pcb defect detetcion application

[ealt-edge.git] / example-apps / PDD / pcb-defect-detection / libs / configs / cfgs_res50_fpn.py

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
new file mode 100755 (executable)
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+++ b/example-apps/PDD/pcb-defect-detection/libs/configs/cfgs_res50_fpn.py
@@ -0,0 +1,208 @@
+# -*- 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
+
+
+