chore: import upstream snapshot with attribution

cleanlab/internal/segmentation_utils.py

@@ -0,0 +1,58 @@
+"""
+Helper functions used internally for segmentation tasks.
+"""
+
+from typing import Optional, List
+
+import numpy as np
+
+
+def _get_valid_optional_params(
+    batch_size: Optional[int] = None,
+    n_jobs: Optional[int] = None,
+):
+    """Takes in optional args and returns good values for them if they are None."""
+    if batch_size is None:
+        batch_size = 10000
+
+    if batch_size <= 0:
+        raise ValueError(f"Batch size must be greater than 0, got {batch_size}")
+    return batch_size, n_jobs
+
+
+def _get_summary_optional_params(
+    class_names: Optional[List[str]] = None,
+    exclude: Optional[List[int]] = None,
+    top: Optional[int] = None,
+):
+    """Takes in optional args and returns good values for them if they are None for summary functions."""
+    if exclude is None:
+        exclude = []
+    if top is None:
+        top = 20
+    return class_names, exclude, top
+
+
+def _check_input(labels: np.ndarray, pred_probs: np.ndarray) -> None:
+    """
+    Checks that the input labels and predicted probabilities are valid.
+
+    Parameters
+    ----------
+    labels:
+        Array of shape ``(N, H, W)`` of integer labels, where `N` is the number of images in the dataset and `H` and `W` are the height and width of the images.
+
+    pred_probs:
+        Array of shape ``(N, K, H, W)`` of predicted probabilities, where `N` is the number of images in the dataset, `K` is the number of classes, and `H` and `W` are the height and width of the images.
+    """
+    if len(labels.shape) != 3:
+        raise ValueError("labels must have a shape of (N, H, W)")
+
+    if len(pred_probs.shape) != 4:
+        raise ValueError("pred_probs must have a shape of (N, K, H, W)")
+
+    num_images, height, width = labels.shape
+    num_images_pred, num_classes, height_pred, width_pred = pred_probs.shape
+
+    if num_images != num_images_pred or height != height_pred or width != width_pred:
+        raise ValueError("labels and pred_probs must have matching dimensions for N, H, and W")