README.md

Faster R-CNN FP32 inference

Description

This document has instructions for running Faster R-CNN FP32 inference using Intel-optimized TensorFlow.

Datasets

The COCO validation dataset is used in the Faster R-CNN quickstart scripts. The scripts require that the dataset has been converted to the TF records format. See the COCO dataset for instructions on downloading and preprocessing the COCO validation dataset.

Quick Start Scripts

Script name	Description
fp32_inference.sh	Runs batch and online inference using the coco dataset
fp32_accuracy.sh	Runs inference and evaluates the model's accuracy

Run the model

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

Setup using AI Kit	Setup without AI Kit
AI Kit does not currently support TF 1.15.2 models	To run without AI Kit you will need: Python 3.6 or 3.7 git numactl wget Protobuf Compilation intel-tensorflow==1.15.2 Cython contextlib2 jupyter lxml matplotlib pillow>=8.1.2 pycocotools A clone of the Model Zoo repo git clone https://github.com/IntelAI/models.git

Running Faster R-CNN FP32 inference also requires cloning the TensorFlow models repo using the tag specified below. Set the TF_MODELS_DIR environment variable to point to the TensorFlow models directory. Run the protobuf-compiler on the research directory.

# Clone the TF models repo
git clone https://github.com/tensorflow/models.git tf_models
pushd tf_models
git checkout tags/v1.12.0
export TF_MODELS_DIR=$(pwd)

# Run the protobuf-compiler from the TF models research directory
pushd research
wget -O protobuf.zip https://github.com/google/protobuf/releases/download/v3.0.0/protoc-3.0.0-linux-x86_64.zip
unzip protobuf.zip
./bin/protoc object_detection/protos/*.proto --python_out=.
rm protobuf.zip
popd
popd

Download and extract the pretrained model. The path to this directory should be set to the PRETRAINED_MODEL environment variable before running the quickstart scripts.

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/faster_rcnn_resnet50_fp32_coco_pretrained_model.tar.gz
tar -xvf faster_rcnn_resnet50_fp32_coco_pretrained_model.tar.gz
export PRETRAINED_MODEL=$(pwd)/faster_rcnn_resnet50_fp32_coco

In addition to the TF_MODELS_DIR and PRETRAINED_MODEL variables from above, set environment variables for the path to your DATASET_DIR (directory where the coco_val.record TF records file is located) and an OUTPUT_DIR where log files will be written, then run a quickstart script.

# cd to your model zoo directory
cd models

export DATASET_DIR=<path to the directory that contains the coco_val.record file>
export OUTPUT_DIR=<directory where log files will be written>
export TF_MODELS_DIR=<path to the TensorFlow models dir>
export PRETRAINED_MODEL=<path to the extracted pretrained model dir>

./quickstart/object_detection/tensorflow/faster_rcnn/inference/cpu/fp32/<script name>.sh

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/faster-rcnn-fp32-inference-tensorflow-container.html.