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ResNet50 FP32 inference

Description

This document has instructions for running ResNet50 FP32 inference.

Datasets

The ImageNet validation dataset is used when testing accuracy. The inference scripts use synthetic data, so no dataset is needed.

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

The accuracy script looks for a folder named val, so 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 when running accuracy (for example: export DATASET_DIR=/home/<user>/imagenet).

Quick Start Scripts

Script name Description
fp32_online_inference.sh Runs online inference using synthetic data (batch_size=1).
fp32_batch_inference.sh Runs batch inference using synthetic data (batch_size=128).
fp32_accuracy.sh Measures the model accuracy (batch_size=128).

Bare Metal

Follow the instructions to setup your bare metal environment on Linux or Windows systems, download and preprocess the dataset, and do the model specific setup. Once all the setup is done, the Model Zoo can be used to run a quickstart script. Ensure that you have enviornment variables set to point to the dataset directory and an output directory.

Follow the instructions below to clone the Model Zoo repo before running the model on Linux or Windows:

# Clone the Model Zoo repo 
git clone https://github.com/IntelAI/models.git
cd models

Run on Linux

Install the following prerequisites in your environment:

  • Python 3
  • intel-extension-for-pytorch
  • torchvision==v0.6.1
  • numactl
  • Run the model: 
    # Env vars
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/resnet50/inference/cpu/fp32/fp32_batch_inference.sh

Run on Windows

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

Using Windows CMD.exe, run:

# 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(for example, FP32 batch inference)
bash quickstart\image_recognition\pytorch\resnet50\inference\cpu\fp32\fp32_batch_inference.sh

License

LICENSE