Training Image sizes

[IsCoelacanth]

Hello,

What spatial dimensions are you using for training on the CMRxRecon dataset? I didn't find any cropping or padding in the data loaders for it.

Is it purely through asymmetric padding in the Unit Layers for the full-resolution scans? [144x512 or 116x382 for the T1/T2 task]

[hellopipu]

no padding or cropping, input as the original resolution

[IsCoelacanth]

Thanks for the clarification! So, for handling non-standard image sizes, you just do the Pad and Unpad steps like here and here

def forward(self, x: torch.Tensor) -> torch.Tensor:
        if not x.shape[-1] == 2:
            raise ValueError("Last dimension must be 2 for complex.")

        # get shapes for unet and normalize
        x = self.complex_to_chan_dim(x)
        x, mean, std = self.norm(x)
        x, pad_sizes = self.pad(x)

        x = self.unet(x)

        # get shapes back and unnormalize
        x = self.unpad(x, *pad_sizes)
        x = self.unnorm(x, mean, std)
        x = self.chan_complex_to_last_dim(x)

        return x