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Reorganize package dependencies (#506)
* Update dependencies and imports for MONAI and typing * Refactor MedicalImage and MedicalImageFolder classes * allow init of MedicalImage class; raise error in methods * Refactor import_optional_module function to allow importing module attributes * Refactor MedicalImage optional module imports * Update dependencies in pyproject.toml * Add test for MedicalImage feature without MONAI * Prevent use of txrv_transforms method at runtime if MONAI is not installed * Move report package deps to core installation * Adjust package installation tests * Formatting fix * Remove report package test action * Formatting fix * remove txrv_transforms, add dictionary wrapper for torchvision transforms, and remove monai deps from monitor package * fix repr for transform * fix Dictd call func * fix monitor-api notebook * Update imports for image transforms * Update metadata for cxr_classification.ipynb Signed-off-by: Franklin <41602287+fcogidi@users.noreply.github.com> * fix transforms in notebooks --------- Signed-off-by: Franklin <41602287+fcogidi@users.noreply.github.com> Co-authored-by: Amrit K <amritk@vectorinstitute.ai> Co-authored-by: akore <akore0x5f@gmail.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,28 +1,84 @@ | ||
| """Transforms for the datasets.""" | ||
| from typing import Any, Callable, Tuple | ||
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| from typing import Tuple | ||
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| from monai.transforms import Lambdad, Resized, ToDeviced # type: ignore | ||
| from torchvision.transforms import Compose | ||
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| def txrv_transforms( | ||
| keys: Tuple[str, ...] = ("features",), | ||
| device: str = "cpu", | ||
| ) -> Compose: | ||
| """Set of transforms for the models in the TXRV library.""" | ||
| return Compose( | ||
| [ | ||
| Resized( | ||
| keys=keys, | ||
| spatial_size=(1, 224, 224), | ||
| allow_missing_keys=True, | ||
| ), | ||
| Lambdad( | ||
| keys=keys, | ||
| func=lambda x: ((2 * (x / 255.0)) - 1.0) * 1024, | ||
| allow_missing_keys=True, | ||
| ), | ||
| ToDeviced(keys=keys, device=device, allow_missing_keys=True), | ||
| ], | ||
| ) | ||
| from torchvision.transforms import Lambda, Resize | ||
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| # generic dictionary-based wrapper for any transform | ||
| class Dictd: | ||
| """Generic dictionary-based wrapper for any transform.""" | ||
|
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| def __init__( | ||
| self, | ||
| transform: Callable[..., Any], | ||
| keys: Tuple[str, ...], | ||
| allow_missing_keys: bool = False, | ||
| ): | ||
| self.transform = transform | ||
| self.keys = keys | ||
| self.allow_missing_keys = allow_missing_keys | ||
|
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| def __call__(self, data: Any) -> Any: | ||
| """Apply the transform to the data.""" | ||
| for key in self.keys: | ||
| if self.allow_missing_keys and key not in data: | ||
| continue | ||
| data[key] = self.transform(data[key]) | ||
| return data | ||
|
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||
| def __repr__(self) -> str: | ||
| """Return a string representation of the transform.""" | ||
| return ( | ||
| f"{self.__class__.__name__}(transform={self.transform}, " | ||
| f"keys={self.keys}, allow_missing_keys={self.allow_missing_keys})" | ||
| ) | ||
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| # dictionary-based wrapper of Lambda transform using Dictd | ||
| class Lambdad: | ||
| """Dictionary-based wrapper of Lambda transform using Dictd.""" | ||
|
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| def __init__( | ||
| self, | ||
| func: Callable[..., Any], | ||
| keys: Tuple[str, ...], | ||
| allow_missing_keys: bool = False, | ||
| ): | ||
| self.transform = Dictd( | ||
| transform=Lambda(func), | ||
| keys=keys, | ||
| allow_missing_keys=allow_missing_keys, | ||
| ) | ||
|
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| def __call__(self, data: Any) -> Any: | ||
| """Apply the transform to the data.""" | ||
| return self.transform(data) | ||
|
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| def __repr__(self) -> str: | ||
| """Return a string representation of the transform.""" | ||
| return f"{self.__class__.__name__}(keys={self.transform.keys}, allow_missing_keys={self.transform.allow_missing_keys})" | ||
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| # dictionary-based wrapper of Resize transform using Dictd | ||
| class Resized: | ||
| """Dictionary-based wrapper of Resize transform using Dictd.""" | ||
|
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||
| def __init__( | ||
| self, | ||
| spatial_size: Tuple[int, int], | ||
| keys: Tuple[str, ...], | ||
| allow_missing_keys: bool = False, | ||
| ): | ||
| self.transform = Dictd( | ||
| transform=Resize(size=spatial_size), | ||
| keys=keys, | ||
| allow_missing_keys=allow_missing_keys, | ||
| ) | ||
|
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| def __call__(self, data: Any) -> Any: | ||
| """Apply the transform to the data.""" | ||
| return self.transform(data) | ||
|
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| def __repr__(self) -> str: | ||
| """Return a string representation of the transform.""" | ||
| return f"{self.__class__.__name__}(keys={self.transform.keys}, allow_missing_keys={self.transform.allow_missing_keys})" |
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