.printStackTrace(); - } - return backupsList; - } - - public static EALTEdgeBackup parseBackupResult(String newstr){ - - EALTEdgeBackup backup = new EALTEdgeBackup(); - List al = new ArrayList(); - - StringTokenizer st = new StringTokenizer(newstr, " "); - StringBuffer sb = new StringBuffer(); - - while(st.hasMoreElements()) { - sb.append(st.nextElement()).append(" "); - } - - String newstrwithProperSpacing = sb.toString(); - String str[] = newstrwithProperSpacing.split(" "); - - str[4] = str[4] + str[5] + str[6] + str[7]; - - al = Arrays.asList(str); - - for(int i = 0; i < al.size(); i++) { - if( i == 0 ) { - backup.setName(al.get(i)); - } - if( i == 1) { - backup.setStatus(al.get(i)); - } - if( i == 2) { - backup.setErrors(al.get(i)); - } - if( i == 3){ - backup.setWarnings(al.get(i)); - } - if( i == 4) { - backup.setCreated(al.get(i)); - } - - } - return backup; - } - - public List executeRestoreCommand(String command) { - - EALTEdgeRestore restore = new EALTEdgeRestore(); - List restoresList = new ArrayList(); - - try { - Process p; - p = Runtime.getRuntime().exec(command); - p.waitFor(); - BufferedReader reader = - new BufferedReader(new InputStreamReader(p.getInputStream())); - - String line = ""; - while ((line = reader.readLine())!= null) { - if(line.startsWith("NAME")) { - continue; - } - else { - restore = parseRestoreResult(line); - restoresList.add(restore); - } - } - } catch (Exception e) { - e.printStackTrace(); - } - return restoresList; - } - - public static EALTEdgeRestore parseRestoreResult(String newstr){ - - EALTEdgeRestore restore = new EALTEdgeRestore(); - - StringTokenizer st = new StringTokenizer(newstr, " "); - StringBuffer sb = new StringBuffer(); - - while(st.hasMoreElements()) { - sb.append(st.nextElement()).append(" "); - } - - String newstrwithProperSpacing = sb.toString(); - String str[] = newstrwithProperSpacing.split(" "); - - List ll = new LinkedList(Arrays.asList(str)); - - for(int i = 0; i < ll.size(); i++) { - if( i == 0 ) { - restore.setName(ll.get(i)); - } - if( i == 1) { - restore.setBackup(ll.get(i)); - } - if( i == 2) { - restore.setStatus(ll.get(i)); - } - } - - return restore; - } -} diff --git a/example-apps/ROBO/backup_BE/src/main/resources/application.properties b/example-apps/ROBO/backup_BE/src/main/resources/application.properties deleted file mode 100755 index 59a2f3c..0000000 --- a/example-apps/ROBO/backup_BE/src/main/resources/application.properties +++ /dev/null @@ -1 +0,0 @@ -server.port = 8091 \ No newline at end of file diff --git a/example-apps/ROBO/deployment/SmartShelf_description.md b/example-apps/ROBO/deployment/SmartShelf_description.md deleted file mode 100644 index 0835c76..0000000 --- a/example-apps/ROBO/deployment/SmartShelf_description.md +++ /dev/null @@ -1,31 +0,0 @@ -# Smart Shelf Application - -This application is an entry level application to demonstrate Remote Office Branch Office usecase (ROBO) for Retail Enterprise for scenario wherein there is a retail chain comprising of multiple retail stores and a retail headquater. -The application provides Smart Shelf solution to monitor shelf's for a retail chain. - -Why Smart Shelf solution: -Smart Shelf is a technological breakthrough that enhances the overall shopping experience and improves a store’s operational efficiency in terms of stock misplacement and notify empty shelves. - -Smart shelf benefits: -- Smart Shelves can notify empty shelf for items when needed without requiring humans, making it easier to prevent - shortages of popular items at peak times. -- Smart Shelves allow retailers to make more informed decisions about stocking and inventory, notify the back-end - system about the existing quantity of items on the shelves, alert when product levels are running low and when items are misplaced. -- Provides access to the store or shelf information remotely, enables visibility of stock position, cross and up - selling of products. -- Better customer experience with proper inventory for each shelf. - -Available Solutions: -- Camera based with Video analytics solution -- weight sensors/optical sensors based solutions -This application is based on 1st solution - -Why Edge: -This use case demands for low latency, jitter for scenarios like theft detection, stock misplacement. -Currently available in-retail enterprise solutions, wherein in store deployment of compute/storage resources - are done, consumes store spaces and are not cost effective. 