configs/BERT_L12_H192_experiments/4tasks_training.yaml

_BASE_: "base_model_bert_l12_h192.yaml"

SHARED_TARGETS:

  - 
    NAME: 'ImageNet1k'
    SHARED_TARGETS_CFG:
      FILE_PATH: 'open_source_dataset/imagenet_class_name_CLIP_with_endoftext.pkl'
      DISTRIBUTED: False
  
  -
    NAME: 'Vocab_Word'
    SHARED_TARGETS_CFG:
      FILE_PATH: 'open_source_dataset/vocabulary_CLIP_with_endoftext.pkl'
      DISTRIBUTED: True



TASKS:

  - 
    NAME: imagenet
    DATASETS:
      TRAIN: 'ImageNetDataset'
      # VAL: 'ImageNetDataset'
      TASK_TYPE: 'image_classification'
      DATASET_NAME: 'ImageNet1k'
      TARGET_SET: ['ImageNet1k']
      
    DATALOADER:
      TRAIN_BATCH_SIZE: 4
      # TEST_BATCH_SIZE: 2
      NUM_WORKERS: 4
      FEATS_FOLDER: 'open_source_dataset/imagenet'
      S3_PATH: 'cluster2:s3://imagenet'
      ANNO_FOLDER:  'open_source_dataset/imagenet/meta'
      SAMPLING_WEIGHT: 1.0
      CLASS_NAME_FILE: 'open_source_dataset/imagenet_class_name.pkl'
      MIXUP: 0.8
      CUTMIX: 1.0
      MIXUP_PROB: 1.0
      MIXUP_SWITCH_PROB: 0.5
      MIXUP_MODE: 'batch'
      MIXUP_LABEL_SMOOTHING: 0.1
    MODEL:
      MAX_SEQ_LEN: -1
      LABELS_NUM: 1000
      TEMP_NAME: logit_scale_img_cls
    LOSSES:
      NAMES: ['SoftTargetCrossEntropy', 'Accuracy']
      LOSS_WEIGHT: 1.0
      REDUCTION: 'mean'
      # LOSS_FP32: True
    INFERENCE:
      NAME: 'ImageNetEvaler'
      ID_KEY: 'image_id'
      VALUE: 'cls_logits'
      VAL_ANNFILE: 'open_source_dataset/imagenet/meta/val.txt'
      TEST_ANNFILE: ''
      GENERATION_MODE: False

  -
    NAME: mscoco_caption
    DATASETS:
      TRAIN: 'ImageTextPairDataset'
      # VAL: 'ImageTextPairDataset'
      # TEST: 'ImageTextPairDataset'
      TASK_TYPE: 'image_caption'
      DATASET_NAME: 'MSCOCO'
      TARGET_SET: ['Vocab_Word']
    DATALOADER:
      TRAIN_BATCH_SIZE: 64
      TEST_BATCH_SIZE: 32
      NUM_WORKERS: 4
      FEATS_FOLDER: 'open_source_dataset/mscoco_dataset/coco_origin'
      ANNO_FOLDER:  'open_source_dataset/mscoco_dataset/new_annotations'
      S3_PATH: 's3://coco/'
      SEQ_PER_SAMPLE:  1
      CACHE_MODE: True
      CIRCULAR_CACHE_MODE: False
      ZIP_MODE: False
      CACHE_ORIGIN_IMAGE: False
      RANDOM_CAPTION: False
      AS_NUMPY_AS_POSSIBLE: False
      SAMPLING_WEIGHT: 1.0
      TRANSFORM: 'clip_transforms'
      RANDOM_MASK: True
    MODEL:
      MAX_SEQ_LEN: 50
      EVAL_MAX_SEQ_LEN: 21
      TEMP_NAME: logit_scale_caption
    LOSSES:
      NAMES: ['CrossEntropy', 'Accuracy']
      LOSS_WEIGHT: 0.33333
      REDUCTION: 'mean'
    DECODE_STRATEGY:
      NAME: 'CaptionBeamSearcherV3'
      BEAM_SIZE: 2
      # LEN_PENALTY: 1.0
    INFERENCE:
      NAME: 'COCOEvaler'
      VOCAB: 'CLIP'
      ID_KEY: 'image_id'
      VALUE: 'caption'
      VAL_ANNFILE: 'open_source_dataset/mscoco_dataset/new_annotations/captions_val5k.json'
      TEST_ANNFILE: 'open_source_dataset/mscoco_dataset/new_annotations/captions_test5k.json'
      GENERATION_MODE: True

