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SSD-MobilenetV1 Inference

SSD-MobileNetV1 Inference best known configurations with Intel® Extension for PyTorch.

Model Information

Use Case Framework Model Repo Branch/Commit/Tag Optional Patch
Inference PyTorch https://github.com/qfgaohao/pytorch-ssd - -

Pre-Requisite

  • Host has Intel® Data Center GPU Arc

  • Host has installed latest Intel® Data Center GPU Arc Series Drivers https://dgpu-docs.intel.com/driver/installation.html

  • The following Intel® oneAPI Base Toolkit components are required:

    • Intel® oneAPI DPC++ Compiler (Placeholder DPCPPROOT as its installation path)
    • Intel® oneAPI Math Kernel Library (oneMKL) (Placeholder MKLROOT as its installation path)
    • Intel® oneAPI MPI Library
    • Intel® oneAPI TBB Library

    Follow instructions at Intel® oneAPI Base Toolkit Download page to setup the package manager repository.

Prepare Dataset

Download, and extract the following dataset, checkpoint and VOC model labels:

Dataset: VOC2007

Download from URL: VOCtrainval_06-Nov-2007.tar and extract, you will find the extract folder as below:

VOCDevkit/
└── VOC2007
    ├── Annotations
    ├── ImageSets
    ├── JPEGImages
    ├── SegmentationClass
    └── SegmentationObject

please indicator the DATASET_DIR to path/to/your/folder/VOCdevkit/VOC2007

Pretrained model

Download form this URL to get mobilenet-v1-ssd-mp-0_675.pth and put under WEIGHT_DIR

Voc-model-labels

Download from this URL to get voc-model-labels.txt and put under LABEL_DIR

Inference

  1. git clone https://github.com/IntelAI/models.git
  2. cd models/models_v2/pytorch/ssd-mobilenetv1/inference/gpu
  3. Create virtual environment venv and activate it: 
    python3 -m venv venv
. ./venv/bin/activate
  4. Run setup.sh 
    ./setup.sh
  5. Install the latest GPU versions of torch, torchvision and intel_extension_for_pytorch:
python -m pip install torch==<torch_version> torchvision==<torchvvision_version> intel-extension-for-pytorch==<ipex_version> --extra-index-url https://pytorch-extension.intel.com/release-whl-aitools/
  1. Set environment variables for Intel® oneAPI Base Toolkit: Default installation location {ONEAPI_ROOT} is /opt/intel/oneapi for root account, ${HOME}/intel/oneapi for other accounts 
    source {ONEAPI_ROOT}/compiler/latest/env/vars.sh
source {ONEAPI_ROOT}/mkl/latest/env/vars.sh
source {ONEAPI_ROOT}/tbb/latest/env/vars.sh
source {ONEAPI_ROOT}/mpi/latest/env/vars.sh
source {ONEAPI_ROOT}/ccl/latest/env/vars.sh
  2. Setup required environment paramaters
Parameter export command
MULTI_TILE export MULTI_TILE=False (False)
PLATFORM export PLATFORM=Arc (Arc)
DATASET_DIR export DATASET_DIR= (the dir path to voc2007 in dataset)
WEIGHT_DIR export WEIGHT_DIR= (contain .pth files)
LABEL_DIR export LABEL_DIR= (contain .txt files)
BATCH_SIZE (optional) export BATCH_SIZE=256
PRECISION export PRECISION=INT8 (INT8, FP16 and FP32 for Arc)
OUTPUT_DIR (optional) export OUTPUT_DIR=$PWD
NUM_ITERATIONS (optional) export NUM_ITERATIONS=500
DATASET_DIR (optional) export DATASET_DIR=--dummy
  1. Run run_model.sh

Output

Single-tile output will typically looks like:

process image 500
Load Image: 0.000002 seconds.
Inference time:  0.02182316780090332
Post time:  0.003971099853515625
Prediction: 0.438544 seconds.

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

results:

  • key: throughput value: 11721.356 unit: inst/s
  • key: latency value: 0.021840476989746095 unit: s
  • key: accuracy value: None unit: accuracy