5G Telco edge based solution is a need here and this - application demonstrates the same. - -This application is developed as part of Akraino EALTEdge BP using EdgeGallery as an upstream project. Kindly refer - below link to get more insights about this application: -https://wiki.akraino.org/display/AK/EALT-EDGE+Landing+Application diff --git a/example-apps/ROBO/deployment/SmartShelf_icon.png b/example-apps/ROBO/deployment/SmartShelf_icon.png deleted file mode 100644 index def1751..0000000 Binary files a/example-apps/ROBO/deployment/SmartShelf_icon.png and /dev/null differ diff --git a/example-apps/ROBO/deployment/setup.md b/example-apps/ROBO/deployment/setup.md deleted file mode 100644 index f24116f..0000000 --- a/example-apps/ROBO/deployment/setup.md +++ /dev/null @@ -1,84 +0,0 @@ - -# Pre-Setup (Before Application Integration) -Step 1: docker login to akraino dockerhub -username/pwd - ealtedge/Huawei_akraino - -or - -pull images manually -docker pull ealtedge/obj-detection -docker pull ealtedge/inventory-be:v1.3 -docker pull ealtedge/robo-be -docker pull ealtedge/robo - -Step 2: pretrained model for obj detection -- copy pretrained model to Host machine where PCB containers are running - 1. cloen model from gitlab git clone https://gitlab.com/gauravagrawal/ealtedge/-/tree/master/ ROBO-Model - 2. create folder /root/model/ in host machine. - 3. scp all files in models cloned to above path. - -Step 3: Install Influx DB -- Install local path storage (if by default is not available on edge node) - 1. git clone https://github.com/rancher/local-path-provisioner.git - 2. cd local-path-provisioner - 3. helm install local-path --namespace kube-system ./deploy/chart/ --set storageClass.provisionerName=rancher.io/local --path --set storageClass.defaultClass=true --set storageClass.name=local-path - -- Install influx db - 1. create my-test namespace - 2. helm repo add influxdata https://influxdata.github.io/helm-charts - 3. helm upgrade -i influxdb influxdata/influxdb --set service.type=NodePort --namespace my-test - -Step 4: update influx db IP and nodeport in smart shelf App yaml -- update smart shelf yaml file - 1. update below enviorment variable in smartshelf-deployment.yaml: INFLUXDB_IP, INFLUXDB_PORT from Nodeport and Node IP for Influx DB in k3s cluster. - -Step 5: Install MINIO storage and create bucket -- Install minio storage server - 1. docker pull minio/minio - 2. docker run -d --name minio -p 9000:9000 -v data:/data minio/minio server /data - 3. docker exec -it minio cat /data/.minio.sys/config/config.json | egrep "(access|secret)_key" - 4. create credentials-velero file... -add values as : -[default] -aws_access_key_id=minioadmin -aws_secret_access_key=minioadmin - -- Install minio cleint and create bucket for backup - 1. docker pull minio/mc - 2. enter to client container to add server and create bucket: docker run -it --entrypoint=/bin/sh minio/mc - 3. first create server alias in mc client: "mc alias set edge_minio http://159.138.33.54:9000" - 4. then create bucket: mc mb edge_minio/mybucket - 5. then list bucket: mc ls edge_minio - -Step 6: Install Velero server and client -- Install velero client - 1. download binary velero amd64/arm64: https://github.com/justmeandopensource/kubernetes/blob/master/docs/setup-velero-notes.md - 2. move binary files to: /usr/local/bin - 3. test velero cli is working by exec velero cmd - -- Install velero server - 1. helm repo add vmware-tanzu https://vmware-tanzu.github.io/helm-charts - 2. kubectl create ns velero - 3. install velero using CLI: - velero install \ - --provider aws \ - --bucket mybucket \ - --secret-file ./credentials-velero \ - --plugins velero/velero-plugin-for-aws:v1.0.0 \ - --backup-location-config -region=minio,s3ForcePathStyle=true,s3Url=http://hostVMIP:9000 - -- Some Pre-Checks - - Check whether the config file is at location /root/.kube/ - - Check that ports are not already occupied - -Step 7: Post setup, cleanup velero -- Install velero resource - 1. kubectl delete namespace velero - 