-
Notifications
You must be signed in to change notification settings - Fork 7
/
defaults.py
157 lines (136 loc) · 5.38 KB
/
defaults.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
148
149
150
151
152
153
154
155
156
157
from yacs.config import CfgNode as CN
_C = CN()
# -------------------------------------------------------- #
# Input #
# -------------------------------------------------------- #
_C.INPUT = CN()
_C.INPUT.DATASET = "CUHK-SYSU"
_C.INPUT.DATA_ROOT = "data/CUHK-SYSU"
# Size of the smallest side of the image
_C.INPUT.MIN_SIZE = 900
# Maximum size of the side of the image
_C.INPUT.MAX_SIZE = 1500
# TODO: support aspect ratio grouping
# Whether to use aspect ratio grouping for saving GPU memory
# _C.INPUT.ASPECT_RATIO_GROUPING_TRAIN = False
# Number of images per batch
_C.INPUT.BATCH_SIZE_TRAIN = 2
_C.INPUT.BATCH_SIZE_TEST = 1
# Number of data loading threads
_C.INPUT.NUM_WORKERS_TRAIN = 5
_C.INPUT.NUM_WORKERS_TEST = 1
# -------------------------------------------------------- #
# Solver #
# -------------------------------------------------------- #
_C.SOLVER = CN()
_C.SOLVER.MAX_EPOCHS = 20
# Learning rate settings
_C.SOLVER.BASE_LR = 0.003
# TODO: add config option WARMUP_EPOCHS
_C.SOLVER.WARMUP_FACTOR = 1.0 / 1000
# _C.SOLVER.WARMUP_EPOCHS = 1
# The epoch milestones to decrease the learning rate by GAMMA
_C.SOLVER.LR_DECAY_MILESTONES = [16]
_C.SOLVER.GAMMA = 0.1
_C.SOLVER.WEIGHT_DECAY = 0.0005
_C.SOLVER.SGD_MOMENTUM = 0.9
# Loss weight of RPN regression
_C.SOLVER.LW_RPN_REG = 1
# Loss weight of RPN classification
_C.SOLVER.LW_RPN_CLS = 1
# Loss weight of proposal regression
_C.SOLVER.LW_PROPOSAL_REG = 10
# Loss weight of proposal classification
_C.SOLVER.LW_PROPOSAL_CLS = 1
# Loss weight of box regression
_C.SOLVER.LW_BOX_REG = 1
# Loss weight of box classification
_C.SOLVER.LW_BOX_CLS = 1
# Loss weight of box OIM (i.e. Online Instance Matching)
_C.SOLVER.LW_BOX_REID = 1
# Set to negative value to disable gradient clipping
_C.SOLVER.CLIP_GRADIENTS = 10.0
# -------------------------------------------------------- #
# RPN #
# -------------------------------------------------------- #
_C.MODEL = CN()
_C.MODEL.BONE = "swin_tiny"
_C.MODEL.SEMANTIC_WEIGHT = 1.0
_C.MODEL.RPN = CN()
# NMS threshold used on RoIs
_C.MODEL.RPN.NMS_THRESH = 0.7
# Number of anchors per image used to train RPN
_C.MODEL.RPN.BATCH_SIZE_TRAIN = 256
# Target fraction of foreground examples per RPN minibatch
_C.MODEL.RPN.POS_FRAC_TRAIN = 0.5
# Overlap threshold for an anchor to be considered foreground (if >= POS_THRESH_TRAIN)
_C.MODEL.RPN.POS_THRESH_TRAIN = 0.7
# Overlap threshold for an anchor to be considered background (if < NEG_THRESH_TRAIN)
_C.MODEL.RPN.NEG_THRESH_TRAIN = 0.3
# Number of top scoring RPN RoIs to keep before applying NMS
_C.MODEL.RPN.PRE_NMS_TOPN_TRAIN = 12000
_C.MODEL.RPN.PRE_NMS_TOPN_TEST = 6000
# Number of top scoring RPN RoIs to keep after applying NMS
_C.MODEL.RPN.POST_NMS_TOPN_TRAIN = 2000
_C.MODEL.RPN.POST_NMS_TOPN_TEST = 300
# -------------------------------------------------------- #
# RoI head #
# -------------------------------------------------------- #
_C.MODEL.ROI_HEAD = CN()
# Whether to use bn neck (i.e. batch normalization after linear)
_C.MODEL.ROI_HEAD.BN_NECK = True
# Number of RoIs per image used to train RoI head
_C.MODEL.ROI_HEAD.BATCH_SIZE_TRAIN = 128
# Target fraction of foreground examples per RoI minibatch
_C.MODEL.ROI_HEAD.POS_FRAC_TRAIN = 0.5
# Overlap threshold for an RoI to be considered foreground (if >= POS_THRESH_TRAIN)
_C.MODEL.ROI_HEAD.POS_THRESH_TRAIN = 0.5
# Overlap threshold for an RoI to be considered background (if < NEG_THRESH_TRAIN)
_C.MODEL.ROI_HEAD.NEG_THRESH_TRAIN = 0.5
# Minimum score threshold
_C.MODEL.ROI_HEAD.SCORE_THRESH_TEST = 0.5
# NMS threshold used on boxes
_C.MODEL.ROI_HEAD.NMS_THRESH_TEST = 0.4
# Maximum number of detected objects
_C.MODEL.ROI_HEAD.DETECTIONS_PER_IMAGE_TEST = 300
# -------------------------------------------------------- #
# Loss #
# -------------------------------------------------------- #
_C.MODEL.LOSS = CN()
# Size of the lookup table in OIM
_C.MODEL.LOSS.LUT_SIZE = 5532
# Size of the circular queue in OIM
_C.MODEL.LOSS.CQ_SIZE = 5000
_C.MODEL.LOSS.OIM_MOMENTUM = 0.5
_C.MODEL.LOSS.OIM_SCALAR = 30.0
# -------------------------------------------------------- #
# Evaluation #
# -------------------------------------------------------- #
# The period to evaluate the model during training
_C.EVAL_PERIOD = 1
# Evaluation with GT boxes to verify the upper bound of person search performance
_C.EVAL_USE_GT = False
# Fast evaluation with cached features
_C.EVAL_USE_CACHE = False
# Evaluation with Context Bipartite Graph Matching (CBGM) algorithm
_C.EVAL_USE_CBGM = False
# -------------------------------------------------------- #
# Miscs #
# -------------------------------------------------------- #
# Save a checkpoint after every this number of epochs
_C.CKPT_PERIOD = 1
# The period (in terms of iterations) to display training losses
_C.DISP_PERIOD = 10
# Whether to use tensorboard for visualization
_C.TF_BOARD = True
# The device loading the model
_C.DEVICE = "cuda"
# Set seed to negative to fully randomize everything
_C.SEED = 1
# Directory where output files are written
_C.OUTPUT_DIR = "./output"
def get_default_cfg():
"""
Get a copy of the default config.
"""
return _C.clone()