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MaskRCNN CPU Inference

MaskRCNN Inference best known configurations with Intel® Extension for PyTorch.

Model Information

Use Case Framework Model Repo Branch/Commit/Tag Optional Patch
Inference PyTorch https://github.com/matterport/Mask_RCNN - -

Pre-Requisite

  • Installation of PyTorch and Intel Extension for PyTorch

  • Installation of Build PyTorch + IPEX + TorchVision Jemalloc and TCMalloc

  • Set Jemalloc and tcmalloc Preload for better performance

    The jemalloc and tcmalloc should be built from the General setup section.

    export LD_PRELOAD="<path to the jemalloc directory>/lib/libjemalloc.so":"path_to/tcmalloc/lib/libtcmalloc.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

  pip install packaging intel-openmp
  export LD_PRELOAD=path/lib/libiomp5.so:$LD_PRELOAD
  • Set ENV to use fp16 AMX if you are using a supported platform
  export DNNL_MAX_CPU_ISA=AVX512_CORE_AMX_FP16

Prepare Dataset

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 <MODEL_DIR=path_to_maskrcnn_inference_cpu>
export DATASET_DIR=<directory where the dataset will be saved>
./download_dataset.sh
cd -

Download pretrained model

cd <MODEL_DIR=path_to_maskrcnn_inference_cpu>
export CHECKPOINT_DIR=<directory where the pretrained model will be saved>
./download_model.sh

Inference

  1. git clone https://github.com/IntelAI/models.git

  2. cd models/models_v2/pytorch/maskrcnn/inference/cpu

  3. Create virtual environment venv and activate it:

    python3 -m venv venv
. ./venv/bin/activate

  4. Install the latest CPU versions of torch, torchvision and intel_extension_for_pytorch.

  5. Run setup scripts

cd <MODEL_DIR=path_to_maskrcnn/inference/cpu>
./setup.sh
cd <path/to/maskrcnn/inference/cpu/maskrcnn-benchmark>
pip install -e setup.py develop
pip install -r requirements.txt
cd -
  1. Setup required environment paramaters
Parameter export command
TEST_MODE (THROUGHPUT, ACCURACY, REALTIME) export TEST_MODE=THROUGHPUT (THROUGHPUT, ACCURACY, REALTIME)
DATASET_DIR export DATASET_DIR=<path-to-coco>
PRECISION export PRECISION=fp32 <Select from: fp32, avx-fp32, bf16, or bf32>
OUTPUT_DIR export OUTPUT_DIR=<path to an output directory>
CHECKPOINT_DIR export CHECKPOINT_DIR=<path to pre-trained model>
MODE export MODE=<set to 'jit' or 'imperative'>
BATCH_SIZE (optional) export BATCH_SIZE=<set a value for batch size, else it will run with default batch size>
  1. Run run_model.sh

Output

2024-05-06 15:01:22,842 maskrcnn_benchmark.inference INFO: P99 Latency 10605.99 ms
2024-05-06 15:01:22,842 - maskrcnn_benchmark.inference - INFO - P99 Latency 10605.99 ms
2024-05-06 15:01:22,843 maskrcnn_benchmark.inference INFO: Total run time: 0:06:53.501260 (20.67506300210953 s / iter per device, on 1 devices)
2024-05-06 15:01:22,843 - maskrcnn_benchmark.inference - INFO - Total run time: 0:06:53.501260 (20.67506300210953 s / iter per device, on 1 devices)
2024-05-06 15:01:22,843 maskrcnn_benchmark.inference INFO: Model inference time: 0:03:27.253329 (10.36266644001007 s / iter per device, on 1 devices)
2024-05-06 15:01:22,843 - maskrcnn_benchmark.inference - INFO - Model inference time: 0:03:27.253329 (10.36266644001007 s / iter per device, on 1 devices)
Throughput: 5.404 fps

Final results of the inference run can be found in results.yaml file.

results:
- key : throughput
  value: 5.404
  unit: fps
- key: latency
  value: 10605.99
  unit: ms
- key: bounding-box accuracy
  value: 0.381
  unit: percentage
- key: segmentation accuracy
  value: 0.381
  unit: percentage