Metadata-Version: 2.1
Name: pytorch-adapt
Version: 0.0.39
Summary: UNKNOWN
Home-page: https://github.com/KevinMusgrave/pytorch-adapt
Author: Kevin Musgrave
License: UNKNOWN
Description: <h1 align="center">
        <a href="https://github.com/KevinMusgrave/pytorch-adapt">
        <img alt="Logo" src="https://github.com/KevinMusgrave/pytorch-adapt/blob/main/docs/imgs/Logo.png">
        </a>
        </h2>
        <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>
        
        ## What does it do?
        PyTorch Adapt provides tools for **domain adaptation**. This is a type of machine learning algorithm used for repurposing existing models to work in new domains.
        
        ## Benefits
        ### 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)
        - [**Overview of modules**](https://github.com/KevinMusgrave/pytorch-adapt/blob/master/CONTENTS.md)
        
        ## Getting started
        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.score(target_train=target_train)
        ```
        
        You can also do this during training with a framework wrapper:
        
        #### 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()))
        ```
        
        #### 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
        Required dependencies: 
        - numpy
        - torch >= 1.6
        - torchvision
        - torchmetrics
        - pytorch-metric-learning >= 1.0.0.dev5
        
        ## Acknowledgements
        
        ### Contributors
        Pull requests are welcome!
        
        ### 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.
        
        ### 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
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.0
Description-Content-Type: text/markdown
Provides-Extra: ignite
Provides-Extra: lightning
Provides-Extra: record-keeper
