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SupContrast: Supervised Contrastive Learning

This repo covers an reference implementation for the following papers in PyTorch, using CIFAR as an illustrative example:
(1) Supervised Contrastive Learning. Paper
(2) A Simple Framework for Contrastive Learning of Visual Representations. Paper

Update

${\color{red}Note}$: if you found it not easy to parse the supcon loss implementation in this repo, we got you. Supcon loss essentially is just a cross-entropy loss (see eq 4 in the StableRep paper). So we got a cleaner and simpler implementation here. Hope it helps.

ImageNet model (small batch size with the trick of the momentum encoder) is released here. It achieved > 79% top-1 accuracy.

Loss Function

The loss function SupConLoss in losses.py takes features (L2 normalized) and labels as input, and return the loss. If labels is None or not passed to the it, it degenerates to SimCLR.

Usage:

from losses import SupConLoss

# define loss with a temperature `temp`
criterion = SupConLoss(temperature=temp)

# features: [bsz, n_views, f_dim]
# `n_views` is the number of crops from each image
# better be L2 normalized in f_dim dimension
features = ...
# labels: [bsz]
labels = ...

# SupContrast
loss = criterion(features, labels)
# or SimCLR
loss = criterion(features)
...

Comparison

Results on CIFAR-10:

Arch Setting Loss Accuracy(%)
SupCrossEntropy ResNet50 Supervised Cross Entropy 95.0
SupContrast ResNet50 Supervised Contrastive 96.0
SimCLR ResNet50 Unsupervised Contrastive 93.6

Results on CIFAR-100:

Arch Setting Loss Accuracy(%)
SupCrossEntropy ResNet50 Supervised Cross Entropy 75.3
SupContrast ResNet50 Supervised Contrastive 76.5
SimCLR ResNet50 Unsupervised Contrastive 70.7

Results on ImageNet (Stay tuned):

Arch Setting Loss Accuracy(%)
SupCrossEntropy ResNet50 Supervised Cross Entropy -
SupContrast ResNet50 Supervised Contrastive 79.1 (MoCo trick)
SimCLR ResNet50 Unsupervised Contrastive -

Running

You might use CUDA_VISIBLE_DEVICES to set proper number of GPUs, and/or switch to CIFAR100 by --dataset cifar100.
(1) Standard Cross-Entropy

python main_ce.py --batch_size 1024 \
  --learning_rate 0.8 \
  --cosine --syncBN \

(2) Supervised Contrastive Learning
Pretraining stage:

python main_supcon.py --batch_size 1024 \
  --learning_rate 0.5 \
  --temp 0.1 \
  --cosine

You can also specify --syncBN but I found it not crucial for SupContrast (syncBN 95.9% v.s. BN 96.0%).

WARN: Currently, --syncBN has no effect since the code is using DataParallel instead of DistributedDataParaleel

Linear evaluation stage:

python main_linear.py --batch_size 512 \
  --learning_rate 5 \
  --ckpt /path/to/model.pth

(3) SimCLR
Pretraining stage:

python main_supcon.py --batch_size 1024 \
  --learning_rate 0.5 \
  --temp 0.5 \
  --cosine --syncBN \
  --method SimCLR

The --method SimCLR flag simply stops labels from being passed to SupConLoss criterion. Linear evaluation stage:

python main_linear.py --batch_size 512 \
  --learning_rate 1 \
  --ckpt /path/to/model.pth

On custom dataset:

python main_supcon.py --batch_size 1024 \
  --learning_rate 0.5  \ 
  --temp 0.1 --cosine \
  --dataset path \
  --data_folder ./path \
  --mean "(0.4914, 0.4822, 0.4465)" \
  --std "(0.2675, 0.2565, 0.2761)" \
  --method SimCLR

The --data_folder must be of form ./path/label/xxx.png folowing https://pytorch.org/docs/stable/torchvision/datasets.html#torchvision.datasets.ImageFolder convension.

and

t-SNE Visualization

(1) Standard Cross-Entropy

(2) Supervised Contrastive Learning

(3) SimCLR

Reference

@Article{khosla2020supervised,
    title   = {Supervised Contrastive Learning},
    author  = {Prannay Khosla and Piotr Teterwak and Chen Wang and Aaron Sarna and Yonglong Tian and Phillip Isola and Aaron Maschinot and Ce Liu and Dilip Krishnan},
    journal = {arXiv preprint arXiv:2004.11362},
    year    = {2020},
}