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https://github.com/BRainynight/awesome-seg
fix from awesome's Nets
https://github.com/BRainynight/awesome-seg
List: awesome-seg
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fix from awesome's Nets
- Host: GitHub
- URL: https://github.com/BRainynight/awesome-seg
- Owner: BRainynight
- License: apache-2.0
- Created: 2019-10-12T06:13:54.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-10-04T00:54:02.000Z (about 2 years ago)
- Last Synced: 2024-11-16T03:00:55.662Z (about 1 month ago)
- Language: Python
- Size: 674 KB
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - awesome-seg - Fix from awesome's Nets. (Other Lists / PowerShell Lists)
README
# 簡介
Fix from [Tramac's repositories](https://github.com/Tramac/awesome-semantic-segmentation-pytorch).
因為是在Google Colab上訓練，擔心被切斷，所以增加了自動上傳到Google Drive的片段。
# 我所做的調整:
## Train 階段
1. \scripts\train.py :
* class Trainer(object):
在`self.criterion = get_segmentation_loss`的參數裡面，多傳入一個(nclass = xx)
在其他地方，nclass可以透過 nclass=datasets[dataset].NUM_CLASS 的方式取得，但是這需要調用到 from dataloader import datasets.
這個檔案對 train.py來說太遙遠，暫且用手動輸入帶過他。
* add function `save_to_Gdrive`:
我自己另外增加了上傳到colab的包，修改過的colab+pydrive也放在這個respository裡面。
2.
## Eval 階段
1. \core\data\dataloader\mydata.py:
基於cityscapes.py 修改而成，其中因為手上自己資料集的label檔案，雖是黑白png，卻是以三通到的方式儲存。
書需要轉換成灰階圖，否則評價 (socre.py )分數上，會因為矩陣大小不符合而報錯。
* `def __getitem__(): `
`mask = Image.open(self.mask_paths[index]).convert('L')`
```
File "/content/awesome-semantic-segmentation-pytorch/core/utils/score.py", line 76, in batch_pix_accuracy
pixel_correct = torch.sum((predict == target) * (target > 0)).item()
RuntimeError: The size of tensor a (640) must match the size of tensor b (3) at non-singleton dimension 3
```
2. \core\models\base_models\resnetv1b.py:
沒有傳入 kwargs 導致 TypeError:
`class ResNetV1b(nn.Module):`: add `**kwargs in __init__() `
```
TypeError: __init__() got an unexpected keyword argument 'local_rank'
```
使用自己的資料集時
## Demo 階段
1. \scripts\demo.py:
沒有將 arg 的整個參數傳送給get_model，添加 `**vars(args)` 。
因為 get_model 內部使用到的是 kwargs (字典方式取值) ，如果只傳入args，**kwargs根本是空的**，所以使用上述方式傳入字典形式參數索引。
```
model = get_model(args.model, **vars(args), pretrained=True, root=args.save_folder).to(device)
```
2. model_zoo.py:
in function get_icnet_resnet50_citys :
```
kwargs.pop("dataset")
net = _models[name](**kwargs)
return net
```
因為決定用哪個模型，是由最初的參數 --model 來決定。
如果把 dataset 傳入 _models 的模型中，會產生 **dataset 被指定兩次**的狀況，所以在這裡先把它刪掉 (_models的模型自己會指定 dataset)。
不能從一開始就少掉這個指令是因為 demo.py 裡面需要這個參數。
```
mask = get_color_pallete(pred, args.dataset)
```
**PS.** `--dataset citys` 這個指令不知道為何，一直沒辦法輸入成功，我直接把預設改成citys。
-------
# Semantic Segmentation on PyTorch
[![python-image]][python-url]
[![pytorch-image]][pytorch-url]
[![lic-image]][lic-url]
This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.
## Installation
```
# semantic-segmentation-pytorch dependencies
pip install ninja tqdm
# follow PyTorch installation in https://pytorch.org/get-started/locally/
conda install pytorch torchvision -c pytorch
# install PyTorch Segmentation
git clone https://github.com/Tramac/awesome-semantic-segmentation-pytorch.git
# the following will install the lib with symbolic links, so that you can modify
# the files if you want and won't need to re-build it
cd awesome-semantic-segmentation-pytorch/core/nn
python setup.py build develop
```
## Usage
### Train
-----------------
- **Single GPU training**
```
# for example, train fcn32_vgg16_pascal_voc:
python train.py --model fcn32s --backbone vgg16 --dataset pascal_voc --lr 0.0001 --epochs 50
```
- **Multi-GPU training**
```
# for example, train fcn32_vgg16_pascal_voc with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py --model fcn32s --backbone vgg16 --dataset pascal_voc --lr 0.0001 --epochs 50
```
### Evaluation
-----------------
- **Single GPU evaluating**
```
# for example, evaluate fcn32_vgg16_pascal_voc
python eval.py --model fcn32s --backbone vgg16 --dataset pascal_voc
```
- **Multi-GPU evaluating**
```
# for example, evaluate fcn32_vgg16_pascal_voc with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS eval.py --model fcn32s --backbone vgg16 --dataset pascal_voc
```
### Demo
```
cd ./scripts
python demo.py --model fcn32s_vgg16_voc --input-pic ./datasets/test.jpg
```
```
.{SEG_ROOT}
├── scripts
│ ├── demo.py
│ ├── eval.py
│ └── train.py
```
## Support
#### Model
- [FCN](https://arxiv.org/abs/1411.4038)
- [ENet](https://arxiv.org/pdf/1606.02147)
- [PSPNet](https://arxiv.org/pdf/1612.01105)
- [ICNet](https://arxiv.org/pdf/1704.08545)
- [DeepLabv3](https://arxiv.org/abs/1706.05587)
- [DeepLabv3+](https://arxiv.org/pdf/1802.02611)
- [DenseASPP](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf)
- [EncNet](https://arxiv.org/abs/1803.08904v1)
- [BiSeNet](https://arxiv.org/abs/1808.00897)
- [PSANet](https://hszhao.github.io/papers/eccv18_psanet.pdf)
- [DANet](https://arxiv.org/pdf/1809.02983)
- [OCNet](https://arxiv.org/pdf/1809.00916)
- [CGNet](https://arxiv.org/pdf/1811.08201)
- [ESPNetv2](https://arxiv.org/abs/1811.11431)
- [CCNet](https://arxiv.org/pdf/1811.11721)
- [DUNet(DUpsampling)](https://arxiv.org/abs/1903.02120)
- [FastFCN(JPU)](https://arxiv.org/abs/1903.11816)
- [LEDNet](https://arxiv.org/abs/1905.02423)
- [Fast-SCNN](https://github.com/Tramac/Fast-SCNN-pytorch)
- [LightSeg](https://github.com/Tramac/Lightweight-Segmentation)
- [DFANet](https://arxiv.org/abs/1904.02216)
[DETAILS](https://github.com/Tramac/awesome-semantic-segmentation-pytorch/blob/master/docs/DETAILS.md) for model & backbone.
