lhutton1 commented on code in PR #13632:
URL: https://github.com/apache/tvm/pull/13632#discussion_r1050617765
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python/tvm/relay/op/nn/_nn.py:
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@@ -47,6 +47,42 @@
# log_softmax
reg.register_strategy("nn.log_softmax", strategy.log_softmax_strategy)
+# upsampling_tanspose
[email protected]_convert_op_layout("nn.upsampling")
+def convert_upsampling(attrs, inputs, tinfos, desired_layouts):
+ """Convert Layout pass registration for upsampling op.
+
+ Parameters
+ ----------
+ attrs : tvm.ir.Attrs
+ Attributes of current convolution
+ inputs : list of tvm.relay.Expr
+ The args of the Relay expr to be legalized
+ tinfos : list of types
+ List of input and output types
+ desired_layouts : list of layout strings
+ List of layouts defining our desired
+ layout for the data and kernel inputs respectively.
+
+ Returns
+ -------
+ result : tvm.relay.Expr
+ The transformed expr
+ """
+ data = inputs[0]
+ new_attrs = dict(attrs)
+ assert len(desired_layouts) == 1, "A desired layout is expected for
upsampling's inputs"
+ desired_data_layout = desired_layouts[0]
+ assert desired_data_layout != "default", "Data layout cannot be default"
+ new_attrs["layout"] = desired_data_layout
+
+ if desired_data_layout == "NCHW":
+ return relay.nn.upsampling(data, **new_attrs)
+ elif desired_data_layout == "NHWC":
+ return relay.nn.upsampling(data, **new_attrs)
Review Comment:
Seems these checks are redundant, we can simply just `return
relay.nn.upsampling(data, **new_attrs)` and defer handling unsupported layouts
to `relay.nn.upsample`.
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