New to this community and still try to learn more.
At the minute, I would like to use TVM to optimise a tensorflow model that uses
some operators from tensorflow 2.2.
Use relay.frontend.from_tensorflow to import the model,
mod, params = relay.frontend.from_tensorflow(graph_def,
Hi, this is my second post on this community.
I would like to use TVM to optimise a tensorflow model that uses tensorflow 2.2
operators that are currently not being supported by the tensorflow frontend.
I then convert the tensorflow model to tensorflow lite model and tried to use
the tflite
Hi Kevin,
Thanks for your reply.
I am trying to adapt the model to tf1, e.g., 'NonMaxSuppressionV5' in tf2 to '
NonMaxSuppressionV3', which is supported by tensorflow frontend.
The computational graph .pb file seems fine now.
However, when running
mod, params = relay.frontend.from_ten
Different operators:
[non_max_suppression/NonMaxSuppressionV3: NonMaxSuppressionV3] in tf1 that is
supported by the current version of TVM. I looked into the source code of
tensorflow frontend and there are two operators NMS, NonMaxSuppressionV2 and
V3.
Look forward to seeing V5. :wink:
[quote="michaelnhw, post:22, topic:3150"]
https://github.com/google/mediapipe/issues/245
[/quote]
Hi FrozenGene,
I wonder if it is possible for you to look into the TFLite frontend to add a
new operator 'NON_MAX_SUPPRESSION_V5', which actually has been implemented in
your TFLite package.
I
