config.sample_path = <your train image path>
config.label_path = <your train gt path>

config.sample_path = <your val image path>
config.label_path = <your val gt path>

# 目前支持resnet18，mv3large
config.arch = "resnet18"

config.is_transform = True        # 是否做数据增强
config.img_size = 640             # 训练图片大小(img_size, img_size)
config.datasets.DBTrainDataset = AttrDict()
config.datasets.DBTrainDataset.shrink_ratio = 0.4
config.datasets.DBTrainDataset.thresh_min = 0.3
config.datasets.DBTrainDataset.thresh_max = 0.7
config.core.DBNetTrain.batch_size = 8
config.core.DBNetTrain.num_workers = 4
config.core.DBNetTrain.train_dataset = DBTrainDataset(config, config.sample_path, config.label_path, config.is_transform, config.img_size)
config.core.DBNetTrain.train_loader = torch.utils.data.DataLoader(
  dataset = config.core.DBNetTrain.train_dataset,
  batch_size = config.core.DBNetTrain.batch_size,
  shuffle = True,
  num_workers = config.core.DBNetTrain.num_workers,
  pin_memory = True,
  sampler = None
)

config.core.DBNetTest.model_path = <your model path>            # 加载模型路径
# config.core.DBNetTest.jit_model_path = <torchscript-model-path> # torchscript model path
config.core.DBNetTest.is_output_polygon = True                  # 输出是否为多边形模型
config.sample_path = <your test image path>                     # 测试图片路径
config.core.DBNetTrain.batch_size = 8
config.core.DBNetTrain.num_workers = 4
config.core.DBNetTest.test_dataset = DBTestDataset(config, config.sample_path, long_size = 1280)
config.core.DBNetTest.test_loader = torch.utils.data.DataLoader(
  dataset = config.core.DBNetTest.test_dataset,
  batch_size = config.core.DBNetTrain.batch_size,
  shuffle = False,
  num_workers = config.core.DBNetTrain.num_workers,
  pin_memory = True
)

config.cast.TraceCast.model_dir = "output/script.pt"

config.core.DBNetTest.jit_model_path = <torchscript-model-path>