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For example, when I convert a model, the original input shape is [1,1,7680], but after conversion, the shape becomes [1,7680,1]. Although I can initialize the data with the corresponding shape when using the model to perform inference without affecting the results, I still want to know if there's a way to avoid modifying the original shape.
The text was updated successfully, but these errors were encountered:
I think it's completely feasible, you should make some change at file(utils/graph_tools.py#35, function build_tf_inputs), make input's layout to FORCE_CHANNEL_FIRST_OP.
For example, when I convert a model, the original input shape is [1,1,7680], but after conversion, the shape becomes [1,7680,1]. Although I can initialize the data with the corresponding shape when using the model to perform inference without affecting the results, I still want to know if there's a way to avoid modifying the original shape.
The text was updated successfully, but these errors were encountered: