-
Notifications
You must be signed in to change notification settings - Fork 1.2k
/
td-hm_4xmspn50_8xb32-210e_coco-256x192.py
152 lines (141 loc) · 4.06 KB
/
td-hm_4xmspn50_8xb32-210e_coco-256x192.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
_base_ = ['../../../_base_/default_runtime.py']
# runtime
train_cfg = dict(max_epochs=210, val_interval=10)
# optimizer
optim_wrapper = dict(optimizer=dict(
type='Adam',
lr=5e-3,
))
# learning policy
param_scheduler = [
dict(
type='LinearLR', begin=0, end=500, start_factor=0.001,
by_epoch=False), # warm-up
dict(
type='MultiStepLR',
begin=0,
end=210,
milestones=[170, 200],
gamma=0.1,
by_epoch=True)
]
# automatically scaling LR based on the actual training batch size
auto_scale_lr = dict(base_batch_size=256)
# hooks
default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater'))
# codec settings
# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach.
kernel_sizes = [15, 11, 9, 7, 5]
codec = [
dict(
type='MegviiHeatmap',
input_size=(192, 256),
heatmap_size=(48, 64),
kernel_size=kernel_size) for kernel_size in kernel_sizes
]
# model settings
model = dict(
type='TopdownPoseEstimator',
data_preprocessor=dict(
type='PoseDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='MSPN',
unit_channels=256,
num_stages=4,
num_units=4,
num_blocks=[3, 4, 6, 3],
norm_cfg=dict(type='BN'),
init_cfg=dict(
type='Pretrained',
checkpoint='torchvision://resnet50',
)),
head=dict(
type='MSPNHead',
out_shape=(64, 48),
unit_channels=256,
out_channels=17,
num_stages=4,
num_units=4,
norm_cfg=dict(type='BN'),
# each sub list is for a stage
# and each element in each list is for a unit
level_indices=[0, 1, 2, 3] * 3 + [1, 2, 3, 4],
loss=([
dict(
type='KeypointMSELoss',
use_target_weight=True,
loss_weight=0.25)
] * 3 + [
dict(
type='KeypointOHKMMSELoss',
use_target_weight=True,
loss_weight=1.)
]) * 4,
decoder=codec[-1]),
test_cfg=dict(
flip_test=True,
flip_mode='heatmap',
shift_heatmap=False,
))
# base dataset settings
dataset_type = 'CocoDataset'
data_mode = 'topdown'
data_root = 'data/coco/'
# pipelines
train_pipeline = [
dict(type='LoadImage'),
dict(type='GetBBoxCenterScale'),
dict(type='RandomFlip', direction='horizontal'),
dict(type='RandomHalfBody'),
dict(type='RandomBBoxTransform'),
dict(type='TopdownAffine', input_size=codec[0]['input_size']),
dict(type='GenerateTarget', multilevel=True, encoder=codec),
dict(type='PackPoseInputs')
]
val_pipeline = [
dict(type='LoadImage'),
dict(type='GetBBoxCenterScale'),
dict(type='TopdownAffine', input_size=codec[0]['input_size']),
dict(type='PackPoseInputs')
]
# data loaders
train_dataloader = dict(
batch_size=32,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_mode=data_mode,
ann_file='annotations/person_keypoints_train2017.json',
data_prefix=dict(img='train2017/'),
pipeline=train_pipeline,
))
val_dataloader = dict(
batch_size=32,
num_workers=4,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_mode=data_mode,
ann_file='annotations/person_keypoints_val2017.json',
bbox_file='data/coco/person_detection_results/'
'COCO_val2017_detections_AP_H_56_person.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=val_pipeline,
))
test_dataloader = val_dataloader
# evaluators
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/person_keypoints_val2017.json',
nms_mode='none')
test_evaluator = val_evaluator