primateface-models / pose /hrnetv2_w18_dark_68kpt_config.py
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auto_scale_lr = dict(base_batch_size=256)
backend_args = dict(backend='local')
codec = dict(
heatmap_size=(
64,
64,
),
input_size=(
256,
256,
),
sigma=2,
type='MSRAHeatmap',
unbiased=True)
custom_hooks = [
dict(type='SyncBuffersHook'),
]
data_mode = 'topdown'
data_root = 'A:\\NonEnclosureProjects\\inprep\\PrimateFace\\data\\annos_from_rex'
dataset_info = dict(
dataset_name='coco_wholebody_face',
joint_weights=[
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
],
keypoint_info=dict({
0:
dict(
color=[
255,
0,
0,
], id=0, name='face-0', swap='face-16', type=''),
1:
dict(
color=[
255,
0,
0,
], id=1, name='face-1', swap='face-15', type=''),
10:
dict(
color=[
255,
0,
0,
], id=10, name='face-10', swap='face-6', type=''),
11:
dict(
color=[
255,
0,
0,
], id=11, name='face-11', swap='face-5', type=''),
12:
dict(
color=[
255,
0,
0,
], id=12, name='face-12', swap='face-4', type=''),
13:
dict(
color=[
255,
0,
0,
], id=13, name='face-13', swap='face-3', type=''),
14:
dict(
color=[
255,
0,
0,
], id=14, name='face-14', swap='face-2', type=''),
15:
dict(
color=[
255,
0,
0,
], id=15, name='face-15', swap='face-1', type=''),
16:
dict(
color=[
255,
0,
0,
], id=16, name='face-16', swap='face-0', type=''),
17:
dict(
color=[
255,
0,
0,
],
id=17,
name='face-17',
swap='face-26',
type=''),
18:
dict(
color=[
255,
0,
0,
],
id=18,
name='face-18',
swap='face-25',
type=''),
19:
dict(
color=[
255,
0,
0,
],
id=19,
name='face-19',
swap='face-24',
type=''),
2:
dict(
color=[
255,
0,
0,
], id=2, name='face-2', swap='face-14', type=''),
20:
dict(
color=[
255,
0,
0,
],
id=20,
name='face-20',
swap='face-23',
type=''),
21:
dict(
color=[
255,
0,
0,
],
id=21,
name='face-21',
swap='face-22',
type=''),
22:
dict(
color=[
255,
0,
0,
],
id=22,
name='face-22',
swap='face-21',
type=''),
23:
dict(
color=[
255,
0,
0,
],
id=23,
name='face-23',
swap='face-20',
type=''),
24:
dict(
color=[
255,
0,
0,
],
id=24,
name='face-24',
swap='face-19',
type=''),
25:
dict(
color=[
255,
0,
0,
],
id=25,
name='face-25',
swap='face-18',
type=''),
26:
dict(
color=[
255,
0,
0,
],
id=26,
name='face-26',
swap='face-17',
type=''),
27:
dict(color=[
255,
0,
0,
], id=27, name='face-27', swap='', type=''),
28:
dict(color=[
255,
0,
0,
], id=28, name='face-28', swap='', type=''),
29:
dict(color=[
255,
0,
0,
], id=29, name='face-29', swap='', type=''),
3:
dict(
color=[
255,
0,
0,
], id=3, name='face-3', swap='face-13', type=''),
30:
dict(color=[
255,
0,
0,
], id=30, name='face-30', swap='', type=''),
31:
dict(
color=[
255,
0,
0,
],
id=31,
name='face-31',
swap='face-35',
type=''),
32:
dict(
color=[
255,
0,
0,
],
id=32,
name='face-32',
swap='face-34',
type=''),
33:
dict(color=[
255,
0,
0,
], id=33, name='face-33', swap='', type=''),
34:
dict(
color=[
255,
0,
0,
],
id=34,
name='face-34',
swap='face-32',
type=''),
35:
dict(
color=[
255,
0,
0,
],
id=35,
name='face-35',
swap='face-31',
type=''),