```
.{SEG_ROOT}
├── core
│ ├── models
│ │ ├── bisenet.py
│ │ ├── danet.py
│ │ ├── deeplabv3.py
│ │ ├── deeplabv3+.py
│ │ ├── denseaspp.py
│ │ ├── dunet.py
│ │ ├── encnet.py
│ │ ├── fcn.py
│ │ ├── pspnet.py
│ │ ├── icnet.py
│ │ ├── enet.py
│ │ ├── ocnet.py
│ │ ├── ccnet.py
│ │ ├── psanet.py
│ │ ├── cgnet.py
│ │ ├── espnet.py
│ │ ├── lednet.py
│ │ ├── dfanet.py
│ │ ├── ......
```
#### Dataset
You can run script to download dataset, such as:
```
cd ./core/data/downloader
python ade20k.py --download-dir ../datasets/ade
```
| Dataset | training set | validation set | testing set |
| :----------------------------------------------------------: | :----------: | :------------: | :---------: |
| [VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) | 1464 | 1449 | ✘ |
| [VOCAug](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz) | 11355 | 2857 | ✘ |
| [ADK20K](http://groups.csail.mit.edu/vision/datasets/ADE20K/) | 20210 | 2000 | ✘ |
| [Cityscapes](https://www.cityscapes-dataset.com/downloads/) | 2975 | 500 | ✘ |
| [COCO](http://cocodataset.org/#download) | | | |
| [SBU-shadow](http://www3.cs.stonybrook.edu/~cvl/content/datasets/shadow_db/SBU-shadow.zip) | 4085 | 638 | ✘ |
| [LIP(Look into Person)](http://sysu-hcp.net/lip/) | 30462 | 10000 | 10000 |
```
.{SEG_ROOT}
├── core
│ ├── data
│ │ ├── dataloader
│ │ │ ├── ade.py
│ │ │ ├── cityscapes.py
│ │ │ ├── mscoco.py
│ │ │ ├── pascal_aug.py
│ │ │ ├── pascal_voc.py
│ │ │ ├── sbu_shadow.py
│ │ └── downloader
│ │ ├── ade20k.py
│ │ ├── cityscapes.py
│ │ ├── mscoco.py
│ │ ├── pascal_voc.py
│ │ └── sbu_shadow.py
```
## Result
- **PASCAL VOC 2012**
|Methods|Backbone|TrainSet|EvalSet|crops_size|epochs|JPU|Mean IoU|pixAcc|
|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
|FCN32s|vgg16|train|val|480|60|✘|47.50|85.39|
|FCN16s|vgg16|train|val|480|60|✘|49.16|85.98|
|FCN8s|vgg16|train|val|480|60|✘|48.87|85.02|
|FCN32s|resnet50|train|val|480|50|✘|54.60|88.57|
|PSPNet|resnet50|train|val|480|60|✘|63.44|89.78|
|DeepLabv3|resnet50|train|val|480|60|✘|60.15|88.36|
Note: `lr=1e-4, batch_size=4, epochs=80`.
## Overfitting Test
See [TEST](https://github.com/Tramac/Awesome-semantic-segmentation-pytorch/tree/master/tests) for details.
```
.{SEG_ROOT}
├── tests
│ └── test_model.py
```
## To Do
- [x] add train script
- [ ] remove syncbn
- [ ] train & evaluate
- [x] test distributed training
- [x] fix syncbn ([Why SyncBN?](https://tramac.github.io/2019/04/08/SyncBN/))
- [x] add distributed ([How DIST?](https://tramac.github.io/2019/04/22/%E5%88%86%E5%B8%83%E5%BC%8F%E8%AE%AD%E7%BB%83-PyTorch/))
## References
- [PyTorch-Encoding](https://github.com/zhanghang1989/PyTorch-Encoding)
- [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark)
- [gloun-cv](https://github.com/dmlc/gluon-cv)
- [imagenet](https://github.com/pytorch/examples/tree/master/imagenet)
[python-image]: https://img.shields.io/badge/Python-2.x|3.x-ff69b4.svg
[python-url]: https://www.python.org/
[pytorch-image]: https://img.shields.io/badge/PyTorch-1.1-2BAF2B.svg
[pytorch-url]: https://pytorch.org/
[lic-image]: https://img.shields.io/badge/Apache-2.0-blue.svg
[lic-url]: https://github.com/Tramac/Awesome-semantic-segmentation-pytorch/blob/master/LICENSE