Skip to content

darts_segmentation.metrics.BinaryInstanceStatScores

Bases: torchmetrics.classification.stat_scores._AbstractStatScores

Base class for binary instance segmentation metrics.

Create a new instance of the BinaryInstanceStatScores metric.

Parameters:

  • threshold (float, default: 0.5 ) –

    Threshold for binarizing the prediction. Has no effect if the prediction is already binarized. Defaults to 0.5.

  • matching_threshold (float, default: 0.5 ) –

    The threshold for matching instances. Defaults to 0.5.

  • multidim_average (typing.Literal['global', 'samplewise'], default: 'global' ) –

    How the average over multiple batches is calculated. Defaults to "global".

  • ignore_index (int | None, default: None ) –

    Ignores an invalid class. Defaults to None.

  • validate_args (bool, default: True ) –

    Weather to validate inputs. Defaults to True.

  • kwargs (typing.Any, default: {} ) –

    Additional arguments for the Metric class, regarding compute-methods. Please refer to torchmetrics for more examples.

Raises:

  • ValueError –

    If matching_threshold is not a float in the [0,1] range.

Source code in darts-segmentation/src/darts_segmentation/metrics/binary_instance_stat_scores.py 
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
def __init__(
    self,
    threshold: float = 0.5,
    matching_threshold: float = 0.5,
    multidim_average: Literal["global", "samplewise"] = "global",
    ignore_index: int | None = None,
    validate_args: bool = True,
    **kwargs: Any,
) -> None:
    """Create a new instance of the BinaryInstanceStatScores metric.

    Args:
        threshold (float, optional): Threshold for binarizing the prediction.
            Has no effect if the prediction is already binarized. Defaults to 0.5.
        matching_threshold (float, optional): The threshold for matching instances. Defaults to 0.5.
        multidim_average (Literal["global", "samplewise"], optional): How the average over multiple batches is
            calculated. Defaults to "global".
        ignore_index (int | None, optional): Ignores an invalid class. Defaults to None.
        validate_args (bool, optional): Weather to validate inputs. Defaults to True.
        kwargs: Additional arguments for the Metric class, regarding compute-methods.
            Please refer to torchmetrics for more examples.

    Raises:
        ValueError: If `matching_threshold` is not a float in the [0,1] range.

    """
    zero_division = kwargs.pop("zero_division", 0)
    super(_AbstractStatScores, self).__init__(**kwargs)
    if validate_args:
        _binary_stat_scores_arg_validation(threshold, multidim_average, ignore_index, zero_division)
        if not (isinstance(matching_threshold, float) and (0 <= matching_threshold <= 1)):
            raise ValueError(
                f"Expected arg `matching_threshold` to be a float in the [0,1] range, but got {matching_threshold}."
            )

    self.threshold = threshold
    self.matching_threshold = matching_threshold
    self.multidim_average = multidim_average
    self.ignore_index = ignore_index
    self.validate_args = validate_args
    self.zero_division = zero_division

    self._create_state(size=1, multidim_average=multidim_average)

full_state_update class-attribute instance-attribute 

full_state_update: bool = False

higher_is_better class-attribute instance-attribute 

higher_is_better: bool | None = None

ignore_index instance-attribute 

ignore_index = darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceStatScores(
    ignore_index
)

is_differentiable class-attribute instance-attribute 

is_differentiable: bool = False

matching_threshold instance-attribute 

matching_threshold = darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceStatScores(
    matching_threshold
)

multidim_average instance-attribute 

multidim_average = darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceStatScores(
    multidim_average
)

threshold instance-attribute 

threshold = darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceStatScores(
    threshold
)

validate_args instance-attribute 

validate_args = darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceStatScores(
    validate_args
)

zero_division instance-attribute 

zero_division = zero_division

compute 

compute() -> torch.Tensor
Source code in darts-segmentation/src/darts_segmentation/metrics/binary_instance_stat_scores.py 
132
133
134
def compute(self) -> Tensor:  # noqa: D102
    tp, fp, tn, fn = self._final_state()
    return _binary_stat_scores_compute(tp, fp, tn, fn, self.multidim_average)

update 

update(preds: torch.Tensor, target: torch.Tensor) -> None

Update the metric state.

If the predictions are logits (not between 0 and 1), they are converted to probabilities using a sigmoid and then binarized using the threshold. If the predictions are probabilities, they are binarized using the threshold.

Parameters:

  • preds (torch.Tensor) –

    Predictions from model (logits or probabilities).

  • target (torch.Tensor) –

    Ground truth labels.

Raises:

  • ValueError –

    If preds and target have different shapes.

  • ValueError –

    If the input targets are not binary masks.

Source code in darts-segmentation/src/darts_segmentation/metrics/binary_instance_stat_scores.py 
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
def update(self, preds: Tensor, target: Tensor) -> None:
    """Update the metric state.

    If the predictions are logits (not between 0 and 1), they are converted to probabilities using a sigmoid and
    then binarized using the threshold.
    If the predictions are probabilities, they are binarized using the threshold.

    Args:
        preds (Tensor): Predictions from model (logits or probabilities).
        target (Tensor): Ground truth labels.

    Raises:
        ValueError: If `preds` and `target` have different shapes.
        ValueError: If the input targets are not binary masks.

    """
    if self.validate_args:
        _binary_stat_scores_tensor_validation(preds, target, self.multidim_average, self.ignore_index)
        if not preds.dim() == 3:
            raise ValueError(f"Expected `preds` and `target` to have 3 dimensions (BHW), but got {preds.dim()}.")
        if self.ignore_index is None and target.max() > 1:
            raise ValueError(
                "Expected binary mask, got more than 1 unique value in target."
                " You can set 'ignore_index' to ignore a class."
            )

    # Format
    if preds.is_floating_point():
        if not torch.all((preds >= 0) * (preds <= 1)):
            # preds is logits, convert with sigmoid
            preds = preds.sigmoid()
        preds = preds > self.threshold

    if self.ignore_index is not None:
        invalid_idx = target == self.ignore_index
        preds = preds.clone()
        preds[invalid_idx] = 0  # This will prevent from counting instances in the ignored area
        target = (target == 1).to(torch.uint8)

    # Update state
    instance_list_target = mask_to_instances(target.to(torch.uint8), self.validate_args)
    instance_list_preds = mask_to_instances(preds.to(torch.uint8), self.validate_args)

    for target_i, preds_i in zip(instance_list_target, instance_list_preds):
        tp, fp, fn = match_instances(
            target_i,
            preds_i,
            match_threshold=self.matching_threshold,
            validate_args=self.validate_args,
        )
        self._update_state(tp, fp, 0, fn)