README.md

MobileNet V1 inference

Description

This document has instructions for running MobileNet V1 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 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 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/intel-optimized-tensorflow/models/v1_8/mobilenet_v1_1.0_224_frozen.pb
export PRETRAINED_MODEL=$(pwd)/mobilenet_v1_1.0_224_frozen.pb

# Int8 Pretrained model:
wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_8/mobilenetv1_int8_pretrained_model.pb
export PRETRAINED_MODEL=$(pwd)/mobilenetv1_int8_pretrained_model.pb

Intel® Neural Compressor int8 quantized MobileNet V1 pre-trained model is available as another option to download and try.

wget https://storage.googleapis.com/intel-optimized-tensorflow/intel-neural-compressor/v1_13/mobilenetv1-inc-int8-inference.pb
export PRETRAINED_MODEL=$(pwd)/mobilenetv1-inc-int8-inference.pb

Check the instructions for more details on how to quantize FP32 model using Intel® Neural Compressor.

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 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_v1/inference/cpu/<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 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_v1\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 Int8 BFloat16 for calling the launch_benchmark.py script directly.
• To run the model using docker, please see the Intel® Developer Catalog workload container:
  https://software.intel.com/content/www/us/en/develop/articles/containers/mobilenetv1-fp32-inference-tensorflow-container.html.