This repository is the official implementation of paper [Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples]. [Paper] [Slide]
- Python 3.6
- PyTorch 1.8.1
- Refer requirements.txt for other requirements
To install requirements:
pip install -r requirements.txt
For Imagenet training, change the path of the validation set in .hocon file. To train the model described in the paper, run one of this command:
./run_cifar10_4bit.sh
./run_cifar100_4bit.sh
./run_imgnet_resnet18_4bit.sh
./run_imgnet_resnet50_4bit.sh
./run_imgnet_mobilenet_v2_4bit.sh
Refer other_train_scripts folder for 5bit settings.
To evaluate trained model, run the command below after training:
./eval_cifar10_4bit.sh
./eval_cifar100_4bit.sh
./eval_imgnet_resnet18_4bit.sh
./eval_imgnet_resnet50_4bit.sh
./eval_imgnet_mobilenet_v2_4bit.sh
Feature space visualization of real or synthetic images described in Figure 3.
python experiments.py --pca_source
python experiments.py --gdfq_generator_path GENERATOR_WEIGHT_PATH --pca_gdfq --image_gdfq
python experiments.py --qimera_generator_path GENERATOR_WEIGHT_PATH --pca_qimera --pca_mix --pca_path --image_gdfq --image_mix
Our model achieves the following performance on :
| Dataset | Model | Teacher Net Accuracy | 4bit Quantized Model Accuracy | 5bit Quantized Model Accuracy |
|---|---|---|---|---|
| Cifar-10 | ResNet-20 | 93.89% | 91.26% | 93.46% |
| Cifar-100 | ResNet-20 | 70.33% | 65.10% | 69.02% |
| ImageNet | ResNet-18 | 71.47% | 63.84% | 69.29% |
| ImageNet | ResNet-50 | 77.73% | 66.25% | 75.32% |
| ImageNet | MobileNetV2 | 73.03% | 61.62% | 70.45% |
Generated Synthetic Images for Cifar10 :
This project is licensed under the terms of the GNU General Public License v3.0
@inproceedings{choi2021qimera,
title={Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples},
author={Choi, Kanghyun and Hong, Deokki and Park, Noseong and Kim, Youngsok and Lee, Jinho},
booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
year={2021}
}
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