Misleading ValueError - Accuracy Metric Multilabel

[niowniow]

🐛 Bug description

When using Accuracy.update() with both inputs having the second dimension 1, e.g. in my case torch.Size([256,1]) the raised error message is misleading.

To reproduce

from ignite.metrics import Accuracy
import torch
acc = Accuracy(is_multilabel=True)
acc.update((torch.zeros((256,1)), torch.zeros((256,1))))

ValueError: y and y_pred must have same shape of (batch_size, num_categories, ...).

In this case the y and y_pred do have the same shape but the issue is that it's not an accepted multilabel input (the and y.shape[1] != 1 in the following code block from _check_shape in _BaseClassification). This should be indicated in the error message (or the if statement changed).

What is the argument to not allow a y.shape[1] of 1?

if self._is_multilabel and not (y.shape == y_pred.shape and y.ndimension() > 1 and y.shape[1] != 1):
            raise ValueError("y and y_pred must have same shape of (batch_size, num_categories, ...).")

Environment

• PyTorch Version (e.g., 1.4):
• Ignite Version (e.g., 0.3.0): 0.3.0
• OS (e.g., Linux): Linux
• How you installed Ignite (conda, pip, source): conda
• Python version:
• Any other relevant information:

[niowniow]

Ok, I'm still working through this. Sorry for having posted too early. Maybe the issue is that the two lines

        self._check_shape((y_pred, y))
        self._check_type((y_pred, y))

should be in reverse order!?
Maybe someone could have a look at it.

[vfdev-5]

@niowniow thanks for posting !
I agree with your first message about misleading error message regarding the input as in your example. Maybe, even the condition y.shape[1] != 1 is a bit wrong and should be y.shape[1] > 1 (to cover cases like t = torch.zeros(10, 0, 2)).

I think, we just need to improve the error message to

"y and y_pred must have same shape of (batch_size, num_categories, ...) and num_categories > 1"

What do you think ?

[sdesrozis]

@niowniow Thank you for the question. The point is num_categories should be greater than one wrt multilabel classification, shouldn't it ?

@vfdev-5 You are too fast!! :)

[niowniow]

@vfdev-5 That would probably do it. However, I'm don't know if it holds for y.shape[1] > 1
@sdesrozis Yes, it should be greater than one. However, I ran multiple tests with varying number of categories. Some happen to have only one category. And I didn't check for it before instantiating Accuracy.

But would there be a difference in the output of the binary case and the multilabel case with num_categories==1?

[sdesrozis]

@sdesrozis Yes, it should be greater than one. However, I ran multiple tests with varying number of categories. Some happen to have only one category. And I didn't check for it before instantiating Accuracy.

Ok it makes sense, thank you for that clarification.

But would there be a difference in the output of the binary case and the multilabel case with num_categories==1?

Actually we don't have the same implementation but I agree that should not be different. Let's see what we can do.

[vfdev-5]

If num_categories changes, another error is raised:

ignite/ignite/metrics/accuracy.py

Lines 88 to 91 in 01a2bc0

	if self._num_classes != num_classes:
	raise ValueError(
	"Input data number of classes has changed from {} to {}".format(self._num_classes, num_classes)
	)

[sdesrozis]

We discuss this issue and the behavior of Accuracy with is_multilabel=True in the case of num_categories == 1 won't change. Actually, the case num_categories == 1 is already covered by binary (i.e. simply is_multilabel=False here). We want to maintain a clear usage and do not have multiple ways (and maybe bad ones wrt performance) to do the same thing. I agree that is_multilabel=True and num_categories == 1 means binary but our implementation does not fit that.

Btw, I can help you about your specific needs. Feel free to share snippets, as you can see, @vfdev-5 is very fast, my challenge is to be faster :)

[niowniow]

Thanks. It's fine for me to have another if-condition checking if my input should use binary.

[sdesrozis]

@niowniow Perfect!