%global _empty_manifest_terminate_build 0
Name: python-SAMITorch
Version: 0.2.7
Release: 1
Summary: Deep Learning Framework For Medical Image Analysis
License: MIT
URL: https://pypi.org/project/SAMITorch/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/63/1e/ce6bfbc60593f8be90c3ed066e4ab01e21672f41b85f3fcacf09a55f09d4/SAMITorch-0.2.7.tar.gz
BuildArch: noarch
%description
# 
SAMITorch
## Welcome to SAMITorch
[](https://travis-ci.com/sami-ets/SAMITorch)
SAMITorch is a deep learning framework for *Shape Analysis in Medical Imaging* laboratory of [École de technologie supérieure](https://www.etsmtl.ca/) using [PyTorch](https://github.com/pytorch) library.
It implements an extensive set of loaders, transformers, models and data sets suited for deep learning in medical imaging.
Our objective is to build a tested, standard framework for quickly producing results in deep learning reasearch applied to medical imaging.
# Table Of Contents
- [Authors](#authors)
- [References](#references)
- [Project architecture](#project-architecture)
- [Folder structure](#folder-structure)
- [Main Components](#main-components)
- [Models](#models)
- [Transformers](#transformers)
- [Configuration](#configs)
- [Main](#main)
- [Contributing](#contributing)
- [Branch naming](#branch-naming)
- [Commits syntax](#commits-syntax)
- [Acknowledgments](#acknowledgments)
## Authors
* Pierre-Luc Delisle - [pldelisle](https://github.com/pldelisle)
* Benoit Anctil-Robitaille - [banctilrobitaille](https://github.com/banctilrobitaille)
## References
#### Segmentation
```
@article{RN10,
author = {Çiçek, Özgün and Abdulkadir, Ahmed and Lienkamp, Soeren S. and Brox, Thomas and Ronneberger, Olaf},
title = {3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation},
journal = {eprint arXiv:1606.06650},
pages = {arXiv:1606.06650},
url = {https://ui.adsabs.harvard.edu/\#abs/2016arXiv160606650C},
year = {2016},
type = {Journal Article}
}
```
#### Classification
```
@inproceedings{RN12,
author = {He, K. and Zhang, X. and Ren, S. and Sun, J.},
title = {Deep Residual Learning for Image Recognition},
booktitle = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {770-778},
ISBN = {1063-6919},
DOI = {10.1109/CVPR.2016.90},
type = {Conference Proceedings}
}
```
#### Diffusion imaging
#### Application
## Setup
> pip install -r [path/to/requirements.txt]
> python3 .py
## Project architecture
### Folder structure
```
── samitorch
| ├── configs - This folder contains the YAML configuration files.
| │ ├── configurations.py - This file contains the definitions of different configuration classes.
| │ |── resnet3d.yaml - Standard ResNet 3D configuration file and model definition.
| │ └── unet3d.yaml - Standard UNet 3D configuration file and model definition.
| |
| ├── initializers - This folder contains custom layer/op initializers.
| | └── initializers.py
| │
| ├── inputs - This folder contains anything relative to inputs to a network.
| | |── batch.py - Contains Batch definition object used in training.
| | |── datasets.py - Contains basic dataset definition for classification and segmentation.
| | |── images.py - Contains Enums for various methods.
| | |── patch.py - Contains Patch definition used in segmentation problems.
| | |── sample.py - Contains a Sample object.
| | |── transformers.py - Contains a series of common transformations.
| | └── utils.py - Contains various utilitary methods.
| |
| ├── models - This folder contains any standard and tested deep learning models.
| │ |── layers.py - Contains layer definitions.
| | |── resnet3d.py - Contains a standard ResNet 3D model.
| | └── unet3d.py - Contains a standard UNet 3D model.
| |
| |── parsers - This folder contains parsers definition used in SAMITorch.
| |
| ├── preprocessing - This folder contains anything relative to input preprocessing, and scripts that must be executed prior training.
| |
| └── utils - This folder contains any utils you may need.
| |── files.py - Contains file related utils methods.
| |── slice_builder.py - Contains an object to build slices out of a data sets (for image segmentation).
| └── tensors.py - Contains tensor related utils methods.
── tests - Folder containing unit tests of the standard framework api and functions.
```
### Main components
(To be documented shortly...)
#### Models
#### Transformers
#### Configs
#### Main
## Contributing
If you find a bug or have an idea for an improvement, please first have a look at our [contribution guideline](https://github.com/sami-ets/SAMITorch/blob/master/CONTRIBUTING.md). Then,
- [X] Create a branch by feature and/or bug fix
- [X] Get the code
- [X] Commit and push
- [X] Create a pull request
## Branch naming
| Instance | Branch | Description, Instructions, Notes |
|-----------------|-----------------------------------------------------|----------------------------------------------------|
| Stable | stable | Accepts merges from Development and Hotfixes |
| Development | dev/ [Short description] [Issue number] | Accepts merges from Features / Issues and Hotfixes |
| Features/Issues | feature/ [Short feature description] [Issue number] | Always branch off HEAD or dev/ |
| Hotfix | fix/ [Short feature description] [Issue number] | Always branch off Stable |
## Commits syntax
##### Adding code:
> \+ Added [Short Description] [Issue Number]
##### Deleting code:
> \- Deleted [Short Description] [Issue Number]
##### Modifying code:
> \* Changed [Short Description] [Issue Number]
##### Merging branches:
> Y Merged [Short Description]
## To build documentation
SAMITorch uses Sphinx Documentation. To build doc, simply execute the following:
> cd docs
> sphinx-build -b html source build
## Acknowledgment
Thanks to [École de technologie supérieure](https://www.etsmtl.ca/), [Hervé Lombaert](https://profs.etsmtl.ca/hlombaert/) and [Christian Desrosiers](https://www.etsmtl.ca/Professeurs/cdesrosiers/Accueil) for providing us a lab and helping us in our research activities.
Icons made by Freepik from www.flaticon.com is licensed by CC 3.0 BY
%package -n python3-SAMITorch
Summary: Deep Learning Framework For Medical Image Analysis
Provides: python-SAMITorch
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-SAMITorch
# SAMITorch
## Welcome to SAMITorch
[](https://travis-ci.com/sami-ets/SAMITorch)
SAMITorch is a deep learning framework for *Shape Analysis in Medical Imaging* laboratory of [École de technologie supérieure](https://www.etsmtl.ca/) using [PyTorch](https://github.com/pytorch) library.
It implements an extensive set of loaders, transformers, models and data sets suited for deep learning in medical imaging.
Our objective is to build a tested, standard framework for quickly producing results in deep learning reasearch applied to medical imaging.
# Table Of Contents
- [Authors](#authors)
- [References](#references)
- [Project architecture](#project-architecture)
- [Folder structure](#folder-structure)
- [Main Components](#main-components)
- [Models](#models)
- [Transformers](#transformers)
- [Configuration](#configs)
- [Main](#main)
- [Contributing](#contributing)
- [Branch naming](#branch-naming)
- [Commits syntax](#commits-syntax)
- [Acknowledgments](#acknowledgments)
## Authors
* Pierre-Luc Delisle - [pldelisle](https://github.com/pldelisle)
* Benoit Anctil-Robitaille - [banctilrobitaille](https://github.com/banctilrobitaille)
## References
#### Segmentation
```
@article{RN10,
author = {Çiçek, Özgün and Abdulkadir, Ahmed and Lienkamp, Soeren S. and Brox, Thomas and Ronneberger, Olaf},
title = {3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation},
journal = {eprint arXiv:1606.06650},
pages = {arXiv:1606.06650},
url = {https://ui.adsabs.harvard.edu/\#abs/2016arXiv160606650C},
year = {2016},
type = {Journal Article}
}
```
#### Classification
```
@inproceedings{RN12,
author = {He, K. and Zhang, X. and Ren, S. and Sun, J.},
title = {Deep Residual Learning for Image Recognition},
booktitle = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {770-778},
ISBN = {1063-6919},
DOI = {10.1109/CVPR.2016.90},
type = {Conference Proceedings}
}
```
#### Diffusion imaging
#### Application
## Setup
> pip install -r [path/to/requirements.txt]
> python3 .py
## Project architecture
### Folder structure
```
── samitorch
| ├── configs - This folder contains the YAML configuration files.
| │ ├── configurations.py - This file contains the definitions of different configuration classes.
| │ |── resnet3d.yaml - Standard ResNet 3D configuration file and model definition.
| │ └── unet3d.yaml - Standard UNet 3D configuration file and model definition.
| |
| ├── initializers - This folder contains custom layer/op initializers.
| | └── initializers.py
| │
| ├── inputs - This folder contains anything relative to inputs to a network.
