| dataset: | |
| video_processor: ShardedVideoProcessor | |
| bert_name: bert-base-uncased | |
| meta_processor: ShardedHow2MetaProcessor | |
| train_path: data/how2/how2_s3d_train.lst | |
| val_path: data/how2/how2_s3d_val.lst | |
| vfeat_dir: data/feat/feat_how2_s3d_shard_small | |
| text_processor: ShardedTextProcessor | |
| tfeat_dir: data/feat/feat_how2_s3d_shard_small/raw_caption_dedup.bert-base-uncased. | |
| aligner: MFMMLMAligner | |
| subsampling: 32 | |
| sampled_min_len: 8 | |
| sampled_max_len: 64 | |
| max_video_len: 32 | |
| max_len: 96 | |
| lazy_vfeat_mask: true | |
| mfm_probability: 0.15 | |
| mlm_probability: 0.15 | |
| mm_prob: 0.5 | |
| fairseq: | |
| common: | |
| tensorboard_logdir: run | |
| log_interval: 1000 | |
| fp16: true | |
| dataset: | |
| num_workers: 4 | |
| batch_size: 256 | |
| optimization: | |
| lr: | |
| - 5.0e-05 | |
| clip_norm: 2.0 | |
| optimizer: adam | |
| adam_betas: (0.9, 0.98) | |
| lr_scheduler: polynomial_decay | |
| total_num_update: 1000000 | |
| warmup_updates: 1000 | |
| weight_decay: 0.0 | |
| ddp_backend: no_c10d | |
| max_epoch: 15 | |
| checkpoint: | |
| save_dir: runs/mtm/vlm | |
| save_interval_updates: 1024 | |
| keep_interval_updates: 2 | |
| keep_last_epochs: 30 | |
| task_type: sweep_big | |
| slurm_config: big | |
| eval: | |
| save_path: runs/mtm/vlm | |
| model: | |
| model_cls: MMFusionMTM | |
| mm_encoder_cls: MMBertForMFMMLM | |
| use_seg_emb: true | |
| loss: | |
| loss_cls: MTM | |
| task: VLMTask | |