Shao-kun-Zhang 4600c661ab | 2 years ago | |
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classification | 2 years ago | |
detection | 2 years ago | |
imgs | 2 years ago | |
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README.md | 2 years ago |
This repository contains the PyTorch implementation for the CVPR 2020 paper ZeroQ: A Novel Zero-Shot Quantization Framework. Below are instructions for reproducing classification results. Please see detection readme for instructions to reproduce object detection results.
You can find a short video explanation of ZeroQ here.
# Code is based on PyTorch 1.2 (Cuda10). Other dependancies could be installed as follows:
cd classification
pip install -r requirements.txt --user
# Set a symbolic link to ImageNet validation data (used only to evaluate model)
mkdir data
ln -s /path/to/imagenet/ data/
The folder structures should be the same as following
zeroq
├── utils
├── data
│ ├── imagenet
│ │ ├── val
Afterwards you can test Zero Shot quantization with W8A8 by running:
bash run.sh
Below are the results that you should get for 8-bit quantization (W8A8 refers to the quantizing model to 8-bit weights and 8-bit activations).
Models | Single Precision Top-1 | W8A8 Top-1 |
---|---|---|
ResNet18 | 71.47 | 71.43 |
ResNet50 | 77.72 | 77.67 |
InceptionV3 | 78.88 | 78.72 |
MobileNetV2 | 73.03 | 72.91 |
ShuffleNet | 65.07 | 64.94 |
SqueezeNext | 69.38 | 69.17 |
export CUDA_VISIBLE_DEVICES=0
python uniform_test.py [--dataset] [--model] [--batch_size] [--test_batch_size]
optional arguments:
--dataset type of dataset (default: imagenet)
--model model to be quantized (default: resnet18)
--batch-size batch size of distilled data (default: 64)
--test-batch-size batch size of test data (default: 512)
ZeroQ has been developed as part of the following paper. We appreciate it if you would please cite the following paper if you found the implementation useful for your work:
@inproceedings{cai2020zeroq,
title={Zeroq: A novel zero shot quantization framework},
author={Cai, Yaohui and Yao, Zhewei and Dong, Zhen and Gholami, Amir and Mahoney, Michael W and Keutzer, Kurt},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={13169--13178},
year={2020}
}
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