In current relay pass, we can merge two consecutive reshape op by
`SimplifyExpr` pass. Sometimes, We may produce useless reshape op in the
process of importing models. For example:
#[version = "0.0.5"]
def @main(%Placeholder: Tensor[(1, 3, 227, 227), float32], %conv1/weights:
Tensor[(96, 3, 11, 11), float32], %conv1/biases: Tensor[(96), float32]) ->
Tensor[(1, 96, 55, 55), float32] {
%0 = nn.conv2d(%Placeholder, %conv1/weights, strides=[4, 4], padding=[0,
0, 0, 0], channels=96, kernel_size=[11, 11]) /* ty=Tensor[(1, 96, 55, 55),
float32] */;
%1 = nn.bias_add(%0, %conv1/biases) /* ty=Tensor[(1, 96, 55, 55),
float32] */;
%2 = reshape(%1, newshape=[1, 96, 55, 55]) /* ty=Tensor[(1, 96, 55, 55),
float32] */;
nn.relu(%2) /* ty=Tensor[(1, 96, 55, 55), float32] */
}
We can see that the output of `%2` have the same shape with `%1`, then the
reshape op is useless...
In order to deal with this problem, we have extended the functionality of
`SimplifyExpr` pass, and now it will remove the useless reshape op. Do you
think it is necessary for me to propose a pr? @haichen
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