ANSHUMAN87 commented on pull request #7656:
URL: https://github.com/apache/tvm/pull/7656#issuecomment-798933062
> It seems to me that the current pattern already covers this case? You can
refer to the test cases I added, which have a ReLU followed by transposes. Is
this what you want?
Thanks @comaniac for clarification. It is indeed supported.
Can we add 1 more test case as below, it will help reviewer to understand
the flow is supported:
```
def before4():
x = relay.var("x", shape=(1, 3, 224, 224), dtype="float32") # NCHW
y = relay.nn.relu(x)
#y = relay.transpose(y, axes=[0, 2, 3, 1])
y = relay.transpose(y) # Reverse
y = relay.transpose(y) # Reverse
y = relay.transpose(y, axes=[0, 2, 3, 1])
y = relay.transpose(y) # Reverse
y = relay.transpose(y) # Reverse
return relay.Function([x], y)
def expected4():
x = relay.var("x", shape=(1, 3, 224, 224), dtype="float32") # NCHW
y = relay.nn.relu(x)
y = relay.transpose(y, axes=[0, 2, 3, 1])
return relay.Function([x], y)
```
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