README.md

ResNet50 v1.5 Int8 inference

Description

This document has instructions for running ResNet50 v1.5 Int8 inference using Intel-optimized TensorFlow.

Datasets

Download and preprocess the ImageNet dataset using the instructions here. After running the conversion script you should have a directory with the ImageNet dataset in the TF records format.

Set the DATASET_DIR to point to the TF records directory when running ResNet50 v1.5.

Quick Start Scripts

Script name	Description
int8_online_inference.sh	Runs online inference (batch_size=1).
int8_batch_inference.sh	Runs batch inference (batch_size=128).
int8_accuracy.sh	Measures the model accuracy (batch_size=100).
multi_instance_batch_inference.sh	Uses numactl to run batch inference (batch_size=128) with one instance per socket for 1500 steps and 50 warmup steps. If no DATASET_DIR is set, synthetic data is used. The script waits for all instances to complete, then prints a summarized throughput value.
multi_instance_online_inference.sh	Uses numactl to run online inference (batch_size=1) using four cores per instance for 1500 steps and 50 warmup steps. If no DATASET_DIR is set, synthetic data is used. The script waits for all instances to complete, then prints a summarized throughput value.

Run the model

Setup your environment using the instructions below, depending on if you are using AI Kit:

Setup using AI Kit on Linux	Setup without AI Kit on Linux	Setup without AI Kit on Windows
To run using AI Kit on Linux you will need: numactl wget openmpi-bin (only required for multi-instance) openmpi-common (only required for multi-instance) openssh-client (only required for multi-instance) openssh-server (only required for multi-instance) libopenmpi-dev (only required for multi-instance) horovod==0.21.0 (only required for multi-instance) Activate the tensorflow conda environment conda activate tensorflow	To run without AI Kit on Linux you will need: Python 3 intel-tensorflow>=2.5.0 git numactl wget openmpi-bin (only required for multi-instance) openmpi-common (only required for multi-instance) openssh-client (only required for multi-instance) openssh-server (only required for multi-instance) libopenmpi-dev (only required for multi-instance) horovod==0.21.0 (only required for multi-instance) A clone of the Model Zoo repo git clone https://github.com/IntelAI/models.git	To run without AI Kit on Windows you will need: Intel Model Zoo on Windows Systems prerequisites A clone of the Model Zoo repo git clone https://github.com/IntelAI/models.git

After finishing the setup above, download the pretrained model and set the PRETRAINED_MODEL environment var to the path to the frozen graph. If you run on Windows, please use a browser to download the pretrained model using the link below. For Linux, run:

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_8/resnet50v1_5_int8_pretrained_model.pb
export PRETRAINED_MODEL=$(pwd)/resnet50v1_5_int8_pretrained_model.pb

Set environment variables for the path to your DATASET_DIR for ImageNet and an OUTPUT_DIR where log files will be written. Navigate to your model zoo directory and then run a quickstart script on either Linux or Windows.

Run on Linux

# cd to your model zoo directory
cd models

export PRETRAINED_MODEL=<path to the frozen graph downloaded above>
export DATASET_DIR=<path to the ImageNet TF records>
export OUTPUT_DIR=<directory where log files and checkpoints will be written>

./quickstart/image_recognition/tensorflow/resnet50v1_5/inference/cpu/int8/<script name>.sh

Run on Windows

Using cmd.exe, run:

# cd to your model zoo directory
cd models

set PRETRAINED_MODEL=<path to the frozen graph downloaded above>
set DATASET_DIR=<path to the ImageNet TF records>
set OUTPUT_DIR=<directory where log files will be written>

bash quickstart\image_recognition\tensorflow\resnet50v1_5\inference\cpu\int8\<script name>.sh

Note: You may use cygpath to convert the Windows paths to Unix paths before setting the environment variables. As an example, if the dataset location on Windows is D:\user\ImageNet, convert the Windows path to Unix as shown:

cygpath D:\user\ImageNet
/d/user/ImageNet

Then, set the DATASET_DIR environment variable set DATASET_DIR=/d/user/ImageNet.

Additional Resources

• To run more advanced use cases, see the instructions here for calling the launch_benchmark.py script directly.
• To run the model using docker, please see the oneContainer workload container:
  https://software.intel.com/content/www/us/en/develop/articles/containers/resnet50v1-5-int8-inference-tensorflow-container.html.