2. kubectl delete clusterrolebinding velero - 3. kubectl delete crds -l component=velero - - - - diff --git a/example-apps/ROBO/deployment/smartshelf-deployment.yaml b/example-apps/ROBO/deployment/smartshelf-deployment.yaml deleted file mode 100644 index 8ceb09d..0000000 --- a/example-apps/ROBO/deployment/smartshelf-deployment.yaml +++ /dev/null @@ -1,217 +0,0 @@ ---- -apiVersion: apps/v1 -kind: Deployment -metadata: - name: robo-deployment -spec: - replicas: 2 - selector: - matchLabels: - app: robo - template: - metadata: - labels: - app: robo - spec: - containers: - - name: robo - image: ealtedge/robo - ports: - - containerPort: 80 ---- -apiVersion: v1 -kind: Service -metadata: - name: robo-service -spec: - type: NodePort - selector: - app: robo - ports: - - protocol: "TCP" - port: 8000 - targetPort: 80 - nodePort: 30001 ---- -apiVersion: apps/v1 -kind: Deployment -metadata: - name: backup-be -spec: - replicas: 1 - selector: - matchLabels: - app: backup - template: - metadata: - labels: - app: backup - spec: - volumes: - - name: task-pv-storage - persistentVolumeClaim: - claimName: task-pv-claim - containers: - - name: backup-be - image: ealtedge/robo-be - env: - - name: KUBE_CONFIG - value: "/usr/app/.kube/config" - - name: HOSTIP - value: "159.138.129.53" - ports: - - containerPort: 8091 - volumeMounts: - - name: task-pv-storage - mountPath: "/usr/app/.kube/" ---- -apiVersion: v1 -kind: Service -metadata: - name: backup-be -spec: - selector: - app: backup - type: NodePort - ports: - - port: 8091 - name: service-port - nodePort: 30081 ---- -apiVersion: v1 -kind: PersistentVolume -metadata: - name: task-pv-volume - labels: - type: local -spec: - storageClassName: manual - capacity: - storage: 1Gi - accessModes: - - ReadWriteOnce - hostPath: - path: "/root/.kube/" ---- -apiVersion: v1 -kind: PersistentVolumeClaim -metadata: - name: task-pv-claim -spec: - storageClassName: manual - accessModes: - - ReadWriteOnce - resources: - requests: - storage: 1Gi ---- -apiVersion: apps/v1 -kind: Deployment -metadata: - name: inventory-be - namespace: my-test -spec: - replicas: 1 - selector: - matchLabels: - app: inventory - template: - metadata: - labels: - app: inventory - spec: - containers: - - name: inventory-be - image: ealtedge/inventory-be:v1.3 - env: - - name: INFLUXDB_IP - value: "159.138.129.53" - - name: INFLUXDB_PORT - value: "30270" - - name: OBJ_DET_SER_IP - value: "159.138.129.53" - - name: OBJ_DET_SER_PORT - value: "30093" - ports: - - containerPort: 9995 ---- -apiVersion: v1 -kind: Service -metadata: - name: inventory-be - namespace: my-test -spec: - selector: - app: inventory - type: NodePort - ports: - - port: 9995 - name: service-port - nodePort: 30092 ---- -apiVersion: apps/v1 -kind: Deployment -metadata: - name: obj-detection -spec: - replicas: 1 - selector: - matchLabels: - app: detection - template: - metadata: - labels: - app: detection - spec: - containers: - - name: obj-detection - image: ealtedge/obj-detection - ports: - - containerPort: 9999 - volumeMounts: - - name: host-path-storage - mountPath: "/usr/app/model/" - volumes: - - name: host-path-storage - persistentVolumeClaim: - claimName: host-path-claim ---- -apiVersion: v1 -kind: PersistentVolume -metadata: - name: host-path-pv - labels: - type: local -spec: - storageClassName: manual - capacity: - storage: 2Gi - accessModes: - - ReadWriteOnce - hostPath: - path: "/root/model/" ---- -apiVersion: v1 -kind: PersistentVolumeClaim -metadata: - name: host-path-claim -spec: - storageClassName: manual - accessModes: - - ReadWriteOnce - resources: - requests: - storage: 2Gi ---- -apiVersion: v1 -kind: Service -metadata: - name: obj-detection -spec: - selector: - app: detection - type: NodePort - ports: - - port: 9999 - name: service-port - nodePort: 30093 diff --git a/example-apps/ROBO/retail_app/.gitignore b/example-apps/ROBO/retail_app/.gitignore deleted file mode 100644 index 329ec83..0000000 --- a/example-apps/ROBO/retail_app/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -venv -__pycache__ -.idea diff --git a/example-apps/ROBO/retail_app/Dockerfile b/example-apps/ROBO/retail_app/Dockerfile deleted file mode 100644 index cad2cf5..0000000 --- a/example-apps/ROBO/retail_app/Dockerfile +++ /dev/null @@ -1,98 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# Prepare stage for multistage image build -## START OF STAGE0 ## -# FROM python:3.6-slim-stretch -# FROM python:3.9 -# FROM python:3.9-slim-buster -# # CREATE APP USER ## - -FROM python:2.7 - -# Set umask -RUN sed -i "s|umask 022|umask 027|g" /etc/profile - -# Create the home directory for the new app user. -RUN mkdir -p /usr/app -RUN mkdir -p /usr/app/bin -RUN mkdir -p /usr/app/inventry -RUN mkdir -p /usr/app/test/resources -RUN mkdir -p /usr/app/images_result - -# Set the home directory to our app user's home. -ENV APP_HOME=/usr/app -ENV UID=166 -ENV GID=166 -ENV USER_NAME=eguser -ENV GROUP_NAME=eggroup -ENV ENV="/etc/profile" -ENV PYTHONUNBUFFERED=0 - -# Create an app user so our program doesn't run as root. -RUN apt-get -y update &&\ - groupadd -r -g $GID $GROUP_NAME &&\ - useradd -r -u $UID -g $GID -d $APP_HOME -s /sbin/nologin -c "Docker image user" $USER_NAME - -RUN apt-get install -y --fix-missing \ - build-essential \ - cmake \ - gfortran \ - git \ - wget \ - curl \ - graphicsmagick \ - libgraphicsmagick1-dev \ - libavcodec-dev \ - libavformat-dev \ - libgtk2.0-dev \ - libjpeg-dev \ - liblapack-dev \ - libswscale-dev \ - pkg-config \ - software-properties-common \ - zip \ - imagemagick \ - && apt-get clean && rm -rf /tmp/* /var/tmp/* - -# Set the working directory. -WORKDIR $APP_HOME - -# Copy the application & scripts -COPY config.py requirements.txt run.py $APP_HOME/ -COPY inventry $APP_HOME/inventry/ - -COPY test $APP_HOME/test/ -COPY test/resources $APP_HOME/test/resources/ -COPY configs/start.sh $APP_HOME/bin - -RUN chmod 750 $APP_HOME &&\ - chmod -R 550 $APP_HOME/bin &&\ - mkdir -p -m 750 $APP_HOME/log &&\ - mkdir -p -m 700 $APP_HOME/ssl &&\ - chown -R $USER_NAME:$GROUP_NAME $APP_HOME - -# Exposed port -EXPOSE 9995 - -# Change to the app user. -USER $USER_NAME - -# Install requirements -RUN pip install -r requirements.txt - -# Execute script & application -ENTRYPOINT ["sh", "./bin/start.sh"] diff --git a/example-apps/ROBO/retail_app/Inventory-be-k8s.yaml b/example-apps/ROBO/retail_app/Inventory-be-k8s.yaml deleted file mode 100644 index 9c05489..0000000 --- a/example-apps/ROBO/retail_app/Inventory-be-k8s.yaml +++ /dev/null @@ -1,58 +0,0 @@ -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. ---- -apiVersion: apps/v1 -kind: Deployment -metadata: - name: inventory-be -spec: - replicas: 1 - selector: - matchLabels: - app: inventory - template: - metadata: - labels: - app: inventory - spec: - containers: - - name: inventory-be - image: ealtedge/inventory-be - env: - - name: INFLUXDB_IP - value: "159.138.33.54" - - name: INFLUXDB_PORT - value: "30944" - - name: OBJ_DET_SER_IP - value: "159.138.33.54" - - name: OBJ_DET_SER_PORT - value: "30093" - ports: - - containerPort: 9995 - - -# backup-be serice ---- -apiVersion: v1 -kind: Service -metadata: - name: inventory-be -spec: - selector: - app: inventory - type: NodePort - ports: - - port: 9995 - name: service-port - nodePort: 30092 diff --git a/example-apps/ROBO/retail_app/camera_driver/capture_frame.py b/example-apps/ROBO/retail_app/camera_driver/capture_frame.py deleted file mode 100644 index eb66edc..0000000 --- a/example-apps/ROBO/retail_app/camera_driver/capture_frame.py +++ /dev/null @@ -1,53 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -import cv2 - - -class VideoCamera(object): - """ - opneCV to capture frame from a camera - """ - def __init__(self, url): - self.video = cv2.VideoCapture(url) - - def delete(self): - self.video.release() - - def get_frame(self): - """ - get a frame from camera url - """ - success, image = self.video.read() - return success, image - - -class VideoFile(object): - """ - opneCV to capture frame from a video stream - """ - def __init__(self, video_name): - self.video = cv2.VideoCapture("./test/resources/" + video_name) - - def delete(self): - self.video.release() - - def get_frame(self): - """ - get a frane from stream - """ - success, image = self.video.read() - return success, image diff --git a/example-apps/ROBO/retail_app/config.py b/example-apps/ROBO/retail_app/config.py deleted file mode 100644 index 896e5a0..0000000 --- a/example-apps/ROBO/retail_app/config.py +++ /dev/null @@ -1,54 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -import os - -# [Server Configurations] -server_port = 9995 -# server_address = os.environ.get('LISTEN_IP') -server_address = "0.0.0.0" - -# [InfluxDB config] -IPADDRESS = os.environ.get('INFLUXDB_IP') -PORT = os.environ.get('INFLUXDB_PORT') -DATABASE_NAME = "Shelf_Inventry" - -# [SSL Configurations] -ssl_enabled = False -ssl_protocol = "TLSv1.2" -ssl_ciphers = ["TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256", - "TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256", - "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384", - "TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384"] -ssl_certfilepath = "/usr/app/ssl/server_tls.crt" -ssl_keyfilepath = "/usr/app/ssl/server_tls.key" -ssl_cacertpath = "/usr/app/ssl/ca.crt" -ssl_server_name = os.environ.get('SERVER_NAME', "ealtedge") - -# [Service Configurations] -api_gateway = os.environ.get("API_GATEWAY", "apigw.mep.org") -# Obj_Det_SER_IP = os.environ.get("OBJ_DET_SER_IP", "localhost") - -Obj_Det_SER_IP = os.environ.get("OBJ_DET_SER_IP") -# Obj_Det_SER_IP = os.environ.get("OBJ_DET_SER_IP") -Obj_Det_SER_PORT = os.environ.get("OBJ_DET_SER_PORT") -# Obj_Det_SER_PORT = os.environ.get("OBJ_DET_SER_PORT") -Obj_Det = os.environ.get("OBJ_DETECTION", "mep/v1/obj_detection") - -# [Constants] -# detection_url = "http://" + api_gateway + "/" + Obj_Det -detection_url = "http://" + Obj_Det_SER_IP + ":" + Obj_Det_SER_PORT + "/" + \ - Obj_Det + "/" diff --git a/example-apps/ROBO/retail_app/config.pyc b/example-apps/ROBO/retail_app/config.pyc deleted file mode 100644 index 6df092d..0000000 Binary files a/example-apps/ROBO/retail_app/config.pyc and /dev/null differ diff --git a/example-apps/ROBO/retail_app/configs/start.sh b/example-apps/ROBO/retail_app/configs/start.sh deleted file mode 100644 index 23796c6..0000000 --- a/example-apps/ROBO/retail_app/configs/start.sh +++ /dev/null @@ -1,47 +0,0 @@ -#!/bin/bash -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Validates if ip is valid -validate_ip() -{ - ip_var="$1" - # checks if variable is unset - if [ -z "$ip_var" ] ; then - echo "ip is not set" - return 1 - fi - - if ! echo "$ip_var" | grep -qE '^(([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])\.)' ; then - return 1 - fi - return 0 -} - -validate_name() { - hostname="$1" - len="${#hostname}" - if [ "${len}" -gt "64" ]; then - return 1 - fi - if ! echo "$hostname" | grep -qE '^[a-zA-Z0-9]*$|^[a-zA-Z0-9][a-zA-Z0-9_\-]*[a-zA-Z0-9]$'; then - return 1 - fi - return 0 -} - -echo "Running Retail App" -umask 0027 -cd /usr/app || exit -python -u run.py \ No newline at end of file diff --git a/example-apps/ROBO/retail_app/images/input_frame/img.jpeg b/example-apps/ROBO/retail_app/images/input_frame/img.jpeg deleted file mode 100644 index df1ebc1..0000000 Binary files a/example-apps/ROBO/retail_app/images/input_frame/img.jpeg and /dev/null differ diff --git a/example-apps/ROBO/retail_app/inventry/__init__.py b/example-apps/ROBO/retail_app/inventry/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/example-apps/ROBO/retail_app/inventry/__init__.pyc b/example-apps/ROBO/retail_app/inventry/__init__.pyc deleted file mode 100644 index 4a779cf..0000000 Binary files a/example-apps/ROBO/retail_app/inventry/__init__.pyc and /dev/null differ diff --git a/example-apps/ROBO/retail_app/inventry/capture_frame.py b/example-apps/ROBO/retail_app/inventry/capture_frame.py deleted file mode 100644 index eb66edc..0000000 --- a/example-apps/ROBO/retail_app/inventry/capture_frame.py +++ /dev/null @@ -1,53 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -import cv2 - - -class VideoCamera(object): - """ - opneCV to capture frame from a camera - """ - def __init__(self, url): - self.video = cv2.VideoCapture(url) - - def delete(self): - self.video.release() - - def get_frame(self): - """ - get a frame from camera url - """ - success, image = self.video.read() - return success, image - - -class VideoFile(object): - """ - opneCV to capture frame from a video stream - """ - def __init__(self, video_name): - self.video = cv2.VideoCapture("./test/resources/" + video_name) - - def delete(self): - self.video.release() - - def get_frame(self): - """ - get a frane from