36:
dict(
color=[
255,
0,
0,
],
id=36,
name='face-36',
swap='face-45',
type=''),
37:
dict(
color=[
255,
0,
0,
],
id=37,
name='face-37',
swap='face-44',
type=''),
38:
dict(
color=[
255,
0,
0,
],
id=38,
name='face-38',
swap='face-43',
type=''),
39:
dict(
color=[
255,
0,
0,
],
id=39,
name='face-39',
swap='face-42',
type=''),
4:
dict(
color=[
255,
0,
0,
], id=4, name='face-4', swap='face-12', type=''),
40:
dict(
color=[
255,
0,
0,
],
id=40,
name='face-40',
swap='face-47',
type=''),
41:
dict(
color=[
255,
0,
0,
],
id=41,
name='face-41',
swap='face-46',
type=''),
42:
dict(
color=[
255,
0,
0,
],
id=42,
name='face-42',
swap='face-39',
type=''),
43:
dict(
color=[
255,
0,
0,
],
id=43,
name='face-43',
swap='face-38',
type=''),
44:
dict(
color=[
255,
0,
0,
],
id=44,
name='face-44',
swap='face-37',
type=''),
45:
dict(
color=[
255,
0,
0,
],
id=45,
name='face-45',
swap='face-36',
type=''),
46:
dict(
color=[
255,
0,
0,
],
id=46,
name='face-46',
swap='face-41',
type=''),
47:
dict(
color=[
255,
0,
0,
],
id=47,
name='face-47',
swap='face-40',
type=''),
48:
dict(
color=[
255,
0,
0,
],
id=48,
name='face-48',
swap='face-54',
type=''),
49:
dict(
color=[
255,
0,
0,
],
id=49,
name='face-49',
swap='face-53',
type=''),
5:
dict(
color=[
255,
0,
0,
], id=5, name='face-5', swap='face-11', type=''),
50:
dict(
color=[
255,
0,
0,
],
id=50,
name='face-50',
swap='face-52',
type=''),
51:
dict(color=[
255,
0,
0,
], id=52, name='face-51', swap='', type=''),
52:
dict(
color=[
255,
0,
0,
],
id=52,
name='face-52',
swap='face-50',
type=''),
53:
dict(
color=[
255,
0,
0,
],
id=53,
name='face-53',
swap='face-49',
type=''),
54:
dict(
color=[
255,
0,
0,
],
id=54,
name='face-54',
swap='face-48',
type=''),
55:
dict(
color=[
255,
0,
0,
],
id=55,
name='face-55',
swap='face-59',
type=''),
56:
dict(
color=[
255,
0,
0,
],
id=56,
name='face-56',
swap='face-58',
type=''),
57:
dict(color=[
255,
0,
0,
], id=57, name='face-57', swap='', type=''),
58:
dict(
color=[
255,
0,
0,
],
id=58,
name='face-58',
swap='face-56',
type=''),
59:
dict(
color=[
255,
0,
0,
],
id=59,
name='face-59',
swap='face-55',
type=''),
6:
dict(
color=[
255,
0,
0,
], id=6, name='face-6', swap='face-10', type=''),
60:
dict(
color=[
255,
0,
0,
],
id=60,
name='face-60',
swap='face-64',
type=''),
61:
dict(
color=[
255,
0,
0,
],
id=61,
name='face-61',
swap='face-63',
type=''),
62:
dict(color=[
255,
0,
0,
], id=62, name='face-62', swap='', type=''),
63:
dict(
color=[
255,
0,
0,
],
id=63,
name='face-63',
swap='face-61',
type=''),
64:
dict(
color=[
255,
0,
0,
],
id=64,
name='face-64',
swap='face-60',
type=''),
65:
dict(
color=[
255,
0,
0,
],
id=65,
name='face-65',
swap='face-67',
type=''),
66:
dict(color=[
255,
0,
0,
], id=66, name='face-66', swap='', type=''),
67:
dict(
color=[
255,
0,
0,
],
id=67,
name='face-67',
swap='face-65',
type=''),
7:
dict(color=[
255,
0,
0,
], id=7, name='face-7', swap='face-9', type=''),
8:
dict(color=[
255,
0,
0,
], id=8, name='face-8', swap='', type=''),
9:
dict(color=[
255,
0,
0,
