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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,101 @@ | ||
| # -*- coding: utf-8 -*- | ||
| from __future__ import division, print_function, absolute_import | ||
| import os | ||
| import tensorflow as tf | ||
| import math | ||
|
|
||
| """ | ||
| epoch-00: 3.90 epoch-01: 12.7 | ||
| epoch-02: 17.1 epoch-03: 21.6 | ||
| epoch-04: 24.1 epoch-05: 24.2 | ||
| epoch-06: 25.4 epoch-07: 26.3 | ||
| epoch-11: 27.5 epoch-12: 32.2 | ||
| epoch-17: 33.4 epoch-18: 33.4 | ||
| """ | ||
|
|
||
| # ------------------------------------------------ | ||
| VERSION = 'RetinaNet_COCO_1x_20190525' | ||
| NET_NAME = 'resnet50_v1d' # 'MobilenetV2' | ||
| ADD_BOX_IN_TENSORBOARD = True | ||
|
|
||
| # ---------------------------------------- System_config | ||
| ROOT_PATH = os.path.abspath('../') | ||
| print(20*"++--") | ||
| print(ROOT_PATH) | ||
| GPU_GROUP = "0,1,2,3,4,5,6,7" | ||
| NUM_GPU = len(GPU_GROUP.strip().split(',')) | ||
| SHOW_TRAIN_INFO_INTE = 20 | ||
| SMRY_ITER = 200 | ||
| SAVE_WEIGHTS_INTE = 80000 | ||
|
|
||
| SUMMARY_PATH = ROOT_PATH + '/output/summary' | ||
| TEST_SAVE_PATH = ROOT_PATH + '/tools/test_result' | ||
|
|
||
| if NET_NAME.startswith("resnet"): | ||
| weights_name = NET_NAME | ||
| elif NET_NAME.startswith("MobilenetV2"): | ||
| weights_name = "mobilenet/mobilenet_v2_1.0_224" | ||
| else: | ||
| raise Exception('net name must in [resnet_v1_101, resnet_v1_50, MobilenetV2]') | ||
|
|
||
| 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/' | ||
|
|
||
| # ------------------------------------------ Train config | ||
| RESTORE_FROM_RPN = False | ||
| FIXED_BLOCKS = 1 # allow 0~3 | ||
| FREEZE_BLOCKS = [True, False, False, False, False] # for gluoncv backbone | ||
| USE_07_METRIC = True | ||
|
|
||
| MUTILPY_BIAS_GRADIENT = 2.0 # if None, will not multipy | ||
| GRADIENT_CLIPPING_BY_NORM = 10.0 # if None, will not clip | ||
|
|
||
| BATCH_SIZE = 1 | ||
| EPSILON = 1e-5 | ||
| MOMENTUM = 0.9 | ||
| LR = 5e-4 * NUM_GPU * BATCH_SIZE | ||
| DECAY_STEP = [SAVE_WEIGHTS_INTE*12, SAVE_WEIGHTS_INTE*16, SAVE_WEIGHTS_INTE*20] | ||
| MAX_ITERATION = SAVE_WEIGHTS_INTE*20 | ||
| WARM_SETP = int(1.0 / 8.0 * SAVE_WEIGHTS_INTE) | ||
|
|
||
| # -------------------------------------------- Data_preprocess_config | ||
| DATASET_NAME = 'coco' # '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_ = [0.485, 0.456, 0.406] | ||
| PIXEL_STD = [0.229, 0.224, 0.225] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR | ||
| IMG_SHORT_SIDE_LEN = 600 | ||
| IMG_MAX_LENGTH = 1000 | ||
| CLASS_NUM = 80 | ||
|
|
||
| # --------------------------------------------- Network_config | ||
| BATCH_SIZE = 1 | ||
| SUBNETS_WEIGHTS_INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.01, seed=None) | ||
| SUBNETS_BIAS_INITIALIZER = tf.constant_initializer(value=0.0) | ||
| PROBABILITY = 0.01 | ||
| FINAL_CONV_BIAS_INITIALIZER = tf.constant_initializer(value=-math.log((1.0 - PROBABILITY) / PROBABILITY)) | ||
| WEIGHT_DECAY = 1e-4 | ||
|
|
||
| # ---------------------------------------------Anchor config | ||
| LEVEL = ['P3', 'P4', 'P5', 'P6', 'P7'] | ||
| BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512] | ||
| ANCHOR_STRIDE = [8, 16, 32, 64, 128] | ||
| ANCHOR_SCALES = [2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)] | ||
| ANCHOR_RATIOS = [0.5, 1.0, 2.0] | ||
| ANCHOR_SCALE_FACTORS = None | ||
| USE_CENTER_OFFSET = True | ||
|
|
||
| # --------------------------------------------RPN config | ||
| SHARE_NET = True | ||
| USE_P5 = True | ||
| IOU_POSITIVE_THRESHOLD = 0.5 | ||
| IOU_NEGATIVE_THRESHOLD = 0.4 | ||
|
|
||
| NMS = True | ||
| NMS_IOU_THRESHOLD = 0.5 | ||
| MAXIMUM_DETECTIONS = 100 | ||
| FILTERED_SCORE = 0.05 | ||
| VIS_SCORE = 0.5 | ||
|
|
||
|
|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,116 @@ | ||
| # -*- coding: utf-8 -*- | ||
| from __future__ import division, print_function, absolute_import | ||
| import os | ||
| import tensorflow as tf | ||
| import math | ||
|
|
||
| """ | ||
| cls : motorbike|| Recall: 0.9969230769230769 || Precison: 0.012111244019138757|| AP: 0.8608716836726598 | ||
| cls : bus|| Recall: 0.9812206572769953 || Precison: 0.010832944591302546|| AP: 0.8665177973232026 | ||
| cls : cow|| Recall: 0.9918032786885246 || Precison: 0.014500569237222122|| AP: 0.8700933653540773 | ||
| cls : cat|| Recall: 0.994413407821229 || Precison: 0.019201725997842502|| AP: 0.8901733848581077 | ||
| cls : pottedplant|| Recall: 0.89375 || Precison: 0.009184918748795684|| AP: 0.5459186868755065 | ||
| cls : sheep|| Recall: 0.987603305785124 || Precison: 0.013457964975505377|| AP: 0.8678004330444818 | ||
| cls : aeroplane|| Recall: 0.9649122807017544 || Precison: 0.02806981729100745|| AP: 0.8901971050794585 | ||
| cls : boat|| Recall: 0.9467680608365019 || Precison: 0.008034849951597289|| AP: 0.7574725882243678 | ||
| cls : bicycle|| Recall: 0.9792284866468842 || Precison: 0.01154047910473859|| AP: 0.8696655881282686 | ||
| cls : car|| Recall: 0.9816819317235637 || Precison: 0.021344775146643492|| AP: 0.8910282687046879 | ||
| cls : tvmonitor|| Recall: 0.9642857142857143 || Precison: 0.007807981492192018|| AP: 0.8129339370651543 | ||
| cls : person|| Recall: 0.9783568904593639 || Precison: 0.04236477698722362|| AP: 0.8492903923966436 | ||
| cls : bottle|| Recall: 0.9253731343283582 || Precison: 0.007948572370469406|| AP: 0.7258658516344598 | ||
| cls : chair|| Recall: 0.9616402116402116 || Precison: 0.008080920357916967|| AP: 0.643958822348765 | ||
| cls : sofa|| Recall: 0.9874476987447699 || Precison: 0.005221354454744574|| AP: 0.7747622585263062 | ||
| cls : horse|| Recall: 0.9942528735632183 || Precison: 0.019444756659548163|| AP: 0.8831973097154177 | ||
| cls : train|| Recall: 0.9432624113475178 || Precison: 0.016586643387167175|| AP: 0.8595258237569346 | ||
| cls : bird|| Recall: 0.9673202614379085 || Precison: 0.02792628467199195|| AP: 0.8461742818633661 | ||
| cls : dog|| Recall: 0.9979550102249489 || Precison: 0.02050764834425954|| AP: 0.8791514392825484 | ||
| cls : diningtable|| Recall: 0.9805825242718447 || Precison: 0.0026451908596870294|| AP: 0.7533332150753614 | ||
| mAP is : 0.8168966116464886 () | ||
| """ | ||
|
|
||
| # ------------------------------------------------ | ||
| VERSION = 'RetinaNet_VOC0712_20190526' | ||
