TorchShard TorchShard is a lightweight engine for slicing a PyTorch tensor into parallel shards. It can reduce GPU memory and scale up the training when the model has massive linear layers (e.g., ViT, BERT and GPT) or huge classes (millions). It has the same API design as PyTorch. Installation pip install torchshard More options in INSTALL.md. Usage import torchshard as ts ts.init_process_group(group_size=2) # init parallel groups m = torch.nn.Sequential( torch.nn.Linear(20, 30, bias=True), ts.nn.ParallelLinear(30, 30, bias=True, dim=None), # equal to nn.Linear() […]

LED2-Net This is PyTorch implementation of our CVPR 2021 Oral paper “LED2-Net: Monocular 360˚ Layout Estimation via Differentiable Depth Rendering”. You can visit our project website and upload your own panorama to see the 3D results! Prerequisite This repo is primarily based on PyTorch. You can use the follwoing command to intall the dependencies. pip install -r requirements.txt Preparing Training Data Under LED2Net/Dataset, we provide the dataloader of Matterport3D and Realtor360. The annotation formats of the two datasets follows PanoAnnotator. […]