Skip to content

Latest commit

 

History

History
97 lines (77 loc) · 4.87 KB

tf_model_binary_input.md

File metadata and controls

97 lines (77 loc) · 4.87 KB

TensorFlow model conversion to IR with scaling for binary input

This document guides how to convert TensorFlow model and deploy it with the OpenVINO™ Model Server. It also explains how to scale the input tensors and adjust to binary JPEG and 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 on the model optimizer documentation.

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

Steps

Prepering the Model

Download the model

wget http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v2_fp32_savedmodel_NHWC.tar.gz

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 neccessery for the prepartion, but in this guide in order to keep the commands as simple as possible the directories are simplified.

Pull the latest openvino ubuntu_dev image from dockerhub

docker pull openvino/ubuntu18_dev:latest

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/ubuntu18_dev:latest deployment_tools/model_optimizer/mo.py --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 a topology- and the pre-processing relies on the application providing an 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.

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

Running the inference requests from the client

cd example_client
virtualenv .venv
. .venv/bin/activate
pip install -r client_requirements.txt

python grpc_binary_client.py --images_list input_images.txt --grpc_port 9000 --input_name input_tensor --output_name  softmax_tensor --model_name resnet
Start processing:
        Model name: resnet
        Images list file: input_images.txt
Batch: 0; Processing time: 36.33 ms; speed 27.52 fps
         1 airliner 404 ; Correct match.
Batch: 1; Processing time: 18.39 ms; speed 54.38 fps
         2 Arctic fox, white fox, Alopex lagopus 279 ; Correct match.
Batch: 2; Processing time: 15.51 ms; speed 64.47 fps
         3 bee 309 ; Correct match.
Batch: 3; Processing time: 12.71 ms; speed 78.68 fps
         4 golden retriever 207 ; Correct match.
Batch: 4; Processing time: 13.15 ms; speed 76.04 fps
         5 gorilla, Gorilla gorilla 366 ; Correct match.
Batch: 5; Processing time: 12.87 ms; speed 77.69 fps
         6 magnetic compass 635 ; Correct match.
Batch: 6; Processing time: 12.22 ms; speed 81.85 fps
         7 peacock 84 ; Correct match.
Batch: 7; Processing time: 13.73 ms; speed 72.84 fps
         8 pelican 144 ; Correct match.
Batch: 8; Processing time: 13.15 ms; speed 76.05 fps
         9 snail 113 ; Correct match.
Batch: 9; Processing time: 13.74 ms; speed 72.76 fps
         10 zebra 340 ; Correct match.
Overall accuracy= 100.0 %
Average latency= 15.7 ms