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