-
Notifications
You must be signed in to change notification settings - Fork 2.8k
/
tinypose_256x256_hand.yml
145 lines (130 loc) · 3.05 KB
/
tinypose_256x256_hand.yml
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
use_gpu: true
log_iter: 5
save_dir: output
snapshot_epoch: 10
weights: output/tinypose_256x256_hand/model_final
epoch: 210
num_joints: &num_joints 21
pixel_std: &pixel_std 200
metric: KeyPointTopDownCOCOWholeBadyHandEval
num_classes: 1
train_height: &train_height 256
train_width: &train_width 256
trainsize: &trainsize [*train_width, *train_height]
hmsize: &hmsize [64, 64]
flip_perm: &flip_perm []
#####model
architecture: TopDownHRNet
TopDownHRNet:
backbone: LiteHRNet
post_process: HRNetPostProcess
flip_perm: *flip_perm
num_joints: *num_joints
width: &width 40
loss: KeyPointMSELoss
use_dark: true
LiteHRNet:
network_type: wider_naive
freeze_at: -1
freeze_norm: false
return_idx: [0]
KeyPointMSELoss:
use_target_weight: true
loss_scale: 1.0
#####optimizer
LearningRate:
base_lr: 0.002
schedulers:
- !PiecewiseDecay
milestones: [170, 200]
gamma: 0.1
- !LinearWarmup
start_factor: 0.001
steps: 500
OptimizerBuilder:
optimizer:
type: Adam
regularizer:
factor: 0.0
type: L2
#####data
TrainDataset:
!KeypointTopDownCocoWholeBodyHandDataset
image_dir: train2017
anno_path: annotations/coco_wholebody_train_v1.0.json
dataset_dir: dataset/coco
num_joints: *num_joints
trainsize: *trainsize
pixel_std: *pixel_std
EvalDataset:
!KeypointTopDownCocoWholeBodyHandDataset
image_dir: val2017
anno_path: annotations/coco_wholebody_val_v1.0.json
dataset_dir: dataset/coco
num_joints: *num_joints
trainsize: *trainsize
pixel_std: *pixel_std
TestDataset:
!ImageFolder
anno_path: dataset/coco/keypoint_imagelist.txt
worker_num: 2
global_mean: &global_mean [0.485, 0.456, 0.406]
global_std: &global_std [0.229, 0.224, 0.225]
TrainReader:
sample_transforms:
- TopDownRandomShiftBboxCenter:
shift_prob: 0.3
shift_factor: 0.16
- TopDownRandomFlip:
flip_prob: 0.5
flip_perm: *flip_perm
- TopDownGetRandomScaleRotation:
rot_prob: 0.6
rot_factor: 90
scale_factor: 0.3
# - AugmentationbyInformantionDropping:
# prob_cutout: 0.5
# offset_factor: 0.05
# num_patch: 1
# trainsize: *trainsize
- TopDownAffine:
trainsize: *trainsize
use_udp: true
- ToHeatmapsTopDown_DARK:
hmsize: *hmsize
sigma: 2
batch_transforms:
- NormalizeImage:
mean: *global_mean
std: *global_std
is_scale: true
- Permute: {}
batch_size: 128
shuffle: true
drop_last: false
EvalReader:
sample_transforms:
- TopDownAffine:
trainsize: *trainsize
use_udp: true
batch_transforms:
- NormalizeImage:
mean: *global_mean
std: *global_std
is_scale: true
- Permute: {}
batch_size: 128
TestReader:
inputs_def:
image_shape: [3, *train_height, *train_width]
sample_transforms:
- Decode: {}
- TopDownEvalAffine:
trainsize: *trainsize
- NormalizeImage:
mean: *global_mean
std: *global_std
is_scale: true
- Permute: {}
batch_size: 1
fuse_normalize: false