BinaryConfusionMatrix does not work with float target

[rafael-patronilo]

🐛 Describe the bug

Binary Confusion Matrix does not work if target is float (even if all values are 0 or 1).

Note: It could be argued that target should always be int. However given that #146 was solved, I assume you will also want to solve this one, or at least include an error for it.
In my case, target was float because my csv was automatically loaded by pandas.

Minimal example:

import torch
from torcheval.metrics import BinaryConfusionMatrix

input = torch.randint(0, 2, (10,)).to(torch.float32)
target = torch.randint(0, 2, (10,))

cm = BinaryConfusionMatrix()
cm.update(input, target) # no error here
print(cm.compute())

cm = BinaryConfusionMatrix()
cm.update(input, target.to(torch.float32)) # error here
print(cm.compute())

Error and Traceback

Traceback (most recent call last):
  File "/mnt/c/Users/rafae/Documents/faculdade/thesis/thesis-framework/temp.py", line 12, in <module>
    cm.update(input, target.to(torch.float32)) # error here
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/mnt/c/Users/rafae/Documents/faculdade/thesis/thesis-framework/.venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/mnt/c/Users/rafae/Documents/faculdade/thesis/thesis-framework/.venv/lib/python3.12/site-packages/torcheval/metrics/classification/confusion_matrix.py", line 311, in update
    self.confusion_matrix += _binary_confusion_matrix_update(
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/mnt/c/Users/rafae/Documents/faculdade/thesis/thesis-framework/.venv/lib/python3.12/site-packages/torcheval/metrics/functional/classification/confusion_matrix.py", line 175, in _binary_confusion_matrix_update
    return _update(input, target, 2)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: The following operation failed in the TorchScript interpreter.
Traceback of TorchScript (most recent call last):
  File "/mnt/c/Users/rafae/Documents/faculdade/thesis/thesis-framework/.venv/lib/python3.12/site-packages/torcheval/metrics/functional/classification/confusion_matrix.py", line 232, in _update

    # Each prediction creates an entry at the position (true, pred)
    sparse_cm = torch.sparse_coo_tensor(coordinates, torch.ones_like(target), cm_shape)
                ~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE

    return sparse_cm.to_dense()
RuntimeError: indices must be an int64 tensor

Interpretation

The coordinates tensor (corresponding to the indices parameter of sparse_coo_tensor) is a vstack of input (after applying the threshold, therefore an int tensor) with target. Since target is float, vstack must create the stacked tensor as float.

Versions

Versions

• torcheval 0.0.7
• torch 2.5.0