|
|
_base_ = 'mmaction::_base_/default_runtime.py'
|
|
|
|
|
|
custom_imports = dict(imports='models')
|
|
|
|
|
|
model = dict(
|
|
|
type='RecognizerGCN',
|
|
|
backbone=dict(
|
|
|
type='MSG3D', graph_cfg=dict(layout='nturgb+d', mode='binary_adj')),
|
|
|
cls_head=dict(type='GCNHead', num_classes=60, in_channels=384))
|
|
|
|
|
|
dataset_type = 'PoseDataset'
|
|
|
ann_file = 'data/skeleton/ntu60_3d.pkl'
|
|
|
train_pipeline = [
|
|
|
dict(type='PreNormalize3D'),
|
|
|
dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']),
|
|
|
dict(type='UniformSampleFrames', clip_len=100),
|
|
|
dict(type='PoseDecode'),
|
|
|
dict(type='FormatGCNInput', num_person=2),
|
|
|
dict(type='PackActionInputs')
|
|
|
]
|
|
|
val_pipeline = [
|
|
|
dict(type='PreNormalize3D'),
|
|
|
dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']),
|
|
|
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='PreNormalize3D'),
|
|
|
dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']),
|
|
|
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))
|
|
|
|
|
|
val_evaluator = [dict(type='AccMetric')]
|
|
|
test_evaluator = val_evaluator
|
|
|
|
|
|
train_cfg = dict(
|
|
|
type='EpochBasedTrainLoop', max_epochs=16, val_begin=1, val_interval=1)
|
|
|
val_cfg = dict(type='ValLoop')
|
|
|
test_cfg = dict(type='TestLoop')
|
|
|
|
|
|
param_scheduler = [
|
|
|
dict(
|
|
|
type='CosineAnnealingLR',
|
|
|
eta_min=0,
|
|
|
T_max=16,
|
|
|
by_epoch=True,
|
|
|
convert_to_iter_based=True)
|
|
|
]
|
|
|
|
|
|
optim_wrapper = dict(
|
|
|
optimizer=dict(
|
|
|
type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True))
|
|
|
|
|
|
default_hooks = dict(checkpoint=dict(interval=1), logger=dict(interval=100))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
auto_scale_lr = dict(enable=False, base_batch_size=128)
|
|
|
|
