mirror of https://github.com/YifanXu74/MQ-Det.git
142 lines
3.5 KiB
YAML
142 lines
3.5 KiB
YAML
MODEL:
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META_ARCHITECTURE: "GeneralizedVLRCNN_New"
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WEIGHT: "MODEL/glip_tiny_model_o365_goldg_cc_sbu.pth"
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# WEIGHT: "MODEL/mq-glip-t" # debug
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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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TOKENIZER_TYPE: "bert-base-uncased"
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MODEL_TYPE: "bert-base-uncased"
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# TOKENIZER_TYPE: "MODEL/THIRD_PARTIES/bert-base-uncased"
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# MODEL_TYPE: "MODEL/THIRD_PARTIES/bert-base-uncased"
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MASK_SPECIAL: False
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ROI_BOX_HEAD:
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POOLER_RESOLUTION: 7
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POOLER_SCALES: (0.125, 0.0625, 0.03125, 0.015625, 0.0078125)
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POOLER_SAMPLING_RATIO: 0
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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: False
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TEST:
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EVAL_TASK: 'detection'
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DURING_TRAINING: False
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IMS_PER_BATCH: 8
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# use for grounding model
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DATASETS:
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TRAIN: ("coco_grounding_train_for_obj365", ) # NOTE: modified for customized usage
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TEST: ("coco_2017_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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EXCLUDE_CROWD: True
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SPECIAL_SAFEGUARD_FOR_COCO_GROUNDING: True
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# FEW_SHOT: 50 # NOTE: for debug
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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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NUM_WORKERS: 0
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SOLVER:
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OPTIMIZER: ADAMW
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BASE_LR: 0.0001
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#### should be modified during fine-tuning #######
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GATE_LR: 0.005
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QUERY_LR: 0.00001
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#################################################
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LANG_LR: 0.00001
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WEIGHT_DECAY: 0.0001
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STEPS: (0.95,)
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MAX_EPOCH: 1 # NOTE: modified for customized usage
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IMS_PER_BATCH: 16
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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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CHECKPOINT_PERIOD: 99999999
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CHECKPOINT_PER_EPOCH: 2.0 # NOTE: modified for customized usage
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TUNING_HIGHLEVEL_OVERRIDE: "vision_query"
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MAX_TO_KEEP: 4
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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
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VISION_QUERY:
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ENABLED: True
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QUERY_BANK_PATH: 'MODEL/coco_query_5000_sel_tiny.pth' # NOTE: modified for customized usage
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PURE_TEXT_RATE: 0.
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TEXT_DROPOUT: 0.4
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VISION_SCALE: 1.0
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NUM_QUERY_PER_CLASS: 5 # NOTE: vision query number for each category in one forward process, modified for customized usage
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RANDOM_KSHOT: False
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ADD_ADAPT_LAYER: False
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CONDITION_GATE: True
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NONLINEAR_GATE: True
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NO_CAT: True
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MAX_QUERY_NUMBER: 5000 # NOTE: vision query number for each category in the bank, modified for customized usage
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