https://github.com/jkoutsikakis/pytorch-wrapper
Provides a systematic and extensible way to build, train, evaluate, and tune deep learning models using PyTorch.
https://github.com/jkoutsikakis/pytorch-wrapper
data-science deep-learning machine-learning neural-network python pytorch pytorch-wrapper tensor
Last synced: 23 days ago
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Provides a systematic and extensible way to build, train, evaluate, and tune deep learning models using PyTorch.
- Host: GitHub
- URL: https://github.com/jkoutsikakis/pytorch-wrapper
- Owner: jkoutsikakis
- License: mit
- Created: 2019-08-11T11:06:32.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-06-17T22:44:06.000Z (over 1 year ago)
- Last Synced: 2025-08-20T21:30:28.708Z (6 months ago)
- Topics: data-science, deep-learning, machine-learning, neural-network, python, pytorch, pytorch-wrapper, tensor
- Language: Python
- Homepage: https://pytorch-wrapper.readthedocs.io/en/latest/
- Size: 246 KB
- Stars: 94
- Watchers: 8
- Forks: 18
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PyTorch Wrapper
PyTorch Wrapper is a library that provides a systematic and extensible way to build, train, evaluate, and tune deep learning models
using PyTorch. It also provides several ready to use modules and functions for fast model development.
| Branch | Build | Coverage |
| :---: | :---: | :---: |
| Master | [](https://travis-ci.com/jkoutsikakis/pytorch-wrapper) | [](https://coveralls.io/github/jkoutsikakis/pytorch-wrapper?branch=master) |
| Develop | [](https://travis-ci.com/jkoutsikakis/pytorch-wrapper)| [](https://coveralls.io/github/jkoutsikakis/pytorch-wrapper?branch=develop) |
## Installation
### From PyPI
```bash
pip install pytorch-wrapper
```
### From Source
```bash
git clone https://github.com/jkoutsikakis/pytorch-wrapper.git
cd pytorch-wrapper
pip install .
```
## Basic abstract usage pattern
```python
import torch
import pytorch_wrapper as pw
train_dataloader = ...
val_dataloader = ...
dev_dataloader = ...
evaluators = { 'acc': pw.evaluators.AccuracyEvaluator(), ... }
loss_wrapper = pw.loss_wrappers.GenericPointWiseLossWrapper(torch.nn.BCEWithLogitsLoss())
model = ...
system = pw.System(model=model, device=torch.device('cuda'))
optimizer = torch.optim.Adam(system.model.parameters())
system.train(
loss_wrapper,
optimizer,
train_data_loader=train_dataloader,
evaluators=evaluators,
evaluation_data_loaders={'val': val_dataloader},
callbacks=[
pw.training_callbacks.EarlyStoppingCriterionCallback(
patience=3,
evaluation_data_loader_key='val',
evaluator_key='acc',
tmp_best_state_filepath='current_best.weights'
)
]
)
results = system.evaluate(dev_dataloader, evaluators)
predictions = system.predict(dev_dataloader)
system.save_model_state('model.weights')
system.load_model_state('model.weights')
```
## Docs & Examples
The docs can be found [here](https://pytorch-wrapper.readthedocs.io/en/latest/).
There are also the following examples in notebook format:
1. [Two Spiral Task](examples/1_two_spiral_task.ipynb)
2. [Image Classification Task](examples/2_image_classification_task.ipynb)
3. [Tuning Image Classifier](examples/3_tuning_image_classifier.ipynb)
4. [Text Classification Task](examples/4_text_classification_task.ipynb)
5. [Token Classification Task](examples/5_token_classification_task.ipynb)
6. [Text Classification Task using BERT](examples/6_text_classification_task_using_bert.ipynb)
7. [Custom Callback](examples/7_custom_callback.ipynb)
8. [Custom Loss Wrapper](examples/8_custom_loss_wrapper.ipynb)
9. [Custom Evaluator](examples/9_custom_evaluator.ipynb)