-
Notifications
You must be signed in to change notification settings - Fork 1
/
cfgs_res101_ohd-sjtu-all_r3det_v1.py
147 lines (129 loc) · 4.58 KB
/
cfgs_res101_ohd-sjtu-all_r3det_v1.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import os
import tensorflow as tf
import math
"""
iou threshold: 0.5
classname: small-vehicle
npos num: 5090
ap: 0.6800277413298225
classname: ship
npos num: 9886
ap: 0.8790966524933848
classname: plane
npos num: 2673
ap: 0.9016083306245202
classname: large-vehicle
npos num: 4293
ap: 0.8115409137256296
classname: helicopter
npos num: 72
ap: 0.5662227133798301
classname: harbor
npos num: 2065
ap: 0.6499725062980641
map: 0.7480781429752086
classaps: [68.00277413 87.90966525 90.16083306 81.15409137 56.62227134 64.99725063]
AP50:95: [0.7480781429752086, 0.7154595274889176, 0.6689263328396268, 0.6004777562391038, 0.5151428248230311, 0.36735942361075624, 0.2254496427225011, 0.0882618213179142, 0.018468081504794384, 0.0008337449643473207]
mmAP: 0.39484572984862015
"""
# ------------------------------------------------
VERSION = 'RetinaNet_OHD-SJTU-ALL_R3Det_2x_20200812'
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"
NUM_GPU = len(GPU_GROUP.strip().split(','))
SHOW_TRAIN_INFO_INTE = 20
SMRY_ITER = 200
SAVE_WEIGHTS_INTE = 20000 * 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
CLS_WEIGHT = 1.0
REG_WEIGHT = 1.0
USE_IOU_FACTOR = False
BATCH_SIZE = 1
EPSILON = 1e-5
MOMENTUM = 0.9
LR = 5e-4
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 = 'OHD-SJTU-ALL-600' # '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 = 800
IMG_MAX_LENGTH = 800
CLASS_NUM = 6
IMG_ROTATE = False
RGB2GRAY = False
VERTICAL_FLIP = False
HORIZONTAL_FLIP = True
IMAGE_PYRAMID = False
# --------------------------------------------- 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
USE_GN = False
NUM_SUBNET_CONV = 4
NUM_REFINE_STAGE = 1
USE_RELU = False
FPN_CHANNEL = 256
# ---------------------------------------------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 = [1, 1 / 2, 2., 1 / 3., 3., 5., 1 / 5.]
ANCHOR_ANGLES = [-90, -75, -60, -45, -30, -15]
ANCHOR_SCALE_FACTORS = None
USE_CENTER_OFFSET = True
METHOD = 'H'
USE_ANGLE_COND = False
ANGLE_RANGE = 90
# --------------------------------------------RPN config
SHARE_NET = True
USE_P5 = True
IOU_POSITIVE_THRESHOLD = 0.5
IOU_NEGATIVE_THRESHOLD = 0.4
REFINE_IOU_POSITIVE_THRESHOLD = [0.6, 0.7]
REFINE_IOU_NEGATIVE_THRESHOLD = [0.5, 0.6]
NMS = True
NMS_IOU_THRESHOLD = 0.1
MAXIMUM_DETECTIONS = 100
FILTERED_SCORE = 0.05
VIS_SCORE = 0.4
# --------------------------------------------MASK config
USE_SUPERVISED_MASK = False
MASK_TYPE = 'r' # r or h
BINARY_MASK = False
SIGMOID_ON_DOT = False
MASK_ACT_FET = True # weather use mask generate 256 channels to dot feat.
GENERATE_MASK_LIST = ["P3", "P4", "P5", "P6", "P7"]
ADDITION_LAYERS = [4, 4, 3, 2, 2] # add 4 layer to generate P2_mask, 2 layer to generate P3_mask
ENLAEGE_RF_LIST = ["P3", "P4", "P5", "P6", "P7"]
SUPERVISED_MASK_LOSS_WEIGHT = 1.0