Create Own Edge TPU Model.

About

The Edge TPU has a great sample. However, the samples are only image classification(slim) and object detection(object detection api), and there is no way to build and learn your own model.
This is a sample for converting your own model from learning to Edge TPU Model.
The model uses TensorFlow's MNIST Network sample (fully_connected_feed).

Environment

• Coral USB Accelerator(May be Devboard)

Installation

• Tensorflow (Learning only)
• Edge TPU Python library (Get started with the USB Accelerator)

How to Train

1.Create Model and Quantization-aware training

It is a sample of Jupyter notebook.
This sample is based on the Tensorflow tutorial (MNIST fully_connected_feed.py).
Open train/MNIST_fully_connected_feed.ipynb and Run All.
When learning is complete, a model is generated in the following directory:

  train
    + logs
    |    + GraphDef
    + checkpoint
    + model.ckpt-xxxx.data-00000-of-00001
    + model.ckpt-xxxx.index
    + model.ckpt-xxxx.meta 
    + mnist_fully_connected_feed_graph.pb

model.ckpt is a ckecpoint file.
The number of learning steps is recorded in "xxxx".
The default is 4999 maximum. mnist_fully_connected_feed_graph.pb is a GrarhDef file.

The GraphDef directory contains the structure of the model.
You can check with tensorboard.
(Move dir and tensorboard --logdir=./)

"Quantization-aware training" is required to generate Edge TPU Model.
See Quantization-aware training for details.

2.Freeze Graph

Converts checkpoint variables into Const ops in a standalone GraphDef file.
Use the freeze_graph command.
The freeze_mnist_fully_connected_feed_graph.pb file is generated in the logs directory.

$ freeze_graph \
  --input_graph=./logs/mnist_fully_connected_feed_graph.pb \
  --input_checkpoint=./logs/model.ckpt-4999 \
  --input_binary=true \
  --output_graph=./logs/freeze_mnist_fully_connected_feed_graph.pb \
  --output_node_names=softmax_tensor

3.Convert TF-Lite Model

Generate a TF-Lite model using the tflite_convert command.
The output_tflite_graph.tflite file is generated in the logs directory.

$ tflite_convert \
  --output_file=./logs/output_tflite_graph.tflite \
  --graph_def_file=./logs/freeze_mnist_fully_connected_feed_graph.pb \
  --inference_type=QUANTIZED_UINT8 \
  --input_arrays=input_tensor \
  --output_arrays=softmax_tensor \
  --mean_values=0 \
  --std_dev_values=255 \
  --input_shapes="1,784"

4.Compile Edge TPU Model

Compile the TensorFlow Lite model for compatibility with the Edge TPU.
See Edge TPU online compiler for details.

How to Run a model on the Edge TPU

First, Copy the Edge TPU Model to the classify directory.
The directory structure is as follows.

  classify
    + classify_mnist.py
    + mnist_data.py
    + xxxx_edgetpu.tflite

Note: "xxxx_edgetpu.tflite" is Edge TPU Model. This repository contains "mnist_tflite_edgetpu.tflite"

Then, classify MNIST data:

$ python3 classify_mnist.py --model=./mnist_tflite_edgetpu.tflite

The result of classification with images is displayed on the console as follows.
"Inference latency:" and "Output tensor:" are return values of the RunInference method of eddypu.basic.basic_engine.BasicEngine.
(See details Python API)
"Inference" is the most accurate value of "output tensor" (correct and predicted numbers).
"Label" is the correct label.

Note: Full command-line options:

$ python3 classify_mnist.py -h

How to Run a model on the TF-Lite

If you want to use the TF-Lite API, take a look at the notebook sample. Classify MNIST using TF-Lite Model.

For more information, please refer my blogs.
(comming soon. but sorry japanese only.)