PyTorch Sparse This package consists of a small extension library of optimized sparse matrix operations with autograd support. This package currently consists of the following methods: All included operations work on varying data types and are implemented both for CPU and GPU.To avoid the hazzle of creating torch.sparse_coo_tensor, this package defines operations on sparse tensors by simply passing index and value tensors as arguments (with same shapes as defined in PyTorch).Note that only value comes with autograd support, as index […]

Keras style model.summary() in PyTorch Keras has a neat API to view the visualization of the model which is very helpful while debugging your network. Here is a barebone code to try and mimic the same in PyTorch. The aim is to provide information complementary to, what is not provided by print(your_model) in PyTorch. Usage pip install torchsummary or git clone https://github.com/sksq96/pytorch-summary from torchsummary import summary summary(your_model, input_size=(channels, H, W)) Note that the input_size is required to make a forward […]

torch-optimizer torch-optimizer — collection of optimizers for PyTorch compatible with optim module. Simple example import torch_optimizer as optim # model = … optimizer = optim.DiffGrad(model.parameters(), lr=0.001) optimizer.step() Installation Installation process is simple, just: $ pip install torch_optimizer Documentation https://pytorch-optimizer.rtfd.io GitHub https://github.com/jettify/pytorch-optimizer    

EfficientNet PyTorch A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!) Quickstart Install with pip install efficientnet_pytorch and load a pretrained EfficientNet with: from efficientnet_pytorch import EfficientNet model = EfficientNet.from_pretrained(‘efficientnet-b0’) Updates Update (April 2, 2021) The EfficientNetV2 paper has been released! I am working on implementing it as you read this 🙂 About EfficientNetV2: EfficientNetV2 is a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models. To develop this family of models, […]