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Reproducing SimCLR in PyTorch

Introduction

This is an unofficial PyTorch implementation of the recent paper 'A Simple Framework for Contrastive Learning of Visual Representations'. The arXiv version of this paper can be cited as

@article{chen2020simple,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  journal={arXiv preprint arXiv:2002.05709},
  year={2020}
}

The focus of this repository is to accurately reproduce the results in the paper using PyTorch. We use the original paper and the official tensorflow repo as our sources.

SimCLR algorithm

Data

For comparison with the original paper, we use the CIFAR-10 and ILSVRC2012 datasets. This PyTorch version also supports CIFAR-100 and STL-10.

Augmentation

The following augmentations are used on the training set

• Random crop and resize. We use RandomResizedCrop in PyTorch with cubic interpolation for the resizing. CIFAR images are resized to 32×32, STL-10 images to 96×96 and ILSVRC2012 images to 224×224.
• Random horizontal flip
• Colour distortion. We use the code provided in the paper (Appendix A)
• Gaussian blur is not yet included

Encoder

We use a ResNet50 as our base architecture. We use the ResNet50 implementation included in torchvision with the following changes:

• Stem adapted to the dataset, for details see models/resnet.py. We adapt the stem for CIFAR in the same way as the original paper: replacing the first 7×7 Conv of stride 2 with 3×3 Conv of stride 1, and also removing the first max pooling operation. For STL, we use the 3×3 Conv of stride 1 but include a max pool.
• We do not make special adjustments to sync the batch norm means and variances across GPU nodes.
• Remove the final fully connected layer, giving a representation of dimension 2048.

Projection head

The projection head consists of the following:

• MLP projection with one hidden layer with dimensions 2048 -> 2048 -> 128
• Following the tensorflow code, we also include batch norm in the projection head

Loss

We use the NT-Xent loss of the original paper. Specifically, we calculate the CosineSimilarity (using PyTorch's implementation) between each of the 2N projected representations z. We rescale these similarities by temperature. We set the diagonal similarities to -inf and treat the one remaining positive example as the correct category in a 2N-way classification task. The scores are fed directly into CrossEntropyLoss.

Optimizer

We use the LARS optimizer with trust_coef=1e-3 to match the tensorflow code. We set the weight decay to 1e-6. The 10 epoch linear ramp and cosine annealing of the original paper are implemented and can be activated using --cosine-anneal, otherwise a constant learning rate is used.

Evaluation

On CIFAR-10, we fitted the downstream classifier using L-BFGS with no augmentation on the training set. This is the approach used in the original paper for transfer learning (and is substantially faster for small datasets). For ImageNet, we use SGD with the same random resized crop and random flip as for the original training, but no colour distortion or other augmentations. This is as in the original paper.

Running the code

Requirements

See requirements.txt. Note we require the torchlars package.

Dataset locations

The dataset file locations should be specified in a JSON file of the following form

dataset-paths.json
{
    "cifar10": "/data/cifar10/",
    "cifar100": "/data/cifar100/",
    "stl10": "/data/stl10/",
    "imagenet": "/data/imagenet/2012/"
}

Running with CIFAR-10

Use the following command to train an encoder from scratch on CIFAR-10

$ python3 simclr.py --num-epochs 1000 --cosine-anneal --filename output.pth --base-lr 1.5

To evaluate the trained encoder using L-BFGS across a range of regularization parameters

$ python3 lbfgs_linear_clf.py --load-from output.pth

Running with ImageNet

Use the following command to train an encoder from scratch on ILSVRC2012

$ python3 simclr.py --num-epochs 1000 --cosine-anneal --filename output.pth --test-freq 0 --num-workers 32 --dataset imagenet 

To evaluate the trained encoder, use

$ python3 gradient_linear_clf.py --load-from output.pth --nesterov --num-workers 32

Outstanding differences with the original paper

• We do not synchronize the batch norm between multiple GPUs. (To use PyTorch's SyncBatchNorm, we would need to change from using DataParallel to DistributedDataParallel.)
• We not use Gaussian blur for any datasets, including ILSVRC2012.
• We are not aware of any other discrepancies with the original work, but any correction is more than welcome and should be suggested by opening an Issue in this repo.

Reproduction results

CIFAR-10 with ResNet50 (1000 epochs)

Acknowledgements

The basis for this repository was pytorch-cifar. We make use of torchlars.