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VGG-11 with batch normalization Inference

Description

This document has instructions for running VGG-11 with batch normalization inference.

Datasets

ImageNet

The ImageNet validation dataset is used to run VGG-11 with batch normalization accuracy tests.

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

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

DataType Throughput Latency Accuracy
FP32 bash batch_inference_baremetal.sh fp32 bash online_inference_baremetal.sh fp32 bash accuracy_baremetal.sh fp32
BF16 bash batch_inference_baremetal.sh bf16 bash online_inference_baremetal.sh bf16 bash accuracy_baremetal.sh bf16

Follow the instructions to setup your bare metal environment on either Linux or Windows systems. Once all the setup is done, the Model Zoo can be used to run a quickstart script. Ensure that you have a clone of the Model Zoo Github repository.

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

Run on Linux

Follow link to install Miniconda and build Pytorch, IPEX, TorchVison and Jemalloc.

  • Set Jemalloc Preload for better performance

    After Jemalloc setup, set the following environment variables.

    export LD_PRELOAD="<path to the jemalloc directory>/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. Set the following environment variables.

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

  • Set ENV to use AMX if you are using SPR

    export DNNL_MAX_CPU_ISA=AVX512_CORE_AMX

  • Run the model:

    cd models

# Set environment variables
export DATASET_DIR=<path_to_Imagenet_Dataset>
export OUTPUT_DIR=<path to the directory where log files will be written>

# Run a quickstart script (for example, FP32 batch inference)
bash quickstart/image_recognition/pytorch/vgg11_bn/inference/cpu/batch_inference_baremetal.sh fp32

Run on Windows

If not already setup, please follow instructions for environment setup on Windows.

Using Windows CMD.exe, run:

cd models

# Env vars
set DATASET_DIR=<path to the Imagenet Dataset>
set OUTPUT_DIR=<path to the directory where log files will be written>

#Run a quickstart script for fp32 precision(FP32 online inference or batch inference or accuracy)
bash quickstart\image_recognition\pytorch\vgg11_bn\inference\cpu\batch_inference_baremetal.sh fp32

License

LICENSE