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A high-level deep learning library build on top of PyTorch.

Project description

TensorNet

MIT License Version

TensorNet is a high-level deep learning library built on top of PyTorch.

NOTE: This documentation applies to the MASTER version of TensorNet only.

Installation

You can use pip to install tensornet

pip install torch-tensornet

If you want to get the latest version of the code before it is released on PyPI you can install the library from GitHub

pip install git+https://github.com/shan18/TensorNet.git#egg=torch-tensornet

Features

TensorNet currently supports the following features

  • Model architectures
    • ResNet18
    • A custom model called BasicNet
  • Model utilities
    • Loss functions
      • Cross Entropy Loss
    • Optimizers
      • Stochastic Gradient Descent
    • Regularizers
      • L1 regularization
      • L2 regularization
    • Callbacks
      • Step LR
      • Reduce LR on Plateau
    • LR Finder
  • Model training and validation
  • Datasets (data is is returned via data loaders)
    • CIFAR10
  • Data Augmentation
    • Horizontal Flip
    • Vertical Flip
    • Gaussian Blur
    • Random Rotation
    • CutOut
  • GradCAM and GradCAM++ (Gradient-weighted Class Activation Map)
  • Result Analysis Tools
    • Plotting changes in validation accuracy and loss during model training
    • Displaying correct and incorrect predictions of a trained model

How to Use

For examples on how to use TensorNet, refer to the examples directory.

Dependencies

TensorNet has the following third-party dependencies

  • torch
  • torchvision
  • torchsummary
  • tqdm
  • matplotlib
  • albumentations
  • opencv-python

Documentation

Documentation making for the library is currently in progress. So until a documentation is available please refer to the following table for various functionalities and their corresponding module names.

Functionality Module Name
Training train
Validation evaluate
Dataset downloading and preprocessing data
GradCAM and GradCAM++ gradcam
Models, loss, optimizers, regularizers and callbacks model
CUDA, random seed and result analysis utils

For a demo on how to use these modules, refer to the notebooks present in the examples directory.

Contact/Getting Help

If you need any help or want to report a bug, raise an issue in the repo.

Project details


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