README.md

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:

Notice that MQBench version is 0.0.2.

git clone https://github.com/ZLkanyo009/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

Accuracy

Model	Acc.(fp32)	Acc.(tensorRT)
VGG16	79.90%	78.95%
GoogleNet	90.20%	89.42%
ResNet18	95.43%	95.44%
RegNetX_200MF	89.47%	89.22%
SENet18	91.69%	91.34%
MobileNetV2	88.42%	87.65%
ResNeXt29(2x64d)	87.07%	86.95%
SimpleDLA	90.24%	89.45%
DenseNet121	85.18%	85.10%
PreActResNet18	92.06%	91.68%