Convert TensorFlow Models to Accept Binary Inputs#

This guide shows how to convert TensorFlow models and deploy them with the OpenVINO Model Server. It also explains how to scale the input tensors and adjust to use binary JPEG or PNG input data.

  • In this example TensorFlow model ResNet will be used.

  • TensorFlow model can be converted into Intermediate Representation format using model_optimizer tool. There are several formats for storing TensorFlow model. In this guide, we present conversion from SavedModel format. More information about conversion process can be found in the model optimizer guide.

  • Binary input format has several requirements for the model and ovms configuration. More information can be found in binary inputs documentation.


Preparing the Model#

Download the model


tar -xvzf resnet_v2_fp32_savedmodel_NHWC.tar.gz 

mv resnet_v2_fp32_savedmodel_NHWC/1538687283/ resnet_v2
rmdir resnet_v2_fp32_savedmodel_NHWC/

Note: Directories operations are not necessary for the preparation, but in this guide the directories are simplified.

Convert the TensorFlow model to Intermediate Representation format using model_optimizer tool:

docker run -u $(id -u):$(id -g) -v ${PWD}/resnet_v2/:/resnet openvino/ubuntu20_dev:2022.1.0 mo --saved_model_dir /resnet/ --output_dir /resnet/models/resnet/1/ --input_shape=[1,224,224,3] --mean_values=[123.68,116.78,103.94] --reverse_input_channels

Note: Some models might require other parameters such as --scale parameter.

  • --reverse_input_channels - required for models that are trained with images in RGB order.

  • --mean_values , --scale - should be provided if input pre-processing operations are not a part of topology- and the pre-processing relies on the application providing input data. They can be determined in several ways described in conversion parameters guide. In this example model pre-processing script was used to determine them.

Note: You can find out more about TensorFlow Model conversion into Intermediate Representation if your model is stored in other formats.

This operation will create model files in ${PWD}/resnet_v2/models/resnet/1/ folder.

tree resnet_v2/models/resnet/1
├── saved_model.bin
├── saved_model.mapping
└── saved_model.xml

OVMS Deployment#

Pull the latest openvino model_server image from dockerhub

docker pull openvino/model_server:latest

Deploy OVMS using the following command:

docker run -d -p 9000:9000 -v ${PWD}/resnet_v2/models:/models openvino/model_server:latest --model_path /models/resnet --model_name resnet --port 9000 --layout NHWC

Note: This model has N... layout by default, but binary inputs feature requires model to have NHWC or N?HWC layout, therefore we specify --layout NHWC option.

Running the inference requests from the client#

git clone
cd model_server/client/python/ovmsclient/samples
virtualenv .venv
. .venv/bin/activate
pip install -r requirements.txt

python --images_dir ../../../../demos/common/static/images --model_name resnet --service_url localhost:9000

Image ../../../../demos/common/static/images/magnetic_compass.jpeg has been classified as magnetic compass
Image ../../../../demos/common/static/images/pelican.jpeg has been classified as pelican
Image ../../../../demos/common/static/images/gorilla.jpeg has been classified as gorilla, Gorilla gorilla
Image ../../../../demos/common/static/images/snail.jpeg has been classified as snail
Image ../../../../demos/common/static/images/zebra.jpeg has been classified as zebra
Image ../../../../demos/common/static/images/arctic-fox.jpeg has been classified as Arctic fox, white fox, Alopex lagopus
Image ../../../../demos/common/static/images/bee.jpeg has been classified as bee
Image ../../../../demos/common/static/images/peacock.jpeg has been classified as peacock
Image ../../../../demos/common/static/images/airliner.jpeg has been classified as airliner
Image ../../../../demos/common/static/images/golden_retriever.jpeg has been classified as golden retriever