Skip to content

anomaly

Anomaly Score Normalization Callback that uses min-max normalization.

ThresholdNormalizationCallback(threshold_type='pixel')

Bases: Callback

Callback that normalizes the image-level and pixel-level anomaly scores dividing by the threshold value.

Parameters:

  • threshold_type (str, default: 'pixel' ) –

    Threshold used to normalize pixel level anomaly scores, either image or pixel (default)

Source code in quadra/utils/anomaly.py 
59
60
61
def __init__(self, threshold_type: str = "pixel"):
    super().__init__()
    self.threshold_type = threshold_type

on_predict_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0)

Called when the predict batch ends, normalizes the predicted scores and anomaly maps.

Source code in quadra/utils/anomaly.py 
85
86
87
88
89
90
91
92
93
94
95
96
97
def on_predict_batch_end(
    self,
    trainer: pl.Trainer,
    pl_module: AnomalyModule,
    outputs: Any,
    batch: Any,
    batch_idx: int,
    dataloader_idx: int = 0,
) -> None:
    """Called when the predict batch ends, normalizes the predicted scores and anomaly maps."""
    del trainer, batch, batch_idx, dataloader_idx  # These variables are not used.

    self._normalize_batch(outputs, pl_module)

on_test_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0)

Called when the test batch ends, normalizes the predicted scores and anomaly maps.

Source code in quadra/utils/anomaly.py 
71
72
73
74
75
76
77
78
79
80
81
82
83
def on_test_batch_end(
    self,
    trainer: pl.Trainer,
    pl_module: AnomalyModule,
    outputs: STEP_OUTPUT | None,
    batch: Any,
    batch_idx: int,
    dataloader_idx: int = 0,
) -> None:
    """Called when the test batch ends, normalizes the predicted scores and anomaly maps."""
    del trainer, batch, batch_idx, dataloader_idx  # These variables are not used.

    self._normalize_batch(outputs, pl_module)

on_test_start(trainer, pl_module)

Called when the test begins.

Source code in quadra/utils/anomaly.py 
63
64
65
66
67
68
69
def on_test_start(self, trainer: pl.Trainer, pl_module: AnomalyModule) -> None:
    """Called when the test begins."""
    del trainer  # `trainer` variable is not used.

    for metric in (pl_module.image_metrics, pl_module.pixel_metrics):
        if metric is not None:
            metric.set_threshold(100.0)

normalize_anomaly_score(raw_score, threshold)

Normalize anomaly score value or map based on threshold.

Parameters:

  • raw_score (MapOrValue) –

    Raw anomaly score valure or map

  • threshold (float) –

    Threshold for anomaly detection

Returns:

  • MapOrValue –

    Normalized anomaly score value or map clipped between 0 and 1000

Source code in quadra/utils/anomaly.py 
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
def normalize_anomaly_score(raw_score: MapOrValue, threshold: float) -> MapOrValue:
    """Normalize anomaly score value or map based on threshold.

    Args:
        raw_score: Raw anomaly score valure or map
        threshold: Threshold for anomaly detection

    Returns:
        Normalized anomaly score value or map clipped between 0 and 1000
    """
    if threshold > 0:
        normalized_score = (raw_score / threshold) * 100.0
    elif threshold == 0:
        # TODO: Is this the best way to handle this case?
        normalized_score = (raw_score + 1) * 100.0
    else:
        normalized_score = 200.0 - ((raw_score / threshold) * 100.0)

    if isinstance(normalized_score, torch.Tensor):
        return torch.clamp(normalized_score, 0.0, 1000.0)

    return np.clip(normalized_score, 0.0, 1000.0)