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Convert Model to Edge TPU TFlite Format for Google Coral

Introduction​

The Coral M.2 Accelerator with Dual Edge TPU is an M.2 module that brings two Edge TPU coprocessors to existing systems and products with an available M.2 E-key slot.Tensorflow and Pytorch is the most popular deep learning frameworks. So in order to use the Edge TPU, we need to compile the model to Edge TPU format.

This wiki article will guide you through the process of compiling a model and running it on the Google Coral TPU, enabling you to leverage its capabilities for high-performance machine learning applications.

Prepare Hardware​

Raspberry Pi 5 8GBRaspberry Pi M.2 HAT+Coral M.2 Accelerator B+M key
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Install Hardware​

pir

Convert Model​

note

Before you start, make sure you have installed the Google Coral TPU to Pi 5 follow the installation guide.

note

And all process have been tested on Python 3.11.9.

Install Tensorflow​

pip install tensorflow

Check tflite_converter​

tflite_convert -h

The result should be like this:

2024-07-23 10:41:03.750087: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-07-23 10:41:04.276520: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
usage: tflite_convert [-h] --output_file OUTPUT_FILE [--saved_model_dir SAVED_MODEL_DIR | --keras_model_file KERAS_MODEL_FILE] [--saved_model_tag_set SAVED_MODEL_TAG_SET]
                      [--saved_model_signature_key SAVED_MODEL_SIGNATURE_KEY] [--enable_v1_converter] [--experimental_new_converter [EXPERIMENTAL_NEW_CONVERTER]]
                      [--experimental_new_quantizer [EXPERIMENTAL_NEW_QUANTIZER]]

Command line tool to run TensorFlow Lite Converter.

optional arguments:
  -h, --help            show this help message and exit
  --output_file OUTPUT_FILE
                        Full filepath of the output file.
  --saved_model_dir SAVED_MODEL_DIR
                        Full path of the directory containing the SavedModel.
  --keras_model_file KERAS_MODEL_FILE
                        Full filepath of HDF5 file containing tf.Keras model.
  --saved_model_tag_set SAVED_MODEL_TAG_SET
                        Comma-separated set of tags identifying the MetaGraphDef within the SavedModel to analyze. All tags must be present. In order to pass in an empty tag set, pass in "". (default "serve")
  --saved_model_signature_key SAVED_MODEL_SIGNATURE_KEY
                        Key identifying the SignatureDef containing inputs and outputs. (default DEFAULT_SERVING_SIGNATURE_DEF_KEY)
  --enable_v1_converter
                        Enables the TensorFlow V1 converter in 2.0
  --experimental_new_converter [EXPERIMENTAL_NEW_CONVERTER]
                        Experimental flag, subject to change. Enables MLIR-based conversion instead of TOCO conversion. (default True)
  --experimental_new_quantizer [EXPERIMENTAL_NEW_QUANTIZER]
                        Experimental flag, subject to change. Enables MLIR-based quantizer instead of flatbuffer conversion. (default True)

Convert Tensorflow Model to TFlite Model​

tflite_convert --saved_model_dir=YOUR_MODEL_PATH --output_file=YOUR_MODEL_NAME.tflite

Convert TFlite Model to Edge TPU Model​

note

You should optimize your model before you convert tflite model to edge tup model, please check the Optimize Tensorflow Model

Install edgetpu compiler​

curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -

echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list

sudo apt-get update

sudo apt-get install edgetpu-compiler

Transform TFlite Model to Edge TPU Model​

edgetpu_compiler YOUR_MODEL_NAME.tflite

And then you should get a new file named YOUR_MODEL_NAME_edgetpu.tflite

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