100 lines
2.2 KiB
YAML
100 lines
2.2 KiB
YAML
MODEL:
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META_ARCHITECTURE: "GeneralizedVLRCNN"
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WEIGHT: "swin_tiny_patch4_window7_224.pth"
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RPN_ONLY: True
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RPN_ARCHITECTURE: "VLDYHEAD"
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BACKBONE:
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CONV_BODY: "SWINT-FPN-RETINANET"
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OUT_CHANNELS: 256
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FREEZE_CONV_BODY_AT: -1
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LANGUAGE_BACKBONE:
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FREEZE: False
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MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip"
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MASK_SPECIAL: False
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RPN:
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USE_FPN: True
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ANCHOR_SIZES: (64, 128, 256, 512, 1024)
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ANCHOR_STRIDE: (8, 16, 32, 64, 128)
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ASPECT_RATIOS: (1.0,)
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SCALES_PER_OCTAVE: 1
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DYHEAD:
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CHANNELS: 256
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NUM_CONVS: 6
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USE_GN: True
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USE_DYRELU: True
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USE_DFCONV: True
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USE_DYFUSE: True
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TOPK: 9 # topk for selecting candidate positive samples from each level
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SCORE_AGG: "MEAN"
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LOG_SCALE: 0.0
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FUSE_CONFIG:
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EARLY_FUSE_ON: True
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TYPE: "MHA-B"
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USE_CLASSIFICATION_LOSS: False
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USE_TOKEN_LOSS: False
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USE_CONTRASTIVE_ALIGN_LOSS: False
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CONTRASTIVE_HIDDEN_DIM: 64
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USE_DOT_PRODUCT_TOKEN_LOSS: True
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USE_FUSED_FEATURES_DOT_PRODUCT: True
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USE_LAYER_SCALE: True
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CLAMP_MIN_FOR_UNDERFLOW: True
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CLAMP_MAX_FOR_OVERFLOW: True
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CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True
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CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True
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CLAMP_DOT_PRODUCT: True
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USE_CHECKPOINT: True
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TEST:
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DURING_TRAINING: False
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IMS_PER_BATCH: 64
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# use for grounding model
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DATASETS:
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TRAIN: ("object365_dt_train", "mixed_train_no_coco", "flickr30k_train", )
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TEST: ("coco_2014_val", )
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DISABLE_SHUFFLE: False
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ADD_DET_PROMPT: False
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RANDOM_SAMPLE_NEG: 85
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CONTROL_PROB: (0.0, 0.0, 0.5, 0.0)
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SEPARATION_TOKENS: ". "
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INPUT:
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PIXEL_MEAN: [ 103.530, 116.280, 123.675 ]
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PIXEL_STD: [ 57.375, 57.120, 58.395 ]
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MIN_SIZE_TRAIN: 800
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MAX_SIZE_TRAIN: 1333
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MIN_SIZE_TEST: 800
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MAX_SIZE_TEST: 1333
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AUGMENT:
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MULT_MIN_SIZE_TRAIN: (480,560,640,720,800)
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DATALOADER:
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SIZE_DIVISIBILITY: 32
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SOLVER:
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OPTIMIZER: ADAMW
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BASE_LR: 0.0001
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LANG_LR: 0.00001
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WEIGHT_DECAY: 0.0001
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STEPS: (0.67, 0.89)
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MAX_EPOCH: 30
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IMS_PER_BATCH: 64
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WARMUP_ITERS: 2000
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WARMUP_FACTOR: 0.001
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USE_AMP: True
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MODEL_EMA: 0.999
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FIND_UNUSED_PARAMETERS: False
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CLIP_GRADIENTS:
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ENABLED: True
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CLIP_TYPE: "full_model"
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CLIP_VALUE: 1.0
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NORM_TYPE: 2.0 |