path: root/python-pytorch-adapt.spec

%global _empty_manifest_terminate_build 0
Name:		python-pytorch-adapt
Version:	0.0.83
Release:	1
Summary:	Domain adaptation made easy. Fully featured, modular, and customizable.
License:	MIT License
URL:		https://github.com/KevinMusgrave/pytorch-adapt
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/db/e5/96520821bbb5f2f38d3f77458e9b47e155b37a35e1e577b34f6dd5a55a49/pytorch-adapt-0.0.83.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-torch
Requires:	python3-torchvision
Requires:	python3-torchmetrics
Requires:	python3-pytorch-metric-learning
Requires:	python3-albumentations
Requires:	python3-black
Requires:	python3-isort
Requires:	python3-nbqa
Requires:	python3-flake8
Requires:	python3-mkdocs-material
Requires:	python3-mkdocstrings[python]
Requires:	python3-griffe
Requires:	python3-mkdocs-gen-files
Requires:	python3-mkdocs-section-index
Requires:	python3-mkdocs-literate-nav
Requires:	python3-pytorch-ignite
Requires:	python3-pytorch-lightning
Requires:	python3-record-keeper
Requires:	python3-tensorboard
Requires:	python3-timm

%description
<h1>
<a href="https://github.com/KevinMusgrave/pytorch-adapt">
<img alt="PyTorch Adapt" src="https://github.com/KevinMusgrave/pytorch-adapt/blob/main/docs/imgs/Logo.png">
</a>
</h1>

<p align="center">
 <a href="https://badge.fury.io/py/pytorch-adapt">
     <img alt="PyPi version" src="https://badge.fury.io/py/pytorch-adapt.svg">
 </a> 
</p>

## Why use PyTorch Adapt?
PyTorch Adapt provides tools for **domain adaptation**, a type of machine learning algorithm that repurposes existing models to work in new domains. This library is:

### 1. **Fully featured**
Build a complete train/val domain adaptation pipeline in a few lines of code.
### 2. **Modular**
Use just the parts that suit your needs, whether it's the algorithms, loss functions, or validation methods.
### 3. **Highly customizable**
Customize and combine complex algorithms with ease.
### 4. **Compatible with frameworks**
Add additional functionality to your code by using one of the framework wrappers. Converting an algorithm into a PyTorch Lightning module is as simple as wrapping it with ```Lightning```.


## Documentation
- [**Documentation**](https://kevinmusgrave.github.io/pytorch-adapt/)
- [**Installation instructions**](https://github.com/KevinMusgrave/pytorch-adapt#installation)
- [**List of papers implemented**](https://kevinmusgrave.github.io/pytorch-adapt/algorithms/uda)

## Examples
See the **[examples folder](https://github.com/KevinMusgrave/pytorch-adapt/blob/main/examples/README.md)** for notebooks you can download or run on Google Colab.

## How to...

### Use in vanilla PyTorch
```python
from pytorch_adapt.hooks import DANNHook
from pytorch_adapt.utils.common_functions import batch_to_device

# Assuming that models, optimizers, and dataloader are already created.
hook = DANNHook(optimizers)
for data in tqdm(dataloader):
    data = batch_to_device(data, device)
    # Optimization is done inside the hook.
    # The returned loss is for logging.
    _, loss = hook({**models, **data})
```

### Build complex algorithms
Let's customize ```DANNHook``` with:

- minimum class confusion
- virtual adversarial training

```python
from pytorch_adapt.hooks import MCCHook, VATHook

# G and C are the Generator and Classifier models
G, C = models["G"], models["C"]
misc = {"combined_model": torch.nn.Sequential(G, C)}
hook = DANNHook(optimizers, post_g=[MCCHook(), VATHook()])
for data in tqdm(dataloader):
    data = batch_to_device(data, device)
    _, loss = hook({**models, **data, **misc})
```

### Wrap with your favorite PyTorch framework
First, set up the adapter and dataloaders:

```python
from pytorch_adapt.adapters import DANN
from pytorch_adapt.containers import Models
from pytorch_adapt.datasets import DataloaderCreator

models_cont = Models(models)
adapter = DANN(models=models_cont)
dc = DataloaderCreator(num_workers=2)
dataloaders = dc(**datasets)
```

Then use a framework wrapper:

#### PyTorch Lightning
```python
import pytorch_lightning as pl
from pytorch_adapt.frameworks.lightning import Lightning

L_adapter = Lightning(adapter)
trainer = pl.Trainer(gpus=1, max_epochs=1)
trainer.fit(L_adapter, dataloaders["train"])
```