stream - """ - success, image = self.video.read() - return success, image diff --git a/example-apps/ROBO/retail_app/inventry/retail_app.py b/example-apps/ROBO/retail_app/inventry/retail_app.py deleted file mode 100644 index 22f7db0..0000000 --- a/example-apps/ROBO/retail_app/inventry/retail_app.py +++ /dev/null @@ -1,452 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -import config -from flask_sslify import SSLify -from flask import Flask, request, jsonify, Response -from flask_cors import CORS -from influxdb import InfluxDBClient -import json -import requests -import os -import cv2 -import os.path -from os import path -import base64 -import time -import sys - -app = Flask(__name__) -CORS(app) -sslify = SSLify(app) -app.config['JSON_AS_ASCII'] = False -app.config['UPLOAD_PATH'] = '/usr/app/images_result/' -app.config['VIDEO_PATH'] = '/usr/app/test/resources/' -app.config['supports_credentials'] = True -app.config['CORS_SUPPORTS_CREDENTIALS'] = True -app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024 -ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg']) -ALLOWED_VIDEO_EXTENSIONS = {'mp4'} -count = 0 -listOfMsgs = [] -listOfCameras = [] -listOfVideos = [] - - -class inventory_info: - """ - Store the data and manage multiple input video feeds - """ - def __init__(self, status="Needs Filling", time=0): - self.type = "Shelf_INV1" - self.labels = "Bottles" - self.status = status - self.currentCount = 1 - self.maxCount = 5 - self.time = time - - def setstatus(self, status): - self.status = status - - def getstatus(self): - return self.status - - def setcurrentCount(self, count): - self.currentCount = count - - def getcurrentCount(self): - return self.currentCount - - def setmaxCount(self, count): - self.maxCount = count - - def getmaxCount(self): - return self.maxCount - - def setlabel(self, labels): - self.labels = labels - - def getlabel(self): - return self.labels - - def settime(self, time): - self.labels = time - - def gettime(self): - return self.time - - -# temporary copied capture_frame file to this due to docker issue for module -# import -class VideoCamera(object): - """ - opneCV to capture frame from a camera - """ - def __init__(self, url): - self.video = cv2.VideoCapture(url) - - def delete(self): - self.video.release() - - def get_frame(self): - """ - get a frame from camera url - """ - success, image = self.video.read() - return success, image - - -class VideoFile(object): - """ - opneCV to capture frame from a video stream - """ - def __init__(self, video_name): - self.video = cv2.VideoCapture(app.config['VIDEO_PATH'] + video_name) - - def delete(self): - self.video.release() - - def get_frame(self): - """ - get a frane from stream - """ - success, image = self.video.read() - return success, image - - -def shelf_inventory(video_capture, camera_info, true=None): - """ - shelf_inventory - """ - global count - global mock_func - - labels = "Bottles" - count_val = 'ObjCount' - process_this_frame = 0 - i = 0 - url = config.detection_url + "detect" - url_get = config.detection_url + "image" - - while True: - success, frame = video_capture.get_frame() - if not success: - print('read frame from file is failed') - break - - i = i+1 - if i < 10: - continue - - i = 0 - - if process_this_frame == 0: - imencoded = cv2.imencode(".jpg", frame)[1] - file = {'file': ( - 'image.jpg', imencoded.tostring(), 'image/jpeg', - {'Expires': '0'})} - res = requests.post(url, files=file) - data = json.loads(res.text) - - # get image - response = requests.get(url_get) - - file = open(app.config['UPLOAD_PATH'] + "sample_image.jpg", "wb") - file.write(response.content) - file.close() - - inven_info = inventory_info() - current_count = data[count_val] - if (current_count >= 3): - status = "Mostly Filled" - elif (current_count == 2): - status = "Partially Filled" - else: - status = "Needs Filling" - - inven_info.setlabel(labels) - inven_info.setstatus(status) - inven_info.setcurrentCount(current_count) - time_sec = time.time() - local_time = time.ctime(time_sec) - inven_info.time = local_time - store_info_db(inven_info) - time.sleep(0.30) - - -def db_drop_table(inven_info): - """ - cleanup measrurment before new trigger - - :param inven_info: inven_info object - :return: None - """ - global db_client - db_client.drop_measurement(inven_info.type) - - -def store_info_db(inven_info): - """ - Send "shelf" data to InfluxDB - - :param inven_info: Inventry object - :return: None - """ - global db_client - json_body = [ - { - "measurement": inven_info.type, - "tags": { - "object": "bottles", - }, - "fields": { - "time": inven_info.time, - "status": inven_info.status, - "currentCount": inven_info.currentCount, - "maxCount": inven_info.maxCount, - } - }] - db_client.write_points(json_body) - - -def retrive_info_db(): - """ - Send "shelf" data to InfluxDB - - :param inven_info: Inventry object - :return: None - """ - global db_client - - # get data last n data points from DB - result = db_client.query('select * from Shelf_INV1 order by desc limit ' - '1;') - - # Get points and iterate over each record - points = result.get_points(tags={"object": "bottles"}) - - # clear the msg list - # listOfMsgs.clear() - del listOfMsgs[:] - - # iterate points and fill the records and insert to list - for point in points: - print("status: %s,Time: %s" % (point['status'], point['time'])) - newdict = {"shelfName": 'Shelf_INV1', "ObjType": "bottles", - "status": point['status'], - "currentCount": point['currentCount'], - "maxCount": point['maxCount'], - "time": point['time']} - listOfMsgs.insert(0, newdict) - - -def create_database(): - """ - Connect to InfluxDB and create the database - - :return: None - """ - global db_client - proxy = {"http": "http://{}:{}".format(config.IPADDRESS, config.PORT)} - db_client = InfluxDBClient(host=config.IPADDRESS, port=config.PORT, - proxies=proxy, database=config.DATABASE_NAME) - db_client.create_database(config.DATABASE_NAME) - - -@app.route('/v1/inventry/table', methods=['GET']) -def inventry_table(): - """ - return inventry table - - :return: inventry table - """ - retrive_info_db() - table = {"InventryData": listOfMsgs} - return jsonify(table) - - -@app.route('/v1/inventry/image', methods=['GET']) -def detected_image(): - """ - detect images with imposed - - :return: result image - """ - detected_image = app.config['UPLOAD_PATH'] + 'sample_image.jpg' - print('file exits:', str(path.exists(detected_image))) - status = str(path.exists(detected_image)) - if status == 'True': - # as base64 string - with open(detected_image, "rb") as img_file: - jpeg_bin = base64.b64encode(img_file.read()) - - response = {'image': jpeg_bin} - return jsonify(response) - else: - response = {'image': 'null'} - print('file not exist') - return jsonify(response) - - -def allowed_videofile(filename): - """ - File types to upload:mp4 - param: filename: - """ - return '.' in filename and \ - filename.rsplit('.', 1)[1].lower() in ALLOWED_VIDEO_EXTENSIONS - - -@app.route('/v1/monitor/video', methods=['POST']) -def upload_video(): - app.logger.info("Received message from ClientIP [" + request.remote_addr - + "] Operation [" + request.method + "]" + - " Resource [" + request.url + "]") - print("videpath:" + app.config['VIDEO_PATH']) - if 'file' in request.files: - files = request.files.getlist("file") - for file in files: - if allowed_videofile(file.filename): - file.save(os.path.join(app.config['VIDEO_PATH'], - file.filename)) - print('file path is:', app.config['VIDEO_PATH'] - + file.filename) - else: - raise IOError('video format error') - msg = {"responce": "failure"} - return jsonify(msg) - msg = {"responce": "success"} - return jsonify(msg) - - -def hash_func(camera_info): - hash_string = camera_info["cameraNumber"] + \ - camera_info["cameraLocation"] + \ - camera_info["rtspUrl"] - # readable_hash = hashlib.sha256(str(hash_string).encode( - # 'utf-8')).hexdigest() - readable_hash = hash(hash_string) - if readable_hash < 0: - readable_hash += sys.maxsize - print(readable_hash) - return readable_hash - - -@app.route('/v1/monitor/cameras', methods=['POST']) -def add_camera(): - camera_detail = request.json - app.logger.info("Received message from ClientIP [" + request.remote_addr - + "] Operation [" + request.method + "]" + - " Resource [" + request.url + "]") - camera_id = hash_func(camera_detail) - camera_id = str(camera_id) - for camera_info in listOfCameras: - if camera_id == camera_info["cameraID"]: - msg = {"responce": "failure"} - return