| | |── batch.py - Contains Batch definition object used in training.
| | |── datasets.py - Contains basic dataset definition for classification and segmentation.
| | |── images.py - Contains Enums for various methods.
| | |── patch.py - Contains Patch definition used in segmentation problems.
| | |── sample.py - Contains a Sample object.
| | |── transformers.py - Contains a series of common transformations.
| | └── utils.py - Contains various utilitary methods.
| |
| ├── models - This folder contains any standard and tested deep learning models.
| │ |── layers.py - Contains layer definitions.
| | |── resnet3d.py - Contains a standard ResNet 3D model.
| | └── unet3d.py - Contains a standard UNet 3D model.
| |
| |── parsers - This folder contains parsers definition used in SAMITorch.
| |
| ├── preprocessing - This folder contains anything relative to input preprocessing, and scripts that must be executed prior training.
| |
| └── utils - This folder contains any utils you may need.
| |── files.py - Contains file related utils methods.
| |── slice_builder.py - Contains an object to build slices out of a data sets (for image segmentation).
| └── tensors.py - Contains tensor related utils methods.
── tests - Folder containing unit tests of the standard framework api and functions.
```
### Main components
(To be documented shortly...)
#### Models
#### Transformers
#### Configs
#### Main
## Contributing
If you find a bug or have an idea for an improvement, please first have a look at our [contribution guideline](https://github.com/sami-ets/SAMITorch/blob/master/CONTRIBUTING.md). Then,
- [X] Create a branch by feature and/or bug fix
- [X] Get the code
- [X] Commit and push
- [X] Create a pull request
## Branch naming
| Instance | Branch | Description, Instructions, Notes |
|-----------------|-----------------------------------------------------|----------------------------------------------------|
| Stable | stable | Accepts merges from Development and Hotfixes |
| Development | dev/ [Short description] [Issue number] | Accepts merges from Features / Issues and Hotfixes |
| Features/Issues | feature/ [Short feature description] [Issue number] | Always branch off HEAD or dev/ |
| Hotfix | fix/ [Short feature description] [Issue number] | Always branch off Stable |
## Commits syntax
##### Adding code:
> \+ Added [Short Description] [Issue Number]
##### Deleting code:
> \- Deleted [Short Description] [Issue Number]
##### Modifying code:
> \* Changed [Short Description] [Issue Number]
##### Merging branches:
> Y Merged [Short Description]
## To build documentation
SAMITorch uses Sphinx Documentation. To build doc, simply execute the following:
> cd docs
> sphinx-build -b html source build
## Acknowledgment
Thanks to [École de technologie supérieure](https://www.etsmtl.ca/), [Hervé Lombaert](https://profs.etsmtl.ca/hlombaert/) and [Christian Desrosiers](https://www.etsmtl.ca/Professeurs/cdesrosiers/Accueil) for providing us a lab and helping us in our research activities.
Icons made by Freepik from www.flaticon.com is licensed by CC 3.0 BY
%package help
Summary: Development documents and examples for SAMITorch
Provides: python3-SAMITorch-doc
%description help
# SAMITorch
## Welcome to SAMITorch
[](https://travis-ci.com/sami-ets/SAMITorch)
SAMITorch is a deep learning framework for *Shape Analysis in Medical Imaging* laboratory of [École de technologie supérieure](https://www.etsmtl.ca/) using [PyTorch](https://github.com/pytorch) library.
It implements an extensive set of loaders, transformers, models and data sets suited for deep learning in medical imaging.
Our objective is to build a tested, standard framework for quickly producing results in deep learning reasearch applied to medical imaging.
