QAT(quantize aware training) for classification with MQBench

Overview

MQBench Quantization Aware Training with PyTorch

I am using MQBench(Model Quantization Benchmark)(http://mqbench.tech/) to quantize the model for deployment.

MQBench is a benchmark and framework for evluating the quantization algorithms under real world hardware deployments.

Prerequisites

  • Python 3.7+
  • PyTorch 1.8.1+

Install MQBench Lib

Before run this repository, you should install MQBench:

git clone https://github.com/ModelTC/MQBench.git
cd MQBench
python setup.py build
python setup.py install

Training Fp32 Model

# Start training fp32 model with: 
# model_name can be ResNet18, MobileNet, ...
python main.py model_name

# You can manually config the training with: 
python main.py --resume --lr=0.01

Training Quantize Model

# Start training quantize model with: 
# model_name can be ResNet18, MobileNet, ...
python main.py model_name --quantize

# You can manually config the training with: 
python main.py --resume --parallel DP --BackendType Tensorrt --quantize
python -m torch.distributed.launch main.py --local_rank 0 --parallel DDP --resume  --BackendType Tensorrt --quantize

Fp32 Accuracy

Model Acc.
VGG16 92.64%
ResNet18 93.02%
ResNet50 93.62%
ResNet101 93.75%
RegNetX_200MF 94.24%
RegNetY_400MF 94.29%
MobileNetV2 94.43%
ResNeXt29(32x4d) 94.73%
ResNeXt29(2x64d) 94.82%
SimpleDLA 94.89%
DenseNet121 95.04%
PreActResNet18 95.11%
DPN92 95.16%
DLA 95.47%
Owner
Ling Zhang
Ling Zhang
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