  -
    NAME: yfcc_caption
    DATASETS:
      TRAIN: 'ImageTextPairDataset'
      TASK_TYPE: 'image_caption'
      DATASET_NAME: 'YFCC'
      TARGET_SET: ['Vocab_Word']
    DATALOADER:
      TRAIN_BATCH_SIZE: 64
      TEST_BATCH_SIZE: 32
      NUM_WORKERS: 2
      S3_ANNO_FOLDER:  'cluster2:s3://yfcc'
      ANNO_FOLDER:  'open_source_dataset/yfcc'
      ANNO_FILENAME: 'yfcc100m_subset_available_untokenized.json'
      FEATS_FOLDER: 'open_source_dataset/yfcc/'
      S3_PATH: 'cluster2:s3://yfcc/'
      SEQ_PER_SAMPLE:  1
      SAMPLER: NodeDistributed
      CACHE_MODE: True
      CIRCULAR_CACHE_MODE: False
      ZIP_MODE: False
      CACHE_ORIGIN_IMAGE: False
      RANDOM_CAPTION: True
      AS_NUMPY_AS_POSSIBLE: False
      SAMPLING_WEIGHT: 1.0
      TRANSFORM: 'clip_transforms'
    MODEL:
      MAX_SEQ_LEN: 50
      TEMP_NAME: logit_scale_caption
    LOSSES:
      NAMES: ['CrossEntropy', 'Accuracy']
      LOSS_WEIGHT: 1.0
      REDUCTION: 'mean'
    INFERENCE:
      VOCAB: 'CLIP'
      GENERATION_MODE: False

  -
    NAME: cc12m_caption
    DATASETS:
      TRAIN: 'ImageTextPairDataset'
      TASK_TYPE: 'image_caption'
      DATASET_NAME: 'CC12M'
      TARGET_SET: ['Vocab_Word']
    DATALOADER:
      TRAIN_BATCH_SIZE: 64
      TEST_BATCH_SIZE: 32
      NUM_WORKERS: 2
      S3_ANNO_FOLDER:  's3://cc12m/'
      ANNO_FOLDER:  'open_source_dataset/c12m/'
      ANNO_FILENAME: 'train_available.json'
      FEATS_FOLDER: 'open_source_dataset/c12m/'
      S3_PATH: 's3://cc12m/'
      SEQ_PER_SAMPLE:  1
      SAMPLER: NodeDistributed
      CACHE_MODE: True
      CIRCULAR_CACHE_MODE: False
      ZIP_MODE: False
      CACHE_ORIGIN_IMAGE: False
      RANDOM_CAPTION: False
      AS_NUMPY_AS_POSSIBLE: False
      SAMPLING_WEIGHT: 1.0
      TRANSFORM: 'clip_transforms'
    MODEL:
      MAX_SEQ_LEN: 50
      TEMP_NAME: logit_scale_caption
    LOSSES:
      NAMES: ['CrossEntropy', 'Accuracy']
      LOSS_WEIGHT: 1.0
      REDUCTION: 'mean'
    INFERENCE:
      VOCAB: 'CLIP'
      GENERATION_MODE: False

  -
    NAME: cc3m_caption
    DATASETS:
      TRAIN: 'ImageTextPairDataset'
      TASK_TYPE: 'image_caption'
      DATASET_NAME: 'CC3M'
      TARGET_SET: ['Vocab_Word']
    DATALOADER:
      TRAIN_BATCH_SIZE: 64
      TEST_BATCH_SIZE: 32
      NUM_WORKERS: 2
      S3_ANNO_FOLDER: 's3://cc3m/'
      ANNO_FOLDER:  'open_source_dataset/cc3m/'
      ANNO_FILENAME: 'train_spacy.json'
      FEATS_FOLDER: 'open_source_dataset/cc3m/'
      S3_PATH: 's3://cc3m/'
      SEQ_PER_SAMPLE:  1
      SAMPLER: NodeDistributed
      CACHE_MODE: True
      CIRCULAR_CACHE_MODE: False
      ZIP_MODE: False
      CACHE_ORIGIN_IMAGE: False
      RANDOM_CAPTION: False
      AS_NUMPY_AS_POSSIBLE: False
      SAMPLING_WEIGHT: 1.0
      TRANSFORM: 'clip_transforms'
    MODEL:
      MAX_SEQ_LEN: 50
      TEMP_NAME: logit_scale_caption
    LOSSES:
      NAMES: ['CrossEntropy', 'Accuracy']
      LOSS_WEIGHT: 1.0
      REDUCTION: 'mean'
    INFERENCE:
      VOCAB: 'CLIP'
      GENERATION_MODE: False

