Extension of channels last converter to models with branches #114
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…e problem now seems to be to move the transpose upwards before the fork and properly reconnect the network. Not sure either what I should do concerning the model tensor shape since I see that for the base case it is modified.
nghielme
commented
Apr 23, 2024
| _channelsLast_node_types = list(channels_last.custom_op.keys()) | ||
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| # Nodes, which do not modify the shape of the tensor | ||
| # And modify all values in the same way. | ||
| _move_through_nodes = ["Quant", "Relu"] | ||
| _move_through_nodes = ["Quant", "Relu", "LeakyRelu", "Resize"] |
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Not sure that Resize should be in _move_through_nodes list since it actually modifies the shape of the tensor
…etter though to handle them in the move up and down transformations
…e in which the transposes passes the special nodes. I added some cleaning transformation of the domain field, it is not very elegant but I think it is strictly necessary.
a removal of eventual input and output transposes
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I extended channels last converted in order to work with models with branches.
I tested with a dummy model composed by a single fork node and with a complete UNet model with multiple fork nodes.