README.md

DenseNet 169 inference

Description

This document has instructions for running DenseNet 169 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 this directory when running DenseNet 169.

Quick Start Scripts

Script name	Description
online_inference.sh	Runs online inference (batch_size=1).
batch_inference.sh	Runs batch inference (batch_size=100).
accuracy.sh	Measures the model accuracy (batch_size=100).

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 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 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 your setup is complete, download the pretrained model. The path to this file should be set to the PRETRAINED_MODEL environment variable before running the quickstart scripts. 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/densenet169_fp32_pretrained_model.pb
export PRETRAINED_MODEL=$(pwd)/densenet169_fp32_pretrained_model.pb

Set the environment variables and run quickstart script on either Linux or Windows systems. See the list of quickstart scripts for details on the different options.

Run on Linux

# cd to your model zoo directory
cd models

export DATASET_DIR=<path to the dataset>
export PRECISION=fp32
export OUTPUT_DIR=<path to the directory where log files will be written>
export PRETRAINED_MODEL=<path to the frozen graph that you downloaded>
# For a custom batch size, set env var `BATCH_SIZE` or it will run with a default value.
export BATCH_SIZE=<customized batch size value>

# Run a script for your desired usage
./quickstart/image_recognition/tensorflow/densenet169/inference/cpu/<script name>.sh

Run on Windows

Using a 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 PRECISION=fp32
set OUTPUT_DIR=<directory where log files will be written>
# For a custom batch size, set env var `BATCH_SIZE` or it will run with a default value.
set BATCH_SIZE=<customized batch size value>

bash quickstart\image_recognition\tensorflow\densenet169\inference\cpu\<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 for the available precisions FP32 for calling the launch_benchmark.py script directly.
• To run the model using docker, please see the Intel® Developer Catalog workload container:
  https://www.intel.com/content/www/us/en/developer/articles/machine-learning-model/densenet169-fp32-inference-tensorflow-model.html.