+++ /dev/null
-# -*- 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
-
-
-
Code Review / ealt-edge.git / blobdiff