%global _empty_manifest_terminate_build 0 Name: python-pytorch-segmentation-models-trainer Version: 0.17.0 Release: 1 Summary: Image segmentation models training of popular architectures. License: GPL URL: https://github.com/phborba/pytorch_segmentation_models_trainer Source0: https://mirrors.nju.edu.cn/pypi/web/packages/23/77/6763d17050316adab53302082893346db749f2b350e85357562331c795ec/pytorch_segmentation_models_trainer-0.17.0.tar.gz BuildArch: noarch Requires: python3-torch Requires: python3-torchvision Requires: python3-pytorch-lightning Requires: python3-torchmetrics Requires: python3-segmentation-models-pytorch Requires: python3-hydra-core Requires: python3-kornia Requires: python3-albumentations Requires: python3-pandas Requires: python3-tensorboardX Requires: python3-pillow Requires: python3-matplotlib Requires: python3-scipy Requires: python3-numpy Requires: python3-pytorch-toolbelt Requires: python3-descartes Requires: python3-fiona Requires: python3-psycopg2 Requires: python3-shapely Requires: python3-geopandas Requires: python3-geoalchemy2 Requires: python3-rasterio Requires: python3-numba Requires: python3-sahi Requires: python3-skan Requires: python3-torch-scatter Requires: python3-tqdm Requires: python3-pygeos Requires: python3-rtree Requires: python3-bidict Requires: python3-Cython Requires: python3-ninja Requires: python3-pyyaml Requires: python3-pycocotools Requires: python3-multiprocess Requires: python3-wget Requires: python3-fastapi Requires: python3-uvicorn Requires: python3-similaritymeasures Requires: python3-colorama Requires: python3-swifter Requires: python3-multipart Requires: python3-pytest Requires: python3-scikit-image Requires: python3-parameterized %description # pytorch_segmentation_models_trainer [![Torch](https://img.shields.io/badge/-PyTorch-red?logo=pytorch&labelColor=gray)](https://pytorch.org/get-started/locally/) [![Pytorch Lightning](https://img.shields.io/badge/code-Lightning-blueviolet?logo=pytorchlightning&labelColor=gray)](https://pytorchlightning.ai/) [![Hydra](https://img.shields.io/badge/conf-hydra-blue)](https://hydra.cc/) [![Segmentation Models](https://img.shields.io/badge/models-segmentation_models_pytorch-yellow)](https://github.com/qubvel/segmentation_models.pytorch) [![Python application](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-app.yml/badge.svg)](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-app.yml) [![Upload Python Package](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-publish.yml/badge.svg)](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-publish.yml) [![Publish Docker image](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/docker-publish.yml/badge.svg)](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/docker-publish.yml) [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/phborba/pytorch_segmentation_models_trainer/main.svg)](https://results.pre-commit.ci/latest/github/phborba/pytorch_segmentation_models_trainer/main) [![PyPI package](https://img.shields.io/pypi/v/pytorch-segmentation-models-trainer?logo=pypi&color=green)](https://pypi.org/project/pytorch-segmentation-models-trainer/) [![codecov](https://codecov.io/gh/phborba/pytorch_segmentation_models_trainer/branch/main/graph/badge.svg?token=PRJL5GVOL2)](https://codecov.io/gh/phborba/pytorch_segmentation_models_trainer) [![CodeQL](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/codeql-analysis.yml) [![maintainer](https://img.shields.io/badge/maintainer-phborba-blue.svg)](https://github.com/phborba) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4573996.svg)](https://doi.org/10.5281/zenodo.4573996) Framework based on Pytorch, Pytorch Lightning, segmentation_models.pytorch and hydra to train semantic segmentation models using yaml config files as follows: ``` model: _target_: segmentation_models_pytorch.Unet encoder_name: resnet34 encoder_weights: imagenet in_channels: 3 classes: 1 loss: _target_: segmentation_models_pytorch.utils.losses.DiceLoss optimizer: _target_: torch.optim.AdamW lr: 0.001 weight_decay: 1e-4 hyperparameters: batch_size: 1 epochs: 2 max_lr: 0.1 pl_trainer: max_epochs: ${hyperparameters.batch_size} gpus: 0 train_dataset: _target_: pytorch_segmentation_models_trainer.dataset_loader.dataset.SegmentationDataset input_csv_path: /path/to/input.csv data_loader: shuffle: True num_workers: 1 pin_memory: True drop_last: True prefetch_factor: 1 augmentation_list: - _target_: albumentations.HueSaturationValue always_apply: false hue_shift_limit: 0.2 p: 0.5 - _target_: albumentations.RandomBrightnessContrast brightness_limit: 0.2 contrast_limit: 0.2 p: 0.5 - _target_: albumentations.RandomCrop always_apply: true height: 256 width: 256 p: 1.0 - _target_: albumentations.Flip always_apply: true - _target_: albumentations.Normalize p: 1.0 - _target_: albumentations.pytorch.transforms.ToTensorV2 always_apply: true val_dataset: _target_: pytorch_segmentation_models_trainer.dataset_loader.dataset.SegmentationDataset input_csv_path: /path/to/input.csv data_loader: shuffle: True num_workers: 1 pin_memory: True drop_last: True prefetch_factor: 1 augmentation_list: - _target_: albumentations.Resize always_apply: true height: 256 width: 256 p: 1.0 - _target_: albumentations.Normalize p: 1.0 - _target_: albumentations.pytorch.transforms.ToTensorV2 always_apply: true ``` To train a model with configuration path ```/path/to/config/folder``` and name ```test.yaml```: ``` pytorch-smt --config-dir /path/to/config/folder --config-name test +mode=train ``` The mode can be stored in configuration yaml as well. In this case, do not pass the +mode= argument. If the mode is stored in the yaml and you want to overwrite the value, do not use the + clause, just mode= . This module suports hydra features such as configuration composition. For further information, please visit https://hydra.cc/docs/intro # Install If you are not using docker and if you want to enable gpu acceleration, before installing this package, you should install pytorch_scatter as instructed in https://github.com/rusty1s/pytorch_scatter After installing pytorch_scatter, just do ``` pip install pytorch_segmentation_models_trainer ``` We have a docker container in which all dependencies are installed and ready for gpu usage. You can pull the image from dockerhub: ``` docker pull phborba/pytorch_segmentation_models_trainer:latest ``` # Citing: ``` @software{philipe_borba_2021_5115127, author = {Philipe Borba}, title = {{phborba/pytorch\_segmentation\_models\_trainer: Version 0.8.0}}, month = jul, year = 2021, publisher = {Zenodo}, version = {v0.8.0}, doi = {10.5281/zenodo.5115127}, url = {https://doi.org/10.5281/zenodo.5115127} } %package -n python3-pytorch-segmentation-models-trainer Summary: Image segmentation models training of popular architectures. Provides: python-pytorch-segmentation-models-trainer BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pytorch-segmentation-models-trainer # pytorch_segmentation_models_trainer [![Torch](https://img.shields.io/badge/-PyTorch-red?logo=pytorch&labelColor=gray)](https://pytorch.org/get-started/locally/) [![Pytorch Lightning](https://img.shields.io/badge/code-Lightning-blueviolet?logo=pytorchlightning&labelColor=gray)](https://pytorchlightning.ai/) [![Hydra](https://img.shields.io/badge/conf-hydra-blue)](https://hydra.cc/) [![Segmentation Models](https://img.shields.io/badge/models-segmentation_models_pytorch-yellow)](https://github.com/qubvel/segmentation_models.pytorch) [![Python application](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-app.yml/badge.svg)](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-app.yml) [![Upload Python Package](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-publish.yml/badge.svg)](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-publish.yml) [![Publish Docker image](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/docker-publish.yml/badge.svg)](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/docker-publish.yml) [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/phborba/pytorch_segmentation_models_trainer/main.svg)](https://results.pre-commit.ci/latest/github/phborba/pytorch_segmentation_models_trainer/main) [![PyPI package](https://img.shields.io/pypi/v/pytorch-segmentation-models-trainer?logo=pypi&color=green)](https://pypi.org/project/pytorch-segmentation-models-trainer/) [![codecov](https://codecov.io/gh/phborba/pytorch_segmentation_models_trainer/branch/main/graph/badge.svg?token=PRJL5GVOL2)](https://codecov.io/gh/phborba/pytorch_segmentation_models_trainer) [![CodeQL](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/codeql-analysis.yml) [![maintainer](https://img.shields.io/badge/maintainer-phborba-blue.svg)](https://github.com/phborba) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4573996.svg)](https://doi.org/10.5281/zenodo.4573996) Framework