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Restructure unit tests and fix ruff issues #2306

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Sep 18, 2024
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Restructure unit tests and fix ruff issues #2306

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85ac1ff
Restructure data unit tests to follow the new data structure
samet-akcay Sep 18, 2024
21a498f
Fix ruff issues
samet-akcay Sep 18, 2024
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54 changes: 36 additions & 18 deletions src/anomalib/data/dataclasses/numpy/image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,22 +36,27 @@ class NumpyImageItem(_ImageInputFields[str], NumpyItem):
>>> path = item.image_path
"""

def _validate_image(self, image: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_image(image: np.ndarray) -> np.ndarray:
assert image.ndim == 3, f"Expected 3D image, got {image.ndim}D image."
if image.shape[0] == 3:
image = image.transpose(1, 2, 0)
return image

def _validate_gt_label(self, gt_label: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_gt_label(gt_label: np.ndarray) -> np.ndarray:
return gt_label

def _validate_gt_mask(self, gt_mask: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_gt_mask(gt_mask: np.ndarray) -> np.ndarray:
return gt_mask

def _validate_mask_path(self, mask_path: str) -> str:
@staticmethod
def _validate_mask_path(mask_path: str) -> str:
return mask_path

def _validate_anomaly_map(self, anomaly_map: np.ndarray | None) -> np.ndarray | None:
@staticmethod
def _validate_anomaly_map(anomaly_map: np.ndarray | None) -> np.ndarray | None:
if anomaly_map is None:
return None
assert isinstance(anomaly_map, np.ndarray), f"Anomaly map must be a numpy array, got {type(anomaly_map)}."
Expand All @@ -66,21 +71,25 @@ def _validate_anomaly_map(self, anomaly_map: np.ndarray | None) -> np.ndarray |
anomaly_map = anomaly_map.squeeze(0)
return anomaly_map.astype(np.float32)

def _validate_pred_score(self, pred_score: np.ndarray | None) -> np.ndarray | None:
@staticmethod
def _validate_pred_score(pred_score: np.ndarray | None) -> np.ndarray | None:
if pred_score is None:
return None
if pred_score.ndim == 1:
assert len(pred_score) == 1, f"Expected single value for pred_score, got {len(pred_score)}."
pred_score = pred_score[0]
return pred_score

def _validate_pred_mask(self, pred_mask: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_pred_mask(pred_mask: np.ndarray) -> np.ndarray:
return pred_mask

def _validate_pred_label(self, pred_label: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_pred_label(pred_label: np.ndarray) -> np.ndarray:
return pred_label

def _validate_image_path(self, image_path: str) -> str:
@staticmethod
def _validate_image_path(image_path: str) -> str:
return image_path


Expand Down Expand Up @@ -115,29 +124,38 @@ class NumpyImageBatch(BatchIterateMixin[NumpyImageItem], _ImageInputFields[list[

item_class = NumpyImageItem

def _validate_image(self, image: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_image(image: np.ndarray) -> np.ndarray:
return image

def _validate_gt_label(self, gt_label: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_gt_label(gt_label: np.ndarray) -> np.ndarray:
return gt_label

def _validate_gt_mask(self, gt_mask: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_gt_mask(gt_mask: np.ndarray) -> np.ndarray:
return gt_mask

def _validate_mask_path(self, mask_path: list[str]) -> list[str]:
@staticmethod
def _validate_mask_path(mask_path: list[str]) -> list[str]:
return mask_path

def _validate_anomaly_map(self, anomaly_map: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_anomaly_map(anomaly_map: np.ndarray) -> np.ndarray:
return anomaly_map

def _validate_pred_score(self, pred_score: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_pred_score(pred_score: np.ndarray) -> np.ndarray:
return pred_score

def _validate_pred_mask(self, pred_mask: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_pred_mask(pred_mask: np.ndarray) -> np.ndarray:
return pred_mask

def _validate_pred_label(self, pred_label: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_pred_label(pred_label: np.ndarray) -> np.ndarray:
return pred_label

