https://github.com/deepvac/hrnet-lite-seg
DeepVAC-compliant HRNet-lite implementation for segmentation.
https://github.com/deepvac/hrnet-lite-seg
Last synced: about 1 year ago
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DeepVAC-compliant HRNet-lite implementation for segmentation.
- Host: GitHub
- URL: https://github.com/deepvac/hrnet-lite-seg
- Owner: DeepVAC
- License: gpl-3.0
- Created: 2021-05-17T02:22:47.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-06-25T10:08:05.000Z (almost 5 years ago)
- Last Synced: 2025-05-09T01:08:19.672Z (about 1 year ago)
- Language: Python
- Size: 31.3 KB
- Stars: 8
- Watchers: 1
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# HRNet-lite-seg
DeepVAC-compliant HRNet-lite implementation for segmentation.
# 简介
本项目实现了符合DeepVAC规范的HRNet-lite-seg 。
### 项目依赖
- deepvac >= 0.5.7
- pytorch >= 1.8.0
- torchvision >= 0.7.0
- opencv-python
- numpy
# 如何运行本项目
## 1. 阅读[DeepVAC规范](https://github.com/DeepVAC/deepvac)
可以粗略阅读，建立起第一印象。
## 2. 准备运行环境
使用Deepvac规范指定[Docker镜像](https://github.com/DeepVAC/deepvac#2-%E7%8E%AF%E5%A2%83%E5%87%86%E5%A4%87)。
## 3. 准备数据集
- TODO
- 在config.py中修改如下配置：
```python
config.train_txt = './data/train.txt'
config.val_txt = './data/val.txt'
config.sample_path_prefix = 'your train images dir'
```
## 4. 训练相关配置
- dataloader相关配置
```python
config.datasets.FileLineCvSegWithMetaInfoDataset = AttrDict()
config.datasets.FileLineCvSegWithMetaInfoDataset.cached_data_file = 'data/clothes.p'
config.datasets.FileLineCvSegWithMetaInfoDataset.classes = config.cls_num
config.datasets.FileLineCvSegWithMetaInfoDataset.norm_val = 1.10
config.data = FileLineCvSegWithMetaInfoDataset(config, config.train_txt, config.sample_path_prefix)()
config.datasets.FileLineCvSegDataset = AttrDict()
config.datasets.FileLineCvSegDataset.composer = LiteHRNetTrainComposer(config)
config.batch_size = 8
config.num_workers = 3
config.core.LiteHRNetTrain.train_dataset = FileLineCvSegDataset(config, config.train_txt, config.delimiter, config.sample_path_prefix)
config.core.LiteHRNetTrain.train_loader = torch.utils.data.DataLoader(config.core.LiteHRNetTrain.train_dataset, batch_size=config.batch_size, shuffle=True, num_workers=config.num_workers, pin_memory=config.pin_memory)
```
## 5. 训练
### 5.1 单卡训练
执行命令：
```bash
python3 train.py
```
### 5.2 分布式训练
在config.py中修改如下配置：
```python
#dist_url，单机多卡无需改动，多机训练一定要修改
config.core.LiteHRNetTrain.dist_url = "tcp://localhost:27030"
#rank的数量，一定要修改
config.core.LiteHRNetTrain.world_size = 2
```
然后执行命令：
```bash
python train.py --rank 0 --gpu 0
python train.py --rank 1 --gpu 1
```
## 6. 测试
- 测试相关配置
```python
config.core.LiteHRNetTest = config.core.LiteHRNetTrain.clone()
config.core.LiteHRNetTest.test_sample_path = 'your test image dir'
config.core.LiteHRNetTest.model_path = 'your trained model path'
```
- 加载模型(*.pth)
```python
config.core.LiteHRNetTest.model_path =
```
- 运行测试脚本：
```bash
python3 test.py
```
## 7. 使用trace模型/script模型
如果训练过程中开启config.cast.TraceCast（或者config.cast.ScriptCast)开关，可以在测试过程中转化torchscript模型
- 转换torchscript模型(*.pt)
```python
# trace
config.cast.TraceCast = AttrDict()
config.cast.TraceCast.model_dir = "./trace.pt"
# script
config.cast.ScriptCast = AttrDict()
config.cast.ScriptCast.model_dir = "./script.pt"
```
按照步骤6完成测试，torchscript模型将保存至model_dir指定文件位置
- 加载torchscript模型
```python
config.core.LiteHRNetTrain.jit_model_path =
config.core.LiteHRNetTest.jit_model_path =
```
## 8. 使用静态量化模型
如果训练过程中未开启config.cast.TraceCast开关，可以在测试过程中转化静态量化模型
- 转换静态模型(*.sq)
```python
# trace
config.cast.TraceCast.static_quantize_dir = "./trace.sq"
# script
config.cast.ScriptCast.static_quantize_dir = "./script.sq"
```
按照步骤6完成测试，静态量化模型将保存至config.static_quantize_dir指定文件位置
- 加载静态量化模型
```python
config.core.LiteHRNetTrain.jit_model_path =
config.core.LiteHRNetTest.jit_model_path =
```
- 动态量化模型对应的配置参数为config.cast.TraceCast.dynamic_quantize_dir(或者config.cast.ScriptCast.dynamic_quantize_dir)
## 9. 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
请参考[DeepVAC](https://github.com/DeepVAC/deepvac)