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README.md

Step-by-Step

This document describes the step-by-step instructions for reproducing PyTorch MASK_RCNN tuning results with Intel® Neural Compressor.

Prerequisite

1. Installation

Environment

PyTorch >=1.8 version is required with pytorch_fx backend.

cd examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx
pip install -r requirements.txt

Maskrcnn_benchmark and Dependencies Installation

Make sure that your conda is setup properly with the right environment. Check that which conda, which pip and which python points to the right path from a clean conda env.

# this installs the right pip and dependencies for the fresh python
conda install ipython pip
# maskrcnn_benchmark and coco api dependencies
pip install ninja yacs cython matplotlib tqdm opencv-python

cd examples/pytorch/object_detection/maskrcnn/quantization/ptq/fx/pytorch
export INSTALL_DIR=$PWD

# install pycocotools
cd $INSTALL_DIR
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
git checkout 8c9bcc3cf640524c4c20a9c40e89cb6a2f2fa0e9
python setup.py build_ext install

# install cityscapesScripts
cd $INSTALL_DIR
git clone https://github.com/mcordts/cityscapesScripts.git
cd cityscapesScripts/
git checkout a7ac7b4062d1a80ed5e22d2ea2179c886801c77d
python setup.py build_ext install

# install PyTorch Detection
# some modifications have been made according to this example
# please apply the patch provided before installing
cd $INSTALL_DIR
git clone https://github.com/facebookresearch/maskrcnn-benchmark.git
cd maskrcnn-benchmark
git checkout 57eec25b75144d9fb1a6857f32553e1574177daf
git apply ../maskrnn.patch
python setup.py build develop

cd ../..
unset INSTALL_DIR

2. Prepare Dataset

You can download COCO2017 dataset use script file:

source download_dataset.sh

Or you can download COCO2017 dataset to your local path, then link it to pytorch/datasets/coco:

ln -s /path/of/COCO2017/annotations pytorch/datasets/coco/annotations
ln -s /path/of/COCO2017/train2017 pytorch/datasets/coco/train2017
ln -s /path/of/COCO2017/val2017 pytorch/datasets/coco/val2017

3. Prepare weights

You can download weights with script file:

bash download_weights.sh

Or you else can link your weights to pytorch folder:

ln -s /path/of/weights pytorch/e2e_mask_rcnn_R_50_FPN_1x.pth

Run

bash run_quant.sh --output_model=/path/to/tuned_checkpoint

Saving and loading model:

  • Saving model: After tuning with Neural Compressor, we can get neural_compressor.model:
from neural_compressor.config import PostTrainingQuantConfig
from neural_compressor import quantization
conf = PostTrainingQuantConfig()
q_model = quantization.fit(model,
                            conf,
                            calib_dataloader=cal_dataloader,
                            eval_func=eval_func)

Here, q_model is Neural Compressor model class, so it has "save" API:

q_model.save("Path_to_save_configure_file")
  • loading model:
from neural_compressor.utils.pytorch import load
from neural_compressor.utils.pytorch import load
q_model = load(os.path.abspath(os.path.expanduser(args.tuned_checkpoint)),
            fp32_model,
            dataloader=your_dataloader)

Please refer to Sample code