diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 0000000..c8cd2d4 --- /dev/null +++ b/.editorconfig @@ -0,0 +1,15 @@ +# EditorConfig is awesome: https://EditorConfig.org + +# top-most EditorConfig file +root = true + +[*] +indent_style = space +indent_size = 4 +end_of_line = lf +charset = utf-8 +trim_trailing_whitespace = true +insert_final_newline = true + +[*.{json,toml}] +indent_size = 2 diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml deleted file mode 100644 index 0c9a416..0000000 --- a/.github/workflows/main.yml +++ /dev/null @@ -1,45 +0,0 @@ -name: Publish Docker image - -on: - push: - branches: master - -jobs: - push_to_registry: - name: Push Docker image - runs-on: ubuntu-latest - steps: - - name: Checkout - uses: actions/checkout@v2 - - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v1 - - - name: Log in to Docker Hub - uses: docker/login-action@v1 - with: - username: milesial - password: ${{ secrets.DOCKER_PASSWORD }} - - - name: Log in to the Container registry - uses: docker/login-action@f054a8b539a109f9f41c372932f1ae047eff08c9 - with: - registry: ghcr.io - username: ${{ github.repository_owner }} - password: ${{ secrets.GITHUB_TOKEN }} - - - name: Extract metadata (tags, labels) for Docker - id: meta - uses: docker/metadata-action@v3 - with: - images: milesial/unet - - - name: Build and push Docker image - id: docker_build - uses: docker/build-push-action@v2 - with: - context: . - push: true - tags: | - milesial/unet:latest - ghcr.io/milesial/pytorch-unet:latest diff --git a/.gitignore b/.gitignore index c064c2a..435f4d5 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,5 @@ -*.pyc -data/ +.venv/ +.mypy_cache/ __pycache__/ + checkpoints/ -*.pth -*.jpg -venv/ -.idea/ -wandb/ diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index ed96ee7..0000000 --- a/Dockerfile +++ /dev/null @@ -1,9 +0,0 @@ -FROM nvcr.io/nvidia/pytorch:22.01-py3 - -RUN rm -rf /workspace/* -WORKDIR /workspace/unet - -ADD requirements.txt . -RUN pip install --no-cache-dir --upgrade --pre pip -RUN pip install --no-cache-dir -r requirements.txt -ADD . . diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 94a9ed0..0000000 --- a/LICENSE +++ /dev/null @@ -1,674 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. - - If the disclaimer of warranty and limitation of liability provided -above cannot be given local legal effect according to their terms, -reviewing courts shall apply local law that most closely approximates -an absolute waiver of all civil liability in connection with the -Program, unless a warranty or assumption of liability accompanies a -copy of the Program in return for a fee. - - END OF TERMS AND CONDITIONS - - How to Apply These Terms to Your New Programs - - If you develop a new program, and you want it to be of the greatest -possible use to the public, the best way to achieve this is to make it -free software which everyone can redistribute and change under these terms. - - To do so, attach the following notices to the program. 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If not, see . - -Also add information on how to contact you by electronic and paper mail. - - If the program does terminal interaction, make it output a short -notice like this when it starts in an interactive mode: - - Copyright (C) - This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. - This is free software, and you are welcome to redistribute it - under certain conditions; type `show c' for details. - -The hypothetical commands `show w' and `show c' should show the appropriate -parts of the General Public License. Of course, your program's commands -might be different; for a GUI interface, you would use an "about box". - - You should also get your employer (if you work as a programmer) or school, -if any, to sign a "copyright disclaimer" for the program, if necessary. -For more information on this, and how to apply and follow the GNU GPL, see -. - - The GNU General Public License does not permit incorporating your program -into proprietary programs. If your program is a subroutine library, you -may consider it more useful to permit linking proprietary applications with -the library. If this is what you want to do, use the GNU Lesser General -Public License instead of this License. But first, please read -. diff --git a/README.md b/README.md deleted file mode 100644 index 350fac9..0000000 --- a/README.md +++ /dev/null @@ -1,189 +0,0 @@ -# U-Net: Semantic segmentation with PyTorch - - - - - -![input and output for a random image in the test dataset](https://i.imgur.com/GD8FcB7.png) - - -Customized implementation of the [U-Net](https://arxiv.org/abs/1505.04597) in PyTorch for Kaggle's [Carvana Image Masking Challenge](https://www.kaggle.com/c/carvana-image-masking-challenge) from high definition images. - -- [Quick start](#quick-start) - - [Without Docker](#without-docker) - - [With Docker](#with-docker) -- [Description](#description) -- [Usage](#usage) - - [Docker](#docker) - - [Training](#training) - - [Prediction](#prediction) -- [Weights & Biases](#weights--biases) -- [Pretrained model](#pretrained-model) -- [Data](#data) - -## Quick start - -### Without Docker - -1. [Install CUDA](https://developer.nvidia.com/cuda-downloads) - -2. [Install PyTorch](https://pytorch.org/get-started/locally/) - -3. Install dependencies -```bash -pip install -r requirements.txt -``` - -4. Download the data and run training: -```bash -bash scripts/download_data.sh -python train.py --amp -``` - -### With Docker - -1. [Install Docker 19.03 or later:](https://docs.docker.com/get-docker/) -```bash -curl https://get.docker.com | sh && sudo systemctl --now enable docker -``` -2. [Install the NVIDIA container toolkit:](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) -```bash -distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ - && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ - && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list -sudo apt-get update -sudo apt-get install -y nvidia-docker2 -sudo systemctl restart docker -``` -3. [Download and run the image:](https://hub.docker.com/repository/docker/milesial/unet) -```bash -sudo docker run --rm --shm-size=8g --ulimit memlock=-1 --gpus all -it milesial/unet -``` - -4. Download the data and run training: -```bash -bash scripts/download_data.sh -python train.py --amp -``` - -## Description -This model was trained from scratch with 5k images and scored a [Dice coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) of 0.988423 on over 100k test images. - -It can be easily used for multiclass segmentation, portrait segmentation, medical segmentation, ... - - -## Usage -**Note : Use Python 3.6 or newer** - -### Docker - -A docker image containing the code and the dependencies is available on [DockerHub](https://hub.docker.com/repository/docker/milesial/unet). -You can download and jump in the container with ([docker >=19.03](https://docs.docker.com/get-docker/)): - -```console -docker run -it --rm --shm-size=8g --ulimit memlock=-1 --gpus all milesial/unet -``` - - -### Training - -```console -> python train.py -h -usage: train.py [-h] [--epochs E] [--batch-size B] [--learning-rate LR] - [--load LOAD] [--scale SCALE] [--validation VAL] [--amp] - -Train the UNet on images and target masks - -optional arguments: - -h, --help show this help message and exit - --epochs E, -e E Number of epochs - --batch-size B, -b B Batch size - --learning-rate LR, -l LR - Learning rate - --load LOAD, -f LOAD Load model from a .pth file - --scale SCALE, -s SCALE - Downscaling factor of the images - --validation VAL, -v VAL - Percent of the data that is used as validation (0-100) - --amp Use mixed precision -``` - -By default, the `scale` is 0.5, so if you wish to obtain better results (but use more memory), set it to 1. - -Automatic mixed precision is also available with the `--amp` flag. [Mixed precision](https://arxiv.org/abs/1710.03740) allows the model to use less memory and to be faster on recent GPUs by using FP16 arithmetic. Enabling AMP is recommended. - - -### Prediction - -After training your model and saving it to `MODEL.pth`, you can easily test the output masks on your images via the CLI. - -To predict a single image and save it: - -`python predict.py -i image.jpg -o output.jpg` - -To predict a multiple images and show them without saving them: - -`python predict.py -i image1.jpg image2.jpg --viz --no-save` - -```console -> python predict.py -h -usage: predict.py [-h] [--model FILE] --input INPUT [INPUT ...] - [--output INPUT [INPUT ...]] [--viz] [--no-save] - [--mask-threshold MASK_THRESHOLD] [--scale SCALE] - -Predict masks from input images - -optional arguments: - -h, --help show this help message and exit - --model FILE, -m FILE - Specify the file in which the model is stored - --input INPUT [INPUT ...], -i INPUT [INPUT ...] - Filenames of input images - --output INPUT [INPUT ...], -o INPUT [INPUT ...] - Filenames of output