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ResNet50v1.5 inference

Description

This document has instructions for running ResNet50v1.5 inference using Intel(R) Extension for PyTorch with GPU.

Hardware Requirements:

  • Intel® Data Center GPU Flex Series

Software Requirements:

  • Ubuntu 20.04 (64-bit)

  • Intel GPU Drivers: Intel® Data Center GPU Flex Series 419.40

    Release OS Intel GPU Install Intel GPU Driver
    v1.0.0 Ubuntu 20.04 Intel® Data Center GPU Flex Series Refer to the Installation Guides for latest driver installation. If install the verified Intel® Data Center GPU Flex Series 419.40, please append the specific version after components, such as apt-get install intel-opencl-icd=22.28.23726.1+i419~u20.04

  • Intel® oneAPI Base Toolkit 2022.3: Need to install components of Intel® oneAPI Base Toolkit

    • Intel® oneAPI DPC++ Compiler
    • Intel® oneAPI Math Kernel Library (oneMKL)

    • Download and install the verified DPC++ compiler and oneMKL in Ubuntu 20.04.

      wget https://registrationcenter-download.intel.com/akdlm/irc_nas/18852/l_BaseKit_p_2022.3.0.8767_offline.sh
# 4 components are necessary: DPC++/C++ Compiler, DPC++ Libiary, Threading Building Blocks and oneMKL
sh ./l_BaseKit_p_2022.3.0.8767_offline.sh

      For any more details, please follow the procedure in https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html.

    • Set environment variables: Default installation location {ONEAPI_ROOT} is /opt/intel/oneapi for root account, ${HOME}/intel/oneapi for other accounts 
      source {ONEAPI_ROOT}/setvars.sh

Datasets

The ImageNet validation dataset is used.

Download and extract the ImageNet2012 dataset from http://www.image-net.org/, then move validation images to labeled subfolders, using the valprep.sh shell script

A after running the data prep script, your folder structure should look something like this:

imagenet
└── val
    ├── ILSVRC2012_img_val.tar
    ├── n01440764
    │   ├── ILSVRC2012_val_00000293.JPEG
    │   ├── ILSVRC2012_val_00002138.JPEG
    │   ├── ILSVRC2012_val_00003014.JPEG
    │   ├── ILSVRC2012_val_00006697.JPEG
    │   └── ...
    └── ...

The folder that contains the val directory should be set as the DATASET_DIR (for example: export DATASET_DIR=/home/<user>/imagenet).

Quick Start Scripts

Script name Description
inference_block_format.sh Runs ResNet50 inference (block format) for the specified precision (int8)

Run the model

Install the following pre-requisites:

  • Python version 3.9
  • Create and activate virtual environment. 
    virtualenv -p python <virtualenv_name>
source <virtualenv_name>/bin/activate
  • Install PyTorch and Intel® Extension for PyTorch for GPU (IPEX): 
    python -m pip install torch==1.10.0a0 -f https://developer.intel.com/ipex-whl-stable-xpu
python -m pip install numpy==1.23.4
python -m pip install intel_extension_for_pytorch==1.10.200+gpu -f https://developer.intel.com/ipex-whl-stable-xpu
    To verify that PyTorch and IPEX are correctly installed: 
    python -c "import torch;print(torch.device('xpu'))"  # Sample output: "xpu"
python -c "import intel_extension_for_pytorch as ipex;print(ipex.xpu.is_available())"  #Sample output True
python -c "import intel_extension_for_pytorch as ipex;print(ipex.xpu.has_onemkl())"  # Sample output: True
  • Navigate to ResNet50v1.5 inference directory and install model specific dependencies for the workload: 
    cd quickstart/image_recognition/pytorch/resnet50v1_5/inference/gpu
./setup.sh
cd -

See the datasets section of this document for instructions on downloading and preprocessing the ImageNet dataset. The path to the ImageNet dataset files will need to be set as the DATASET_DIR environment variable prior to running a quickstart script.

Run the model on Baremetal

Set environment variables for the path to your dataset, an output directory, and specify the precision to run the quickstart script:

To run with ImageNet data, the dataset directory will need to be specified in addition to an output directory and precision.
export DATASET_DIR=<path to the preprocessed imagenet dataset>
export OUTPUT_DIR=<Path to save the output logs>
export PRECISION=int8

# Run a quickstart script
./quickstart/image_recognition/pytorch/resnet50v1_5/inference/gpu/inference_block_format.sh

License

LICENSE