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modality = 'j'
graph = 'nturgb+d'
work_dir = './work_dirs/test_prototype/ntu60_xview/j_2'
model = dict(
type='RecognizerGCN_7_1_1',
backbone=dict(
type='GCN_7_1_2',
tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'],
graph_cfg=dict(
layout='nturgb+d',
mode='random',
num_filter=8,
init_off=0.04,
init_std=0.02)),
cls_head=dict(type='SimpleHead_7_4_2', num_classes=60, in_channels=384))
dataset_type = 'PoseDataset'
ann_file = '/data/lhd/pyskl_data/nturgbd/ntu60_3danno.pkl'
train_pipeline = [
dict(type='PreNormalize3D', align_spine=False),
dict(type='RandomRot', theta=0.2),
dict(type='GenSkeFeat', feats=['j']),
dict(type='UniformSampleDecode', clip_len=100),
dict(type='FormatGCNInput'),
dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['keypoint'])
]
val_pipeline = [
dict(type='PreNormalize3D', align_spine=False),
dict(type='GenSkeFeat', feats=['j']),
dict(type='UniformSampleDecode', clip_len=100, num_clips=1),
dict(type='FormatGCNInput'),
dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['keypoint'])
]
test_pipeline = [
dict(type='PreNormalize3D', align_spine=False),
dict(type='GenSkeFeat', feats=['j']),
dict(type='UniformSampleDecode', clip_len=100, num_clips=10),
dict(type='FormatGCNInput'),
dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['keypoint'])
]
data = dict(
videos_per_gpu=16,
workers_per_gpu=4,
test_dataloader=dict(videos_per_gpu=1),
train=dict(
type='PoseDataset',
ann_file='/data/lhd/pyskl_data/nturgbd/ntu60_3danno.pkl',
pipeline=[
dict(type='PreNormalize3D', align_spine=False),
dict(type='RandomRot', theta=0.2),
dict(type='GenSkeFeat', feats=['j']),
dict(type='UniformSampleDecode', clip_len=100),
dict(type='FormatGCNInput'),
dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['keypoint'])
],
split='xview_train'),
val=dict(
type='PoseDataset',
ann_file='/data/lhd/pyskl_data/nturgbd/ntu60_3danno.pkl',
pipeline=[
dict(type='PreNormalize3D', align_spine=False),
dict(type='GenSkeFeat', feats=['j']),
dict(type='UniformSampleDecode', clip_len=100, num_clips=1),
dict(type='FormatGCNInput'),
dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['keypoint'])
],
split='xview_val'),
test=dict(
type='PoseDataset',
ann_file='/data/lhd/pyskl_data/nturgbd/ntu60_3danno.pkl',
pipeline=[
dict(type='PreNormalize3D', align_spine=False),
dict(type='GenSkeFeat', feats=['j']),
dict(type='UniformSampleDecode', clip_len=100, num_clips=10),
dict(type='FormatGCNInput'),
dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['keypoint'])
],
split='xview_val'))
optimizer = dict(
type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True)
optimizer_config = dict(grad_clip=None)
lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False)
total_epochs = 150
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metrics=['top_k_accuracy'])
log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
gpu_ids = range(0, 1)
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