  -
    NAME: sbu_caption
    DATASETS:
      TRAIN: 'ImageTextPairDataset'
      TASK_TYPE: 'image_caption'
      DATASET_NAME: 'SBU'
      TARGET_SET: ['Vocab_Word']
    DATALOADER:
      TRAIN_BATCH_SIZE: 64
      TEST_BATCH_SIZE: 32
      NUM_WORKERS: 1
      S3_ANNO_FOLDER: 's3://SBU/annotations'
      ANNO_FOLDER: 'open_source_dataset/sbucaption/annotations'
      ANNO_FILENAME: 'subcaption.json'
      FEATS_FOLDER: 'open_source_dataset/sbucaption/'
      S3_PATH: 's3://SBU/images'
      SEQ_PER_SAMPLE:  1
      SAMPLER: NodeDistributed
      CACHE_MODE: True
      CIRCULAR_CACHE_MODE: False
      ZIP_MODE: False
      CACHE_ORIGIN_IMAGE: False
      RANDOM_CAPTION: False
      AS_NUMPY_AS_POSSIBLE: False
      SAMPLING_WEIGHT: 1.0
      TRANSFORM: 'clip_transforms'
    MODEL:
      MAX_SEQ_LEN: 50
      TEMP_NAME: logit_scale_caption
    LOSSES:
      NAMES: ['CrossEntropy', 'Accuracy']
      LOSS_WEIGHT: 1.0
      REDUCTION: 'mean'
    INFERENCE:
      VOCAB: 'CLIP'
      GENERATION_MODE: False

  -
    NAME: vg_caption
    DATASETS:
      TRAIN: 'ImageTextPairDataset'
      TASK_TYPE: 'image_caption'
      DATASET_NAME: 'VG'
      TARGET_SET: ['Vocab_Word']
    DATALOADER:
      TRAIN_BATCH_SIZE: 64
      TEST_BATCH_SIZE: 32
      NUM_WORKERS: 2
      FEATS_FOLDER: 'open_source_dataset/visual_genome/images'
      ANNO_FOLDER:  'open_source_dataset/visual_genome/annotations'
      S3_PATH: 's3://visual_genome/images'
      ANNO_FILENAME: 'vg_captions_128filter.json'
      SEQ_PER_SAMPLE:  1
      CACHE_MODE: True
      CIRCULAR_CACHE_MODE: False
      ZIP_MODE: False
      CACHE_ORIGIN_IMAGE: False
      RANDOM_CAPTION: False
      AS_NUMPY_AS_POSSIBLE: False
      SAMPLING_WEIGHT: 1.0
      TRANSFORM: 'clip_transforms'
    MODEL:
      MAX_SEQ_LEN: 30
      TEMP_NAME: logit_scale_caption
    LOSSES:
      NAMES: ['CrossEntropy', 'Accuracy']
      LOSS_WEIGHT: 1.0
      REDUCTION: 'mean'
    INFERENCE:
      VOCAB: 'CLIP'
      GENERATION_MODE: True

