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207 changes: 103 additions & 104 deletions
207
libs/configs/cfgs_res50_coco_1x_v1.py → libs/configs/COCO/cfgs_res50_coco_1x_v1.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,104 +1,103 @@ | ||
| # -*- coding: utf-8 -*- | ||
| from __future__ import division, print_function, absolute_import | ||
| import os | ||
| import tensorflow as tf | ||
| import math | ||
|
|
||
| """ | ||
| epoch-00: 00.0 epoch-01: 7.40 | ||
| epoch-02: 15.4 epoch-03: 18.8 | ||
| epoch-04: 20.7 epoch-05: 23.0 | ||
| epoch-06: 23.6 epoch-07: 25.3 | ||
| epoch-08: 24.7 epoch-09: 26.7 | ||
| epoch-11: 26.2 epoch-12: 30.7 | ||
| epoch-13: 30.8 epoch-14: 31.1 | ||
| epoch-15: 31.2 epoch-16: 31.4 | ||
| epoch-19: 31.5 | ||
| """ | ||
|
|
||
| # ------------------------------------------------ | ||
| VERSION = 'RetinaNet_COCO_1x_20190522' | ||
| NET_NAME = 'resnet_v1_50' # '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 = 20000 * 5 | ||
|
|
||
| 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 = None # 2.0 # if None, will not multipy | ||
| GRADIENT_CLIPPING_BY_NORM = None # 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 | ||
|
|
||
|
|
||
| # -*- coding: utf-8 -*- | ||
| from __future__ import division, print_function, absolute_import | ||
| import os | ||
| import tensorflow as tf | ||
| import math | ||
|
|
||
| """ | ||
| epoch-00: 00.0 epoch-01: 7.40 | ||
| epoch-02: 15.4 epoch-03: 18.8 | ||
| epoch-04: 20.7 epoch-05: 23.0 | ||
| epoch-06: 23.6 epoch-07: 25.3 | ||
| epoch-08: 24.7 epoch-09: 26.7 | ||
| epoch-11: 26.2 epoch-12: 30.7 | ||
| epoch-13: 30.8 epoch-14: 31.1 | ||
| epoch-15: 31.2 epoch-16: 31.4 | ||
| epoch-19: 31.5 | ||
| """ | ||
|
|
||
| # ------------------------------------------------ | ||
| VERSION = 'RetinaNet_COCO_1x_20190522' | ||
| NET_NAME = 'resnet_v1_50' # '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 = 20000 * 5 | ||
|
|
||
| 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 = None # 2.0 # if None, will not multipy | ||
| GRADIENT_CLIPPING_BY_NORM = None # 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 | ||
| 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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