based on Pytorch, Pytorch Lightning, segmentation_models.pytorch and hydra to train semantic segmentation models using yaml config files as follows: ``` model: _target_: segmentation_models_pytorch.Unet encoder_name: resnet34 encoder_weights: imagenet in_channels: 3 classes: 1 loss: _target_: segmentation_models_pytorch.utils.losses.DiceLoss optimizer: _target_: torch.optim.AdamW lr: 0.001 weight_decay: 1e-4 hyperparameters: batch_size: 1 epochs: 2 max_lr: 0.1 pl_trainer: max_epochs: ${hyperparameters.batch_size} gpus: 0 train_dataset: _target_: pytorch_segmentation_models_trainer.dataset_loader.dataset.SegmentationDataset input_csv_path: /path/to/input.csv data_loader: shuffle: True num_workers: 1 pin_memory: True drop_last: True prefetch_factor: 1 augmentation_list: - _target_: albumentations.HueSaturationValue always_apply: false hue_shift_limit: 0.2 p: 0.5 - _target_: albumentations.RandomBrightnessContrast brightness_limit: 0.2 contrast_limit: 0.2 p: 0.5 - _target_: albumentations.RandomCrop always_apply: true height: 256 width: 256 p: 1.0 - _target_: albumentations.Flip always_apply: true - _target_: albumentations.Normalize p: 1.0 - _target_: albumentations.pytorch.transforms.ToTensorV2 always_apply: true val_dataset: _target_: pytorch_segmentation_models_trainer.dataset_loader.dataset.SegmentationDataset input_csv_path: /path/to/input.csv data_loader: shuffle: True num_workers: 1 pin_memory: True drop_last: True prefetch_factor: 1 augmentation_list: - _target_: albumentations.Resize always_apply: true height: 256 width: 256 p: 1.0 - _target_: albumentations.Normalize p: 1.0 - _target_: albumentations.pytorch.transforms.ToTensorV2 always_apply: true ``` To train a model with configuration path ```/path/to/config/folder``` and name ```test.yaml```: ``` pytorch-smt --config-dir /path/to/config/folder --config-name test +mode=train ``` The mode can be stored in configuration yaml as well. In this case, do not pass the +mode= argument. If the mode is stored in the yaml and you want to overwrite the value, do not use the + clause, just mode= . This module suports hydra features such as configuration composition. For further information, please visit https://hydra.cc/docs/intro # Install If you are not using docker and if you want to enable gpu acceleration, before installing this package, you should install pytorch_scatter as instructed in https://github.com/rusty1s/pytorch_scatter After installing pytorch_scatter, just do ``` pip install pytorch_segmentation_models_trainer ``` We have a docker container in which all dependencies are installed and ready for gpu usage. You can pull the image from dockerhub: ``` docker pull phborba/pytorch_segmentation_models_trainer:latest ``` # Citing: ``` @software{philipe_borba_2021_5115127, author = {Philipe Borba}, title = {{phborba/pytorch\_segmentation\_models\_trainer: Version 0.8.0}}, month = jul, year = 2021, publisher = {Zenodo}, version = {v0.8.0}, doi = {10.5281/zenodo.5115127}, url = {https://doi.org/10.5281/zenodo.5115127} } %package help Summary: Development documents and examples for pytorch-segmentation-models-trainer Provides: python3-pytorch-segmentation-models-trainer-doc %description help # pytorch_segmentation_models_trainer [![Torch](https://img.shields.io/badge/-PyTorch-red?logo=pytorch&labelColor=gray)](https://pytorch.org/get-started/locally/) [![Pytorch Lightning](https://img.shields.io/badge/code-Lightning-blueviolet?logo=pytorchlightning&labelColor=gray)](https://pytorchlightning.ai/) [![Hydra](https://img.shields.io/badge/conf-hydra-blue)](https://hydra.cc/) [![Segmentation Models](https://img.shields.io/badge/models-segmentation_models_pytorch-yellow)](https://github.com/qubvel/segmentation_models.pytorch) [![Python application](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-app.yml/badge.svg)](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-app.yml) [![Upload Python Package](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-publish.yml/badge.svg)](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-publish.yml) [![Publish Docker