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ResNet101 inference

Description

This document has instructions for running ResNet101 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 ResNet101.

Quick Start Scripts

Script name Description
online_inference.sh Runs online inference (batch_size=1).
batch_inference.sh Runs batch inference (batch_size=128).
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:

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

# FP32 Pretrained model:
wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_8/resnet101_fp32_pretrained_model.pb
export PRETRAINED_MODEL=$(pwd)/resnet101_fp32_pretrained_model.pb

# Int8 Pretrained model:
wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_8/resnet101_int8_pretrained_model.pb
export PRETRAINED_MODEL=$(pwd)/resnet101_int8_pretrained_model.pb

Intel® Neural Compressor int8 quantized ResNet101 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/resnet-101-inc-int8-inference.pb
export PRETRAINED_MODEL=$(pwd)/resnet-101-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">
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/resnet101/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>

bash quickstart\image_recognition\tensorflow\resnet101\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