Better normalization supporting different normalization transforms for input/target and different normalization per channel#34
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…eDataset and CropImageDataset for training with input and target that can take different value ranges
…ated as a subpackage, moved crop generator protocol to its own stand aloen module and center and point-centered crop generator functions in their respective modules under the new subpackage.
… the full FOV best effort
…ubpackage while supporting older import signatures for compatibility
d33bs
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Jul 13, 2026
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| def _apply_input_transforms( | ||
| self, | ||
| image: np.ndarray, | ||
| ) -> np.ndarray: | ||
| for transform in self.input_transforms: | ||
| image = transform.apply(img=image) | ||
| return image | ||
|
|
||
| def _apply_target_transforms( | ||
| self, | ||
| image: np.ndarray, | ||
| ) -> np.ndarray: | ||
| for transform in self.target_transforms: | ||
| image = transform.apply(img=image) | ||
| return image |
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These feel mostly duplicative. Is there an abstraction you could use for both cases?
…and summary reporting for center, point-centered, and tile crops.
…explanation on non-overlapping tile benefits for convolutional neural networks.
…opGenerator type directly
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Thanks @d33bs for reviewing. Merging now1 |
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Useful for training models whose input and target takes different ranges and experimenting with input engineering combining multiple channels of different value ranges