Signed-off-by: Mikael Brudfors [email protected]

part of #5740

Description

Makes the binary and categorical metrics from MetricsReloaded available in MONAI via a wrapper module (monai/metrics/wrapper.py).

This module allows to use the MetricsReloaded metrics as, e.g.:

import torch
from monai.metrics import MetricsReloadedBinary

metric_name = "Cohens Kappa"
metric = MetricsReloadedBinary(metric_name=metric_name)

# first iteration
# shape [batch=1, channel=1, 2, 2]
y_pred = torch.tensor([[[[1.0, 0.0], [0.0, 1.0]]]])
y = torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]])
print(metric(y_pred, y))

# second iteration
# shape [batch=1, channel=1, 2, 2]
y_pred = torch.tensor([[[[1.0, 0.0], [0.0, 0.0]]]])
y = torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]])
print(metric(y_pred, y))

# aggregate
# shape ([batch=2, channel=1])
print(metric.aggregate(reduction="none"))  # tensor([[0.5], [0.2]])

# reset
metric.reset()

Tests of all metrics are in tests/test_metrics_reloaded.py.

Note that MetricsReloaded is an optional dependency of MONAI; so in order to use the wrapper, MONAI needs to be installed with:

pip install '.[metricsreloaded]'

Types of changes

• Non-breaking change (fix or new feature that would not break existing functionality).
• Breaking change (fix or new feature that would cause existing functionality to change).
• New tests added to cover the changes.
• Integration tests passed locally by running ./runtests.sh -f -u --net --coverage.
• Quick tests passed locally by running ./runtests.sh --quick --unittests --disttests.
• In-line docstrings updated.
• Documentation updated, tested make html command in the docs/ folder.