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tf-fastmri-data's Issues

Implement fake equidistant mask

Right now we only have the possibility to implement the real equidistant mask scheme for retrospective undersampling.

However, due to a slight coding mistake, the so-called equidistant scheme for undersampling in the fastMRI dataset is not really equidistant.

We should implement this mask by simply copy-pasting what was done here, and maybe refactoring a bit.

Why do we restrict to 320x320?

self._raw_ds = self._raw_ds.map(
lambda _, kspace: crop(ortho_ifft2d(kspace), (320, 320)),
num_parallel_calls=self.num_parallel_calls,
deterministic=True,
)

In above code, I feel its best to just not hardcode and rather just crop the data by later pipelines and not initially itself as this makes the data shapes as None, None etc. Also, using adjust_image_size is surely a better alternative (however, I dont want to crop here at all)

Benchmark this

In particular, a screenshot of tf profiling with a simple U-net could be nice.

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