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, […]

Simple-implementation-of-Mobile-Former At present, only the model but no trained. There may be some bug in the code, and some details may be different from the original paper, if you are interested in this, welcome to discuss. Add: CutUp,MixUp,RandomErasing,SyncBatchNorm for DDP train Inference: paper:https://arxiv.org/pdf/2108.05895.pdf https://github.com/xiaolai-sqlai/mobilenetv3 https://github.com/lucidrains/vit-pytorch https://github.com/Islanna/DynamicReLU GitHub https://github.com/ACheun9/Pytorch-implementation-of-Mobile-Former    

This codebase provides a PyTorch implementation of Protagonist Antagonist Induced Regret Environment Design (PAIRED), which was first introduced in “Emergent Complexity and Zero-Shot Transfer via Unsupervised Environment Design” (Dennis et al, 2020). This implementation comes integrated with custom adversarial maze environments based on MiniGrid environment (Chevalier-Boisvert et al, 2018), as used in Dennis et al, 2020. Unsupervised environment design (UED) methods propose a curriculum of tasks or environment instances (levels) that aims to foster more sample efficient learning and robust […]

Simple torch module implementation of Alias-Free GAN. This repository including Note: Since this repository is unofficial, filter and upsample could be different with official implementation. Note: 2d lowpass filter is applying sinc instead of jinc (first order Bessel function of the first kind) in paper Requirements Due to torch.kaiser_window and torch.i0 are implemeted after 1.7.0, our repository need torch>=1.7.0. TODO [x] 2d sinc filter [x] 2d resample [x] devide 1d and 2d modules [ ] pip packaging Test results 1d […]

DepthNet training on Still Box This code can replicate the results of our paper that was published in UAVg-17.If you use this repo in your work, please cite us with the following bibtex : @Article{isprs-annals-IV-2-W3-67-2017, AUTHOR = {Pinard, C. and Chevalley, L. and Manzanera, A. and Filliat, D.}, TITLE = {END-TO-END DEPTH FROM MOTION WITH STABILIZED MONOCULAR VIDEOS}, JOURNAL = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences}, VOLUME = {IV-2/W3}, YEAR = {2017}, PAGES = {67–74}, URL […]

EDSR-PyTorch PyTorch version of the paper ‘Enhanced Deep Residual Networks for Single Image Super-Resolution’ (CVPRW 2017) About PyTorch 1.1.0 There have been minor changes with the 1.1.0 update. Now we support PyTorch 1.1.0 by default, and please use the legacy branch if you prefer older version. This repository is an official PyTorch implementation of the paper “Enhanced Deep Residual Networks for Single Image Super-Resolution” from CVPRW 2017, 2nd NTIRE.You can find the original code and more information from here. If […]

e2c-pytorch Paper can be found here: https://arxiv.org/abs/1506.07365 You will need a patched version of OpenAI Gym in order to generate the dataset. See https://github.com/ethanluoyc/gym/tree/pendulum_internal For the planar task, we use code from. The source code of the repository has been modified for our needs and included under e2c/e2c_tf. What’s included ? E2C model, VAE and AE baselines. Allow configuration for different network architecture for the different setups (see Appendix of the paper). TODO Documentation, tests… (Soon to follow) GitHub https://github.com/ethanluoyc/e2c-pytorch […]