基于mmdetection与mmclassification,实现无需模型训练，专注于对模型量化的学习。
This project is just a naive quantization toy which is based on mmdetection and mmclassification. The best thing we can focus on quantization, the model can be downloaded from openmmlab.
Step 1: create a conda environment, activate it and install pytorch
# The code is based on CUDA10.2 pytorch1.8.1
conda install pytorch==1.8.1 torchvision==0.9.1 torchaudio==0.8.1 cudatoolkit=10.2 -c pytorch
Step 2: install mmcv
pip install -U openmim
mim install mmcv-full
# code change, if you do not do this step, the process of downloading trained model will raise error.
# assuming envs is your python install path
vim envs/lib/python3.7/site-packages/mim/commands/search.py
# about line:392, there is a code as
`if collection_name :`
# we need rewrite it as
`if collection_name and collection_name in name2collection.keys():`
# and done
Step 3: install the necessary library
pip install -r requirements.txt
Step 4: install MQBench
git clone https://github.com/ModelTC/MQBench.git
cd MQBench
python setup.py install
- If you want to test a fp model you can just run
python fp_classify
- If you want to quantize a model you can just run
python fp2int_cls.py
- if you want to compare the speed of origin model and quanted model, you can run you also can find out how to load a quantitized model here which follows link
python ./tools/cls_benchmark ...your_cifar10_resnet18.pth ...your_quanted_cifar10_resnet18.pth
0708 Attention: Right now we only support classification test Although you can find fp_detection.py work as well if you sample the coco data and give the right to the config/coco_detection.py