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MODEL:
BACKBONE:
TYPE: 'swin' # 'resnet' or 'swin'
PRETRAINED_WEIGHTS:
IS_TRAINING: True
RESNETS:
DEPTH: 50
STEM_OUT_CHANNELS: 64
STRIDE_IN_1X1: False
OUT_FEATURES: ["res2", "res3", "res4", "res5"]
SWIN:
TYPE: "base" # "tiny" or "small" or "base" or "large"
EMBED_DIM: 96
DEPTHS: [2 2 6 2]
NUM_HEADS: [3 6 12 24]
PATCH_SIZE: 4
WINDOW_SIZE: 7
MLP_RATIO: 4.
QKV_BIAS: True
QK_SCALE:
DROP_RATE: 0.
ATTN_DROP_RATE: 0.
DROP_PATH_RATE: 0.3
APE: False
PATCH_NORM: True
OUT_INDICES: (0 1 2 3)
PRETRAIN_IMG_SIZE: 384
USE_CHECKPOINT: False
OUT_FEATURES: ["res2", "res3", "res4", "res5"]
DATASETS:
TRAIN: 'dataset/training.odgt'
VALID: 'dataset/validation.odgt'
ROOT_DIR: 'nuImages/ImageData/nuimages-v1.0-all-samples/'
PIXEL_MEAN: [0.485, 0.456, 0.406]
PIXEL_STD: [0.229, 0.224, 0.225]
SOLVER:
IMS_PER_BATCH: 16
BASE_LR: 0.0001
MAX_ITER: 160000
WARMUP_FACTOR: 1.0
WARMUP_ITERS: 0
WEIGHT_DECAY: 0.05
OPTIMIZER: "ADAMW"
LR_SCHEDULER_NAME: "WarmupPolyLR"
BACKBONE_MULTIPLIER: 0.1
CLIP_GRADIENTS:
ENABLED: True
CLIP_TYPE: "full_model"
CLIP_VALUE: 0.01
NORM_TYPE: 2.0
AMP:
ENABLED: True
INPUT:
MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"]
MIN_SIZE_TRAIN_SAMPLING: "choice"
CROP:
ENABLED: True
TYPE: "absolute"
SIZE: [224, 320, 480, 512] # [640, 800, 960, 1120]
MAX_SIZE: [1024, 576] # [width, height]
SINGLE_CATEGORY_MAX_AREA: 1.0
COLOR_AUG_SSD: True
SIZE_DIVISIBILITY: 640 # used in dataset mapper
FORMAT: "RGB"
DATASET_MAPPER_NAME: "mask_former_instance"
TRAIN:
LOG_DIR: 'logs'
CKPT_DIR: 'ckpt'
BATCH_SIZE: 9
WORKERS: 8
EPOCH: 300
SOLVER:
LR: 0.00006
OPTIMIZER: "ADAMW"
CLIP_GRADIENTS:
ENABLED: True
CLIP_TYPE: "full_model"
CLIP_VALUE: 0.01
NORM_TYPE: 2.0
TEST:
EVAL_PERIOD: 5000
TEST_DIR: 'test'
SAVE_DIR: 'output'
AUG:
ENABLED: False
MIN_SIZES: [320, 480, 640, 800, 960, 1120]
MAX_SIZE: 4480
FLIP: True
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: True
NUM_WORKERS: 4
VERSION: 2
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