Allow passing metrics objects directly to create_metrics_collection

[ashwinvaidya17]

📝 Description

• Adds the ability to pass metric objects directly.
• Alternative to solution proposed in how to calculate Precision/Recall/Accuracy in v1.1.10? #2205

To test

from torchmetrics.classification import Accuracy, Precision, Recall

from anomalib.data import MVTec
from anomalib.engine import Engine
from anomalib.models import Padim

if __name__ == "__main__":
    model = Padim()
    data = MVTec()
    engine = Engine(image_metrics=[Accuracy(task="binary"), Precision(task="binary"), Recall(task="binary")])
    engine.train(model, datamodule=data)

✨ Changes

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• 🐞 Bug fix (non-breaking change which fixes an issue)
• 🔨 Refactor (non-breaking change which refactors the code base)
• 🚀 New feature (non-breaking change which adds functionality)
• 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected)
• 📚 Documentation update
• 🔒 Security update

✅ Checklist

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• 📋 I have summarized my changes in the CHANGELOG and followed the guidelines for my type of change (skip for minor changes, documentation updates, and test enhancements).
• 📚 I have made the necessary updates to the documentation (if applicable).
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[codecov]

Codecov Report

Attention: Patch coverage is 87.50000% with 1 line in your changes missing coverage. Please review.

Project coverage is 80.79%. Comparing base (cfd3d8e) to head (4ce9d62).

Files	Patch %	Lines
src/anomalib/metrics/__init__.py	87.50%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #2212      +/-   ##
==========================================
- Coverage   80.80%   80.79%   -0.01%     
==========================================
  Files         248      248              
  Lines       10859    10864       +5     
==========================================
+ Hits         8775     8778       +3     
- Misses       2084     2086       +2     

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[samet-akcay]

Would this mean that a user cannot pass the following:

from torchmetrics.classification import Accuracy, Precision, Recall

from anomalib.data import MVTec
from anomalib.engine import Engine
from anomalib.models import Padim

if __name__ == "__main__":
    model = Padim()
    data = MVTec()
    engine = Engine(image_metrics=["F1Score", Accuracy(task="binary")])
    engine.train(model, datamodule=data)

[samet-akcay]

Also, would it be an idea to have an additional check like;

from torchmetrics.classification import Accuracy, Precision, Recall
import types

def instantiate_if_needed(metric, task="binary"):
    if isinstance(metric, types.FunctionType) or isinstance(metric, type):
        # If metric is a function or a class (not instantiated)
        return metric(task=task)
    else:
        # If metric is already instantiated
        return metric

Or do you think if this is overkill?