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@@ -0,0 +1 @@ +/pytorch_segmentation_models_trainer-0.17.0.tar.gz diff --git a/python-pytorch-segmentation-models-trainer.spec b/python-pytorch-segmentation-models-trainer.spec new file mode 100644 index 0000000..1307401 --- /dev/null +++ b/python-pytorch-segmentation-models-trainer.spec @@ -0,0 +1,531 @@ +%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 + + +[](https://pytorch.org/get-started/locally/) +[](https://pytorchlightning.ai/) +[](https://hydra.cc/) +[](https://github.com/qubvel/segmentation_models.pytorch) +[](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-app.yml) +[](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-publish.yml) +[](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/docker-publish.yml) +[](https://results.pre-commit.ci/latest/github/phborba/pytorch_segmentation_models_trainer/main) +[](https://pypi.org/project/pytorch-segmentation-models-trainer/) +[](https://codecov.io/gh/phborba/pytorch_segmentation_models_trainer) +[](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/codeql-analysis.yml) +[](https://github.com/phborba) +[](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 + + +[](https://pytorch.org/get-started/locally/) +[](https://pytorchlightning.ai/) +[](https://hydra.cc/) +[](https://github.com/qubvel/segmentation_models.pytorch) +[](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-app.yml) +[](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-publish.yml) +[](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/docker-publish.yml) +[](https://results.pre-commit.ci/latest/github/phborba/pytorch_segmentation_models_trainer/main) +[](https://pypi.org/project/pytorch-segmentation-models-trainer/) +[](https://codecov.io/gh/phborba/pytorch_segmentation_models_trainer) +[](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/codeql-analysis.yml) +[](https://github.com/phborba) +[](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 + + +[](https://pytorch.org/get-started/locally/) +[](https://pytorchlightning.ai/) +[](https://hydra.cc/) +[](https://github.com/qubvel/segmentation_models.pytorch) +[](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-app.yml) +[](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/python-publish.yml) +[](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/docker-publish.yml) +[](https://results.pre-commit.ci/latest/github/phborba/pytorch_segmentation_models_trainer/main) +[](https://pypi.org/project/pytorch-segmentation-models-trainer/) +[](https://codecov.io/gh/phborba/pytorch_segmentation_models_trainer) +[](https://github.com/phborba/pytorch_segmentation_models_trainer/actions/workflows/codeql-analysis.yml) +[](https://github.com/phborba) +[](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 +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.17.0-1 +- Package Spec generated @@ -0,0 +1 @@ +1c08dbd207e7be8762d1f82ea09466f7 pytorch_segmentation_models_trainer-0.17.0.tar.gz |