TensorNet is a high-level deep learning library built on top of PyTorch.
NOTE: This documentation applies to the MASTER version of TensorNet only.
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
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
- LR Scheduler
- LR Finder
- Loss functions
- 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
For examples on how to use TensorNet, refer to the examples directory.
TensorNet has the following third-party dependencies
- torch
- torchvision
- torchsummary
- tqdm
- matplotlib
- albumentations
- opencv-python
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.
If you need any help or want to report a bug, raise an issue in the repo.