#### PyTorch Ignite
```python
trainer = Ignite(adapter)
trainer.run(datasets, dataloader_creator=dc)
```

### Check your model's performance
You can do this in vanilla PyTorch:
```python
from pytorch_adapt.validators import SNDValidator

# Assuming predictions have been collected
target_train = {"preds": preds}
validator = SNDValidator()
score = validator(target_train=target_train)
```

You can also do this during training with a framework wrapper:

#### PyTorch Lightning
```python
from pytorch_adapt.frameworks.utils import filter_datasets

validator = SNDValidator()
dataloaders = dc(**filter_datasets(datasets, validator))
train_loader = dataloaders.pop("train")

L_adapter = Lightning(adapter, validator=validator)
trainer = pl.Trainer(gpus=1, max_epochs=1)
trainer.fit(L_adapter, train_loader, list(dataloaders.values()))
```

#### Pytorch Ignite
```python
from pytorch_adapt.validators import ScoreHistory

validator = ScoreHistory(SNDValidator())
trainer = Ignite(adapter, validator=validator)
trainer.run(datasets, dataloader_creator=dc)
```

### Run the above examples
See [this notebook](https://github.com/KevinMusgrave/pytorch-adapt/blob/main/examples/other/ReadmeExamples.ipynb) and [the examples page](https://github.com/KevinMusgrave/pytorch-adapt/tree/main/examples/) for other notebooks.

## Installation

### Pip
```
pip install pytorch-adapt
```

**To get the latest dev version**:
```
pip install pytorch-adapt --pre
```

**To use ```pytorch_adapt.frameworks.lightning```**:
```
pip install pytorch-adapt[lightning]
```

**To use ```pytorch_adapt.frameworks.ignite```**:
```
pip install pytorch-adapt[ignite]
```


### Conda
Coming soon...

### Dependencies
See [setup.py](https://github.com/KevinMusgrave/pytorch-adapt/blob/main/setup.py)

## Acknowledgements

### Contributors
Thanks to the contributors who made pull requests!
| Contributor | Highlights |
| -- | -- |
| [deepseek-eoghan](https://github.com/deepseek-eoghan) | Improved the TargetDataset class |

### Advisors
Thank you to [Ser-Nam Lim](https://research.fb.com/people/lim-ser-nam/), and my research advisor, [Professor Serge Belongie](https://vision.cornell.edu/se3/people/serge-belongie/).

### Logo
Thanks to [Jeff Musgrave](https://www.designgenius.ca/) for designing the logo.

### Citing this library
If you'd like to cite pytorch-adapt in your paper, you can refer to [this paper](https://arxiv.org/abs/2211.15673) by copy-pasting this bibtex reference: 
```latex
@article{Musgrave2022PyTorchA,
  title={PyTorch Adapt},
  author={Kevin Musgrave and Serge J. Belongie and Ser Nam Lim},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.15673}
}
```

### Code references (in no particular order)
- https://github.com/wgchang/DSBN
- https://github.com/jihanyang/AFN
- https://github.com/thuml/Versatile-Domain-Adaptation
- https://github.com/tim-learn/ATDOC
- https://github.com/thuml/CDAN
- https://github.com/takerum/vat_chainer
- https://github.com/takerum/vat_tf
- https://github.com/RuiShu/dirt-t
- https://github.com/lyakaap/VAT-pytorch
- https://github.com/9310gaurav/virtual-adversarial-training
- https://github.com/thuml/Deep-Embedded-Validation
- https://github.com/lr94/abas
- https://github.com/thuml/Batch-Spectral-Penalization
- https://github.com/jvanvugt/pytorch-domain-adaptation
- https://github.com/ptrblck/pytorch_misc




%package -n python3-pytorch-adapt
Summary:	Domain adaptation made easy. Fully featured, modular, and customizable.
Provides:	python-pytorch-adapt
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-pytorch-adapt
<h1>
<a href="https://github.com/KevinMusgrave/pytorch-adapt">
<img alt="PyTorch Adapt" src="https://github.com/KevinMusgrave/pytorch-adapt/blob/main/docs/imgs/Logo.png">
</a>
</h1>

<p align="center">
 <a href="https://badge.fury.io/py/pytorch-adapt">
     <img alt="PyPi version" src="https://badge.fury.io/py/pytorch-adapt.svg">
 </a> 
</p>

## Why use PyTorch Adapt?
PyTorch Adapt provides tools for **domain adaptation**, a type of machine learning algorithm that repurposes existing models to work in new domains. This library is:

### 1. **Fully featured**
Build a complete train/val domain adaptation pipeline in a few lines of code.
### 2. **Modular**
Use just the parts that suit your needs, whether it's the algorithms, loss functions, or validation methods.
### 3. **Highly customizable**
Customize and combine complex algorithms with ease.
### 4. **Compatible with frameworks**
Add additional functionality to your code by using one of the framework wrappers. Converting an algorithm into a PyTorch Lightning module is as simple as wrapping it with ```Lightning```.


