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Cross Domain Generalization of AutoFormer

Please find our poster here: Poster

Background

AutoFormer [1] is an one-shot architecture search framework dedicated to vision transformer search. It entangles the weights of different vision transformer blocks in the same layers during supernet training. Benefiting from the strategy, the trained supernet allows thousands of subnets to be very well-trained. Specifically, the performance of these subnets with weights inherited from the supernet is comparable to those retrained from scratch.

AutoFormer overview

Overview

In this work, we evaluate the cross-domain generalization performace of Autoformer.

Specifically we explore AutoFormer performance on:

  • Image Classification. [1] does it for ImageNet. We experiment with CIFAR-100.
  • Image Super-Resolution. We experiment with an AutoFormer version of SwinIR [2] trained on the DIV2K dataset and evaluated on Set14.
  • (Masked) Language Modeling. We experiment with an AutoFormer version of RoBERTa [3] with WikiText-103.

Setup

To set up the enviroment you can easily run the following command:

conda create -n Autoformer python=3.6
conda activate Autoformer
pip install -r AutoFormer/requirements-dev.txt

Also, setup fairseq, whose RoBERTa implementation we have modified.

cd AutFormer/AutoFormer/fairseq
pip install --editable ./

Autoformer Classification

Run supernet train

python3 -m torch.distributed.launch --use_env -m AutoFormer.experiments.classification.supernet_train --num_workers=8 --data-path ./data/cifar-100 --data-set CIFAR100 --gp  --change_qk --relative_position --mode super --dist-eval --cfg ./AutoFormer/experiments_configs/classification/supernet-T.yaml --epochs 500 --warmup-epochs 20 --output ./output_change --batch-size 128```

Run evolutionary search

python -m torch.distributed.launch --use_env -m AutoFormer.experiments.classification.evolution --data-path ./data/evol_cifar100 --gp  --change_qk --relative_position --dist-eval --cfg ./AutoFormer/experiments_configs/classification/supernet-T.yaml --resume {/PATH/checkpoint.pth}  --min-param-limits 0.0 --param-limits 5.560792 --data-set CIFAR100 --output_dir ./search_output

Run retrain (From scratch)

python3 -m torch.distributed.launch --use_env -m AutoFormer.experiments.classification.supernet_train --data-path ./data/cifar-100 --data-set CIFAR100 --gp --change_qk --relative_position --mode retrain --dist-eval --cfg ./AutoFormer/experiments_configs/classification/AutoFormer-T.yaml --epochs 500 --output ./output_cifar_scratchretrain

Run retrain (Finetune)

python3 -m torch.distributed.launch --use_env -m AutoFormer.experiments.classification.supernet_train --data-path ./data/cifar-100 --data-set CIFAR100 --gp --change_qk --relative_position --mode retrain --dist-eval --cfg ./AutoFormer/experiments_configs/classification/AutoFormer-T.yaml --resume {/PATH/checkpoint.pth} --start_epoch 500 --epochs 540 --output ./output_cifar_finetrain

Autoformer for image super-resolution (SwinIR)

Run supernet train

python3 -m torch.distributed.launch --use_env -m AutoFormer.experiments.super_resolution.supernet_train  --gp --change_qk --relative_position --mode super --dist-eval --cfg ./AutoFormer/experiments_configs/supernet-swinir/supernet-T.yaml --opt-doc ./AutoFormer/experiments_configs/supernet-swinir/train_swinir_sr_lightweight.json --epochs 300 --output ./output_swinir_train

Run evolutionary search

python3 -m torch.distributed.launch --use_env -m AutoFormer.experiments.super_resolution.random_swin --gp  --change_qk --relative_position --dist-eval --cfg ./AutoFormer/experiments_configs/supernet-swinir/supernet-T.yaml  --opt-doc ./AutoFormer/experiments_configs/supernet-swinir/train_swinir_sr_lightweight.json --resume {/PATH/checkpoint.pth}  --min-param-limits 0.0 --param-limits 9.29628  --output_dir ./search_swinir_random```

Run retrain (From scratch)

python3 -m torch.distributed.launch --use_env -m AutoFormer.experiments.super_resolution.supernet_train  --gp --change_qk --relative_position --mode retrain --dist-eval --cfg ./AutoFormer/experiments_configs/supernet-swinir/AutoFormer-T.yaml  --opt-doc ./AutoFormer/experiments_configs/supernet-swinir/train_swinir_sr_lightweight.json --epochs 300 --output ./output_swinir_retrain

Run Finetune

python3 -m torch.distributed.launch --use_env -m AutoFormer.experiments.super_resolution.supernet_train  --gp --change_qk --relative_position --mode retrain --dist-eval --cfg ./AutoFormer/experiments_configs/supernet-swinir/AutoFormer-T.yaml --resume {/PATH/checkpoint.pth} --opt-doc ./AutoFormer/experiments_configs/supernet-swinir/train_swinir_sr_lightweight.json  --start_epoch 300 --epochs 340 --output ./output_swinir_finetrain

Autoformer for RoBERTa (Masked-LM)

Note: Assumes you are inside AutoFormer/AutoFormer/fairseq.

Setup data

bash 01-data-setup.sh

Run supernet train

bash 02-train.sh

Run retrain (From scratch)

bash 05-retrain-from-scratch.sh

Run retrain (Finetune)

bash 06-retrain-finetune.sh

Evaluate on test set

bash 03-eval-test.sh

Model Checkpoints

Code Sources

References

[1] Chen, Minghao, et al. "Autoformer: Searching transformers for visual recognition." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.

[2] Liang, Jingyun, et al. "Swinir: Image restoration using swin transformer." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.

[3] Liu, Yinhan, et al. "Roberta: A robustly optimized bert pretraining approach." arXiv preprint arXiv:1907.11692 (2019).