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https://github.com/yu-changqian/TorchSeg

Fast, modular reference implementation and easy training of Semantic Segmentation algorithms in PyTorch.
https://github.com/yu-changqian/TorchSeg

Last synced: 3 months ago
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Fast, modular reference implementation and easy training of Semantic Segmentation algorithms in PyTorch.

Host: GitHub
URL: https://github.com/yu-changqian/TorchSeg
Owner: yu-changqian
License: mit
Created: 2019-01-10T09:06:41.000Z (almost 6 years ago)
Default Branch: master
Last Pushed: 2020-03-11T09:55:12.000Z (over 4 years ago)
Last Synced: 2024-07-22T14:18:22.708Z (4 months ago)
Language: Python
Size: 2.39 MB
Stars: 1,400
Watchers: 47
Forks: 253
Open Issues: 41
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-robotic-tooling - TorchSeg - This project aims at providing a fast, modular reference implementation for semantic segmentation models using PyTorch. (Sensor Processing / Image Processing)

README

        # TorchSeg

This project aims at providing a fast, modular reference implementation for semantic segmentation models using PyTorch.

![demo image](demo/cityscapes_demo_img.png)

## Highlights

- **Modular Design:** easily construct customized semantic segmentation models by combining different components.

- **Distributed Training:** **>60%** faster than the multi-thread parallel method([nn.DataParallel](https://pytorch.org/docs/stable/nn.html#dataparallel)), we use the multi-processing parallel method.

- **Multi-GPU training and inference:** support different manners of inference.

- Provides pre-trained models and implement different semantic segmentation models.

## Prerequisites

- PyTorch 1.0

  - `pip3 install torch torchvision`

- Easydict

  - `pip3 install easydict`

- [Apex](https://nvidia.github.io/apex/index.html)

- Ninja

  - `sudo apt-get install ninja-build`

- tqdm

  - `pip3 install tqdm`

  

## Updates

v0.1.1 (05/14/2019)

- Release the pre-trained models and all trained models

- Add PSANet for ADE20K

- Add support for CamVid, PASCAL-Context datasets

- Start only supporting the distributed training manner

## Model Zoo

### Pretrained Model

- [ResNet18](https://drive.google.com/file/d/1PP7mEMvqW6vBMDeNhrS6l13RU5TwI6cA/view?usp=sharing)

- [ResNet50](https://drive.google.com/file/d/1iEshXXzI3tCexo2CH92TNNOyizf2R_db/view?usp=sharing)

- [ResNet101](https://drive.google.com/file/d/1iELk6WeQ1smockQJGKU_DEG6slcqw6Mu/view?usp=sharing)

### Supported Model

- FCN

- [DFN](https://arxiv.org/abs/1804.09337) 

- [BiSeNet](https://arxiv.org/abs/1808.00897)

- PSPNet

- PSANet

### Performance and Benchmarks

SS:Single Scale MSF:Multi-scale + Flip

### PASCAL VOC 2012

 Methods | Backbone | TrainSet | EvalSet | Mean IoU(ss) | Mean IoU(msf) | Model 

:--:|:--:|:--:|:--:|:--:|:--:|:--:

 FCN-32s     | R101_v1c | *train_aug*  | *val*  | 71.26 | -                 |  

 DFN(paper)  | R101_v1c | *train_aug*  | *val*  | 79.67 | 80.6^*  |  

 DFN(ours)   | R101_v1c | *train_aug*  | *val*  | 79.40 | 81.40             | [GoogleDrive](https://drive.google.com/file/d/1dK5v1oakTMP1UKMARfYf5kdBP15mEgiL/view?usp=sharing) 

80.6^*: this result reported in paper is further finetuned on *train* dataset. 

### Cityscapes

#### Non-real-time Methods

 Methods | Backbone |OHEM| TrainSet | EvalSet | Mean IoU(ss) | Mean IoU(msf) | Model 

:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:

 DFN(paper)                 | R101_v1c |✗| *train_fine* | *val*  | 78.5  | 79.3   | 

 DFN(ours)                  | R101_v1c |✗| *train_fine* | *val*  | 79.09 | 80.41  | [GoogleDrive](https://drive.google.com/file/d/1QGM652rWQWZx83oe2A5r48HGYtfZCRrm/view?usp=sharing)

 DFN(ours)                  | R101_v1c |✓| *train_fine* | *val*  | 79.16 | 80.53  | [GoogleDrive](https://drive.google.com/file/d/1KEX5g5dXF2cNpCh1NUe9iKvVye9g9JaD/view?usp=sharing) 

 BiSeNet(paper)             | R101_v1c |✓| *train_fine* | *val*  |  -    | 80.3   |  

 BiSeNet(ours)              | R101_v1c |✓| *train_fine* | *val*  | 79.09 | 80.39  | [GoogleDrive](https://drive.google.com/file/d/1yTbozInCLGiCklJ8plNTl5GgeGnW_fC0/view?usp=sharing) 

 BiSeNet(paper)             | R18      |✓| *train_fine* | *val*  | 76.21 | 78.57  |  

 BiSeNet(ours)              | R18      |✓| *train_fine* | *val*  | 76.28 | 78.00  | [GoogleDrive](https://drive.google.com/file/d/1hFF-J9qoXlbVRRUr29aWeQpL4Lwn45mU/view?usp=sharing)  

 BiSeNet(paper)             | X39      |✓| *train_fine* | *val*  | 70.1  | 72     | 

 BiSeNet(ours)^*  | X39      |✓| *train_fine* | *val*  | 70.32 | 72.06  | [GoogleDrive](https://drive.google.com/file/d/1hb_qk3QLwZtQUmevZUFOHNiRHhRr0IQB/view?usp=sharing)  

