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|
%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.aliyun.com/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
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.17.0-1
- Package Spec generated
|
