+++ /dev/null
-# -*- coding: utf-8 -*-
-from __future__ import division, print_function, absolute_import
-import os
-import tensorflow as tf
-
-"""
-cls : car|| Recall: 0.9542048293089093 || Precison: 0.002486730925298256|| AP: 0.8861557852184543
-____________________
-cls : aeroplane|| Recall: 0.9333333333333333 || Precison: 0.0005925041542114572|| AP: 0.842025283046165
-____________________
-cls : diningtable|| Recall: 0.8932038834951457 || Precison: 0.00040969553387335954|| AP: 0.7380756727563009
-____________________
-cls : cow|| Recall: 0.9672131147540983 || Precison: 0.0005127837421479989|| AP: 0.8598673192007477
-____________________
-cls : boat|| Recall: 0.870722433460076 || Precison: 0.0005048556531707801|| AP: 0.614924861322453
-____________________
-cls : person|| Recall: 0.9112190812720848 || Precison: 0.00890183387270766|| AP: 0.8403573910074297
-____________________
-cls : bottle|| Recall: 0.8550106609808102 || Precison: 0.0008528612324589202|| AP: 0.6431100249936452
-____________________
-cls : bus|| Recall: 0.9624413145539906 || Precison: 0.00045113940207524124|| AP: 0.8462278674877455
-____________________
-cls : chair|| Recall: 0.8465608465608465 || Precison: 0.0013795928045612787|| AP: 0.5683751395230314
-____________________
-cls : train|| Recall: 0.9361702127659575 || Precison: 0.0005837401825514753|| AP: 0.8364460305413468
-____________________
-cls : dog|| Recall: 0.9754601226993865 || Precison: 0.0010383537847668929|| AP: 0.9033364857501106
-____________________
-cls : motorbike|| Recall: 0.92 || Precison: 0.0006513139551094382|| AP: 0.8295629935581179
-____________________
-cls : cat|| Recall: 0.9608938547486033 || Precison: 0.0007397197236372707|| AP: 0.8871965890034861
-____________________
-cls : sofa|| Recall: 0.9539748953974896 || Precison: 0.0005096076691483964|| AP: 0.8040267075520897
-____________________
-cls : bird|| Recall: 0.9150326797385621 || Precison: 0.0009065830883400464|| AP: 0.8237404489482196
-____________________
-cls : horse|| Recall: 0.9568965517241379 || Precison: 0.0007317362585204424|| AP: 0.8735567893480496
-____________________
-cls : pottedplant|| Recall: 0.7895833333333333 || Precison: 0.0008196455411498826|| AP: 0.4870711669635938
-____________________
-cls : bicycle|| Recall: 0.9109792284866469 || Precison: 0.0006712950308861314|| AP: 0.8361496835573192
-____________________
-cls : tvmonitor|| Recall: 0.9512987012987013 || Precison: 0.0006329305331737686|| AP: 0.7944641602539069
-____________________
-cls : sheep|| Recall: 0.9214876033057852 || Precison: 0.0004898504308706817|| AP: 0.7823429444259854
-____________________
-mAP is : 0.7848506672229101 USE_12_METRIC
-
-cls : tvmonitor|| Recall: 0.9512987012987013 || Precison: 0.0006329305331737686|| AP: 0.7680923930233127
-____________________
-cls : sheep|| Recall: 0.9214876033057852 || Precison: 0.0004898504308706817|| AP: 0.7605331894258903
-____________________
-cls : cat|| Recall: 0.9608938547486033 || Precison: 0.0007397197236372707|| AP: 0.8561115936556246
-____________________
-cls : train|| Recall: 0.9361702127659575 || Precison: 0.0005837401825514753|| AP: 0.7978443126776701
-____________________
-cls : aeroplane|| Recall: 0.9333333333333333 || Precison: 0.0005925041542114572|| AP: 0.8033498986573708
-____________________
-cls : motorbike|| Recall: 0.92 || Precison: 0.0006513139551094382|| AP: 0.7991141448766482
-____________________
-cls : bus|| Recall: 0.9624413145539906 || Precison: 0.00045113940207524124|| AP: 0.8181027979596772
-____________________
-cls : bird|| Recall: 0.9150326797385621 || Precison: 0.0009065830883400464|| AP: 0.7851247014320806
-____________________
-cls : pottedplant|| Recall: 0.7895833333333333 || Precison: 0.0008196455411498826|| AP: 0.49033575375142174
-____________________
-cls : cow|| Recall: 0.9672131147540983 || Precison: 0.0005127837421479989|| AP: 0.8304367006298838
-____________________
-cls : person|| Recall: 0.9112190812720848 || Precison: 0.00890183387270766|| AP: 0.797530517185023
-____________________
-cls : bottle|| Recall: 0.8550106609808102 || Precison: 0.0008528612324589202|| AP: 0.6320745719617634
-____________________
-cls : sofa|| Recall: 0.9539748953974896 || Precison: 0.0005096076691483964|| AP: 0.7868518534567335
-____________________
-cls : boat|| Recall: 0.870722433460076 || Precison: 0.0005048556531707801|| AP: 0.6036612374959088
-____________________
-cls : car|| Recall: 0.9542048293089093 || Precison: 0.002486730925298256|| AP: 0.8623955910304107
-____________________
-cls : bicycle|| Recall: 0.9109792284866469 || Precison: 0.0006712950308861314|| AP: 0.8029062441256611
-____________________
-cls : dog|| Recall: 0.9754601226993865 || Precison: 0.0010383537847668929|| AP: 0.8661350949646617
-____________________
-cls : diningtable|| Recall: 0.8932038834951457 || Precison: 0.00040969553387335954|| AP: 0.7127169697539509
-____________________
-cls : horse|| Recall: 0.9568965517241379 || Precison: 0.0007317362585204424|| AP: 0.8422342978325045
-____________________
-cls : chair|| Recall: 0.8465608465608465 || Precison: 0.0013795928045612787|| AP: 0.563170854101135
-____________________
-mAP is : 0.7589361358998666 USE_07_METRIC
-"""
-
-# ------------------------------------------------
-VERSION = 'FPN_Res101_20181201_v2'
-NET_NAME = 'resnet_v1_101'
-ADD_BOX_IN_TENSORBOARD = True
-
-# ---------------------------------------- System_config
-ROOT_PATH = os.path.abspath('../')
-print (20*"++--")
-print (ROOT_PATH)
-GPU_GROUP = "3"
-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 = False
-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