[onert/python] Introduce metric API

This commit introduces metric APIs.
  - categorical_accuracy.py : CategoricalAccuracy class
  - metric : Metric class to be provided custom metric

ONE-DCO-1.0-Signed-off-by: ragmani <[email protected]>

runtime/onert/api/python/package/experimental/train/metrics/__init__.py

@@ -0,0 +1,4 @@
+from .metric import Metric
+from .categorical_accuracy import CategoricalAccuracy
+
+__all__ = ["Metric", "CategoricalAccuracy"]

runtime/onert/api/python/package/experimental/train/metrics/categorical_accuracy.py

@@ -0,0 +1,56 @@
+import numpy as np
+from .metric import Metric
+
+
+class CategoricalAccuracy(Metric):
+    """
+    Metric for computing categorical accuracy.
+    """
+    def __init__(self):
+        self.correct = 0
+        self.total = 0
+        self.axis = 0
+
+    def reset_state(self):
+        """
+        Reset the metric's state.
+        """
+        self.correct = 0
+        self.total = 0
+
+    def update_state(self, outputs, expecteds):
+        """
+        Update the metric's state based on the outputs and expecteds.
+        Args:
+            outputs (list of np.ndarray): List of model outputs for each output layer.
+            expecteds (list of np.ndarray): List of expected ground truth values for each output layer.
+        """
+        if len(outputs) != len(expecteds):
+            raise ValueError(
+                "The number of outputs and expecteds must match. "
+                f"Got {len(outputs)} outputs and {len(expecteds)} expecteds.")
+
+        for output, expected in zip(outputs, expecteds):
+            if output.shape[self.axis] != expected.shape[self.axis]:
+                raise ValueError(
+                    f"Output and expected shapes must match along the specified axis {self.axis}. "
+                    f"Got output shape {output.shape} and expected shape {expected.shape}."
+                )
+
+            batch_size = output.shape[self.axis]
+            for b in range(batch_size):
+                output_idx = np.argmax(output[b])
+                expected_idx = np.argmax(expected[b])
+                if output_idx == expected_idx:
+                    self.correct += 1
+            self.total += batch_size
+
+    def result(self):
+        """
+        Compute and return the final metric value.
+        Returns:
+            float: Metric value.
+        """
+        if self.total == 0:
+            return 0.0
+        return self.correct / self.total

runtime/onert/api/python/package/experimental/train/metrics/metric.py

@@ -0,0 +1,26 @@
+class Metric:
+    """
+    Abstract base class for all metrics.
+    """
+    def reset_state(self):
+        """
+        Reset the metric's state.
+        """
+        raise NotImplementedError
+
+    def update_state(self, outputs, expecteds):
+        """
+        Update the metric's state based on the outputs and expecteds.
+        Args:
+            outputs (np.ndarray): Model outputs.
+            expecteds (np.ndarray): Expected ground truth values.
+        """
+        raise NotImplementedError
+
+    def result(self):
+        """
+        Compute and return the final metric value.
+        Returns:
+            float: Metric value.
+        """
+        raise NotImplementedError

runtime/onert/api/python/package/experimental/train/metrics/registry.py

@@ -0,0 +1,24 @@
+from .categorical_accuracy import CategoricalAccuracy
+
+
+class MetricsRegistry:
+    """
+    Registry for creating metrics by name.
+    """
+    _metrics = {
+        "categorical_accuracy": CategoricalAccuracy,
+    }
+
+    @staticmethod
+    def create_metric(name):
+        """
+        Create a metric instance by name.
+        Args:
+            name (str): Name of the metric.
+        Returns:
+            BaseMetric: Metric instance.
+        """
+        if name not in MetricsRegistry._metrics:
+            raise ValueError(
+                f"Unknown Metric: {name}. Custom metric is not supported yet")
+        return MetricsRegistry._metrics[name]()