📖TransCL: Transformer Makes Strong and Flexible Compressive Learning (TPAMI 2022)

Chong Mou, Jian Zhang

🔧 Dependencies and Installation

• Python >= 3.6 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 1.4
• At least two V100 GPUs are required.

Installation

1. Clone repo

   git clone https://github.com/MC-E/TransCL.git
   cd TransCL-main

2. Install dependent packages

   pip install tensorboardX
   conda install pytorch=1.6.0 torchvision cudatoolkit=10.1 -c pytorch -y
   pip install mmcv-full==1.2.2 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html

Training for Classification

Please run the commands in the folder of classification.

1. Prepare the training data of ImageNet1K
2. Download the pre-trained checkpoints of vision transformer from link-ImageNet and link-CIFAR.
3. The training support ViT-base with patch size being 16 (-a B_16_imagenet1k) and 32 (-a B_32_imagenet1k).

Training on ImageNet with a fixed CS ratio

python main_imagenet.py -a 'B_32_imagenet1k' -b 128 --image_size 384 --gpu 0  --lr 1e-3 --log_dir logs/transcl_384_imagenet_p32_01 --cs=1 --mm=1 --save_path=transcl_384_imagenet_p32_01 --devices=4 --rat 0.1 --data /group/30042/public_datasets/imagenet1k

Training on ImageNet with arbitrary CS ratios

python main_arb.py -a 'B_32_imagenet1k' -b 128 --image_size 384 --gpu 0  --lr 1e-3 --log_dir logs/transcl_384_imagenet_p32_01 --cs=1 --mm=1 --save_path=transcl_384_imagenet_p32_01 --devices=4 --rat 0.1 --data /group/30042/public_datasets/imagenet1k

Training on Cifar10 with a fixed CS ratio

python main_cifar10.py -a 'B_32_imagenet1k' -b 128 --image_size 384 --gpu 0  --lr 1e-3 --log_dir logs/transcl_384_cifar10_p32_01 --cs=1 --mm=1 --save_path=transcl_384_cifar10_p32_01 --devices=4 --rat 0.1

Training on Cifar100 with a fixed CS ratio

python main_cifar100.py -a 'B_32_imagenet1k' -b 128 --image_size 384 --gpu 0  --lr 1e-3 --log_dir logs/transcl_384_cifar100_p32_01 --cs=1 --mm=1 --save_path=transcl_384_cifar100_p32_01 --devices=4 --rat 0.1

Training for Segmentation

Coming soon

Testing for Classification

You can download the pre-trained checkpoints from our model zoo.

Testing on ImageNet with a fixed CS ratio

python test_imagenet.py -a 'B_32_imagenet1k' -b 128 --image_size 384

Testing on ImageNet with arbitrary CS ratios

python test_arb.py -a 'B_32_imagenet1k' -b 128 --image_size 384

Testing for Segmentation

Coming soon

🏰 Model Zoo

Classification

Mode	Download link
Pre-trained ViT	ImageNet, CIFAR
ImageNet classification (patch size=16, ratio={0.1, 0.05, 0.025, 0.01})	URL
ImageNet classification (patch size=32, ratio={0.1, 0.05, 0.025, 0.01})	URL
Distilled ImageNet classification (patch size=16, ratio={0.1, 0.05, 0.025, 0.01})	URL
Cifar10 classification	URL
Cifar100 classification	URL
Arbitrary ratio classification (patch size=32)	URL
Binary sampling classification	URL
Shuffled classification	URL

Segmentation

Mode	Download link
Pre-trained ViT-large	URL
Segmentation on ADE20K with fixed ratio (patch size=16, ratio={0.1, 0.05, 0.025, 0.01})	URL
Segmentation on Cityscapes with fixed ratio (patch size=16, ratio={0.1, 0.05, 0.025, 0.01})	URL
Segmentation on Pascal Context with fixed ratio (patch size=16, ratio={0.1, 0.05, 0.025, 0.01})	URL
Segmentation with arbitrary ratios	URL
Segmentation with binary sampling	URL

BibTeX

@article{mou2022transcl,
  title={TransCL: Transformer makes strong and flexible compressive learning},
  author={Mou, Chong and Zhang, Jian},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2022},
  publisher={IEEE}
}