## Documentation
- [**Documentation**](https://kevinmusgrave.github.io/pytorch-adapt/)
- [**Installation instructions**](https://github.com/KevinMusgrave/pytorch-adapt#installation)
- [**List of papers implemented**](https://kevinmusgrave.github.io/pytorch-adapt/algorithms/uda)

## Examples
See the **[examples folder](https://github.com/KevinMusgrave/pytorch-adapt/blob/main/examples/README.md)** for notebooks you can download or run on Google Colab.

## How to...

### Use in vanilla PyTorch
```python
from pytorch_adapt.hooks import DANNHook
from pytorch_adapt.utils.common_functions import batch_to_device

# Assuming that models, optimizers, and dataloader are already created.
hook = DANNHook(optimizers)
for data in tqdm(dataloader):
    data = batch_to_device(data, device)
    # Optimization is done inside the hook.
    # The returned loss is for logging.
    _, loss = hook({**models, **data})
```

### Build complex algorithms
Let's customize ```DANNHook``` with:

- minimum class confusion
- virtual adversarial training

```python
from pytorch_adapt.hooks import MCCHook, VATHook

# G and C are the Generator and Classifier models
G, C = models["G"], models["C"]
misc = {"combined_model": torch.nn.Sequential(G, C)}
hook = DANNHook(optimizers, post_g=[MCCHook(), VATHook()])
for data in tqdm(dataloader):
    data = batch_to_device(data, device)
    _, loss = hook({**models, **data, **misc})
```

### Wrap with your favorite PyTorch framework
First, set up the adapter and dataloaders:

```python
from pytorch_adapt.adapters import DANN
from pytorch_adapt.containers import Models
from pytorch_adapt.datasets import DataloaderCreator

models_cont = Models(models)
adapter = DANN(models=models_cont)
dc = DataloaderCreator(num_workers=2)
dataloaders = dc(**datasets)
```

Then use a framework wrapper:

#### PyTorch Lightning
```python
import pytorch_lightning as pl
from pytorch_adapt.frameworks.lightning import Lightning

L_adapter = Lightning(adapter)
trainer = pl.Trainer(gpus=1, max_epochs=1)
trainer.fit(L_adapter, dataloaders["train"])
```

#### PyTorch Ignite
```python
trainer = Ignite(adapter)
trainer.run(datasets, dataloader_creator=dc)
```

### Check your model's performance
You can do this in vanilla PyTorch:
```python
from pytorch_adapt.validators import SNDValidator

# Assuming predictions have been collected
target_train = {"preds": preds}
validator = SNDValidator()
score = validator(target_train=target_train)
```

You can also do this during training with a framework wrapper:

#### PyTorch Lightning
```python
from pytorch_adapt.frameworks.utils import filter_datasets

validator = SNDValidator()
dataloaders = dc(**filter_datasets(datasets, validator))
train_loader = dataloaders.pop("train")

L_adapter = Lightning(adapter, validator=validator)
trainer = pl.Trainer(gpus=1, max_epochs=1)
trainer.fit(L_adapter, train_loader, list(dataloaders.values()))
```

#### Pytorch Ignite
```python
from pytorch_adapt.validators import ScoreHistory

validator = ScoreHistory(SNDValidator())
trainer = Ignite(adapter, validator=validator)
trainer.run(datasets, dataloader_creator=dc)
```

### Run the above examples
See [this notebook](https://github.com/KevinMusgrave/pytorch-adapt/blob/main/examples/other/ReadmeExamples.ipynb) and [the examples page](https://github.com/KevinMusgrave/pytorch-adapt/tree/main/examples/) for other notebooks.