images - --viz, -v Visualize the images as they are processed - --no-save, -n Do not save the output masks - --mask-threshold MASK_THRESHOLD, -t MASK_THRESHOLD - Minimum probability value to consider a mask pixel white - --scale SCALE, -s SCALE - Scale factor for the input images -``` -You can specify which model file to use with `--model MODEL.pth`. - -## Weights & Biases - -The training progress can be visualized in real-time using [Weights & Biases](https://wandb.ai/). Loss curves, validation curves, weights and gradient histograms, as well as predicted masks are logged to the platform. - -When launching a training, a link will be printed in the console. Click on it to go to your dashboard. If you have an existing W&B account, you can link it - by setting the `WANDB_API_KEY` environment variable. If not, it will create an anonymous run which is automatically deleted after 7 days. - - -## Pretrained model -A [pretrained model](https://github.com/milesial/Pytorch-UNet/releases/tag/v3.0) is available for the Carvana dataset. It can also be loaded from torch.hub: - -```python -net = torch.hub.load('milesial/Pytorch-UNet', 'unet_carvana', pretrained=True, scale=0.5) -``` -Available scales are 0.5 and 1.0. - -## Data -The Carvana data is available on the [Kaggle website](https://www.kaggle.com/c/carvana-image-masking-challenge/data). - -You can also download it using the helper script: - -``` -bash scripts/download_data.sh -``` - -The input images and target masks should be in the `data/imgs` and `data/masks` folders respectively (note that the `imgs` and `masks` folder should not contain any sub-folder or any other files, due to the greedy data-loader). For Carvana, images are RGB and masks are black and white. - -You can use your own dataset as long as you make sure it is loaded properly in `utils/data_loading.py`. - - ---- - -Original paper by Olaf Ronneberger, Philipp Fischer, Thomas Brox: - -[U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597) - -![network architecture](https://i.imgur.com/jeDVpqF.png) diff --git a/hubconf.py b/hubconf.py deleted file mode 100644 index d9c39d9..0000000 --- a/hubconf.py +++ /dev/null @@ -1,21 +0,0 @@ -import torch -from unet import UNet as _UNet - -def unet_carvana(pretrained=False, scale=0.5): - """ - UNet model trained on the Carvana dataset ( https://www.kaggle.com/c/carvana-image-masking-challenge/data ). - Set the scale to 0.5 (50%) when predicting. - """ - net = _UNet(n_channels=3, n_classes=2, bilinear=False) - if pretrained: - if scale == 0.5: - checkpoint = 'https://github.com/milesial/Pytorch-UNet/releases/download/v3.0/unet_carvana_scale0.5_epoch2.pth' - elif scale == 1.0: - checkpoint = 'https://github.com/milesial/Pytorch-UNet/releases/download/v3.0/unet_carvana_scale1.0_epoch2.pth' - else: - raise RuntimeError('Only 0.5 and 1.0 scales are available') - - net.load_state_dict(torch.hub.load_state_dict_from_url(checkpoint, progress=True)) - - return net - diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..1faf19c --- /dev/null +++ b/poetry.lock @@ -0,0 +1,992 @@ +[[package]] +name = "albumentations" +version = "1.2.0" +description = "Fast image augmentation library and easy to use wrapper around other libraries" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +numpy = ">=1.11.1" +opencv-python-headless = ">=4.1.1" +PyYAML = "*" +qudida = ">=0.0.4" +scikit-image = ">=0.16.1,<0.19" +scipy = "*" + +[package.extras] +develop = ["pytest", "imgaug (>=0.4.0)"] +imgaug = ["imgaug (>=0.4.0)"] +tests = ["pytest"] + +[[package]] +name = "black" +version = "22.3.0" +description = "The uncompromising code formatter." +category = "dev" +optional = false +python-versions = ">=3.6.2" + +[package.dependencies] +click = ">=8.0.0" +mypy-extensions = ">=0.4.3" +pathspec = ">=0.9.0" +platformdirs = ">=2" +tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""} +typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""} + +[package.extras] +colorama = ["colorama (>=0.4.3)"] +d = ["aiohttp (>=3.7.4)"] +jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"] +uvloop = ["uvloop (>=0.15.2)"] + +[[package]] +name = "certifi" +version = "2022.6.15" +description = "Python package for providing Mozilla's CA Bundle." +category = "main" +optional = false +python-versions = ">=3.6" + +[[package]] +name = "charset-normalizer" +version = "2.0.12" +description = "The Real First Universal Charset Detector. 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