ipex

SSD-ResNet34 Inference

Note: IPEX version >= 1.10

Model Specific Setup

• Install dependencies

    pip install -r requirements.txt

• Download pretrained model

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

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

Run

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_tuning.sh --dataset_location=dataset --input_model=model

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