removed exmple apps code

[ealt-edge.git] / example-apps / PDD / pcb-defect-detection / libs / configs / cfgs.py

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 (executable)
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
-
-
-