MQ-Det/configs/pretrain/mq-glip-l.yaml

166 lines
3.8 KiB
YAML

MODEL:
META_ARCHITECTURE: "GeneralizedVLRCNN_New"
WEIGHT: "MODEL/glip_large_model.pth"
RPN_ONLY: True
RPN_ARCHITECTURE: "VLDYHEAD"
BACKBONE:
CONV_BODY: "SWINT-FPN-RETINANET"
OUT_CHANNELS: 256
SWINT:
EMBED_DIM: 192
DEPTHS: (2, 2, 18, 2)
NUM_HEADS: (6, 12, 24, 48)
WINDOW_SIZE: 12
OUT_CHANNELS: (192, 384, 768, 1536)
DROP_PATH_RATE: 0.4
LANGUAGE_BACKBONE:
FREEZE: False
TOKENIZER_TYPE: "bert-base-uncased"
MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip"
# TOKENIZER_TYPE: "MODEL/THIRD_PARTIES/bert-base-uncased"
# MODEL_TYPE: "MODEL/THIRD_PARTIES/bert-base-uncased" # "roberta-base", "clip"
MASK_SPECIAL: False
ROI_BOX_HEAD:
POOLER_RESOLUTION: 7
POOLER_SCALES: (0.125, 0.0625, 0.03125, 0.015625, 0.0078125) # TODO: check
POOLER_SAMPLING_RATIO: 0
RPN:
USE_FPN: True
ANCHOR_SIZES: (64, 128, 256, 512, 1024)
ANCHOR_STRIDE: (8, 16, 32, 64, 128)
ASPECT_RATIOS: (1.0,)
SCALES_PER_OCTAVE: 1
DYHEAD:
CHANNELS: 256
NUM_CONVS: 8
USE_GN: True
USE_DYRELU: True
USE_DFCONV: True
USE_DYFUSE: True
TOPK: 9 # topk for selecting candidate positive samples from each level
SCORE_AGG: "MEAN"
LOG_SCALE: 0.0
# USE_CHECKPOINT: True
USE_CHECKPOINT: False
FUSE_CONFIG:
USE_FUSED_FEATURES_DOT_PRODUCT: True
EARLY_FUSE_ON: True
TYPE: "MHA-B"
USE_CLASSIFICATION_LOSS: False
USE_TOKEN_LOSS: False
USE_CONTRASTIVE_ALIGN_LOSS: False
CONTRASTIVE_HIDDEN_DIM: 64
USE_DOT_PRODUCT_TOKEN_LOSS: True
USE_LAYER_SCALE: True
CLAMP_MIN_FOR_UNDERFLOW: True
CLAMP_MAX_FOR_OVERFLOW: True
CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True
CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True
CLAMP_DOT_PRODUCT: True
TEST:
EVAL_TASK: 'detection'
DURING_TRAINING: False
IMS_PER_BATCH: 8
DATASETS:
TRAIN: ("object365_grounding_train", )
TEST: ("coco_2017_val", )
ONE_HOT: False
FLICKR_COPY: 8 # 0.15 * 8 = ~1.2M
MIXED_COPY: 4 # 0.6 * 4 = ~2.4M
OBJECT365_COPY: 2 # 1.4 * 2 = ~2.8M
VG_COPY: 3 # 0.4 * 3 = ~1.2M
IN_COPY: 2 # 0.67 * 2 = ~1.33M
OI_COPY: 1 # 2M * 1 = 2M
DISABLE_SHUFFLE: False
ADD_DET_PROMPT: False
RANDOM_SAMPLE_NEG: 85
CONTROL_PROB: (0.0, 0.0, 0.5, 0.0)
FURTHER_SCREEN: True
CAPTION_CONF: 0.5
CAPTION_NMS: -1.0
CAPTION_MIN_BOX: 1
SEPARATION_TOKENS: ". "
PACK_RANDOM_CAPTION_NUMBER: 20
NO_RANDOM_PACK_PROBABILITY: 0.4
RANDOM_PACK_PROB: 0.5
CAPTION_FORMAT_VERSION: "v2"
EXCLUDE_CROWD: True
SPECIAL_SAFEGUARD_FOR_COCO_GROUNDING: True
INPUT:
PIXEL_MEAN: [ 103.530, 116.280, 123.675 ]
PIXEL_STD: [ 57.375, 57.120, 58.395 ]
MIN_SIZE_TRAIN: 800
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
AUGMENT:
MULT_MIN_SIZE_TRAIN: (480,560,640,720,800)
DATALOADER:
SIZE_DIVISIBILITY: 32
NUM_WORKERS: 0
SOLVER:
OPTIMIZER: ADAMW
BASE_LR: 0.0001
#### should be modified during fine-tuning #######
GATE_LR: 0.0025
QUERY_LR: 0.00001
#################################################
LANG_LR: 0.00001
WEIGHT_DECAY: 0.01
WEIGHT_DECAY_SCHEDULE: True
# STEPS: (0.67, 0.89)
STEPS: (0.95,)
# MAX_ITER: 1000000
MAX_EPOCH: 1
# IMS_PER_BATCH: 64
IMS_PER_BATCH: 8
WARMUP_ITERS: 2000
WARMUP_FACTOR: 0.001
FIND_UNUSED_PARAMETERS: False
USE_AMP: True
CHECKPOINT_PERIOD: 99999999
CHECKPOINT_PER_EPOCH: 2.0
TUNING_HIGHLEVEL_OVERRIDE: "vision_query"
MAX_TO_KEEP: 4
CLIP_GRADIENTS:
ENABLED: True
CLIP_TYPE: "full_model"
CLIP_VALUE: 1.0
NORM_TYPE: 2.0
VISION_QUERY:
ENABLED: True
QUERY_BANK_PATH: 'MODEL/object365_query_5000_pool7_sel_large.pth'
PURE_TEXT_RATE: 0.
TEXT_DROPOUT: 0.4
VISION_SCALE: 1.0
NUM_QUERY_PER_CLASS: 5
RANDOM_KSHOT: False
ADD_ADAPT_LAYER: False
CONDITION_GATE: True
NONLINEAR_GATE: True
NO_CAT: True
QUERY_ADDITION_NAME: '_L'