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# dataset settings
data_keys = ['motion', 'motion_mask', 'motion_length', 'clip_feat']
meta_keys = ['text', 'token']
train_pipeline = [
dict(
type='Normalize',
mean_path='data/datasets/human_ml3d/mean.npy',
std_path='data/datasets/human_ml3d/std.npy'),
dict(type='Crop', crop_size=196),
dict(type='ToTensor', keys=data_keys),
dict(type='Collect', keys=data_keys, meta_keys=meta_keys)
]
data = dict(
samples_per_gpu=128,
workers_per_gpu=1,
train=dict(
type='RepeatDataset',
dataset=dict(
type='TextMotionDataset',
dataset_name='human_ml3d',
data_prefix='data',
pipeline=train_pipeline,
ann_file='train.txt',
motion_dir='motions',
text_dir='texts',
token_dir='tokens',
clip_feat_dir='clip_feats',
),
times=200
),
test=dict(
type='TextMotionDataset',
dataset_name='human_ml3d',
data_prefix='data',
pipeline=train_pipeline,
ann_file='test.txt',
motion_dir='motions',
text_dir='texts',
token_dir='tokens',
clip_feat_dir='clip_feats',
eval_cfg=dict(
shuffle_indexes=True,
replication_times=20,
replication_reduction='statistics',
text_encoder_name='human_ml3d',
text_encoder_path='data/evaluators/human_ml3d/finest.tar',
motion_encoder_name='human_ml3d',
motion_encoder_path='data/evaluators/human_ml3d/finest.tar',
metrics=[
dict(type='R Precision', batch_size=32, top_k=3),
dict(type='Matching Score', batch_size=32),
dict(type='FID'),
dict(type='Diversity', num_samples=300),
dict(type='MultiModality', num_samples=100, num_repeats=30, num_picks=10)
]
),
test_mode=True
)
) |