jsonify(msg) - break - camera_info = {"cameraID": camera_id, - "cameraNumber": camera_detail["cameraNumber"], - "rtspUrl": camera_detail["rtspUrl"], - "cameraLocation": camera_detail["cameraLocation"]} - listOfCameras.append(camera_info) - msg = {"responce": "success"} - return jsonify(msg) - - -@app.route('/v1/monitor/cameras/', methods=['GET']) -def get_camera(cameraID): - """ - register camera with location - """ - app.logger.info("Received message from ClientIP [" + request.remote_addr - + "] Operation [" + request.method + "]" + - " Resource [" + request.url + "]") - valid_id = 0 - for camera_info in listOfCameras: - # cameraID = int(cameraID) - if cameraID == camera_info["cameraID"]: - valid_id = 1 - break - - if valid_id == 0: - app.logger.info("camera ID is not valid") - msg = {"responce": "failure"} - return jsonify(msg) - - if "mp4" in camera_info["rtspUrl"]: - video_file = VideoFile(camera_info["rtspUrl"]) - video_dict = {camera_info["cameraNumber"]: video_file} - listOfVideos.append(video_dict) - # return Response(shelf_inventory(video_file, camera_info[ - # "cameraNumber"]), - # mimetype='multipart/x-mixed-replace; boundary=frame') - shelf_inventory(video_file, camera_info["cameraNumber"]) - app.logger.info("get_camera: Added json") - msg = {"responce": "success"} - return jsonify(msg) - - else: - video_file = VideoCamera(camera_info["rtspUrl"]) - video_dict = {camera_info["cameraNumber"]: video_file} - listOfVideos.append(video_dict) - return Response(shelf_inventory(video_file, - camera_info["cameraNumber"]), - mimetype='multipart/x-mixed-replace; boundary=frame') - - -@app.route('/v1/monitor/cameras/', methods=['DELETE']) -def delete_camera(camera_name): - app.logger.info("Received message from ClientIP [" + request.remote_addr - + "] Operation [" + request.method + "]" + - " Resource [" + request.url + "]") - for video1 in listOfVideos: - if camera_name in video1: - video_obj = video1[camera_name] - video_obj.delete() - for camera in listOfCameras: - if camera_name == camera["cameraNumber"]: - listOfCameras.remove(camera) - return Response("success") - - -@app.route('/v1/monitor/cameras') -def query_cameras(): - app.logger.info("Received message from ClientIP [" + request.remote_addr - + "] Operation [" + request.method + "]" + - " Resource [" + request.url + "]") - camera_info = {"roboCamera": listOfCameras} - return jsonify(camera_info) - - -@app.route('/', methods=['GET']) -def hello_world(): - app.logger.info("Received message from ClientIP [" + request.remote_addr - + "] Operation [" + request.method + "]" + - " Resource [" + request.url + "]") - return Response("Hello MEC Developer") - - -def start_server(handler): - app.logger.addHandler(handler) - create_database() - if config.ssl_enabled: - context = (config.ssl_certfilepath, config.ssl_keyfilepath) - app.run(host=config.server_address, debug=True, ssl_context=context, - threaded=True, port=config.server_port) - else: - app.run(host=config.server_address, debug=True, threaded=True, - port=config.server_port) diff --git a/example-apps/ROBO/retail_app/requirements.txt b/example-apps/ROBO/retail_app/requirements.txt deleted file mode 100644 index 00afd2f..0000000 --- a/example-apps/ROBO/retail_app/requirements.txt +++ /dev/null @@ -1,24 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -flask~=1.1.2 -requests~=2.18.4 -flask_sslify -opencv-python~=3.3.1.11 -flask_cors -numpy -influxdb~=5.3.1 -Wand \ No newline at end of file diff --git a/example-apps/ROBO/retail_app/retail_app.log b/example-apps/ROBO/retail_app/retail_app.log deleted file mode 100644 index e69de29..0000000 diff --git a/example-apps/ROBO/retail_app/run.py b/example-apps/ROBO/retail_app/run.py deleted file mode 100644 index ca3ad61..0000000 --- a/example-apps/ROBO/retail_app/run.py +++ /dev/null @@ -1,25 +0,0 @@ -# -# Copyright 2020 Huawei Technologies Co., Ltd. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -import logging -from logging.handlers import RotatingFileHandler -from inventry.retail_app import start_server - -if __name__ == '__main__': - handler = RotatingFileHandler('/usr/app/log/obj_detection.log', - maxBytes=10000000, backupCount=10) - handler.setLevel(logging.INFO) - start_server(handler)