Refactor TF Dataset

[andrevitorelli]

After talking with @EiffL and @b-remy, we decided to converge on a more usable format for our tfds.

We will have a simple galaxy generator dataset and one based on parametric COSMOS galaxies drawn as CFIS-like stamp images.

This issue is to discuss some design choices and track/discuss this development.

Issues/todo:

• sample COSMOS realistic images (gal_type='parametric')
• rescale the flux for CFIS like images
• should we put a threshold to remove too faint galaxies?
• do we want to add random knots for realistic images?
• After the CFIS magnitude cut we may want to augment our dataset. What kind of augmentation? Ellipticities only or also size, SNR, etc.

What do you think about these open questions, @EiffL ?

[EiffL]

Yep :-) but I'm a bit confused, I thought we had already done all of that. I remember discussions like here: #39

[EiffL]

Otherwise I want to say no to knots for now, and we will need to keep fairly faint galaxies in order to apply the selection cuts.

[EiffL]

And the only form of augmentation we can make is generating the noise on the fly instead of hardcoded in the dataset

[andrevitorelli]

Yep :-) but I'm a bit confused, I thought we had already done all of that. I remember discussions like here: #39

We did, but the current state of the module is not satisfactory, still.

And the only form of augmentation we can make is generating the noise on the fly instead of hardcoded in the dataset

This is kind of a problem. The COSMOS dataset is small, isn't it? We need more than 80k galaxy models.

[EiffL]

No it's not really a problem, you can apply random rotations to galaxies, and generate independent noise realisations. At least for training a NN it's more than enough

[andrevitorelli]

Ok, that's kind of the data aug I was talking about.

[EiffL]

Yep but this can be done on the fly most likely.