def training_step(self, batch, batch_idx):
masked_kspace, mask, target, fname, _, max_value = batch
output = self.forward(masked_kspace, mask)
target, output = T.center_crop_to_smallest(target, output)
ssim_loss = self.ssim_loss(output.unsqueeze(
1), target.unsqueeze(1), data_range=max_value)
return {'loss': ssim_loss, 'log': {'train_loss': ssim_loss.item()}}
def validation_step(self, batch, batch_idx):
masked_kspace, mask, target, fname, slice, max_value = batch
output = self.forward(masked_kspace, mask)
target, output = T.center_crop_to_smallest(target, output)
return {
'fname': fname,
'slice': slice,
'output': output.cpu().numpy(),
'target': target.cpu().numpy(),
'val_loss': self.ssim_loss(output.unsqueeze(1), target.unsqueeze(1), data_range=max_value),
}