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backend_args = dict(backend='local')
codec = dict(
heatmap_size=(
72,
96,
),
input_size=(
288,
384,
),
sigma=3,
type='MSRAHeatmap',
unbiased=True)
data_mode = 'topdown'
dataset_type = 'CocoDataset'
default_scope = 'mmpose'
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=(
48,
96,
),
num_modules=1),
stage3=dict(
block='BASIC',
num_blocks=(
4,
4,
4,
),
num_branches=3,
num_channels=(
48,
96,
192,
),
num_modules=4),
stage4=dict(
block='BASIC',
num_blocks=(
4,
4,
4,
4,
),
num_branches=4,
num_channels=(
48,
96,
192,
384,
),
num_modules=3)),
in_channels=3,
init_cfg=dict(
checkpoint=
'/scratch/users/yonigoz/mmpose_data/ckpts/hrnet/td-hm_hrnet-w48_dark-8xb32-210e_coco-384x288-39c3c381_20220916.pth',
prefix='backbone',
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(
decoder=dict(
heatmap_size=(
72,
96,
),
input_size=(
288,
384,
),
sigma=3,
type='MSRAHeatmap',
unbiased=True),
deconv_out_channels=None,
in_channels=48,
loss=dict(type='KeypointMSELoss', use_target_weight=True),
out_channels=52,
type='HeatmapHead'),
test_cfg=dict(flip_mode='heatmap', flip_test=True, shift_heatmap=True),
type='TopdownPoseEstimator')
test_dataloader = dict(
batch_size=32,
dataset=dict(
data_mode='topdown',
data_prefix=dict(img=''),
data_root='',
pipeline=[
dict(type='LoadImage'),
dict(type='GetBBoxCenterScale'),
dict(input_size=(
288,
384,
), type='TopdownAffine'),
dict(type='PackPoseInputs'),
],
test_mode=True,
type='CocoDataset',
used_data_keys=[
'nose',
'left_eye',
'right_eye',
'left_ear',
'right_ear',
'left_shoulder',
'right_shoulder',
'left_elbow',
'right_elbow',
'left_wrist',
'right_wrist',
'left_hip',
'right_hip',
'left_knee',
'right_knee',
'left_ankle',
'right_ankle',
'sternum',
'rshoulder',
'lshoulder',
'r_lelbow',
'l_lelbow',
'r_melbow',
'l_melbow',
'r_lwrist',
'l_lwrist',
'r_mwrist',
'l_mwrist',
'r_ASIS',
'l_ASIS',
'r_PSIS',
'l_PSIS',
'r_knee',
'l_knee',
'r_mknee',
'l_mknee',
'r_ankle',
'l_ankle',
'r_mankle',
'l_mankle',
'r_5meta',
'l_5meta',
'r_toe',
'l_toe',
'r_big_toe',
'l_big_toe',
'l_calc',
'r_calc',
'C7',
'L2',
'T11',
'T6',
]),
drop_last=False,
num_workers=4,
persistent_workers=True,
sampler=dict(round_up=False, shuffle=False, type='DefaultSampler'))
visualizer = dict(
name='visualizer',
type='PoseLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
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