# Table Of Contents
- [Authors](#authors)
- [References](#references)
- [Project architecture](#project-architecture)
- [Folder structure](#folder-structure)
- [Main Components](#main-components)
- [Models](#models)
- [Transformers](#transformers)
- [Configuration](#configs)
- [Main](#main)
- [Contributing](#contributing)
- [Branch naming](#branch-naming)
- [Commits syntax](#commits-syntax)
- [Acknowledgments](#acknowledgments)
## Authors
* Pierre-Luc Delisle - [pldelisle](https://github.com/pldelisle)
* Benoit Anctil-Robitaille - [banctilrobitaille](https://github.com/banctilrobitaille)
## References
#### Segmentation
```
@article{RN10,
author = {Çiçek, Özgün and Abdulkadir, Ahmed and Lienkamp, Soeren S. and Brox, Thomas and Ronneberger, Olaf},
title = {3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation},
journal = {eprint arXiv:1606.06650},
pages = {arXiv:1606.06650},
url = {https://ui.adsabs.harvard.edu/\#abs/2016arXiv160606650C},
year = {2016},
type = {Journal Article}
}
```
#### Classification
```
@inproceedings{RN12,
author = {He, K. and Zhang, X. and Ren, S. and Sun, J.},
title = {Deep Residual Learning for Image Recognition},
booktitle = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {770-778},
ISBN = {1063-6919},
DOI = {10.1109/CVPR.2016.90},
type = {Conference Proceedings}
}
```
#### Diffusion imaging
#### Application
## Setup
> pip install -r [path/to/requirements.txt]
> python3 .py
## Project architecture
### Folder structure
```
── samitorch
| ├── configs - This folder contains the YAML configuration files.
| │ ├── configurations.py - This file contains the definitions of different configuration classes.
| │ |── resnet3d.yaml - Standard ResNet 3D configuration file and model definition.
| │ └── unet3d.yaml - Standard UNet 3D configuration file and model definition.
| |
| ├── initializers - This folder contains custom layer/op initializers.
| | └── initializers.py
| │
| ├── inputs - This folder contains anything relative to inputs to a network.
| | |── batch.py - Contains Batch definition object used in training.
| | |── datasets.py - Contains basic dataset definition for classification and segmentation.
| | |── images.py - Contains Enums for various methods.
| | |── patch.py - Contains Patch definition used in segmentation problems.
| | |── sample.py - Contains a Sample object.
| | |── transformers.py - Contains a series of common transformations.
| | └── utils.py - Contains various utilitary methods.
| |
| ├── models - This folder contains any standard and tested deep learning models.
| │ |── layers.py - Contains layer definitions.
| | |── resnet3d.py - Contains a standard ResNet 3D model.
| | └── unet3d.py - Contains a standard UNet 3D model.
| |
| |── parsers - This folder contains parsers definition used in SAMITorch.
| |
| ├── preprocessing - This folder contains anything relative to input preprocessing, and scripts that must be executed prior training.
| |
| └── utils - This folder contains any utils you may need.
| |── files.py - Contains file related utils methods.
| |── slice_builder.py - Contains an object to build slices out of a data sets (for image segmentation).
| └── tensors.py - Contains tensor related utils methods.
── tests - Folder containing unit tests of the standard framework api and functions.
```
### Main components
(To be documented shortly...)
#### Models
#### Transformers
#### Configs
#### Main
## Contributing
If you find a bug or have an idea for an improvement, please first have a look at our [contribution guideline](https://github.com/sami-ets/SAMITorch/blob/master/CONTRIBUTING.md). Then,
- [X] Create a branch by feature and/or bug fix
- [X] Get the code
- [X] Commit and push
- [X] Create a pull request
## Branch naming
| Instance | Branch | Description, Instructions, Notes |
|-----------------|-----------------------------------------------------|----------------------------------------------------|
| Stable | stable | Accepts merges from Development and Hotfixes |
| Development | dev/ [Short description] [Issue number] | Accepts merges from Features / Issues and Hotfixes |
| Features/Issues | feature/ [Short feature description] [Issue number] | Always branch off HEAD or dev/ |
| Hotfix | fix/ [Short feature description] [Issue number] | Always branch off Stable |
## Commits syntax
##### Adding code:
> \+ Added [Short Description] [Issue Number]
##### Deleting code:
> \- Deleted [Short Description] [Issue Number]
##### Modifying code:
> \* Changed [Short Description] [Issue Number]
##### Merging branches:
> Y Merged [Short Description]
## To build documentation
SAMITorch uses Sphinx Documentation. To build doc, simply execute the following:
> cd docs
> sphinx-build -b html source build
## Acknowledgment
Thanks to [École de technologie supérieure](https://www.etsmtl.ca/), [Hervé Lombaert](https://profs.etsmtl.ca/hlombaert/) and [Christian Desrosiers](https://www.etsmtl.ca/Professeurs/cdesrosiers/Accueil) for providing us a lab and helping us in our research activities.
Icons made by Freepik from www.flaticon.com is licensed by CC 3.0 BY
%prep
%autosetup -n SAMITorch-0.2.7
%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-SAMITorch -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 15 2023 Python_Bot - 0.2.7-1
- Package Spec generated