| 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 |
|
|
