OUTPUT_DIR: '' LOG_DIR: '' GPUS: [0,] WORKERS: 4 PRINT_FREQ: 20 AUTO_RESUME: False PIN_MEMORY: True RANK: 0 # Cudnn related params CUDNN: BENCHMARK: True DETERMINISTIC: False ENABLED: True # common params for NETWORK MODEL: NAME: 'dual-hrnet' PRETRAINED: './Checkpoints/HRNet/hrnetv2_w32_imagenet_pretrained.pth' USE_FPN: False IS_DISASTER_PRED: False IS_SPLIT_LOSS: True FUSE_CONV_K_SIZE: 1 # high_resoluton_net related params for segmentation EXTRA: PRETRAINED_LAYERS: ['*'] STEM_INPLANES: 64 FINAL_CONV_KERNEL: 1 WITH_HEAD: True STAGE1: NUM_MODULES: 1 NUM_BRANCHES: 1 NUM_BLOCKS: [4] NUM_CHANNELS: [64] BLOCK: 'BOTTLENECK' FUSE_METHOD: 'SUM' STAGE2: NUM_MODULES: 1 NUM_BRANCHES: 2 NUM_BLOCKS: [4, 4] NUM_CHANNELS: [32, 64] BLOCK: 'BASIC' FUSE_METHOD: 'SUM' STAGE3: NUM_MODULES: 4 NUM_BRANCHES: 3 NUM_BLOCKS: [4, 4, 4] NUM_CHANNELS: [32, 64, 128] BLOCK: 'BASIC' FUSE_METHOD: 'SUM' STAGE4: NUM_MODULES: 3 NUM_BRANCHES: 4 NUM_BLOCKS: [4, 4, 4, 4] NUM_CHANNELS: [32, 64, 128, 256] BLOCK: 'BASIC' FUSE_METHOD: 'SUM' #_C.MODEL.EXTRA= CN(new_allowed=True) LOSS: CLASS_BALANCE: True # DATASET related params DATASET: NUM_CLASSES: 4 # training TRAIN: # Augmentation FLIP: True MULTI_SCALE: [0.8, 1.2] CROP_SIZE: [512, 512] LR_FACTOR: 0.1 LR_STEP: [90, 110] LR: 0.05 EXTRA_LR: 0.001 OPTIMIZER: 'sgd' MOMENTUM: 0.9 WD: 0.0001 NESTEROV: False IGNORE_LABEL: -1 NUM_EPOCHS: 500 RESUME: False BATCH_SIZE_PER_GPU: 16 SHUFFLE: True # only using some training samples NUM_SAMPLES: 0 CLASS_WEIGHTS: [0.4, 1.2, 1.2, 1.2] # testing TEST: BATCH_SIZE_PER_GPU: 32 # only testing some samples NUM_SAMPLES: 0 MODEL_FILE: '' FLIP_TEST: False MULTI_SCALE: False CENTER_CROP_TEST: False SCALE_LIST: [1] # debug DEBUG: DEBUG: False SAVE_BATCH_IMAGES_GT: False SAVE_BATCH_IMAGES_PRED: False SAVE_HEATMAPS_GT: False SAVE_HEATMAPS_PRED: False