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ipex

README.md

Step-by-Step

Prerequisite

1. Environment

    pip install -r requirements.txt

Note: Validated PyTorch Version.

install Intel® Extension for PyTorch

python -m pip install intel_extension_for_pytorch -f https://software.intel.com/ipex-whl-stable

Note: Intel® Extension for PyTorch* has PyTorch version requirement. Please check more detailed information via the URL below.

More installation methods can be found at Installation Guide

2. Prepare Model

    cd <path to your clone of the model zoo>/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex
    bash download_model.sh

3. Prepare Dataset

Download the 2017 COCO dataset using the download_dataset.sh script.

    cd <path to your clone of the model zoo>/examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/ipex

    bash download_dataset.sh

4. Setting of Tuning Performance

  • 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

Quantization

python infer.py
    --data DATASET_DIR
    --device 0
    --checkpoint  PRETAINED_MODEL   #./pretrained/resnet34-ssd1200.pth 
    -w 10 
    -j 0 
    --no-cuda 
    --batch-size 16 
    --tune 
    --accuracy-mode

or

bash run_quant.sh --dataset_location=dataset --input_model=model

bash run_benchmark.sh --dataset_location=dataset --input_model=model --mode=accuracy/performance --int8=True/False