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PyTorch YOLOv7 inference

Description

This document has instructions for running YOLOv7 inference using Intel-optimized PyTorch and PyTorch inductor.

Model Information

Use Case Framework Model Repository Branch/Commit Patch
Inference Pytorch https://github.com/WongKinYiu/yolov7 main/a207844 yolov7_ipex_and_inductor.patch. Enable yolov7 inference with IPEX and torch inductor for specified precision (fp32, int8, bf16, fp16, or bf32).

Pre-Requisite

Follow link to install Miniforge and build Pytorch, IPEX, TorchVison and Tcmalloc.

  • Set Tcmalloc Preload for better performance

    After Tcmalloc setup, set the following environment variables.

    export LD_PRELOAD="<path to the tcmalloc directory>/lib/libtcmalloc.so":$LD_PRELOAD

  • Set IOMP preload for better performance

    IOMP should be installed in your conda env. Set the following environment variables.

    pip install packaging intel-openmp
export LD_PRELOAD=<path to the intel-openmp directory>/lib/libiomp5.so:$LD_PRELOAD

Model specific instructions

  • If you are running fp16 on a platform supporting AMX-FP16, add the following environment variables to leverage AMX-FP16 for better performance.
    export DNNL_MAX_CPU_ISA=AVX512_CORE_AMX_FP16
  • If you are running bf16/fp16/int8 on a platform supporting AVX2-VNNI-2, add the following environment variables to leverage AVX2-VNNI-2 for better performance.
    export DNNL_MAX_CPU_ISA=AVX2_VNNI_2

For more detailed information about DNNL_MAX_CPU_ISA of oneDNN, see oneDNN link.

Download pretrained model

Export the CHECKPOINT_DIR environment variable to specify the directory where the pretrained model will be saved. This environment variable will also be used when running quickstart scripts.

cd <clone of AIRM/models_v2/pytorch/yolov7/inference/cpu>
export CHECKPOINT_DIR=<directory where the pretrained model will be saved>
chmod a+x *.sh
./download_model.sh

Prepare Dataset

Prepare the 2017 COCO dataset for yolov7 using the download_dataset.sh script. Export the DATASET_DIR environment variable to specify the directory where the dataset will be saved. This environment variable will also be used when running quickstart scripts.

cd <clone of AIRM/models_v2/pytorch/yolov7/inference/cpu>
export DATASET_DIR=<directory where the dataset will be saved>
./download_dataset.sh

Inference

  1. git clone https://github.com/IntelAI/models.git

  2. cd models/models_v2/pytorch/yolov7/inference/cpu

  3. Create virtual environment venv and activate it:

    python3 -m venv venv
. ./venv/bin/activate

  4. Install general model requirements

    ./setup.sh

  5. Install the latest CPU versions of torch, torchvision and intel_extension_for_pytorch.

  6. Setup required environment paramaters

Parameter export command
TEST_MODE (THROUGHPUT, ACCURACY, REALTIME) export TEST_MODE=THROUGHPUT (THROUGHPUT, ACCURACY, REALTIME)
DATASET_DIR export DATASET_DIR=<path-to-dlrm_data> or <path-to-preprocessed-data>
CHECKPOINT_DIR export CHECKPOINT_DIR=<directory where the pretrained model will be saved>
PRECISION export PRECISION=fp32 <specify the precision to run: fp32, int8, bf32, bf16, or fp16>
OUTPUT_DIR export OUTPUT_DIR=<path to the directory where log files will be written>
MODEL_DIR export MODEL_DIR=$PWD (set the current path)
BATCH_SIZE (optional) export BATCH_SIZE=<set a value for batch size, else it will run with default batch size>
TORCH_INDUCTOR (optional) export TORCH_INDUCTOR=< 0 or 1> (Compile model with PyTorch Inductor backend)
  1. Run run_model.sh

Output

Output typically looks like this: 100%|██████████| 4/4 [00:17<00:00, 4.29s/it] time per prompt(s): 73.46 Inference latency 73.46 ms Throughput: 13.61 fps

Final results of the inference run can be found in results.yaml file.

results:
- key : throughput
  value: 13.61
  unit: fps
- key: latency
  value: 73.46
  unit: ms
- key: accuracy
  value: 0.20004
  unit: percentage

License

LICENSE