image](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/docker-publish.yml/badge.svg)](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/docker-publish.yml) [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/phborba/pytorch_segmentation_models_trainer/main.svg)](https://results.pre-commit.ci/latest/github/phborba/pytorch_segmentation_models_trainer/main) [![PyPI package](https://img.shields.io/pypi/v/pytorch-segmentation-models-trainer?logo=pypi&color=green)](https://pypi.org/project/pytorch-segmentation-models-trainer/) [![codecov](https://codecov.io/gh/phborba/pytorch_segmentation_models_trainer/branch/main/graph/badge.svg?token=PRJL5GVOL2)](https://codecov.io/gh/phborba/pytorch_segmentation_models_trainer) [![CodeQL](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/codeql-analysis.yml) [![maintainer](https://img.shields.io/badge/maintainer-phborba-blue.svg)](https://github.com/phborba) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4573996.svg)](https://doi.org/10.5281/zenodo.4573996) Framework based on Pytorch, Pytorch Lightning, segmentation_models.pytorch and hydra to train semantic segmentation models using yaml config files as follows: ``` model: _target_: segmentation_models_pytorch.Unet encoder_name: resnet34 encoder_weights: imagenet in_channels: 3 classes: 1 loss: _target_: segmentation_models_pytorch.utils.losses.DiceLoss optimizer: _target_: torch.optim.AdamW lr: 0.001 weight_decay: 1e-4 hyperparameters: batch_size: 1 epochs: 2 max_lr: 0.1 pl_trainer: max_epochs: ${hyperparameters.batch_size} gpus: 0 train_dataset: _target_: pytorch_segmentation_models_trainer.dataset_loader.dataset.SegmentationDataset input_csv_path: /path/to/input.csv data_loader: shuffle: True num_workers: 1 pin_memory: True drop_last: True prefetch_factor: 1 augmentation_list: - _target_: albumentations.HueSaturationValue always_apply: false hue_shift_limit: 0.2 p: 0.5 - _target_: albumentations.RandomBrightnessContrast brightness_limit: 0.2 contrast_limit: 0.2 p: 0.5 - _target_: albumentations.RandomCrop always_apply: true height: 256 width: 256 p: 1.0 - _target_: albumentations.Flip always_apply: true - _target_: albumentations.Normalize p: 1.0 - _target_: albumentations.pytorch.transforms.ToTensorV2 always_apply: true val_dataset: _target_: pytorch_segmentation_models_trainer.dataset_loader.dataset.SegmentationDataset input_csv_path: /path/to/input.csv data_loader: shuffle: True num_workers: 1 pin_memory: True drop_last: True prefetch_factor: 1 augmentation_list: - _target_: albumentations.Resize always_apply: true height: 256 width: 256 p: 1.0 - _target_: albumentations.Normalize p: 1.0 - _target_: albumentations.pytorch.transforms.ToTensorV2 always_apply: true ``` To train a model with configuration path ```/path/to/config/folder``` and name ```test.yaml```: ``` pytorch-smt --config-dir /path/to/config/folder --config-name test +mode=train ``` The mode can be stored in configuration yaml as well. In this case, do not pass the +mode= argument. If the mode is stored in the yaml and you want to overwrite the value, do not use the + clause, just mode= . This module suports hydra features such as configuration composition. For further information, please visit https://hydra.cc/docs/intro # Install If you are not using docker and if you want to enable gpu acceleration, before installing this package, you should install pytorch_scatter as instructed in https://github.com/rusty1s/pytorch_scatter After installing pytorch_scatter, just do ``` pip install pytorch_segmentation_models_trainer ``` We have a docker container in which all dependencies are installed and ready for gpu usage. You can pull the image from dockerhub: ``` docker pull phborba/pytorch_segmentation_models_trainer:latest ``` # Citing: ``` @software{philipe_borba_2021_5115127, author = {Philipe Borba}, title = {{phborba/pytorch\_segmentation\_models\_trainer: Version 0.8.0}}, month = jul, year = 2021, publisher = {Zenodo}, version = {v0.8.0}, doi = {10.5281/zenodo.5115127}, url = {https://doi.org/10.5281/zenodo.5115127} } %prep %autosetup -n pytorch-segmentation-models-trainer-0.17.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-pytorch-segmentation-models-trainer -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue May 30 2023 Python_Bot - 0.17.0-1 - Package Spec generated