  -
    NAME: mscoco_retrieve
    DATASETS:
      TRAIN: 'ImageTextPairDataset'
      # TEST: 'ImageTextPairDataset'
      TASK_TYPE: 'image_retrieval'
      DATASET_NAME: 'MSCOCO'
    DATALOADER:
      TRAIN_BATCH_SIZE: 100
      TEST_BATCH_SIZE: 32
      NUM_WORKERS: 1
      FEATS_FOLDER: 'open_source_dataset/mscoco_dataset/coco_origin'
      ANNO_FOLDER:  'open_source_dataset/mscoco_dataset/new_annotations'
      S3_PATH: 's3://coco/'
      SEQ_PER_SAMPLE:  1
      CACHE_MODE: True
      CIRCULAR_CACHE_MODE: False
      ZIP_MODE: False
      CACHE_ORIGIN_IMAGE: False
      RANDOM_CAPTION: False
      AS_NUMPY_AS_POSSIBLE: False
      SAMPLING_WEIGHT: 1.0
      TRANSFORM: 'clip_transforms'
    MODEL:
      MAX_SEQ_LEN: 50
      TEMP_NAME: logit_scale_retrieve
    LOSSES:
      NAMES: ['LabelSmoothingCrossEntropy', 'Accuracy']
      LABELSMOOTHING: 0.1
      LOSS_WEIGHT: 1.0
      REDUCTION: 'mean'
    INFERENCE:
      VOCAB: 'CLIP'
      ID_KEY: 'image_id'
      VALUE: 'caption'
      NAME: 'RetrievalEvaler'
      VAL_ANNFILE: 'open_source_dataset/flickr30k/all_data_final_val_set0_2014.jsonline'
      TEST_ANNFILE: 'open_source_dataset/flickr30k/all_data_final_test_set0_2014.jsonline'
      GENERATION_MODE: False

  -
    NAME: yfcc_retrieve
    DATASETS:
      TRAIN: 'ImageTextPairDataset'
      TASK_TYPE: 'image_retrieval'
      DATASET_NAME: 'YFCC'
    DATALOADER:
      TRAIN_BATCH_SIZE: 64
      TEST_BATCH_SIZE: 32
      NUM_WORKERS: 2
      S3_ANNO_FOLDER:  'cluster2:s3://yfcc'
      ANNO_FOLDER:  'open_source_dataset/yfcc'
      ANNO_FILENAME: 'yfcc100m_subset_available_untokenized.json'
      FEATS_FOLDER: 'open_source_dataset/yfcc/'
      S3_PATH: 'cluster2:s3://yfcc/'
      SAMPLER: NodeDistributed
      CACHE_MODE: True
      CIRCULAR_CACHE_MODE: False
      ZIP_MODE: False
      CACHE_ORIGIN_IMAGE: False
      RANDOM_CAPTION: True
      AS_NUMPY_AS_POSSIBLE: False
      SAMPLING_WEIGHT: 1.0
      TRANSFORM: 'clip_transforms'
    MODEL:
      MAX_SEQ_LEN: 50
      TEMP_NAME: logit_scale_retrieve
    LOSSES:
      NAMES: ['LabelSmoothingCrossEntropy', 'Accuracy']
      LABELSMOOTHING: 0.1
      LOSS_WEIGHT: 0.5
      REDUCTION: 'mean'
    INFERENCE:
      VOCAB: 'CLIP'
      GENERATION_MODE: False

  -
    NAME: cc12m_retrieve
    DATASETS:
      TRAIN: 'ImageTextPairDataset'
      TASK_TYPE: 'image_retrieval'
      DATASET_NAME: 'CC12M'
    DATALOADER:
      TRAIN_BATCH_SIZE: 64
      TEST_BATCH_SIZE: 32
      NUM_WORKERS: 2
      S3_ANNO_FOLDER:  's3://cc12m/'
      ANNO_FOLDER:  'open_source_dataset/c12m/'
      ANNO_FILENAME: 'train_available.json'
      FEATS_FOLDER: 'open_source_dataset/c12m/'
      S3_PATH: 's3://cc12m/'
      SAMPLER: NodeDistributed
      CACHE_MODE: True
      CIRCULAR_CACHE_MODE: False
      ZIP_MODE: False
      CACHE_ORIGIN_IMAGE: False
      RANDOM_CAPTION: False
      AS_NUMPY_AS_POSSIBLE: False
      SAMPLING_WEIGHT: 1.0
      TRANSFORM: 'clip_transforms'
    MODEL:
      MAX_SEQ_LEN: 50
      TEMP_NAME: logit_scale_retrieve
    LOSSES:
      NAMES: ['LabelSmoothingCrossEntropy', 'Accuracy']
      LABELSMOOTHING: 0.1
      LOSS_WEIGHT: 0.5
      REDUCTION: 'mean'
    INFERENCE:
      VOCAB: 'CLIP'
      GENERATION_MODE: False

