Skip to content
Code base for SRSGD
Jupyter Notebook Python
Branch: master
Clone or download

Latest commit

Fetching latest commit…
Cannot retrieve the latest commit at this time.

Files

Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
experiments_with_quadratic_function
models add code on 5March 2020 Mar 5, 2020
optimizers add code on 5March 2020 Mar 5, 2020
pics
result4plot add code Feb 20, 2020
utils add code on 5March 2020 Mar 5, 2020
README.md
cifar.py
imagenet.py add code Feb 18, 2020
plot_code_srsgd.ipynb add code Feb 20, 2020
recipes.md

README.md

Scheduled Restart SGD

Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent

Resources

Paper, Slides, Blog

Key Results

Figure 1: Error vs. depth of ResNet models trained with SRSGD and the baseline SGD with constant momemtum. Advantage of SRSGD continues to grow with depth.

Figure 2: Test error vs. number of epoch reduction in CIFAR10 and ImageNet training. The dashed lines are test errors of the SGD baseline. On both CIFAR and ImageNet, SRSGD reaches similar or even better error rates with fewer training epochs compared to the SGD baseline.

Requirements

This code is tested inside the NVIDIA Pytorch docker container release 19.09. This container can be pulled from NVIDIA GPU Cloud as follows:

docker pull nvcr.io/nvidia/pytorch:19.09-py3

Detailed information on packages included in the NVIDIA Pytorch containter 19.09 can be found at NVIDIA Pytorch Release 19.09. In addition to those packages, the following packages are required:

  • Sklearn: pip install -U scikit-learn --user
  • OpenCV: pip install opencv-python
  • Progress: pip install progress

In order to reproduce the plots in our papers, the following packages are needed:

  • Pandas: pip install pandas
  • Seaborn: pip install seaborn

To run our code without using the NVIDIA Pytorch containter, at least the following packages are required:

  • Ubuntu 18.04 including Python 3.6 environment
  • PyTorch 1.2.0
  • NVIDIA CUDA 10.1.243 including cuBLAS 10.2.1.243
  • NVIDIA cuDNN 7.6.3
  • NVIDIA APEX

ImageNet Experiments Requires ImageNet Datasets in LMDB Format

Using the default datasets.ImageFolder + data.DataLoader is not efficient due to the slow reading of discontinuous small chunks. In order to speed up the training on ImageNet, we convert small JPEG images into a large binary file in Lighting Memory-Mapped Database (LMDB) format and load the training data with data.distributed.DistributedSampler and data.DataLoader. You can follow the instructions for Caffe to build the LMDB dataset of ImageNet. Alternatively, you can use these following two sets of instructions to build the LMDB dataset of ImageNet:https://github.com/intel/caffe/wiki/How-to-create-ImageNet-LMDB and https://github.com/rioyokotalab/caffe/wiki/How-to-Create-Imagenet-ILSVRC2012-LMDB.

The ImageNet LMDB dataset should be placed inside the directory /datasets/imagenet in your computer and contains the following files:

fid_mean_cov.npz train_faster_imagefolder.lmdb train_faster_imagefolder.lmdb.pt val_faster_imagefolder.lmdb val_faster_imagefolder.lmdb.pt

SRSGD Optimizer

We provide the SRSGD class in ./optimizers/srsgd.py. Our train functions in cifar.py and imagenet.pycall iter_count, iter_total = optimizer.update_iter() after optimizer.step().

Code for Plotting Figures in Our Paper

We provide code for plotting figures in our paper in the jupyter notebook plot_code_srsgd.ipynb. For Figure 7 in the Appendix, we followed this github: https://github.com/wronnyhuang/gen-viz/tree/master/minefield. Instead of using SGD, we trained the model using SRSGD and plotted the trajectories. Since this visualization code took 2 or 3 days to finish, we didn't include it in plot_code_srsgd.ipynb.

Training

A training recipe is provided for image classification experiments (see recipes.md). The recipe contains the commands to run experiments for Table 1, 2, 3, 4 and 5 in our paper, which includes full and short trainings on CIFAR10, CIFAR100, and ImageNet using SRSGD, as well as the baseline SGD trainings. Other experiments in our paper and in the appendix can be run using the same cifar.py and imagenet.py files with different values of parameters.

Citation

Please cite the following paper if you found our SRSGD useful. Thanks!

Bao Wang, Tan M Nguyen, Andrea L Bertozzi, Richard G Baraniuk, and Stanley J Osher. "Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent." arXiv preprint arXiv:2002.10583 (2020).

@article{wang2020scheduled,
  title={Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent},
  author={Wang, Bao and Nguyen, Tan M and Bertozzi, Andrea L and Baraniuk, Richard G and Osher, Stanley J},
  journal={arXiv preprint arXiv:2002.10583},
  year={2020}
}
You can’t perform that action at this time.