stq-hydra/dbnet

DeepVAC-compliant DB Net implementation.

DBNet

DeepVAC-compliant DBNet implementation.

简介

本项目实现了符合DeepVAC规范的OCR检测模型DBNet

项目依赖

• deepvac >= 0.5.6
• pytorch >= 1.8.0
• torchvision >= 0.7.0
• opencv-python
• numpy
• pyclipper
• shapely
• pillow

如何运行本项目

1. 阅读DeepVAC规范

可以粗略阅读，建立起第一印象

2. 准备运行环境

可以使用DeepVAC规范指定的Docker镜像

3. 准备数据集

• 获取文本检测数据集 CTW1500格式的数据集，CTW1500下载地址:

  ch4_training_images.zip

  ch4_training_localization_transcription_gt.zip

  ch4_test_images.zip

  Challenge4_Test_Task1_GT.zip

• 数据集配置 在config.py文件中作如下配置:

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>

5. 模型相关配置

• DB backbone配置

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

4. 训练相关配置

• dataloader相关配置

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
)

5. 训练

python3 train.py

6. 测试

• 测试相关配置

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
)

• 运行测试脚本:

python3 test.py

7. 使用torchscript模型

如果训练过程中未开启config.cast.TraceCast.model_dir开关，可以在测试过程中转化torchscript模型

• 转换torchscript模型(.pt)

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

按照步骤6完成测试，torchscript模型会保存至config.cast.TraceCast.model_dir指定位置

• 加载torchscript模型

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

然后按照步骤6测试，会读取script_model

更多功能

如果要在本项目中开启如下功能:

• 预训练模型加载
• checkpoint加载
• 使用tensorboard
• 启用TorchScript
• 转换ONNX
• 转换NCNN
• 转换CoreML
• 开启量化
• 开启自动混合精度训练
• 采用ema策略(config.ema)
• 采用梯度积攒到一定数量再进行反向更新梯度策略(config.nominal_batch_factor)

请参考DeepVAC