## Installation

### Pip
```
pip install pytorch-adapt
```

**To get the latest dev version**:
```
pip install pytorch-adapt --pre
```

**To use ```pytorch_adapt.frameworks.lightning```**:
```
pip install pytorch-adapt[lightning]
```

**To use ```pytorch_adapt.frameworks.ignite```**:
```
pip install pytorch-adapt[ignite]
```


### Conda
Coming soon...

### Dependencies
See [setup.py](https://github.com/KevinMusgrave/pytorch-adapt/blob/main/setup.py)

## Acknowledgements

### Contributors
Thanks to the contributors who made pull requests!
| Contributor | Highlights |
| -- | -- |
| [deepseek-eoghan](https://github.com/deepseek-eoghan) | Improved the TargetDataset class |

### Advisors
Thank you to [Ser-Nam Lim](https://research.fb.com/people/lim-ser-nam/), and my research advisor, [Professor Serge Belongie](https://vision.cornell.edu/se3/people/serge-belongie/).

### Logo
Thanks to [Jeff Musgrave](https://www.designgenius.ca/) for designing the logo.

### Citing this library
If you'd like to cite pytorch-adapt in your paper, you can refer to [this paper](https://arxiv.org/abs/2211.15673) by copy-pasting this bibtex reference: 
```latex
@article{Musgrave2022PyTorchA,
  title={PyTorch Adapt},
  author={Kevin Musgrave and Serge J. Belongie and Ser Nam Lim},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.15673}
}
```

### Code references (in no particular order)
- https://github.com/wgchang/DSBN
- https://github.com/jihanyang/AFN
- https://github.com/thuml/Versatile-Domain-Adaptation
- https://github.com/tim-learn/ATDOC
- https://github.com/thuml/CDAN
- https://github.com/takerum/vat_chainer
- https://github.com/takerum/vat_tf
- https://github.com/RuiShu/dirt-t
- https://github.com/lyakaap/VAT-pytorch
- https://github.com/9310gaurav/virtual-adversarial-training
- https://github.com/thuml/Deep-Embedded-Validation
- https://github.com/lr94/abas
- https://github.com/thuml/Batch-Spectral-Penalization
- https://github.com/jvanvugt/pytorch-domain-adaptation
- https://github.com/ptrblck/pytorch_misc




%package help
Summary:	Development documents and examples for pytorch-adapt
Provides:	python3-pytorch-adapt-doc
%description help
<h1>
<a href="https://github.com/KevinMusgrave/pytorch-adapt">
<img alt="PyTorch Adapt" src="https://github.com/KevinMusgrave/pytorch-adapt/blob/main/docs/imgs/Logo.png">
</a>
</h1>

<p align="center">
 <a href="https://badge.fury.io/py/pytorch-adapt">
     <img alt="PyPi version" src="https://badge.fury.io/py/pytorch-adapt.svg">
 </a> 
</p>

## Why use PyTorch Adapt?
PyTorch Adapt provides tools for **domain adaptation**, a type of machine learning algorithm that repurposes existing models to work in new domains. This library is:

### 1. **Fully featured**
Build a complete train/val domain adaptation pipeline in a few lines of code.
### 2. **Modular**
Use just the parts that suit your needs, whether it's the algorithms, loss functions, or validation methods.
### 3. **Highly customizable**
Customize and combine complex algorithms with ease.
### 4. **Compatible with frameworks**
Add additional functionality to your code by using one of the framework wrappers. Converting an algorithm into a PyTorch Lightning module is as simple as wrapping it with ```Lightning```.


## Documentation
- [**Documentation**](https://kevinmusgrave.github.io/pytorch-adapt/)
- [**Installation instructions**](https://github.com/KevinMusgrave/pytorch-adapt#installation)
- [**List of papers implemented**](https://kevinmusgrave.github.io/pytorch-adapt/algorithms/uda)

## Examples
See the **[examples folder](https://github.com/KevinMusgrave/pytorch-adapt/blob/main/examples/README.md)** for notebooks you can download or run on Google Colab.

## How to...

### Use in vanilla PyTorch
```python
from pytorch_adapt.hooks import DANNHook
from pytorch_adapt.utils.common_functions import batch_to_device

# Assuming that models, optimizers, and dataloader are already created.
hook = DANNHook(optimizers)
for data in tqdm(dataloader):
    data = batch_to_device(data, device)
    # Optimization is done inside the hook.
    # The returned loss is for logging.
    _, loss = hook({**models, **data})
```

### Build complex algorithms
Let's customize ```DANNHook``` with:

- minimum class confusion
- virtual adversarial training

```python
from pytorch_adapt.hooks import MCCHook, VATHook

# G and C are the Generator and Classifier models
G, C = models["G"], models["C"]
misc = {"combined_model": torch.nn.Sequential(G, C)}
hook = DANNHook(optimizers, post_g=[MCCHook(), VATHook()])
for data in tqdm(dataloader):
    data = batch_to_device(data, device)
    _, loss = hook({**models, **data, **misc})
```

