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https://github.com/mit-han-lab/haq

[CVPR 2019, Oral] HAQ: Hardware-Aware Automated Quantization with Mixed Precision
https://github.com/mit-han-lab/haq

automl efficient-model mixed-precision quantization

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[CVPR 2019, Oral] HAQ: Hardware-Aware Automated Quantization with Mixed Precision

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# HAQ: Hardware-Aware Automated Quantization with Mixed Precision



## Introduction



This repo contains PyTorch implementation for paper [HAQ: Hardware-Aware Automated Quantization with Mixed Precision](http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_HAQ_Hardware-Aware_Automated_Quantization_With_Mixed_Precision_CVPR_2019_paper.pdf) (CVPR2019, oral)



![overview](https://hanlab.mit.edu/projects/haq/images/overview.png)



```

@inproceedings{haq,

author = {Wang, Kuan and Liu, Zhijian and Lin, Yujun and Lin, Ji and Han, Song},

title = {HAQ: Hardware-Aware Automated Quantization With Mixed Precision},

booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

year = {2019}

}

```



Other papers related to automated model design:

- AMC: AutoML for Model Compression and Acceleration on Mobile Devices ([ECCV 2018](https://arxiv.org/abs/1802.03494))



- ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware ([ICLR 2019](https://arxiv.org/abs/1812.00332))



## Dependencies

We evaluate this code with Pytorch 1.1 (cuda10) and torchvision 0.3.0, you can install pytorch with conda:

```

# install pytorch

conda install -y pytorch torchvision cudatoolkit=10.0 -c pytorch

```

And you can use the following command to set up the environment:

```

# install packages and download the pretrained model

bash run/setup.sh

```

(If the server is down, you can download the pretrained model from google drive: [mobilenetv2-150.pth.tar](https://drive.google.com/open?id=1fZ1gNSzSZTQfJ0dL-bNYULNvZJxp_Y53))



Current code base is tested under following environment:

1. Python         3.7.3

2. PyTorch        1.1

3. torchvision    0.3.0

4. numpy          1.14

5. matplotlib     3.0.1

6. scikit-learn   0.21.0

7. easydict       1.8

8. progress       1.4

9. tensorboardX   1.7



## Dataset

If you already have the ImageNet dataset for pytorch, you could create a link to data folder and use it:

```

# prepare dataset, change the path to your own

ln -s /path/to/imagenet/ data/

```

If you do not have the ImageNet yet, you can download the ImageNet dataset and move validation images to labeled subfolders. To do this, you can use the following script: 

[https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh](https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh)



We use a subset of ImageNet in the linear quantizaiton search phase to save the training time, to create the link of the subset, you can use the following tool:

 ```

# prepare imagenet100 dataset

python lib/utils/make_data.py

```



## Reinforcement learning search

- You can run the bash file as following to search the K-Means quantization strategy, which only quantizes the weights with K-Means to compress model size of specific model.

```

# K-Means quantization, for model size

bash run/run_kmeans_quantize_search.sh

```

- You can run the bash file as following to search the linear quantization strategy, which linearly quantizes both the weights and activations to reduce latency/energy of specific model.

```

# Linear quantization, for latency/energy

bash run/run_linear_quantize_search.sh

```

- Usage details

```

python rl_quantize.py --help

```



## Finetune Policy

- After searching, you can get the quantization strategy list, and you can replace the strategy list in **finetune.py** to finetune and evaluate the performance on ImageNet dataset.

- We set the default K-Means quantization strategy searched under preserve ratio = 0.1 like:

```

# preserve ratio 10%

strategy = [6, 6, 5, 5, 5, 5, 4, 5, 5, 4, 5, 5, 5, 5, 5, 5, 3, 5, 4, 3, 5, 4, 3, 4, 4, 4, 2, 5, 4, 3, 3, 5, 3, 2, 5, 3, 2, 4, 3, 2, 5, 3, 2, 5, 3, 4, 2, 5, 2, 3, 4, 2, 3, 4]

```

You can follow the following bash file to finetune the K-Means quantized model to get a better performance:

```

bash run/run_kmeans_quantize_finetune.sh

```

- We set the default linear quantization strategy searched under preserve ratio = 0.6 like:

```

# preserve ratio 60%

strategy = [[8, -1], [7, 7], [5, 6], [4, 6], [5, 6], [5, 7], [5, 6], [7, 4], [4, 6], [4, 6], [7, 7], [5, 6], [4, 6], [7, 3], [5, 7], [4, 7], [7, 3], [5, 7], [4, 7], [7, 7], [4, 7], [4, 7], [6, 4], [6, 7], [4, 7], [7, 4], [6, 7], [5, 7], [7, 4], [6, 7], [5, 7], [7, 4], [6, 7], [6, 7], [6, 4], [5, 7], [6, 7], [6, 4], [5, 7], [6, 7], [7, 7], [4, 7], [7, 7], [7, 7], [4, 7], [7, 7], [7, 7], [4, 7], [7, 7], [7, 7], [4, 7], [7, 7], [8, 8]]

```

You can follow the following bash file to finetune the linear quantized model to get a better performance:

```

bash run/run_linear_quantize_finetune.sh

```

- Usage details

```

python finetune.py --help

```

## Evaluate

You can download the pretrained quantized model like this:

```

# download checkpoint

mkdir -p checkpoints/resnet50/

mkdir -p checkpoints/mobilenetv2/

cd checkpoints/resnet50/

wget https://hanlab.mit.edu/files/haq/resnet50_0.1_75.48.pth.tar

cd ../mobilenetv2/

wget https://hanlab.mit.edu/files/haq/qmobilenetv2_0.6_71.23.pth.tar

cd ../..

```

(If the server is down, you can download the pretrained model from google drive: [qmobilenetv2_0.6_71.23.pth.tar](https://drive.google.com/open?id=1oW1Jq17LIwcOckOzZPWDlKEhGWkZ3F_r)) 



You can evaluate the K-Means quantized model like this:

```

# evaluate K-Means quantization

bash run/run_kmeans_quantize_eval.sh

```

| Models                   | preserve ratio | Top1 Acc (%) | Top5 Acc (%) |

| ------------------------ | -------------- | ------------ | ------------ |

| resnet50 (original)      |       1.0      |     76.15    |    92.87     |

| resnet50 (10x compress)  |       0.1      |     75.48    |    92.42     |



You can evaluate the linear quantized model like this:

```

# evaluate linear quantization

bash run/run_linear_quantize_eval.sh

```



| Models                    | preserve ratio | Top1 Acc (%) | Top5 Acc (%) |

| ------------------------  | -------------- | ------------ | ------------ |

| mobilenetv2 (original)    |       1.0      |     72.05    |    90.49     |

| mobilenetv2 (0.6x latency)|       0.6      |     71.23    |    90.00     |