 

 #### Real-time Methods

  Methods | Backbone |OHEM| TrainSet | EvalSet | Mean IoU | Model 

:--:|:--:|:--:|:--:|:--:|:--:|:--:

 BiSeNet(paper)             | R18      |✓| *train_fine* | *val*  | 74.8  | 

 BiSeNet(ours)              | R18      |✓| *train_fine* | *val*  | 74.83 | [GoogleDrive](https://drive.google.com/file/d/1bLc7YC0qePcKZQTLrqsrNRnzx3cmC-1C/view?usp=sharing) 

 BiSeNet(paper)             | X39      |✓| *train_fine* | *val*  | 69    | 

 BiSeNet(ours)^*  | X39      |✓| *train_fine* | *val*  | 68.51 | [GoogleDrive](https://drive.google.com/file/d/1xZEQLtJR-FSYt6ri7kfSTJ7UT9fXZtxc/view?usp=sharing) 

 

BiSeNet(ours)^*: because we didn't pre-train the Xception39 model on ImageNet in PyTorch, we train this experiment from scratch. We will release the pre-trained Xception39 model in PyTorch and the corresponding experiment.

### ADE

  Methods | Backbone | TrainSet | EvalSet | Mean IoU(ss) | Accuracy(ss)|  Model 

:--:|:--:|:--:|:--:|:--:|:--:|:--:

 PSPNet(paper)             | R50_v1c  | *train* | *val*  | 41.68 | 80.04 |  

 PSPNet(ours)              | R50_v1c  | *train* | *val*  | 41.65 | 79.74 | [GoogleDrive](https://drive.google.com/file/d/1jDj3UJCAAffmPQ4ckTOFxQdvFWxThCoy/view?usp=sharing)

 PSPNet(paper)             | R101_v1c | *train* | *val*  | 41.96 | 80.64 |  

 PSPNet(ours)              | R101_v1c | *train* | *val*  | 42.89 | 80.55 | [GoogleDrive](https://drive.google.com/file/d/1Y6OErgb9F1qc2cJmGhW6VlUfaSdFfmfu/view?usp=sharing)

 PSANet(paper)             | R50_v1c  | *train* | *val*  | 41.92 | 80.17 |  

 PSANet(ours)^*  | R50_v1c  | *train* | *val*  | 41.67 | 80.09 | [GoogleDrive](https://drive.google.com/file/d/1bHD1NBgJUY1PSDgyuXH2FUXPXUB7DlLj/view?usp=sharing)

 PSANet(paper)             | R101_v1c | *train* | *val*  | 42.75 | 80.71 |  

 PSANet(ours)              | R101_v1c | *train* | *val*  | 43.04 | 80.56 | [GoogleDrive](https://drive.google.com/file/d/1nnq0-pDNTttvSgtqiSo-QZ4HeYVaZl5T/view?usp=sharing)

PSANet(ours)^*: The original PSANet in the paper constructs the 

attention map with over-parameters, while we only predict the attention map with 

the same size of the feature map. The performance is almost similar to the 

original one.

### To Do

- [ ] offer comprehensive documents 

- [ ] support more semantic segmentation models

  - [ ] Deeplab v3 / Deeplab v3+

  - [ ] DenseASPP

  - [ ] EncNet

  - [ ] OCNet

## Training

1. create the config file of dataset:`train.txt`, `val.txt`, `test.txt`   

    file structure：(split with `tab`)

    ```txt

    path-of-the-image   path-of-the-groundtruth

    ```

2. modify the `config.py` according to your requirements

3. train a network:

### Distributed Training

We use the official `torch.distributed.launch` in order to launch multi-gpu training. 

This utility function from PyTorch spawns as many Python processes as the number 

of GPUs we want to use, and each Python process will only use a single GPU.

For each experiment, you can just run this script:

```bash

export NGPUS=8

python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py

```

## Inference

In the evaluator, we have implemented the multi-gpu inference base on the multi-process. In the inference phase, the function will spawns as many Python processes as the number of GPUs we want to use, and each Python process will handle a subset of the whole evaluation dataset on a single GPU.

1. evaluate a trained network on the validation set:

    ```bash

    python3 eval.py

    ```

2. input arguments:

    ```bash

    usage: -e epoch_idx -d device_idx [--verbose ] 

    [--show_image] [--save_path Pred_Save_Path]

    ```

## Disclaimer

This project is under active development. So things that are currently working might break in a future release. However, feel free to open issue if you get stuck anywhere.

## Citation

The following are BibTeX references. The BibTeX entry requires the url LaTeX package.

Please consider citing this project in your publications if it helps your research. 

```

@misc{torchseg2019,

  author =       {Yu, Changqian},

  title =        {TorchSeg},

  howpublished = {\url{https://github.com/ycszen/TorchSeg}},

  year =         {2019}

}

```

Please consider citing the [DFN](https://arxiv.org/abs/1804.09337) in your publications if it helps your research. 

```

@inproceedings{yu2018dfn,

  title={Learning a Discriminative Feature Network for Semantic Segmentation},

  author={Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong},

  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

  year={2018}

}

```

Please consider citing the [BiSeNet](https://arxiv.org/abs/1808.00897) in your publications if it helps your research. 

```

@inproceedings{yu2018bisenet,

  title={Bisenet: Bilateral segmentation network for real-time semantic segmentation},

  author={Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong},

  booktitle={European Conference on Computer Vision},

  pages={334--349},

  year={2018},

  organization={Springer}

}

```

## Why this name, Furnace?

Furnace means the **Alchemical Furnace**. We all are the **Alchemist**, so I hope everyone can have a good alchemical furnace to practice the **Alchemy**. Hope you can be a excellent alchemist.