Migrate `IoU` segmentaiton metrics from `keras.metrics` to `keras_cv.metrics`

Intersection-Over-Union is a common evaluation metric for semantic image segmentation.

• tf.keras.metrics.MeanIoU
• tf.keras.metrics.IoU
• tf.keras.metrics.BinaryIoU
• tf.keras.metrics.OneHotMeanIoU
• tf.keras.metrics.OneHotIoU

tf.keras.metrics.OneHotIoU(
    num_classes: int,
    target_class_ids: Union[List[int], Tuple[int, ...]],
    name=None,
    dtype=None,
    ignore_class: Optional[int] = None,
    sparse_y_pred: bool = False,
    axis: int = -1
)

[DavidLandup0]

The issue with the class was that it was expecting exclusively categorical vectors, so when using sparse categorical crossentropy and an output fit for it - there would be a shape mismatch. The common fix was to simply get the argmax and thus convert it into a sparse representation.

However, as @tanzhenyu pointed out in the previous comment:

As of tf 2.10, this is fixed by accepting sparse_y_true and sparse_y_pred – @innat can you verify it before I close this?

Not sure if it’s a better API choice to have MeanIoU(arg) or SparseMeanIoU and CategoricalMeanIoU. KerasCV losses have gone with the latter format so far, which also allows for more specialization down the line.

[LukeWood]

It’s reported here: tensorflow/tensorflow#32875 Sparse categorical crossentropy doesn’t play well with it, apparently. Most fixes boil down to subclassing it modifying one line that throws the exception:

class MeanIoU(tf.keras.metrics.MeanIoU):
  def __init__(self,
               y_true=None,
               y_pred=None,
               num_classes=None,
               name=None,
               dtype=None):
    super(UpdatedMeanIoU, self).__init__(num_classes = num_classes,name=name, dtype=dtype)

  def update_state(self, y_true, y_pred, sample_weight=None):
    y_pred = tf.math.argmax(y_pred, axis=-1)
    return super().update_state(y_true, y_pred, sample_weight)

Depending on what we want. So if this metrics is particularly for segmentation, we should have it at keras_cv. The Keras version MeanIOU is intended for a wider usage which doesn’t really think about the segmentation map tensor shape. So I’d say in this case, we should do the migration.

Yeah gotcha, that makes sense to me. We also could use a bounding box variant of IOU too.