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Mask R-CNN Inference

Description

This document has instructions for running Mask R-CNN inference using Intel-optimized PyTorch.

Bare Metal

General setup

Follow link to install Conda and build Pytorch, IPEX, TorchVison and Jemalloc.

Model Specific Setup

  • Install dependencies

    pip install yacs opencv-python pycocotools cityscapesscripts
conda install intel-openmp

  • Install model

    cd models/object_detection/pytorch/maskrcnn/maskrcnn-benchmark/
python setup.py develop

  • Download pretrained model

    cd <path to your clone of the model zoo>/quickstart/object_detection/pytorch/maskrcnn/inference/cpu
export CHECKPOINT_DIR=<directory where the pretrained model will be saved>
bash download_model.sh

  • Set Jemalloc Preload for better performance

    The jemalloc should be built from the General setup section.

    export LD_PRELOAD="path/lib/libjemalloc.so":$LD_PRELOAD
export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000"

  • Set IOMP preload for better performance

    IOMP should be installed in your conda env from the General setup section.

    export LD_PRELOAD=path/lib/libiomp5.so:$LD_PRELOAD

  • Set ENV to use AMX if you are using SPR

    export DNNL_MAX_CPU_ISA=AVX512_CORE_AMX

Datasets

Download the 2017 COCO dataset using the download_dataset.sh script. Export the DATASET_DIR environment variable to specify the directory where the dataset will be downloaded. This environment variable will be used again when running quickstart scripts.

cd <path to your clone of the model zoo>/quickstart/object_detection/pytorch/maskrcnn/inference/cpu
export DATASET_DIR=<directory where the dataset will be saved>
bash download_dataset.sh

Quick Start Scripts

DataType mode Throughput Latency Accuracy
FP32 imperative bash inference_throughput.sh fp32 imperative bash inference_realtime.sh fp32 imperative bash accuracy.sh fp32 imperative
BF16 imperative bash inference_throughput.sh bf16 imperative bash inference_realtime.sh bf16 imperative bash accuracy.sh bf16 imperative
FP32 jit bash inference_throughput.sh fp32 jit bash inference_realtime.sh fp32 jit bash accuracy.sh fp32 jit
BF16 jit bash inference_throughput.sh bf16 jit bash inference_realtime.sh bf16 jit bash accuracy.sh bf16 jit

Run the model

Follow the instructions above to setup your bare metal environment, download and preprocess the dataset, and do the model specific setup. Once all the setup is done, the Model Zoo can be used to run a quickstart script. Ensure that you have an enviornment variables set to point to the dataset directory, the downloaded pretrained model, and an output directory.

# Clone the model zoo repo and set the MODEL_DIR
git clone https://github.com/IntelAI/models.git
cd models
export MODEL_DIR=$(pwd)

# Env vars
export DATASET_DIR=<path to the COCO dataset>
export CHECKPOINT_DIR=<path to the downloaded pretrained model>
export OUTPUT_DIR=<path to an output directory>
export MODE=<set to 'jit' or 'imperative'>

# Run a quickstart script (for example, FP32 batch inference jit)
cd ${MODEL_DIR}/quickstart/object_detection/pytorch/maskrcnn/inference/cpu
bash inference_throughput.sh fp32 jit

License

LICENSE