OSCAR This repository contains the source code of our ICML 2021 paper How could Neural Networks understand Programs?. Environment Run following commands to build a docker image for the environment: cd docker sudo docker build -t oscar:latest . And you can launch a container with nvidia-docker command. sudo nvidia-docker run -it –mount type=bind,source=”$(pwd)”,target=/oscar oscar:latest To compile the binaries for processing the data: cd /oscar/bin make Then the OSCAR LLVM analyzer pass (located in analyzer), IR Lexer (located in irlexer), and […]

Robust-Vision-Transformer Note: Since the model is trained on our private platform, this transferred code has not been tested and may have some bugs. If you meet any problems, feel free to open an issue! This repository contains PyTorch code for Robust Vision Transformers. For details see our paper “Rethinking the Design Principles of Robust Vision Transformer” First, clone the repository locally: git clone https://github.com/vtddggg/Robust-Vision-Transformer.git Install PyTorch 1.7.0+ and torchvision 0.8.1+ and pytorch-image-models 0.3.2: conda install -c pytorch pytorch torchvision pip […]

pytorch-lr-scheduler PyTorch implementation of some learning rate schedulers for deep learning researcher. Usage WarmupReduceLROnPlateauScheduler import torch from lr_scheduler.warmup_reduce_lr_on_plateau_scheduler import WarmupReduceLROnPlateauScheduler if __name__ == ‘__main__’: max_epochs, steps_in_epoch = 10, 10000 model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))] optimizer = torch.optim.Adam(model, 1e-10) scheduler = WarmupReduceLROnPlateauScheduler( optimizer, init_lr=1e-10, peak_lr=1e-4, warmup_steps=30000, patience=1, factor=0.3, ) for epoch in range(max_epochs): for timestep in range(steps_in_epoch): … … if timestep < warmup_steps: scheduler.step() val_loss = validate() scheduler.step(val_loss) TransformerLRScheduler import torch from lr_scheduler.transformer_lr_scheduler import TransformerLRScheduler if __name__ == '__main__': max_epochs, steps_in_epoch [...]