Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples

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Deep Learningqimera
Overview

Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples

This repository is the official implementation of paper [Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples].

Overview of Qimera

Requirements

  • Python 3.6
  • PyTorch 1.8.1
  • Refer requirements.txt for other requirements

To install requirements:

pip install -r requirements.txt

Training

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.

Evaluation

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

Visualizing Feature Space

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

Results

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 :

Cifar10 Synthetic Images Generated By Qimera

License

This project is licensed under the terms of the GNU General Public License v3.0

Owner
Kanghyun Choi
Grad Student, ACSys Lab., CS, Yonsei Univ.
Kanghyun Choi
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