### Wrap with your favorite PyTorch framework
First, set up the adapter and dataloaders:

```python
from pytorch_adapt.adapters import DANN
from pytorch_adapt.containers import Models
from pytorch_adapt.datasets import DataloaderCreator

models_cont = Models(models)
adapter = DANN(models=models_cont)
dc = DataloaderCreator(num_workers=2)
dataloaders = dc(**datasets)
```

Then use a framework wrapper:

#### PyTorch Lightning
```python
import pytorch_lightning as pl
from pytorch_adapt.frameworks.lightning import Lightning

L_adapter = Lightning(adapter)
trainer = pl.Trainer(gpus=1, max_epochs=1)
trainer.fit(L_adapter, dataloaders["train"])
```

#### PyTorch Ignite
```python
trainer = Ignite(adapter)
trainer.run(datasets, dataloader_creator=dc)
```

### Check your model's performance
You can do this in vanilla PyTorch:
```python
from pytorch_adapt.validators import SNDValidator

# Assuming predictions have been collected
target_train = {"preds": preds}
validator = SNDValidator()
score = validator(target_train=target_train)
```

You can also do this during training with a framework wrapper:

#### PyTorch Lightning
```python
from pytorch_adapt.frameworks.utils import filter_datasets

validator = SNDValidator()
dataloaders = dc(**filter_datasets(datasets, validator))
train_loader = dataloaders.pop("train")

L_adapter = Lightning(adapter, validator=validator)
trainer = pl.Trainer(gpus=1, max_epochs=1)
trainer.fit(L_adapter, train_loader, list(dataloaders.values()))
```

#### Pytorch Ignite
```python
from pytorch_adapt.validators import ScoreHistory

validator = ScoreHistory(SNDValidator())
trainer = Ignite(adapter, validator=validator)
trainer.run(datasets, dataloader_creator=dc)
```

### Run the above examples
See [this notebook](https://github.com/KevinMusgrave/pytorch-adapt/blob/main/examples/other/ReadmeExamples.ipynb) and [the examples page](https://github.com/KevinMusgrave/pytorch-adapt/tree/main/examples/) for other notebooks.

## Installation

### Pip
```
pip install pytorch-adapt
```

**To get the latest dev version**:
```
pip install pytorch-adapt --pre
```

**To use ```pytorch_adapt.frameworks.lightning```**:
```
pip install pytorch-adapt[lightning]
```

**To use ```pytorch_adapt.frameworks.ignite```**:
```
pip install pytorch-adapt[ignite]
```


### Conda
Coming soon...

### Dependencies
See [setup.py](https://github.com/KevinMusgrave/pytorch-adapt/blob/main/setup.py)

## Acknowledgements

### Contributors
Thanks to the contributors who made pull requests!
| Contributor | Highlights |
| -- | -- |
| [deepseek-eoghan](https://github.com/deepseek-eoghan) | Improved the TargetDataset class |

### Advisors
Thank you to [Ser-Nam Lim](https://research.fb.com/people/lim-ser-nam/), and my research advisor, [Professor Serge Belongie](https://vision.cornell.edu/se3/people/serge-belongie/).

### Logo
Thanks to [Jeff Musgrave](https://www.designgenius.ca/) for designing the logo.

### Citing this library
If you'd like to cite pytorch-adapt in your paper, you can refer to [this paper](https://arxiv.org/abs/2211.15673) by copy-pasting this bibtex reference: 
```latex
@article{Musgrave2022PyTorchA,
  title={PyTorch Adapt},
  author={Kevin Musgrave and Serge J. Belongie and Ser Nam Lim},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.15673}
}
```

### Code references (in no particular order)
- https://github.com/wgchang/DSBN
- https://github.com/jihanyang/AFN
- https://github.com/thuml/Versatile-Domain-Adaptation
- https://github.com/tim-learn/ATDOC
- https://github.com/thuml/CDAN
- https://github.com/takerum/vat_chainer
- https://github.com/takerum/vat_tf
- https://github.com/RuiShu/dirt-t
- https://github.com/lyakaap/VAT-pytorch
- https://github.com/9310gaurav/virtual-adversarial-training
- https://github.com/thuml/Deep-Embedded-Validation
- https://github.com/lr94/abas
- https://github.com/thuml/Batch-Spectral-Penalization
- https://github.com/jvanvugt/pytorch-domain-adaptation
- https://github.com/ptrblck/pytorch_misc




%prep
%autosetup -n pytorch-adapt-0.0.83

%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-adapt -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.83-1
- Package Spec generated