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MobileNet V2 inference

Description

This document has instructions for running MobileNet V2 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 MobileNet V1.

Quick Start Scripts

Script name Description
inference.sh Runs realtime inference using a default batch_size=1 for the specified precision (int8, fp32, bfloat16). To run inference for throughtput, set BATCH_SIZE environment variable.
inference_realtime_multi_instance.sh A multi-instance run that uses 4 cores per instance with batch_size=1 for the specified precision (fp32, int8, bfloat16, bfloat32). Uses synthetic data if no DATASET_DIR is set
inference_throughput_multi_instance.sh A multi-instance run that uses 4 cores per instance with batch_size=448 for the specified precision (fp32, int8, bfloat16, bfloat32). Uses synthetic data if no DATASET_DIR is set
accuracy.sh Measures the model accuracy (batch_size=100) for the specified precision (fp32, int8, bfloat16, bfloat32).

Run the model

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

Setup using AI Tools on Linux Setup without AI Tools on Linux Setup without AI Tools on Windows

To run using AI Tools on Linux you will need:

  • numactl
  • wget
  • Activate the tensorflow conda environment 
    conda activate tensorflow

To run without AI Tools on Linux you will need:

  • Python 3
  • intel-tensorflow>=2.5.0
  • git
  • numactl
  • wget
  • A clone of the AI Reference Models repo
    git clone https://github.com/IntelAI/models.git

To run without AI Tools on Windows you will need:

After finishing the setup above, download the pretrained model based on PRECISIONand 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:

# FP32, BFloat16 and BFloat32 Pretrained model:
wget https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz
tar -xvzf mobilenet_v2_1.4_224.tgz
export PRETRAINED_MODEL=$(pwd)/mobilenet_v2_1.4_224_frozen.pb

Intel® Neural Compressor int8 quantized MobileNet V2 pre-trained model:

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/2_11_0/mobilenetv2_inc_int8.pb
export PRETRAINED_MODEL=$(pwd)/mobilenetv2_inc_int8.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 AI Reference Models directory
cd models

export PRETRAINED_MODEL=<path to the frozen graph downloaded above>
export DATASET_DIR=<path to the ImageNet TF records>
export PRECISION=<set the precision to "int8" or "fp32" or "bfloat16" or "bfloat32">
export OUTPUT_DIR=<path to the 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.
export BATCH_SIZE=<customized batch size value>

./quickstart/image_recognition/tensorflow/mobilenet_v2/inference/cpu/<script name>.sh

Run on Windows

Using cmd.exe run:

# cd to your AI Reference Models directory
cd models

set PRETRAINED_MODEL=<path to the frozen graph downloaded above>
set DATASET_DIR=<path to the ImageNet TF records>
set PRECISION=<set the precision to "int8" or "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>
# Run a quick start script for inference or accuracy
bash quickstart\image_recognition\tensorflow\mobilenet_v2\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.