I noticed that in version 0.4.1 of Pytorch the nn.Upsample is being replace by F.interpolate
However due to that change, reusable blocks that contain a
nn.Upsample step become difficult (or at least less elegant) to refactor:
self.block = nn.Sequential( nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False), ConvRelu(in_channels, middle_channels), ConvRelu(middle_channels, out_channels), )
As a result I need to put to introduce a wrapper function or put an extra line of code in the forward method for every used block. It doesn’t seem to add to the maintainability of the model.
I was wondering is there a more elegant way to refactor the above that I missed?
Or perhaps in the future a nn.interpolate will be introduced? (similar to having both nn.ReLU and F.relu)