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from detectron2.config import CfgNode as CN
def add_grit_config(cfg):
_C = cfg
_C.MODEL.BEAM_SIZE = 1
_C.MODEL.TRAIN_TASK = ["ObjectDet", "DenseCap"]
_C.MODEL.TEST_TASK = "DenseCap" # This can be varied if the model is jointly trained on multiple tasks
_C.MODEL.ROI_BOX_HEAD.USE_BIAS = 0.0 # >= 0: not use
_C.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE = False
_C.MODEL.ROI_HEADS.MASK_WEIGHT = 1.0
_C.MODEL.ROI_HEADS.OBJECT_FEAT_POOLER_RES = 14
_C.MODEL.ROI_HEADS.SOFT_NMS_ENABLED = False
# Backbones
_C.MODEL.VIT_LAYERS = 12
# Text Decoder
_C.TEXT_DECODER = CN()
_C.TEXT_DECODER.VOCAB_SIZE = 30522
_C.TEXT_DECODER.HIDDEN_SIZE = 768
_C.TEXT_DECODER.NUM_LAYERS = 6
_C.TEXT_DECODER.ATTENTION_HEADS = 12
_C.TEXT_DECODER.FEEDFORWARD_SIZE = 768 * 4
# Multi-dataset dataloader
_C.DATALOADER.DATASET_RATIO = [1, 1] # sample ratio
_C.DATALOADER.DATASET_BS = 1
_C.DATALOADER.DATASET_INPUT_SIZE = [1024, 1024]
_C.DATALOADER.DATASET_INPUT_SCALE = [(0.1, 2.0), (0.1, 2.0)]
_C.DATALOADER.DATASET_MIN_SIZES = [(640, 800), (640, 800)]
_C.DATALOADER.DATASET_MAX_SIZES = [1333, 1333]
_C.SOLVER.USE_CUSTOM_SOLVER = True
_C.SOLVER.OPTIMIZER = 'ADAMW'
_C.SOLVER.VIT_LAYER_DECAY = True
_C.SOLVER.VIT_LAYER_DECAY_RATE = 0.7
_C.INPUT.CUSTOM_AUG = 'EfficientDetResizeCrop'
_C.INPUT.TRAIN_SIZE = 1024
_C.INPUT.TEST_SIZE = 1024
_C.INPUT.SCALE_RANGE = (0.1, 2.)
# 'default' for fixed short / long edge
_C.INPUT.TEST_INPUT_TYPE = 'default'
_C.FIND_UNUSED_PARAM = True
_C.USE_ACT_CHECKPOINT = True |