Fix num_samples for paired data loader

[mathpluscode]

Subject of the issue

For the data loader, we used num_samples // batch_size to define the number of batches per dataset, as before TensorFlow would raise an error when the generator ends.

https://github.com/DeepRegNet/DeepReg/blob/d3edf264b8685b47f1bdd9bb73aca79b1a72790b/deepreg/util.py#L52

In particular, for paired loader, we used the number of images as the number of samples. It is ok when the images are not labeled, or having one label.

https://github.com/DeepRegNet/DeepReg/blob/d3edf264b8685b47f1bdd9bb73aca79b1a72790b/deepreg/dataset/loader/paired_loader.py#L77

However, when each image has multiple samples, the number of samples should be sum of number of labels. Which means that the number of steps calculated is smaller.

Then, for validation, it caused a big problem when each image has many labels. For example, given 9 images, if each image has 4 labels. There should be 36 samples, but now we estimated 9 samples. With a batch size of 4, we will actually define two steps of validation, which should be 9 instead.

Eventually, this causes the printed metrics inconsistent with the model inference.

So, we should

• define the number of samples correctly for paired loader
• check unpaired and grouped loader
• print step info using logger before training/prediction :)

[mathpluscode]

FYI @zacbaum @YipengHu

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