def _validate_image_path(self, image_path: list[str]) -> list[str]:
@staticmethod
def _validate_image_path(image_path: list[str]) -> list[str]:
return image_path
27 changes: 18 additions & 9 deletions src/anomalib/data/dataclasses/numpy/video.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,16 +20,20 @@ class NumpyVideoItem(_VideoInputFields[np.ndarray, np.ndarray, np.ndarray, str],
for Anomalib's video-based models.
"""

def _validate_image(self, image: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_image(image: np.ndarray) -> np.ndarray:
return image

def _validate_gt_label(self, gt_label: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_gt_label(gt_label: np.ndarray) -> np.ndarray:
return gt_label

def _validate_gt_mask(self, gt_mask: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_gt_mask(gt_mask: np.ndarray) -> np.ndarray:
return gt_mask

def _validate_mask_path(self, mask_path: str) -> str:
@staticmethod
def _validate_mask_path(mask_path: str) -> str:
return mask_path


Expand All @@ -48,17 +52,22 @@ class NumpyVideoBatch(

item_class = NumpyVideoItem

def _validate_image(self, image: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_image(image: np.ndarray) -> np.ndarray:
return image

def _validate_gt_label(self, gt_label: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_gt_label(gt_label: np.ndarray) -> np.ndarray:
return gt_label

def _validate_gt_mask(self, gt_mask: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_gt_mask(gt_mask: np.ndarray) -> np.ndarray:
return gt_mask

def _validate_mask_path(self, mask_path: list[str]) -> list[str]:
@staticmethod
def _validate_mask_path(mask_path: list[str]) -> list[str]:
return mask_path

def _validate_anomaly_map(self, anomaly_map: np.ndarray) -> np.ndarray:
@staticmethod
def _validate_anomaly_map(anomaly_map: np.ndarray) -> np.ndarray:
return anomaly_map
66 changes: 44 additions & 22 deletions src/anomalib/data/dataclasses/torch/depth.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,37 +45,48 @@ class DepthItem(

numpy_class = NumpyImageItem

def _validate_image(self, image: Image) -> Image:
@staticmethod
def _validate_image(image: Image) -> Image:
return image

def _validate_gt_label(self, gt_label: torch.Tensor) -> torch.Tensor:
@staticmethod
def _validate_gt_label(gt_label: torch.Tensor) -> torch.Tensor:
return gt_label

def _validate_gt_mask(self, gt_mask: Mask) -> Mask:
@staticmethod
def _validate_gt_mask(gt_mask: Mask) -> Mask:
return gt_mask

def _validate_mask_path(self, mask_path: str) -> str:
@staticmethod
def _validate_mask_path(mask_path: str) -> str:
return mask_path

def _validate_anomaly_map(self, anomaly_map: torch.Tensor) -> torch.Tensor:
@staticmethod
def _validate_anomaly_map(anomaly_map: torch.Tensor) -> torch.Tensor:
return anomaly_map

def _validate_pred_score(self, pred_score: torch.Tensor) -> torch.Tensor:
@staticmethod
def _validate_pred_score(pred_score: torch.Tensor) -> torch.Tensor:
return pred_score

def _validate_pred_mask(self, pred_mask: torch.Tensor) -> torch.Tensor:
@staticmethod
def _validate_pred_mask(pred_mask: torch.Tensor) -> torch.Tensor:
return pred_mask

def _validate_pred_label(self, pred_label: torch.Tensor) -> torch.Tensor:
@staticmethod
def _validate_pred_label(pred_label: torch.Tensor) -> torch.Tensor:
return pred_label

def _validate_image_path(self, image_path: str) -> str:
@staticmethod
def _validate_image_path(image_path: str) -> str:
return image_path

def _validate_depth_map(self, depth_map: torch.Tensor) -> torch.Tensor:
@staticmethod
def _validate_depth_map(depth_map: torch.Tensor) -> torch.Tensor:
return depth_map

def _validate_depth_path(self, depth_path: str) -> str:
@staticmethod
def _validate_depth_path(depth_path: str) -> str:
return depth_path