| NET_NAME = 'resnet101_v1d' # 'MobilenetV2' | ||
| ADD_BOX_IN_TENSORBOARD = True | ||
|
|
||
| # ---------------------------------------- System_config | ||
| ROOT_PATH = os.path.abspath('../') | ||
| print(20*"++--") | ||
| print(ROOT_PATH) | ||
| GPU_GROUP = "0,1,2,3,4,5,6,7" | ||
| NUM_GPU = len(GPU_GROUP.strip().split(',')) | ||
| SHOW_TRAIN_INFO_INTE = 10 | ||
| SMRY_ITER = 100 | ||
| SAVE_WEIGHTS_INTE = (11540 + 5000) * 2 | ||
|
|
||
| SUMMARY_PATH = ROOT_PATH + '/output/summary' | ||
| TEST_SAVE_PATH = ROOT_PATH + '/tools/test_result' | ||
|
|
||
| if NET_NAME.startswith("resnet"): | ||
| weights_name = NET_NAME | ||
| elif NET_NAME.startswith("MobilenetV2"): | ||
| weights_name = "mobilenet/mobilenet_v2_1.0_224" | ||
| else: | ||
| raise Exception('net name must in [resnet_v1_101, resnet_v1_50, MobilenetV2]') | ||
|
|
||
| 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/' | ||
|
|
||
| # ------------------------------------------ Train config | ||
| RESTORE_FROM_RPN = False | ||
| FIXED_BLOCKS = 1 # allow 0~3 | ||
| FREEZE_BLOCKS = [True, False, False, False, False] # for gluoncv backbone | ||
| USE_07_METRIC = True | ||
|
|
||
| MUTILPY_BIAS_GRADIENT = 2.0 # if None, will not multipy | ||
| GRADIENT_CLIPPING_BY_NORM = 10.0 # if None, will not clip | ||
|
|
||
| BATCH_SIZE = 1 | ||
| EPSILON = 1e-5 | ||
| MOMENTUM = 0.9 | ||
| LR = 5e-4 * NUM_GPU * BATCH_SIZE | ||
| DECAY_STEP = [SAVE_WEIGHTS_INTE*12, SAVE_WEIGHTS_INTE*16, SAVE_WEIGHTS_INTE*20] | ||
| MAX_ITERATION = SAVE_WEIGHTS_INTE*20 | ||
| WARM_SETP = int(1.0 / 4.0 * SAVE_WEIGHTS_INTE) | ||
|
|
||
| # -------------------------------------------- Data_preprocess_config | ||
| DATASET_NAME = 'pascal' # '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_ = [0.485, 0.456, 0.406] | ||
| PIXEL_STD = [0.229, 0.224, 0.225] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR | ||
| IMG_SHORT_SIDE_LEN = 600 | ||
| IMG_MAX_LENGTH = 1000 | ||
| CLASS_NUM = 20 | ||
|
|
||
| # --------------------------------------------- Network_config | ||
| BATCH_SIZE = 1 | ||
| SUBNETS_WEIGHTS_INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.01, seed=None) | ||
| SUBNETS_BIAS_INITIALIZER = tf.constant_initializer(value=0.0) | ||
| PROBABILITY = 0.01 | ||
| FINAL_CONV_BIAS_INITIALIZER = tf.constant_initializer(value=-math.log((1.0 - PROBABILITY) / PROBABILITY)) | ||
| WEIGHT_DECAY = 1e-4 | ||
|
|
||
| # ---------------------------------------------Anchor config | ||
| LEVEL = ['P3', 'P4', 'P5', 'P6', 'P7'] | ||
| BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512] | ||
| ANCHOR_STRIDE = [8, 16, 32, 64, 128] | ||
| ANCHOR_SCALES = [2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)] | ||
| ANCHOR_RATIOS = [0.5, 1.0, 2.0] | ||
| ANCHOR_SCALE_FACTORS = None | ||
| USE_CENTER_OFFSET = True | ||
|
|
||
| # --------------------------------------------RPN config | ||
| SHARE_NET = True | ||
| USE_P5 = True | ||
| IOU_POSITIVE_THRESHOLD = 0.5 | ||
| IOU_NEGATIVE_THRESHOLD = 0.4 | ||
|
|
||
| NMS = True | ||
| NMS_IOU_THRESHOLD = 0.5 | ||
| MAXIMUM_DETECTIONS = 100 | ||
| FILTERED_SCORE = 0.01 | ||
| VIS_SCORE = 0.5 | ||
|
|
||
|
|
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