mmf/projects/mmbt/configs/mmimdb/paper_ablations_reducedlabel.yaml [1:110]:
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model_config:
  mmf_transformer:
    transformer_base: bert-base-uncased
    num_labels: 24
    backend:
      type: huggingface
      freeze: false
      params: {}
    modalities:
    - type: text
      key: text
      position_dim: 512
      segment_id: 0
      embedding_dim: 768
      layer_norm_eps: 1e-12
      hidden_dropout_prob: 0.1
    - type: image
      key: image
      embedding_dim: 2048
      position_dim: 1
      segment_id: 1
      layer_norm_eps: 1e-12
      hidden_dropout_prob: 0.1
      encoder:
        type: resnet152
        params:
            pretrained: true
            pool_type: avg
            num_output_features: 1
    heads:
      - type: refiner_classifier
        refiner_config:
          type: refiner
          freeze: false
          lr_multiplier: 1.0
          hidden_size: 768
          vocab_size: 30522
          loss_type: "cosine"
          refiner_target_pooler: "average_k_from_last"
          refiner_target_layer_depth: 2
          modalities:
            - "text"
            - "image"
          weights:
            - 0.0
            - 0.0
        mlp_loss_config:
          config:
            type: mlp
            num_labels: 24
            hidden_size: 768
            hidden_dropout_prob: 0.1
            layer_norm_eps: 0.000001
            hidden_act: gelu
            pooler_name: bert_pooler
            num_layers: 1
          loss_name: classification_loss
          loss: logit_bce
          max_sample_size: 33
        ms_loss_weight: 0.0
        use_msloss: true
        num_labels: 24
    self_weight_decay: 0.997
dataset_config:
  mmimdb:
    return_features_info: false
    processors:
      text_processor:
        type: bert_tokenizer
        params:
          tokenizer_config:
            type: bert-base-uncased
            params:
              do_lower_case: true
          mask_probability: 0
          max_seq_length: 256


optimizer:
  type: adam_w
  params:
    lr: 5e-5
    eps: 1e-8

evaluation:
  metrics:
  - multilabel_macro_f1
  - multilabel_micro_f1

scheduler:
  type: warmup_linear
  params:
    num_warmup_steps: 0
    num_training_steps: 10000

training:
  batch_size: 32
  lr_scheduler: true
  # Don't forget to update schedule_attributes if you update this
  max_updates: 10000
  find_unused_parameters: true
  early_stop:
    criteria: mmimdb/multilabel_micro_f1
    minimize: false

checkpoint:
  pretrained_state_mapping:
    pooler: pooler
    backend.transformer: backend.transformer
    backend.embeddings: backend.embeddings
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projects/mmbt/configs/mmimdb/paper_ablations_reducedlabel.yaml [1:110]:
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model_config:
  mmf_transformer:
    transformer_base: bert-base-uncased
    num_labels: 24
    backend:
      type: huggingface
      freeze: false
      params: {}
    modalities:
    - type: text
      key: text
      position_dim: 512
      segment_id: 0
      embedding_dim: 768
      layer_norm_eps: 1e-12
      hidden_dropout_prob: 0.1
    - type: image
      key: image
      embedding_dim: 2048
      position_dim: 1
      segment_id: 1
      layer_norm_eps: 1e-12
      hidden_dropout_prob: 0.1
      encoder:
        type: resnet152
        params:
            pretrained: true
            pool_type: avg
            num_output_features: 1
    heads:
      - type: refiner_classifier
        refiner_config:
          type: refiner
          freeze: false
          lr_multiplier: 1.0
          hidden_size: 768
          vocab_size: 30522
          loss_type: "cosine"
          refiner_target_pooler: "average_k_from_last"
          refiner_target_layer_depth: 2
          modalities:
            - "text"
            - "image"
          weights:
            - 0.0
            - 0.0
        mlp_loss_config:
          config:
            type: mlp
            num_labels: 24
            hidden_size: 768
            hidden_dropout_prob: 0.1
            layer_norm_eps: 0.000001
            hidden_act: gelu
            pooler_name: bert_pooler
            num_layers: 1
          loss_name: classification_loss
          loss: logit_bce
          max_sample_size: 33
        ms_loss_weight: 0.0
        use_msloss: true
        num_labels: 24
    self_weight_decay: 0.997
dataset_config:
  mmimdb:
    return_features_info: false
    processors:
      text_processor:
        type: bert_tokenizer
        params:
          tokenizer_config:
            type: bert-base-uncased
            params:
              do_lower_case: true
          mask_probability: 0
          max_seq_length: 256


optimizer:
  type: adam_w
  params:
    lr: 5e-5
    eps: 1e-8

evaluation:
  metrics:
  - multilabel_macro_f1
  - multilabel_micro_f1

scheduler:
  type: warmup_linear
  params:
    num_warmup_steps: 0
    num_training_steps: 10000

training:
  batch_size: 32
  lr_scheduler: true
  # Don't forget to update schedule_attributes if you update this
  max_updates: 10000
  find_unused_parameters: true
  early_stop:
    criteria: mmimdb/multilabel_micro_f1
    minimize: false

checkpoint:
  pretrained_state_mapping:
    pooler: pooler
    backend.transformer: backend.transformer
    backend.embeddings: backend.embeddings
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