Expand Down Expand Up @@ -110,35 +121,46 @@ class DepthBatch(

item_class = DepthItem

def _validate_image(self, image: Image) -> Image:
@staticmethod
def _validate_image(image: Image) -> Image:
return image

def _validate_gt_label(self, gt_label: torch.Tensor) -> torch.Tensor:
@staticmethod
def _validate_gt_label(gt_label: torch.Tensor) -> torch.Tensor:
return gt_label

def _validate_gt_mask(self, gt_mask: Mask) -> Mask:
@staticmethod
def _validate_gt_mask(gt_mask: Mask) -> Mask:
return gt_mask

def _validate_mask_path(self, mask_path: list[str]) -> list[str]:
@staticmethod
def _validate_mask_path(mask_path: list[str]) -> list[str]:
return mask_path

def _validate_anomaly_map(self, anomaly_map: torch.Tensor) -> torch.Tensor:
@staticmethod
def _validate_anomaly_map(anomaly_map: torch.Tensor) -> torch.Tensor:
return anomaly_map

def _validate_pred_score(self, pred_score: torch.Tensor) -> torch.Tensor:
@staticmethod
def _validate_pred_score(pred_score: torch.Tensor) -> torch.Tensor:
return pred_score

def _validate_pred_mask(self, pred_mask: torch.Tensor) -> torch.Tensor:
@staticmethod
def _validate_pred_mask(pred_mask: torch.Tensor) -> torch.Tensor:
return pred_mask

def _validate_pred_label(self, pred_label: torch.Tensor) -> torch.Tensor:
@staticmethod
def _validate_pred_label(pred_label: torch.Tensor) -> torch.Tensor:
return pred_label

def _validate_image_path(self, image_path: list[str]) -> list[str]:
@staticmethod
def _validate_image_path(image_path: list[str]) -> list[str]:
return image_path

def _validate_depth_map(self, depth_map: torch.Tensor) -> torch.Tensor:
@staticmethod
def _validate_depth_map(depth_map: torch.Tensor) -> torch.Tensor:
return depth_map

def _validate_depth_path(self, depth_path: list[str]) -> list[str]:
@staticmethod
def _validate_depth_path(depth_path: list[str]) -> list[str]:
return depth_path
36 changes: 24 additions & 12 deletions src/anomalib/data/dataclasses/torch/image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,13 +61,15 @@ class ImageItem(

numpy_class = NumpyImageItem

def _validate_image(self, image: torch.Tensor) -> Image:
@staticmethod
def _validate_image(image: torch.Tensor) -> Image:
assert isinstance(image, torch.Tensor), f"Image must be a torch.Tensor, got {type(image)}."
assert image.ndim == 3, f"Image must have shape [C, H, W], got shape {image.shape}."
assert image.shape[0] == 3, f"Image must have 3 channels, got {image.shape[0]}."
return to_dtype_image(image, torch.float32, scale=True)

def _validate_gt_label(self, gt_label: torch.Tensor | int | None) -> torch.Tensor:
@staticmethod
def _validate_gt_label(gt_label: torch.Tensor | int | None) -> torch.Tensor:
if gt_label is None:
return None
if isinstance(gt_label, int):
Expand All @@ -80,7 +82,8 @@ def _validate_gt_label(self, gt_label: torch.Tensor | int | None) -> torch.Tenso
assert not torch.is_floating_point(gt_label), f"Ground truth label must be boolean or integer, got {gt_label}."
return gt_label.bool()

def _validate_gt_mask(self, gt_mask: torch.Tensor | None) -> Mask | None:
@staticmethod
def _validate_gt_mask(gt_mask: torch.Tensor | None) -> Mask | None:
if gt_mask is None:
return None
assert isinstance(gt_mask, torch.Tensor), f"Ground truth mask must be a torch.Tensor, got {type(gt_mask)}."
Expand All @@ -93,12 +96,14 @@ def _validate_gt_mask(self, gt_mask: torch.Tensor | None) -> Mask | None:
gt_mask = gt_mask.squeeze(0)
return Mask(gt_mask, dtype=torch.bool)

