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Quantize model

Why quantization ?

The fixed-point model has many advantages over the fp32 model:

  • Smaller size, 8-bit model reduces file size by 75%
  • Benefit from the smaller model, the Cache hit rate is improved and inference would be faster
  • Chips tend to have corresponding fixed-point acceleration instructions which are faster and less energy consumed (int8 on a common CPU requires only about 10% of energy)

APK file size and heat generation are key indicators while evaluating mobile APP; On server side, quantization means that you can increase model size in exchange for precision and keep the same QPS.

Post training quantization scheme

Taking ncnn backend as an example, the complete workflow is as follows:

mmdeploy generates quantization table based on static graph (onnx) and uses backend tools to convert fp32 model to fixed point.

mmdeploy currently support ncnn with PTQ.

How to convert model

After mmdeploy installation, install ppq

git clone https://github.com/openppl-public/ppq.git
cd ppq
pip install -r requirements.txt
python3 setup.py install

Back in mmdeploy, enable quantization with the option 'tools/deploy.py --quant'.

cd /path/to/mmdeploy

export MODEL_CONFIG=/home/rg/konghuanjun/mmclassification/configs/resnet/resnet18_8xb32_in1k.py
export MODEL_PATH=https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth

# get some imagenet sample images
git clone https://github.com/nihui/imagenet-sample-images --depth=1

# quantize
python3 tools/deploy.py  configs/mmcls/classification_ncnn-int8_static.py  ${MODEL_CONFIG}  ${MODEL_PATH}   /path/to/self-test.png   --work-dir work_dir --device cpu --quant --quant-image-dir /path/to/imagenet-sample-images
...

Description

Parameter Meaning
--quant Enable quantization, the default value is False
--quant-image-dir Calibrate dataset, use Validation Set in MODEL_CONFIG by default

Custom calibration dataset

Calibration set is used to calculate quantization layer parameters. Some DFQ (Data Free Quantization) methods do not even require a dataset.

  • Create a folder, just put in some images (no directory structure, no negative example, no special filename format)
  • The image needs to be the data comes from real scenario otherwise the accuracy would be drop
  • You can not quantize model with test dataset
    Type Train dataset Validation dataset Test dataset Calibration dataset
    Usage QAT PTQ Test accuracy PTQ

It is highly recommended that verifying model precision after quantization. Here is some quantization model test result.