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