move result types

Signed-off-by: Ashwin Vaidya <[email protected]>

Author: ashwinvaidya17

model_api/python/model_api/models/result_types.py

@@ -0,0 +1,33 @@
+"""Result types."""
+
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+from .anomaly import AnomalyResult
+from .classification import ClassificationResult
+from .detection import Detection, DetectionResult
+from .keypoint import DetectedKeypoints
+from .segmentation import (
+    Contour,
+    ImageResultWithSoftPrediction,
+    InstanceSegmentationResult,
+    SegmentedObject,
+    SegmentedObjectWithRects,
+)
+from .visual_prompting import PredictedMask, VisualPromptingResult, ZSLVisualPromptingResult
+
+__all__ = [
+    "AnomalyResult",
+    "ClassificationResult",
+    "Contour",
+    "Detection",
+    "DetectionResult",
+    "DetectedKeypoints",
+    "SegmentedObject",
+    "SegmentedObjectWithRects",
+    "ImageResultWithSoftPrediction",
+    "InstanceSegmentationResult",
+    "PredictedMask",
+    "VisualPromptingResult",
+    "ZSLVisualPromptingResult",
+]

model_api/python/model_api/models/result_types/__init__.py

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+"""Anomaly result type."""
+
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import numpy as np
+
+
+class AnomalyResult:
+    """Results for anomaly models."""
+
+    def __init__(
+        self,
+        anomaly_map: np.ndarray | None = None,
+        pred_boxes: np.ndarray | None = None,
+        pred_label: str | None = None,
+        pred_mask: np.ndarray | None = None,
+        pred_score: float | None = None,
+    ) -> None:
+        self.anomaly_map = anomaly_map
+        self.pred_boxes = pred_boxes
+        self.pred_label = pred_label
+        self.pred_mask = pred_mask
+        self.pred_score = pred_score
+
+    def _compute_min_max(self, tensor: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        """Computes min and max values of the tensor."""
+        return tensor.min(), tensor.max()
+
+    def __str__(self) -> str:
+        assert self.anomaly_map is not None
+        assert self.pred_mask is not None
+        anomaly_map_min, anomaly_map_max = self._compute_min_max(self.anomaly_map)
+        pred_mask_min, pred_mask_max = self._compute_min_max(self.pred_mask)
+        return (
+            f"anomaly_map min:{anomaly_map_min} max:{anomaly_map_max};"
+            f"pred_score:{np.round(self.pred_score, 1) if self.pred_score else 0.0};"
+            f"pred_label:{self.pred_label};"
+            f"pred_mask min:{pred_mask_min} max:{pred_mask_max};"
+        )

model_api/python/model_api/models/result_types/anomaly.py

@@ -0,0 +1,37 @@
+"""Classification result type."""
+
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from .utils import array_shape_to_str
+
+if TYPE_CHECKING:
+    import numpy as np
+
+
+class ClassificationResult:
+    """Results for classification models."""
+
+    def __init__(
+        self,
+        top_labels: list[tuple[int, str, float]] | None = None,
+        saliency_map: np.ndarray | None = None,
+        feature_vector: np.ndarray | None = None,
+        raw_scores: np.ndarray | None = None,
+    ) -> None:
+        self.top_labels = top_labels
+        self.saliency_map = saliency_map
+        self.feature_vector = feature_vector
+        self.raw_scores = raw_scores
+
+    def __str__(self) -> str:
+        assert self.top_labels is not None
+        labels = ", ".join(f"{idx} ({label}): {confidence:.3f}" for idx, label, confidence in self.top_labels)
+        return (
+            f"{labels}, {array_shape_to_str(self.saliency_map)}, {array_shape_to_str(self.feature_vector)}, "
+            f"{array_shape_to_str(self.raw_scores)}"
+        )