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DiffRate

This a Pytorch implementation of our paper "DiffRate : Differentiable Compression Rate for Efficient Vision Transformers"

Requirements

- python >= 3.8
- pytorch >= 1.12.1  # For scatter_reduce
- torchvision        # With matching version for your pytorch install

Data Preparation

  • The ImageNet dataset should be prepared as follows:
ImageNet
├── train
│   ├── folder 1 (class 1)
│   ├── folder 2 (class 2)
│   ├── ...
├── val
│   ├── folder 1 (class 1)
│   ├── folder 2 (class 2)
│   ├── ...

Pre-Trained Models

Our proposed DiffRate is designed to operate utilizing the officially endorsed pre-trained models of MAE and DeiT. To facilitate seamless integration, our code is programmed to automatically download and load these pre-trained models. However, users who prefer manual downloads can acquire the pre-trained MAE models via this link, and the pre-trained DeiT models through this link.

Evaluation

We provide the discovered compression rates in the compression_rate.json file. To evaluate these rates, utilize the --eval_with_compression_rate option, which will load the appropriate compression rate from compression_rate.json based on the specified model and target_flops.

  • For the ViT-S (DeiT) model, we currently offer support for the --target_flops option with {2.3,2.5,2.7,2.9,3.1}. To illustrate, an example evaluating the ViT-S (DeiT) model with 2.9G FLOPs would be:
python main.py --eval --eval_with_compression_rate --data-path $path_to_imagenet$ --model vit_deit_small_patch16_224 --target_flops 2.9

This should give:

Acc@1 79.538 Acc@5 94.828 loss 0.902 flops 2.905
  • For the ViT-B (DeiT) model, we currently offer support for the --target_flops option with {8.7,10.0,10.4,11.5,12.5}. To illustrate, an example evaluating the ViT-B (DeiT) model with 11.5G FLOPs would be:
python main.py --eval --eval_with_compression_rate --data-path $path_to_imagenet$ --model vit_deit_base_patch16_224 --target_flops 11.5

This should give:

Acc@1 81.498 Acc@5 95.404 loss 0.861 flops 11.517
  • For the ViT-B (MAE) model, we currently offer support for the --target_flops option with {8.7,10.0,10.4,11.5}. To illustrate, an example evaluating the ViT-B (MAE) model with 11.5G FLOPs would be:
python main.py --eval --eval_with_compression_rate --data-path $path_to_imagenet$ --model vit_base_patch16_mae --target_flops 11.5

This should give:

Acc@1 82.864 Acc@5 96.148 loss 0.794 flops 11.517
  • For the ViT-L (MAE) model, we currently offer support for the --target_flops option with {31.0,34.7,38.5,42.3,46.1}. To illustrate, an example evaluating the ViT-L (MAE) model with 42.3G FLOPs would be:
python main.py --eval --eval_with_compression_rate --data-path $path_to_imagenet$ --model vit_large_patch16_mae --target_flops 42.3

This should give:

Acc@1 85.658 Acc@5 97.442 loss 0.683 flops 42.290
  • For the ViT-H (MAE) model, we currently offer support for the --target_flops option with {83.7,93.2,103.4,124.5}. To illustrate, an example evaluating the ViT-H (MAE) model with 103.4G FLOPs would be:
python main.py --eval --eval_with_compression_rate --data-path $path_to_imagenet$ --model vit_huge_patch14_mae --target_flops 103.4

This should give:

Acc@1 86.664 Acc@5 97.894 loss 0.602 flops 103.337

Training

To find the optimal compression rate by proposed DiffRate, run the following code:

python -m torch.distributed.launch \
--nproc_per_node=4 --use_env  \
--master_port 29513 main.py \
--arch-lr 0.01 --arch-min-lr 0.001 \
--epoch 3 --batch-size 256 \
--data-path $path_to_imagenet$ \
--output_dir $path_to_save_log$ \
--model $model_name$ \
--target_flops $target_flops$
  • supported $model_name$: {vit_deit_tiny_patch16_224,vit_deit_small_patch16_224,vit_deit_base_patch16_224,vit_base_patch16_mae,vit_large_patch16_mae,vit_huge_patch14_mae}
  • supported $target_flops$: a floating point number

For example, search a 2.9G compression rate schedule for ViT-S (DeiT):

python -m torch.distributed.launch \
--nproc_per_node=4 --use_env  \
--master_port 29513 main.py \
--arch-lr 0.01 --arch-min-lr 0.001 \
--epoch 3 --batch-size 256 \
--data-path $path_to_imagenet$ \
--output_dir $path_to_save_log$ \
--model vit_deit_small_patch16_224 \
--target_flops 2.9

Visualization

See visualization.ipynb for more details.