pcb defect detetcion application

[ealt-edge.git] / example-apps / PDD / pcb-defect-detection / libs / configs / cfgs_res101_fpn.py

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
new file mode 100755 (executable)
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+++ b/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs_res101_fpn.py
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+# -*- 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
+
+
+