Requirements

To follow along with this example, you will need:

  • A python environment with tensorflow (tip: Use conda)

    • conda install tensorflow
      

      or

    • conda install tensorflow-gpu
      
  • A TensorFlow SavedModel (download)

The TensorFlow model used for this tutorial is a PoseNet model with a ResNet architecture. You can download the exact model here.

Usage

Installation

You can install the library using pip:

pip install -U tf2onnx

Steps

  1. Make sure the SavedModel file is named saved_model.pb

  2. At a minimum, you need to specify the source model format, the path to the folder containing the SavedModel, and a name for the ONNX file.

    For example:

    • Model Format: --saved-model

    • Model Folder: ./savedmodel

      Note: Do not include a / at the end of the path.

    • Output Name: model.onnx

python -m tf2onnx.convert --saved-model ./savedmodel --opset 10 --output model.onnx

With these parameters you might receive some warnings, but the output should include something like this.

2020-10-21 12:54:11,024 - INFO - Using tensorflow=2.3.0, onnx=1.7.0, tf2onnx=1.6.3/d4abc8
2020-10-21 12:54:11,024 - INFO - Using opset <onnx, 10>
2020-10-21 12:54:12,423 - INFO - Optimizing ONNX model
2020-10-21 12:54:14,047 - INFO - After optimization: Add -4 (20->16), Const -1 (115->114), Identity -4 (4->0), Transpose -117 (122->5)
2020-10-21 12:54:14,138 - INFO -
2020-10-21 12:54:14,138 - INFO - Successfully converted TensorFlow model ./savedmodel to ONNX
2020-10-21 12:54:14,215 - INFO - ONNX model is saved at model.onnx

Next Steps

Be sure to check out the GitHub repo if you want to learn what else you can do with the tool. The README page goes in to greater detail about the following:

  • Current TensorFlow support
  • Parameter options
  • Advanced use cases
  • How the tool converts TensorFlow Models