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ResNet152 Inference

Description

This document has instructions for running ResNet152 inference.

Datasets

ImageNet

The ImageNet validation dataset is used to run ResNet152 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 the 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/resnet152/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\resnet152\inference\cpu\batch_inference_baremetal.sh fp32

License

LICENSE