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PFSegNets

(★ 125)

PointFlow (CVPR-2021)

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- __init__.py
- edge_utils.py
- iSAID.py
- nullloader.py
- Posdam.py
- sampler.py
- uniform.py
- Vaihingen.py
- - __init__.py
  - cc_attention.py
- - __init__.py
  - mynn.py
  - operators.py
  - point_flow.py
  - sampling_points.py
- __init__.py
- ccnet.py
- ccnet_pfnet.py
- danet.py
- danet_fpn.py
- danet_pfnet.py
- datten_net.py
- dgmnnet.py
- emanet.py
- encodingnet.py
- pointflow_resnet_with_max_avg_pool.py
- pointrend.py
- pspnet.py
- refinenet.py
- resnet.py
- resnet_d.py
- uper_resnet.py
- - train_iSAID_pfnet_r101.sh
  - train_iSAID_pfnet_r50.sh
  - train_posdam896_pfnet_r50.sh
  - train_vaihingen768_pfnet_r50.sh
- convert_iSAID_mask2graymask.py
- convert_isprs_mask2graymask.py
- split_iSAID.py
- split_isprs.py
- __init__.py
- joint_transforms.py
- transforms.py
- __init__.py
- attr_dict.py
- f_boundary.py
- flops_counter.py
- misc.py
- my_data_parallel.py
- seg_edge.py
.gitignore
config.py
eval.py
loss.py
optimizer.py
README.md
setup.py
train.py

# Installation Guide

1. Get the code

git clone https://github.com/lxtGH/PFSegNets

Downloads the entire project code from GitHub to your computer.

cd PFSegNets

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install .

Installs the package published on PyPI directly — no need to clone the source.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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# PFSegNets
This repo contains the the implementation of Our CVPR-2021 work: PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation

## PFSegNet implemented by [Jittor from THU](https://github.com/Jittor/PFSegNets-Jittor).

# To do

- [x] Support PointFlow Models
- [x] Add more Models (at least 15 models)

The master branch works with PyTorch 1.5 and python 3.7.6.
# DataSet preparation
1. Downloading [iSAID](https://captain-whu.github.io/iSAID/), [Potsdam](https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-potsdam/) and
 [Vahihigen](https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-vaihingen/) dataset.
2. Using scripts to crop [iSAID](tools/split_iSAID.py) and [Potsdam, Vaihigen](tools/split_isprs.py) into patches.
3. Using scripts to convert the original mask of [iSAID](tools/convert_iSAID_mask2graymask.py) and [Potsdam, Vaihigen](tools/convert_isprs_mask2graymask.py) 
into gray mask for training and evaluating.
4. Finally, you can either change the `config.py` or do the soft link according to the default path in config.

For example, suppose you store your iSAID dataset at `~/username/data/iSAID`, please update the dataset path in `config.py`,
```
__C.DATASET.iSAID_DIR = '~/username/data/iSAID'
``` 
Or, you can link the data path into current folder.

```
mkdir data 
cd data
ln -s your_iSAID_root_data_path iSAID

```

# Install the extension for other models

```
# install the extension 
python setup.py develop
```

Actually, the order of steps 2 and 3 is interchangeable.

## Pretrained Models

Baidu Pan Link: https://pan.baidu.com/s/1MWzpkI3PwtnEl1LSOyLrLw  4lwf

Google Drive Link: https://drive.google.com/drive/folders/1C7YESlSnqeoJiR8DWpmD4EVWvwf9rreB?usp=sharing

After downloading the pretrained ResNet, you can either change the model path of `network/resnet_d.py` or do the soft link according to the default path in `network/resnet_d.py`.

For example, 
Suppose you store the pretrained ResNet50 model at `~/username/pretrained_model/resnet50-deep.pth`, please update the 
dataset path in Line315 of `config.py`,
```
model.load_state_dict(torch.load("~/username/pretrained_model/resnet50-deep.pth", map_location='cpu'))
```
Or, you can link the pretrained model path into current folder.
```
mkdir pretrained_models
ln -s your_pretrained_model_path path_to_pretrained_models_folder
```

# Model Checkpoints

<table><thead><tr><th>Dataset</th><th>Backbone</th><th>mIoU</th><th>Model</th></tr></thead><tbody>
<tr><td>iSAID</td><td>ResNet50</td><td>66.9</td><td><a href="https://drive.google.com/file/d/1igB0y-5IybcIxf0cALFoqh0Pg36OxWR-/view?usp=sharing" target="_blank" rel="noopener noreferrer">Google Drive</a>&nbsp;|&nbsp;<a href="https://pan.baidu.com/s/1xX2DXdQ5SdpKA3w2EAdZUA" target="_blank" rel="noopener noreferrer">Baidu Pan</a>(v3oj)</td></tr>
<tr><td>Potsdam</td><td>ResNet50</td><td>75.4</td><td><a href="https://drive.google.com/file/d/1tVvPLaMLBp55HfyDhRgmRcMOW44CSc6s/view?usp=sharing" target="_blank" rel="noopener noreferrer">Google Drive</a>&nbsp;|&nbsp;<a href="https://pan.baidu.com/s/1NX1k80NBIrA_G03AsmzZ1w" target="_blank" rel="noopener noreferrer">Baidu Pan</a>(lhlf)</td></tr>
<tr><td>Vaihigen</td><td>ResNet50</td><td>70.4</td><td><a href="https://drive.google.com/file/d/1C3FrXPo8-LuBGUJcC6PCcMP-FP8zVXXb/view?usp=sharing" rel="noopener noreferrer">Google Drive</a>&nbsp;|&nbsp;<a href="https://pan.baidu.com/s/1LSOViE817pS2XpzMPCBbwA" target="_blank" rel="noopener noreferrer">Baidu Pan</a>(54qm)</td></tr>
</tbody></table>

# Evaluation

For example, when evaluating PFNet on validation set of iSAID dasaset:
```bash
sh scripts/pointflow/test/test_iSAID_pfnet_R50.pth path_to_checkpoint path_to_save_results
```
If you want to save images during evaluating, add args: `dump_images`, which will take more time.

# Training

To be note that, our models are trained on 8 V-100 GPUs with 32GB memory.
 **It is hard to reproduce such best results if you do not have such resources.**
For example, when training PFNet on iSAID dataset:
```bash
sh scripts/pointflow/train_iSAID_pfnet_r50.sh
```

# Citation
If you find this repo is helpful to your research. Please consider cite our work.

```
@inproceedings{li2021pointflow,
  title={PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation},
  author={Li, Xiangtai and He, Hao and Li, Xia and Li, Duo and Cheng, Guangliang and Shi, Jianping and Weng, Lubin and Tong, Yunhai and Lin, Zhouchen},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4217--4226},
  year={2021}
}
```

# Acknowledgement
This repo is based on NVIDIA segmentation [repo](https://github.com/NVIDIA/semantic-segmentation). 
We fully thank their open-sourced code.