X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fconfigs%2Fcfgs_res101_fpn.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fconfigs%2Fcfgs_res101_fpn.py;h=0000000000000000000000000000000000000000;hb=3ed2c61d9d7e7916481650c41bfe5604f7db22e9;hp=d9daad26a7d41849ffc2cd222dbe30b0fba0cad6;hpb=e6d40ddb2640f434a9d7d7ed99566e5e8fa60cc1;p=ealt-edge.git 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 - - -