], id=9, name='face-9', swap='face-7', type='')
}),
paper_info=dict(
author=
'Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping',
container=
'Proceedings of the European Conference on Computer Vision (ECCV)',
homepage='https://github.com/jin-s13/COCO-WholeBody/',
title='Whole-Body Human Pose Estimation in the Wild',
year='2020'),
sigmas=[
0.042,
0.043,
0.044,
0.043,
0.04,
0.035,
0.031,
0.025,
0.02,
0.023,
0.029,
0.032,
0.037,
0.038,
0.043,
0.041,
0.045,
0.013,
0.012,
0.011,
0.011,
0.012,
0.012,
0.011,
0.011,
0.013,
0.015,
0.009,
0.007,
0.007,
0.007,
0.012,
0.009,
0.008,
0.016,
0.01,
0.017,
0.011,
0.009,
0.011,
0.009,
0.007,
0.013,
0.008,
0.011,
0.012,
0.01,
0.034,
0.008,
0.008,
0.009,
0.008,
0.008,
0.007,
0.01,
0.008,
0.009,
0.009,
0.009,
0.007,
0.007,
0.008,
0.011,
0.008,
0.008,
0.008,
0.01,
0.008,
],
skeleton_info=dict())
dataset_type = 'CocoWholeBodyFaceDataset'
default_hooks = dict(
badcase=dict(
badcase_thr=5,
enable=False,
metric_type='loss',
out_dir='badcase',
type='BadCaseAnalysisHook'),
checkpoint=dict(
interval=1, rule='less', save_best='NME', type='CheckpointHook'),
logger=dict(interval=50, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
timer=dict(type='IterTimerHook'),
visualization=dict(enable=False, type='PoseVisualizationHook'))
default_scope = 'mmpose'
env_cfg = dict(
cudnn_benchmark=False,
dist_cfg=dict(backend='gloo'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
launcher = 'pytorch'
load_from = None
log_level = 'INFO'
log_processor = dict(
by_epoch=True, num_digits=6, type='LogProcessor', window_size=50)
model = dict(
backbone=dict(
extra=dict(
stage1=dict(
block='BOTTLENECK',
num_blocks=(4, ),
num_branches=1,
num_channels=(64, ),
num_modules=1),
stage2=dict(
block='BASIC',
num_blocks=(
4,
4,
),
num_branches=2,
num_channels=(
18,
36,
),
num_modules=1),
stage3=dict(
block='BASIC',
num_blocks=(
4,
4,
4,
),
num_branches=3,
num_channels=(
18,
36,
72,
),
num_modules=4),
stage4=dict(
block='BASIC',
multiscale_output=True,
num_blocks=(
4,
4,
4,
4,
),
num_branches=4,
num_channels=(
18,
36,
72,
144,
),
num_modules=3),
upsample=dict(align_corners=False, mode='bilinear')),
in_channels=3,
init_cfg=dict(
checkpoint='open-mmlab://msra/hrnetv2_w18', type='Pretrained'),
type='HRNet'),
data_preprocessor=dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
std=[
58.395,
57.12,
57.375,
],
type='PoseDataPreprocessor'),
head=dict(
conv_kernel_sizes=(1, ),
conv_out_channels=(270, ),
decoder=dict(
heatmap_size=(
64,
64,
),
input_size=(
256,
256,
),
sigma=2,
type='MSRAHeatmap',
unbiased=True),
deconv_out_channels=None,
in_channels=270,
loss=dict(type='KeypointMSELoss', use_target_weight=True),
out_channels=68,
type='HeatmapHead'),
neck=dict(concat=True, type='FeatureMapProcessor'),
test_cfg=dict(flip_mode='heatmap', flip_test=True, shift_heatmap=True),
type='TopdownPoseEstimator')
optim_wrapper = dict(
loss_scale='dynamic',
optimizer=dict(lr=0.002, type='Adam'),
type='AmpOptimWrapper')
param_scheduler = [
dict(
begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'),
dict(
begin=0,
by_epoch=True,
end=210,
gamma=0.1,
milestones=[
40,
55,
],
type='MultiStepLR'),
]
resume = False
test_cfg = dict()
test_dataloader = dict(
batch_size=32,
dataset=dict(
ann_file=
'A:\\NonEnclosureProjects\\inprep\\PrimateFace\\data\\annos_from_rex\\test.json',
data_mode='topdown',
pipeline=[
dict(type='LoadImage'),
dict(type='GetBBoxCenterScale'),
dict(input_size=(
256,
256,
), type='TopdownAffine'),
dict(type='PackPoseInputs'),
],
test_mode=True,
type='CocoWholeBodyFaceDataset'),
drop_last=False,
num_workers=2,
persistent_workers=True,
sampler=dict(round_up=False, shuffle=False, type='DefaultSampler'))
test_evaluator = dict(norm_mode='keypoint_distance', type='NME')
train_cfg = dict(by_epoch=True, max_epochs=60, val_interval=1)
train_dataloader = dict(
batch_size=64,
dataset=dict(
ann_file=
'A:\\NonEnclosureProjects\\inprep\\PrimateFace\\data\\annos_from_rex\\train.json',
data_mode='topdown',
pipeline=[
dict(type='LoadImage'),
dict(type='GetBBoxCenterScale'),
dict(direction='horizontal', type='RandomFlip'),
dict(direction='vertical', type='RandomFlip'),
dict(
rotate_factor=60,
scale_factor=(
0.75,
1.25,
),
type='RandomBBoxTransform'),
dict(input_size=(
256,
256,
), type='TopdownAffine'),
dict(
encoder=dict(
heatmap_size=(
64,
64,
),
input_size=(
256,
256,
),
sigma=2,
type='MSRAHeatmap',
unbiased=True),
type='GenerateTarget'),
dict(type='PackPoseInputs'),
],
type='CocoWholeBodyFaceDataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=True, type='DefaultSampler'))
train_pipeline = [
dict(type='LoadImage'),
dict(type='GetBBoxCenterScale'),
dict(direction='horizontal', type='RandomFlip'),
dict(direction='vertical', type='RandomFlip'),
dict(
rotate_factor=60,
scale_factor=(
0.75,
1.25,
),
type='RandomBBoxTransform'),
dict(input_size=(
256,
256,
), type='TopdownAffine'),
dict(
encoder=dict(
heatmap_size=(
64,
64,
),
input_size=(
256,
256,
),
sigma=2,
type='MSRAHeatmap',
unbiased=True),
type='GenerateTarget'),
dict(type='PackPoseInputs'),
]
val_cfg = dict()
val_dataloader = dict(
batch_size=32,
dataset=dict(
ann_file=
'A:\\NonEnclosureProjects\\inprep\\PrimateFace\\data\\annos_from_rex\\val.json',
data_mode='topdown',
pipeline=[
dict(type='LoadImage'),
dict(type='GetBBoxCenterScale'),
dict(input_size=(
256,
256,
), type='TopdownAffine'),
dict(type='PackPoseInputs'),
],
test_mode=True,
type='CocoWholeBodyFaceDataset'),
drop_last=False,
num_workers=2,
persistent_workers=True,
sampler=dict(round_up=False, shuffle=False, type='DefaultSampler'))
val_evaluator = dict(norm_mode='keypoint_distance', type='NME')
val_pipeline = [
dict(type='LoadImage'),
dict(type='GetBBoxCenterScale'),
dict(input_size=(
256,
256,
), type='TopdownAffine'),
dict(type='PackPoseInputs'),
]
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='PoseLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = 'A:\\NonEnclosureProjects\\inprep\\PrimateFace\\results\\hrnet-dark_rex_4.5k_68kpt_250528'