PyPI 官网下载 | torch-tensornet-0.7.tar.gz

# TensorNet [](https://github.com/shan18/TensorNet/blob/master/LICENSE) []() 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 ```[bash] 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 ```[bash] 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 - Binary Cross Entropy Loss - Mean Square Error Loss - SSIM and MS-SSIM Loss - Dice Loss - Evaluation Metrics - Accuracy - RMSE - MAE - ABS_REL - Optimizers - Stochastic Gradient Descent - Regularizers - L1 regularization - L2 regularization - LR Schedulers - Step LR - Reduce LR on Plateau - One Cycle Policy - LR Range Test - Model Checkpointing - Tensorboard - Model training and validation - Datasets (data is is returned via data loaders) - MNIST - CIFAR10 - TinyImageNet - MODEST Museum Dataset - 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 ## How to Use For examples on how to use TensorNet, refer to the [examples](https://github.com/shan18/TensorNet/tree/master/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 | | ------- | ----- | | Learner, Callbacks and Tensorboard | engine | | Dataset downloading and preprocessing | data | | GradCAM and GradCAM++ | gradcam | | Models, loss functions and optimizers | model | | CUDA setup and result analysis | utils | For a demo on how to use these modules, refer to the notebooks present in the [examples](https://github.com/shan18/TensorNet/tree/master/examples) directory. ## Contact/Getting Help If you need any help or want to report a bug, raise an issue in the repo.