Converting Tensorflow Model for use with ONNX

[helloimalastair]

Hey y'all! I'm new to WONNX, and ML in general, so please bear with me if I am missing something obvious.

I have an Image Classifier model I've trained with Tensorflow, with the following architecture:

from tensorflow import keras
from keras import layers
from keras.models import Sequential

data_augmentation = Sequential(
  [
    layers.RandomFlip("horizontal",
    input_shape=(img_height, img_width, 3)),
    layers.RandomRotation(0.1),
    layers.RandomZoom(0.1),
  ]
)

model = Sequential([
  data_augmentation,
  layers.Rescaling(1./255),
  layers.Conv2D(16, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(32, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(64, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Dropout(0.2),
  layers.Flatten(),
  layers.Dense(128, activation='relu'),
  layers.Dense(num_classes, name="outputs")
], "model_name")

This model works well against my test data, and I have converted it to an ONNX model with tf2onnx.convert. The trouble is when I try to import said model with wonnx, I get the error

opset error: the model references an unknown opset: 'ai.onnx.ml'

As far as I can tell, by default, tf2onnx will choose either the ONNX_DOMAIN(""), which should work with wonnx, or the AI_ONNX_ML_DOMAIN, and I'm not sure how to get it to compile with the first to make it work.

Anyone have any ideas what I could be missing?

[pixelspark]

You are not using any ops from ai.onnx.ml, so the quickest fix would be to remove the reference from the ONNX file after exporting it from TF (you could use the ONNX python package to do so, it is a bit cumbersome though)

[helloimalastair]

Do you know if there are any docs/posts about how to do so? I'm looking through the ONNX docs(specifically the Python API and the API Reference), but I'm not 100% sure what I'm looking for...