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SSD-ResNet34 Inference

Description

This document has instructions for running SSD-ResNet34 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 matplotlib Pillow pycocotools defusedxml

  • Download pretrained model

    cd <path to your clone of the model zoo>/quickstart/object_detection/pytorch/ssd-resnet34/inference/cpu
export CHECKPOINT_DIR=<directory where to save the pretrained model>
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/ssd-resnet34/inference/cpu
export DATASET_DIR=<directory where the dataset will be saved>
bash download_dataset.sh

Quick Start Scripts

DataType Throughput Latency Accuracy
FP32 bash inference_throughput.sh fp32 bash inference_realtime.sh fp32 bash accuracy.sh fp32
BF16 bash inference_throughput.sh bf16 bash inference_realtime.sh bf16 bash accuracy.sh bf16
INT8 bash inference_throughput.sh int8 bash inference_realtime.sh int8 bash accuracy.sh int8
BF32 bash inference_throughput.sh bf32 bash inference_realtime.sh bf32 bash accuracy.sh bf32

To do the int8 calibration bash bare_metal_int8_calibration.sh int8 <file where to save the calibrated model> <steps to run calibration>, for example bash bare_metal_int8_calibration.sh int8 test.json 10.

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 pretrained model>
export OUTPUT_DIR=<path to an output directory>

# Run a quickstart script (for example, FP32 batch inference)
cd ${MODEL_DIR}/quickstart/object_detection/pytorch/ssd-resnet34/inference/cpu
bash inference_throughput.sh fp32

License

LICENSE