/
stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d.py
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stgcn_8xb16-bone-u100-80e_ntu120-xsub-keypoint-2d.py
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_base_ = 'stgcn_8xb16-joint-u100-80e_ntu120-xsub-keypoint-2d.py'
dataset_type = 'PoseDataset'
ann_file = 'data/skeleton/ntu120_2d.pkl'
train_pipeline = [
dict(type='PreNormalize2D'),
dict(type='GenSkeFeat', dataset='coco', feats=['b']),
dict(type='UniformSampleFrames', clip_len=100),
dict(type='PoseDecode'),
dict(type='FormatGCNInput', num_person=2),
dict(type='PackActionInputs')
]
val_pipeline = [
dict(type='PreNormalize2D'),
dict(type='GenSkeFeat', dataset='coco', feats=['b']),
dict(
type='UniformSampleFrames', clip_len=100, num_clips=1, test_mode=True),
dict(type='PoseDecode'),
dict(type='FormatGCNInput', num_person=2),
dict(type='PackActionInputs')
]
test_pipeline = [
dict(type='PreNormalize2D'),
dict(type='GenSkeFeat', dataset='coco', feats=['b']),
dict(
type='UniformSampleFrames', clip_len=100, num_clips=10,
test_mode=True),
dict(type='PoseDecode'),
dict(type='FormatGCNInput', num_person=2),
dict(type='PackActionInputs')
]
train_dataloader = dict(
batch_size=16,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=5,
dataset=dict(
type=dataset_type,
ann_file=ann_file,
pipeline=train_pipeline,
split='xsub_train')))
val_dataloader = dict(
batch_size=16,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
ann_file=ann_file,
pipeline=val_pipeline,
split='xsub_val',
test_mode=True))
test_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
ann_file=ann_file,
pipeline=test_pipeline,
split='xsub_val',
test_mode=True))