将 ONNX model 转换为 TensorFlow Lite

Question

I've got some models for the ONNX Model Zoo . I'd like to use models from here in a TensorFlow Lite (Android) application and I'm running into problems figuring out how to get the models converted.

From what I've read, the process I need to follow is to convert the ONNX model to a TensorFlow model, then convert that TensorFlow model to a TensorFlow Lite model.

import onnx
from onnx_tf.backend import prepare
import tensorflow as tf

onnx_model = onnx.load('./some-model.onnx') 
tf_rep = prepare(onnx_model)
tf_rep.export_graph("some-model.pb") 

After the above executes, I have the file some-model.pb which I believe contains a TensorFlow Freeze Graph. From here I am not sure where to go. When I search I find a lot of answers that are for TensorFlow 1.x (which I only realize after the samples I find fail to execute). I'm trying to use TensorFlow 2.x.

If it matters, the specific model I'm starting off with is here .

Per the ReadMe.md, the shape of the input is (1x3x416x416) and the output shape is (1x125x13x13).

Answer 1

I got my anser. I was able to use the code below to complete the conversion.

import tensorflow as tf
converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph('model.pb', #TensorFlow freezegraph 
                                                  input_arrays=['input.1'], # name of input
                                                  output_arrays=['218']  # name of output
                                                  )
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
                                   tf.lite.OpsSet.SELECT_TF_OPS]      
# tell converter which type of optimization techniques to use
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tf_lite_model = converter.convert()
open('model.tflite', 'wb').write(tf_lite_model)