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YOLOv4 inference

Description

This document has instructions for running YOLOv4 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

Download and extract the 2017 training/validation images and annotations from the COCO dataset website to a coco folder and unzip the files. After extracting the zip files, your dataset directory structure should look something like this:

coco
├── annotations
│   ├── captions_train2017.json
│   ├── captions_val2017.json
│   ├── instances_train2017.json
│   ├── instances_val2017.json
│   ├── person_keypoints_train2017.json
│   └── person_keypoints_val2017.json
├── train2017
│   ├── 000000454854.jpg
│   ├── 000000137045.jpg
│   ├── 000000129582.jpg
│   └── ...
└── val2017
    ├── 000000000139.jpg
    ├── 000000000285.jpg
    ├── 000000000632.jpg
    └── ...

The parent of the annotations, train2017, and val2017 directory (in this example coco) is the directory that should be used when setting the image environment variable for YOLOv4 (for example: export image=/home/<user>/coco/val2017/000000581781.jpg). In addition, we should also set the size environment to match the size of image. (for example: export size=416)

Quick Start Scripts

Script name Description
inference_with_dummy_data.sh Inference with int8 batch_size64 dummy data

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 Yolov4 inference directory and install model specific dependencies for the workload: 
    cd quickstart/object_detection/pytorch/yolov4/inference/gpu
./setup.sh
cd -
  • Download the pretrained weights file, and set the PRETRAINED_MODEL environment variable to point to where it was saved: 
    wget https://drive.google.com/open?id=1wv_LiFeCRYwtpkqREPeI13-gPELBDwuJ

Run the model on Baremetal

Set environment variables:
export DATASET_DIR=<path where yolov4 COCO dataset>
export PRETRAINED_MODEL=<path to directory where the pretrained weights file was saved>
export OUTPUT_DIR=<Path to save the output logs>

Run the inference script, only int8 precision is supported:
./quickstart/object_detection/pytorch/yolov4/inference/gpu/inference_with_dummy_data.sh

License

LICENSE