Hi there,
I am trying to convert a PyTorch model which uses a 3D convolution to be used with Tensorflow.js for inference.
I saved my PyTorch model in ONNX (with torch.onnx.export) and then converted to a TF Saved Model with the following Python code:
import onnx
from   onnx_tf.backend import prepare

onnx_model = onnx.load('path/to/my/onnx/model')
tf_model = prepare(onnx_model)
tf_model.export_graph('model')

The above procedure generates a ‘model’ directory with contains the .pb file and other stuff.
Then I run the conversion tool:
tensorflowjs_converter --input_format=tf_saved_model model model_tfjs

and I get the following error:
[... omitted stack trace ...]
ValueError: Unsupported Ops in the model before optimization
Conv3DBackpropInputV2

Is there any chance to have the Conv3D backprop op to be enabled (or ignored) in tfjs converter?
Please note that I don’t need to backprop at the web client, but just use the model for inference.
Thank you so much,
m.