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Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. In CVPR 2022.

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Nonuniform-to-Uniform Quantization

This repository contains the training code of N2UQ introduced in our CVPR 2022 paper: "Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation"

In this study, we propose a quantization method that can learn the non-uniform input thresholds to maintain the strong representation ability of nonuniform methods, while output uniform quantized levels to be hardware-friendly and efficient as the uniform quantization for model inference.

To train the quantized network with learnable input thresholds, we introduce a generalized straight-through estimator (G-STE) for intractable backward derivative calculation w.r.t. threshold parameters.

The formula for N2UQ is simply as follows,

Forward pass:

Backward pass:

Moreover, we proposed L1 norm based entropy preserving weight regularization for weight quantization.

Citation

If you find our code useful for your research, please consider citing:

@inproceedings{liu2022nonuniform,
  title={Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation},
  author={Liu, Zechun and Cheng, Kwang-Ting and Huang, Dong and Xing, Eric and Shen, Zhiqiang},
  journal={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

Run

1. Requirements:

  • python 3.6, pytorch 1.7.1, torchvision 0.8.2
  • gdown

2. Data:

  • Download ImageNet dataset

3. Pretrained Models:

  • pip install gdown # gdown will automatically download the models
  • If gdown doesn't work, you may need to manually download the pretrained models and put them in the correponding ./models/ folder.

4. Steps to run:

(1) For ResNet architectures:

  • Change directory to ./resnet/
  • Run bash run.sh architecture n_bits quantize_downsampling
  • E.g., bash run.sh resnet18 2 0 for quantize resnet18 to 2-bit without quantizing downsampling layers

(2) For MobileNet architectures:

  • Change directory to ./mobilenetv2/
  • Run bash run.sh

Models

1. ResNet

Network Methods W2/A2 W3/A3 W4/A4
ResNet-18
PACT 64.4 68.1 69.2
DoReFa-Net 64.7 67.5 68.1
LSQ 67.6 70.2 71.1
N2UQ 69.4 Model-Res18-2bit 71.9 Model-Res18-3bit 72.9 Model-Res18-4bit
N2UQ * 69.7 Model-Res18-2bit 72.1 Model-Res18-3bit 73.1 Model-Res18-4bit
ResNet-34
LSQ 71.6 73.4 74.1
N2UQ 73.3 Model-Res34-2bit 75.2 Model-Res34-3bit 76.0 Model-Res34-4bit
N2UQ * 73.4 Model-Res34-2bit 75.3 Model-Res34-3bit 76.1 Model-Res34-4bit
ResNet-50
PACT 64.4 68.1 69.2
LSQ 67.6 70.2 71.1
N2UQ 75.8 Model-Res50-2bit 77.5 Model-Res50-3bit 78.0 Model-Res50-4bit
N2UQ * 76.4 Model-Res50-2bit 77.6 Model-Res50-3bit 78.0 Model-Res50-4bit

Note that N2UQ without * denotes quantizing all the convolutional layers except the first input convolutional layer.

N2UQ with * denotes quantizing all the convolutional layers except the first input convolutional layer and three downsampling layers.

W2/A2, W3/A3, W4/A4 denote the cases where the weights and activations are both quantized to 2 bits, 3 bits, and 4 bits, respectively.

2. MobileNet

Network Methods W4/A4
MobileNet-V2 N2UQ 72.1 Model-MBV2-4bit

Contact

Zechun Liu, HKUST (zliubq at connect.ust.hk)

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Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. In CVPR 2022.

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