def _validate_mask_path(self, mask_path: str | None) -> str | None:
@staticmethod
def _validate_mask_path(mask_path: str | None) -> str | None:
if mask_path is None:
return None
return str(mask_path)

def _validate_anomaly_map(self, anomaly_map: torch.Tensor | None) -> Mask | None:
@staticmethod
def _validate_anomaly_map(anomaly_map: torch.Tensor | None) -> Mask | None:
if anomaly_map is None:
return None
assert isinstance(anomaly_map, torch.Tensor), f"Anomaly map must be a torch.Tensor, got {type(anomaly_map)}."
Expand Down Expand Up @@ -126,7 +131,8 @@ def _validate_pred_score(self, pred_score: torch.Tensor | np.ndarray | None) ->
assert pred_score.ndim == 0, f"Predicted score must be a scalar, got shape {pred_score.shape}."
return pred_score.to(torch.float32)

def _validate_pred_mask(self, pred_mask: torch.Tensor | None) -> Mask | None:
@staticmethod
def _validate_pred_mask(pred_mask: torch.Tensor | None) -> Mask | None:
if pred_mask is None:
return None
assert isinstance(pred_mask, torch.Tensor), f"Predicted mask must be a torch.Tensor, got {type(pred_mask)}."
Expand All @@ -139,7 +145,8 @@ def _validate_pred_mask(self, pred_mask: torch.Tensor | None) -> Mask | None:
pred_mask = pred_mask.squeeze(0)
return Mask(pred_mask, dtype=torch.bool)

def _validate_pred_label(self, pred_label: torch.Tensor | np.ndarray | None) -> torch.Tensor | None:
@staticmethod
def _validate_pred_label(pred_label: torch.Tensor | np.ndarray | None) -> torch.Tensor | None:
if pred_label is None:
return None
if not isinstance(pred_label, torch.Tensor):
Expand All @@ -152,7 +159,8 @@ def _validate_pred_label(self, pred_label: torch.Tensor | np.ndarray | None) ->
assert pred_label.ndim == 0, f"Predicted label must be a scalar, got shape {pred_label.shape}."
return pred_label.to(torch.bool)

def _validate_image_path(self, image_path: str | None) -> str | None:
@staticmethod
def _validate_image_path(image_path: str | None) -> str | None:
if image_path is None:
return None
return str(image_path)
Expand Down Expand Up @@ -198,7 +206,8 @@ class ImageBatch(
item_class = ImageItem
numpy_class = NumpyImageBatch

def _validate_image(self, image: Image) -> Image:
@staticmethod
def _validate_image(image: Image) -> Image:
assert isinstance(image, torch.Tensor), f"Image must be a torch.Tensor, got {type(image)}."
assert image.ndim in {3, 4}, f"Image must have shape [C, H, W] or [N, C, H, W], got shape {image.shape}."
if image.ndim == 3:
Expand Down Expand Up @@ -286,11 +295,14 @@ def _validate_pred_score(self, pred_score: torch.Tensor | None) -> torch.Tensor
return torch.amax(self.anomaly_map, dim=(-2, -1))
return pred_score

def _validate_pred_mask(self, pred_mask: torch.Tensor) -> torch.Tensor | None:
@staticmethod
def _validate_pred_mask(pred_mask: torch.Tensor) -> torch.Tensor | None:
return pred_mask

def _validate_pred_label(self, pred_label: torch.Tensor) -> torch.Tensor | None:
@staticmethod
def _validate_pred_label(pred_label: torch.Tensor) -> torch.Tensor | None:
return pred_label

def _validate_image_path(self, image_path: list[str]) -> list[str] | None:
@staticmethod
def _validate_image_path(image_path: list[str]) -> list[str] | None:
return image_path
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