torch_soft is a high level implementation of pytorch for some of claasification implementation with ease...

Installation

You can use pip to install torch_soft using pip.

pip install torch-soft==0.1.1

features

TensorNet currently supports the following features

Model architectures
- ResNet18
- A custom model called naiveresnet
Model utilities
- Loss functions
  - Cross entropy loss
  - nll_loss
- Optimizers
  - Stochastic Gradient Descent
- Regularizers
  - L1 regularization
  - L2 regularization
- LR Schedulers
  - Step LR
  - Reduce LR on Plateau
  - One Cycle Policy
- LR Range Test
Model training and validation
Datasets (data is is returned via data loaders)
- MNIST
- CIFAR10
- TinyImageNet
Data Augmentation
- Resize
- Padding
- Random Crop
- 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
- Plotting images in a batch for visualization
- Plotting gradcam outputs

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

torch_soft has the following third-party dependencies