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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 | |
| ) | |
| ) |