  -
    NAME: cc3m_retrieve
    DATASETS:
      TRAIN: 'ImageTextPairDataset'
      TASK_TYPE: 'image_retrieval'
      DATASET_NAME: 'CC3M'
    DATALOADER:
      TRAIN_BATCH_SIZE: 64
      TEST_BATCH_SIZE: 32
      NUM_WORKERS: 2
      S3_ANNO_FOLDER: 's3://cc3m/'
      ANNO_FOLDER:  'open_source_dataset/cc3m/'
      ANNO_FILENAME: 'train_spacy.json'
      FEATS_FOLDER: 'open_source_dataset/cc3m/'
      S3_PATH: 's3://cc3m/'
      SAMPLER: NodeDistributed
      CACHE_MODE: True
      CIRCULAR_CACHE_MODE: False
      ZIP_MODE: False
      CACHE_ORIGIN_IMAGE: False
      RANDOM_CAPTION: False
      AS_NUMPY_AS_POSSIBLE: False
      SAMPLING_WEIGHT: 1.0
      TRANSFORM: 'clip_transforms'
    MODEL:
      MAX_SEQ_LEN: 50
      TEMP_NAME: logit_scale_retrieve
    LOSSES:
      NAMES: ['LabelSmoothingCrossEntropy', 'Accuracy']
      LABELSMOOTHING: 0.1
      LOSS_WEIGHT: 0.5
      REDUCTION: 'mean'
    INFERENCE:
      VOCAB: 'CLIP'
      GENERATION_MODE: False



  -
    NAME: vg_retrieve
    DATASETS:
      TRAIN: 'ImageTextPairDataset'
      TASK_TYPE: 'image_retrieval'
      DATASET_NAME: 'VG'
    DATALOADER:
      TRAIN_BATCH_SIZE: 64
      TEST_BATCH_SIZE: 32
      NUM_WORKERS: 2
      FEATS_FOLDER: 'open_source_dataset/visual_genome/images'
      ANNO_FOLDER:  'open_source_dataset/visual_genome/annotations'
      S3_PATH: 's3://visual_genome/images'
      ANNO_FILENAME: 'vg_captions_128filter.json'
      SEQ_PER_SAMPLE:  1
      CACHE_MODE: True
      CIRCULAR_CACHE_MODE: False
      ZIP_MODE: False
      CACHE_ORIGIN_IMAGE: False
      RANDOM_CAPTION: False
      AS_NUMPY_AS_POSSIBLE: False
      SAMPLING_WEIGHT: 1.0
      TRANSFORM: 'clip_transforms'
    MODEL:
      MAX_SEQ_LEN: 30
      TEMP_NAME: logit_scale_retrieve
    LOSSES:
      NAMES: ['LabelSmoothingCrossEntropy', 'Accuracy']
      LABELSMOOTHING: 0.1
      LOSS_WEIGHT: 0.5
      REDUCTION: 'mean'
    INFERENCE:
      VOCAB: 'CLIP'
      GENERATION_MODE: False

  -
    NAME: sbu_retrieve
    DATASETS:
      TRAIN: 'ImageTextPairDataset'
      TASK_TYPE: 'image_retrieval'
      DATASET_NAME: 'SBU'
    DATALOADER:
      TRAIN_BATCH_SIZE: 64
      TEST_BATCH_SIZE: 32
      NUM_WORKERS: 1
      S3_ANNO_FOLDER: 's3://SBU/annotations'
      ANNO_FOLDER: 'open_source_dataset/sbucaption/annotations'
      ANNO_FILENAME: 'subcaption.json'
      FEATS_FOLDER: 'open_source_dataset/sbucaption/'
      S3_PATH: 's3://SBU/images'
      SAMPLER: NodeDistributed
      CACHE_MODE: True
      CIRCULAR_CACHE_MODE: False
      ZIP_MODE: False
      CACHE_ORIGIN_IMAGE: False
      RANDOM_CAPTION: False
      AS_NUMPY_AS_POSSIBLE: False
      SAMPLING_WEIGHT: 1.0
      TRANSFORM: 'clip_transforms'
    MODEL:
      MAX_SEQ_LEN: 50
      TEMP_NAME: logit_scale_retrieve
    LOSSES:
      NAMES: ['LabelSmoothingCrossEntropy', 'Accuracy']
      LABELSMOOTHING: 0.1
      LOSS_WEIGHT: 0.5
      REDUCTION: 'mean'
    INFERENCE:
      VOCAB: 'CLIP'
      GENERATION_MODE: False

