It's been two months and I think I've finally discovered the True reasons why Simsiam/BYOL avoids collapsed solutions using stop gradient and predictor!!! Follow me on twitter and stay tuned!
A PyTorch implementation for the paper Exploring Simple Siamese Representation Learning by Xinlei Chen & Kaiming He
If you don't have python 3 environment:
conda create -n simsiam python=3.8
conda activate simsiam
Then install the required packages:
pip install -r requirements.txt
CUDA_VISIBLE_DEVICES=0 python main.py --data_dir ../Data/ --log_dir ../logs/ -c configs/simsiam_cifar.yaml --ckpt_dir ~/.cache/ --hide_progress
The data folder ../Data/
should look like this:
➜ ~ tree ../Data/
├── cifar-10-batches-py
│ ├── batches.meta
│ ├── data_batch_1
│ ├── ...
└── stl10_binary
├── ...
Training: 100%|#################################################################| 800/800 [11:46:06<00:00, 52.96s/it, epoch=799, accuracy=90.3]
Model saved to /root/.cache/simsiam-cifar10-experiment-resnet18_cifar_variant1.pth
Evaluating: 100%|##########################################################################################################| 100/100 [08:29<00:00, 5.10s/it]
Accuracy = 90.83
Log file has been saved to ../logs/completed-simsiam-cifar10-experiment-resnet18_cifar_variant1(2)
To evaluate separately:
CUDA_VISIBLE_DEVICES=4 python linear_eval.py --data_dir ../Data/ --log_dir ../logs/ -c configs/simsiam_cifar_eval.yaml --ckpt_dir ~/.cache/ --hide_progress --eval_from ~/simsiam-cifar10-experiment-resnet18_cifar_variant1.pth
creating file ../logs/in-progress_0111061045_simsiam-cifar10-experiment-resnet18_cifar_variant1
Evaluating: 100%|##########################################################################################################| 200/200 [16:52<00:00, 5.06s/it]
Accuracy = 90.87
export DATA="/path/to/your/datasets/"
andexport LOG="/path/to/your/log/"
will save you the trouble of entering the folder name every single time!
CUDA_VISIBLE_DEVICES=1 python main.py --data_dir ../Data/ --log_dir ../logs/ -c configs/simclr_cifar.yaml --ckpt_dir ~/.cache/ --hide_progress
CUDA_VISIBLE_DEVICES=2 python main.py --data_dir ../Data/ --log_dir ../logs/ -c configs/byol_cifar.yaml --ckpt_dir ~/.cache/ --hide_progress
- convert from data-parallel (DP) to distributed data-parallel (DDP)
- create PyPI package
pip install simsiam-pytorch
If you find this repo helpful, please consider star so that I have the motivation to improve it.