https://github.com/satyajitghana/pysodium

PySodium - A Deep Learning Library wrapper for PyTorch
https://github.com/satyajitghana/pysodium

deep deep-learning learning models pysodium pytorch tsai

Last synced: 22 days ago
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PySodium - A Deep Learning Library wrapper for PyTorch

• Host: GitHub
• URL: https://github.com/satyajitghana/pysodium
• Owner: satyajitghana
• License: mit
• Created: 2020-03-03T11:58:03.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-04-25T17:01:57.000Z (over 4 years ago)
• Last Synced: 2024-12-15T12:08:25.960Z (about 1 month ago)
• Topics: deep, deep-learning, learning, models, pysodium, pytorch, tsai
• Language: Python
• Homepage:
• Size: 22.4 MB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# PySodium V0.0.2

![release](https://img.shields.io/github/v/release/satyajitghana/PySodium?include_prereleases)

![PyPI-Python-version](https://img.shields.io/pypi/pyversions/py-sodium)

![PyPI-wheel](https://img.shields.io/pypi/wheel/py-sodium)

![release-date](https://img.shields.io/github/release-date-pre/satyajitghana/PySodium)

![license](https://img.shields.io/github/license/satyajitghana/pysodium)

![maintainer](https://img.shields.io/badge/maintainer-shadowleaf-blue)

## Usage

1. Install the PySodium Library

`pip install git+https://github.com/satyajitghana/PySodium.git#egg=sodium`

2. Create a config.yml

```yaml

name: CIFAR10_V2

save_dir: saved/

seed: 1

target_device: 0

arch:

    type: CIFAR10S8Model

    args: {}

augmentation:

    type: CIFAR10Albumentations

    args: {}

data_loader:

    type: CIFAR10DataLoader

    args:

        batch_size: 128

        data_dir: data/

        nworkers: 4

        shuffle: True

criterion: cross_entropy_loss

lr_scheduler:

    type: ReduceLROnPlateau

    args:

        mode: 'min'

        batch_scheduler: False

optimizer:

    type: SGD

    args:

        lr: 0.001

        momentum: 0.95

        weight_decay: 0.0005

training:

    epochs: 10

```

3. Run the Model !

```python

# import my baby-library

from sodium.utils import load_config

import sodium.runner as runner

# create a runner

config = load_config('config.yml', tsai_mode=True)

# setup trainer

runner.setup_train(tsai_mode=True)

# find best lr

runner.find_lr()

# train the network using the best lr

runner.train(use_bestlr=True)

# plot metrics

runner.plot_metrics()

# plot grad cam

target_layers = ["layer1", "layer2", "layer3", "layer4"]

runner.plot_gradcam(target_layers=target_layers)

# plot misclassifications

runner.plot_misclassifications(target_layers=target_layers)

```

## NOTE

if you are using the library on a terminal, you can use the main.py and call

`python main.py --config=config.yml`

To install OpenCV first update your anaconda environment

```

conda update --all

conda install -c conda-forge opencv

```

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