  -
    NAME: flickr30k_retrieve
    DATASETS:
      TRAIN: 'ImageTextPairDataset'
      TASK_TYPE: 'image_retrieval'
      TEST: 'ImageTextPairDataset'
      DATASET_NAME: 'FLICKR'
    DATALOADER:
      TRAIN_BATCH_SIZE: 128
      TEST_BATCH_SIZE: 128
      NUM_WORKERS: 2
      FEATS_FOLDER: 'open_source_dataset/flickr30k_images/flickr30k_images/flickr30k_images'
      ANNO_FOLDER:  'open_source_dataset/flickr30k'
      S3_PATH: "s3://open_dataset/flickr30k/flickr30k_images"
      SEQ_PER_SAMPLE:  1
      CACHE_MODE: True
      CIRCULAR_CACHE_MODE: False
      ZIP_MODE: False
      CACHE_ORIGIN_IMAGE: False
      RANDOM_CAPTION: False
      AS_NUMPY_AS_POSSIBLE: False 
      SAMPLING_WEIGHT: 1.0
      TRANSFORM: 'clip_transforms'
    MODEL:
      MAX_SEQ_LEN: 77
      TEMP_NAME: logit_scale_retrieve
    LOSSES:
      NAMES: ['LabelSmoothingCrossEntropy']
      LABELSMOOTHING: 0.1
      LOSS_WEIGHT: 1.0
      REDUCTION: 'mean'
    INFERENCE:
      VOCAB: 'CLIP'
      ID_KEY: 'image_id'
      VALUE: 'caption'
      NAME: 'RetrievalEvaler'
      VAL_ANNFILE: 'open_source_dataset/flickr30k/all_data_final_val_set0_2014.jsonline'
      TEST_ANNFILE: 'open_source_dataset/flickr30k/all_data_final_test_set0_2014.jsonline'
      GENERATION_MODE: False

  -
    NAME: flickr30k_caption
    DATASETS:
      TRAIN: 'ImageTextPairDataset'
      TASK_TYPE: 'image_caption'
      TEST: 'ImageTextPairDataset'
      DATASET_NAME: 'FLICKR'
      TARGET_SET: ['Vocab_Word']
    DATALOADER:
      TRAIN_BATCH_SIZE: 32 
      TEST_BATCH_SIZE: 8
      NUM_WORKERS: 4
      FEATS_FOLDER: 'open_source_dataset/flickr30k_images/flickr30k_images/flickr30k_images'
      ANNO_FOLDER:  'open_source_dataset/flickr30k'
      S3_PATH: "s3://open_dataset/flickr30k/flickr30k_images"
      SEQ_PER_SAMPLE:  1
      CACHE_MODE: True
      CIRCULAR_CACHE_MODE: False
      ZIP_MODE: False
      CACHE_ORIGIN_IMAGE: False
      RANDOM_CAPTION: False
      AS_NUMPY_AS_POSSIBLE: False 
      SAMPLING_WEIGHT: 1.0
      TRANSFORM: 'clip_transforms'
      TASK_TYPE: caption
      # DATA_PERCENTAGE: 0.01
    MODEL:
      MAX_SEQ_LEN: 21
      TEMP_NAME: logit_scale_caption
    LOSSES:
      NAMES: ['LabelSmoothingCrossEntropy', 'Accuracy']
      LABELSMOOTHING: 0.1
      LOSS_WEIGHT: 1.0
      REDUCTION: 'mean'
    DECODE_STRATEGY:
      NAME: 'CaptionBeamSearcherV3'
      BEAM_SIZE: 2
    INFERENCE:
      NAME: 'COCOEvaler'
      VOCAB: 'CLIP'
      ID_KEY: 'image_id'
      VALUE: 'caption'
      VAL_ANNFILE: 'open_source_dataset/flickr30k/captions_val.json'
      TEST_ANNFILE: 'open_source_dataset/flickr30k/captions_test.json'
      GENERATION_MODE: True


ENGINE:
  NAME: 'UnifiedTrainer'
 
MODEL:
  META_ARCHITECTURE: 'MultiTaskTransformerEncoder'
  ENCODER: 'UnifiedBertEncoder'

  IN_TUNING: True # use IN1k instead of 22k
  SHARE_LAYERNORM: True
  BERT:
    NORMALIZE_DECISION: "BERTPre" 
    DROP_PATH_PROB: 0.1
    NUM_HIDDEN_LAYERS: 1
    DROP_PATH_PROB_FIXED: True

    UNIFY_QKV: True
  
  MODEL_EMA: False
  MODEL_EMA_DECAY: 0.9999

  MAEParamsInit: True
  POSEMBEDFIX: True


  IMG_INPUT_SIZE: 224
  PATCH_SIZE: 16

  LAYER_SCALE: True 
  LAYER_SCALE_INIT: 1e-3


DATALOADER:
  USE_WEIGHTED_SAMPLER: True
  UNIFIED_DATASET: True 
  NUM_WORKERS: 16

  PADDING_TO_MAX: False # True for debugging or token moe with distributed moe 


  
####################################### Optimizer #######################################
SOLVER:
  NAME: 'Adam'
  TORCH_OPTIMIZER: True
  PARAMS_SEPERATE: True
  # PARAMS_GROUP: True
  # EPOCH: 1
  MAX_ITER: 150000
  CHECKPOINT_PERIOD: 5000
  EVAL_PERIOD: 500000
  BASE_LR: 0.001
  BIAS_LR_FACTOR: 1.0
  WEIGHT_DECAY: 0.05
  WEIGHT_DECAY_NORM: 0.0
  WEIGHT_DECAY_BIAS: 0.0
  WEIGHT_DECAY_EMBEDDING: 0.0
  MOMENTUM: 0.9
  DAMPENING: 0.0
  NESTEROV: 0.0
  BETAS: [0.9, 0.95]
  EPS: 1e-6
  GRAD_CLIP: 0.1
  GRAD_CLIP_TYPE: 'norm'
  ACCUM_ITER: 0
  AMP_FP16: True
  APEX_FP16: False # dangerous

  WRITE_PERIOD: 50
  MIN_LOSS_SCLE: 2048.0
  # BF16: False # True
  # ZEROSTAGE: 2

  LOSS_SCALE_WINDOW: 200





  
####################################### lr scheduler ####################################### 
LR_SCHEDULER:
  NAME: 'WarmupCosine'
  WARMUP: 5000
  MIN_LR: 0.000001




####################################### evaluation ####################################### 
INFERENCE:

  VOCAB: 'CLIP'
  ITER_BASED: True


find_unused_parameters: true

# ENCODERS:
#   -
#     NAME: VisualEncoder
#     TYPE: VisualEncoder
#     DROP_PATH_PROB: 0.0
#     HIDDEN_SIZE: 192
#     HIDDEN_DROPOUT_PROB: 0.
#     HIDDEN_ACT: "gelu"
#     NUM_ATTENTION_HEADS: 3
#     INTERMEDIATE_SIZE: 768
#     INTERMEDIATE_DROP: 0.
#     FFN_DROPOUT_PROB: 0.
#     ATTENTION_PROBS_DROPOUT_PROB: 0.
#     NUM_HIDDEN_LAYERS: 6
#     NUM_GENERATION_LAYERS: 0
#     DROP_PATH_PROB_FIXED: True

#   -
#     NAME: TextEncoder
#     TYPE: TextEncoder
#     DROP_PATH_PROB: 0.0
#     HIDDEN_SIZE: 192
#     HIDDEN_DROPOUT_PROB: 0.
#     HIDDEN_ACT: "gelu"
#     NUM_ATTENTION_HEADS: 3
#     INTERMEDIATE_SIZE: 768
#     INTERMEDIATE_DROP: 0.
#     FFN_DROPOUT_PROB: 0.
#     ATTENTION_PROBS_DROPOUT_PROB: 0.
#     NUM_HIDDEN_LAYERS: 6
#     NUM_GENERATION_LAYERS: 0
#     DROP_PATH_PROB_FIXED: True