Skip to content

binary_instance_stat_scores

darts_segmentation.metrics.binary_instance_stat_scores

Binary instance segmentation metrics.

BinaryInstanceAccuracy 

BinaryInstanceAccuracy(
    threshold: float = 0.5,
    matching_threshold: float = 0.5,
    multidim_average: typing.Literal[
        "global", "samplewise"
    ] = "global",
    ignore_index: int | None = None,
    validate_args: bool = True,
    **kwargs: typing.Any,
)

Bases: darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceStatScores

Binary instance accuracy metric.

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 = True

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
)

plot_lower_bound class-attribute instance-attribute 

plot_lower_bound: float = 0.0

plot_upper_bound class-attribute instance-attribute 

plot_upper_bound: float = 1.0

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 
206
207
208
209
210
211
212
213
214
215
def compute(self) -> Tensor:  # noqa: D102
    tp, fp, tn, fn = self._final_state()
    return _accuracy_reduce(
        tp,
        fp,
        tn,
        fn,
        average="binary",
        multidim_average=self.multidim_average,
    )

plot 

plot(
    val: torch.Tensor
    | collections.abc.Sequence[torch.Tensor]
    | None = None,
    ax: torchmetrics.utilities.plot._AX_TYPE | None = None,
) -> torchmetrics.utilities.plot._PLOT_OUT_TYPE
Source code in darts-segmentation/src/darts_segmentation/metrics/binary_instance_stat_scores.py 
217
218
219
220
221
222
def plot(  # noqa: D102
    self,
    val: Tensor | Sequence[Tensor] | None = None,
    ax: _AX_TYPE | None = None,  # type: ignore
) -> _PLOT_OUT_TYPE:  # type: ignore
    return self._plot(val, ax)

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)

BinaryInstanceConfusionMatrix 

BinaryInstanceConfusionMatrix(
    normalize: bool | None = None,
    threshold: float = 0.5,
    matching_threshold: float = 0.5,
    multidim_average: typing.Literal[
        "global", "samplewise"
    ] = "global",
    ignore_index: int | None = None,
    validate_args: bool = True,
    **kwargs: typing.Any,
)

Bases: darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceStatScores

Binary instance confusion matrix metric.

Create a new instance of the BinaryInstanceConfusionMatrix metric.

Parameters:

  • normalize (bool, default: None ) –

    If True, return the confusion matrix normalized by the number of instances. If False, return the confusion matrix without normalization. Defaults to None.

  • 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:

Source code in darts-segmentation/src/darts_segmentation/metrics/binary_instance_stat_scores.py 
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
def __init__(
    self,
    normalize: bool | None = None,
    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 BinaryInstanceConfusionMatrix metric.

    Args:
        normalize (bool, optional): If True, return the confusion matrix normalized by the number of instances.
            If False, return the confusion matrix without normalization. Defaults to None.
        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 `normalize` is not a bool.

    """
    super().__init__(
        threshold=threshold,
        matching_threshold=matching_threshold,
        multidim_average=multidim_average,
        ignore_index=ignore_index,
        validate_args=False,
        **kwargs,
    )
    if normalize is not None and not isinstance(normalize, bool):
        raise ValueError(f"Argument `normalize` needs to be of bool type but got {type(normalize)}")
    self.normalize = normalize

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
)

normalize instance-attribute 

normalize = darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceConfusionMatrix(
    normalize
)

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 
385
386
387
388
389
390
391
392
def compute(self) -> Tensor:  # noqa: D102
    tp, fp, tn, fn = self._final_state()
    # tn is always 0
    if self.normalize:
        all = tp + fp + fn
        return torch.tensor([[0, fp / all], [fn / all, tp / all]], device=tp.device)
    else:
        return torch.tensor([[tn, fp], [fn, tp]], device=tp.device)

plot 

plot(
    val: torch.Tensor | None = None,
    ax: torchmetrics.utilities.plot._AX_TYPE | None = None,
    add_text: bool = True,
    labels: list[str] | None = None,
    cmap: torchmetrics.utilities.plot._CMAP_TYPE
    | None = None,
) -> torchmetrics.utilities.plot._PLOT_OUT_TYPE
Source code in darts-segmentation/src/darts_segmentation/metrics/binary_instance_stat_scores.py 
394
395
396
397
398
399
400
401
402
403
404
405
406
def plot(  # noqa: D102
    self,
    val: Tensor | None = None,
    ax: _AX_TYPE | None = None,  # type: ignore
    add_text: bool = True,
    labels: list[str] | None = None,  # type: ignore
    cmap: _CMAP_TYPE | None = None,  # type: ignore
) -> _PLOT_OUT_TYPE:  # type: ignore
    val = val or self.compute()
    if not isinstance(val, Tensor):
        raise TypeError(f"Expected val to be a single tensor but got {val}")
    fig, ax = plot_confusion_matrix(val, ax=ax, add_text=add_text, labels=labels, cmap=cmap)
    return fig, ax

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)

BinaryInstanceF1Score 

BinaryInstanceF1Score(
    threshold: float = 0.5,
    multidim_average: typing.Literal[
        "global", "samplewise"
    ] = "global",
    ignore_index: int | None = None,
    validate_args: bool = True,
    zero_division: float = 0,
    **kwargs: typing.Any,
)

Bases: darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceFBetaScore

Binary instance F1 score metric.

Create a new instance of the BinaryInstanceF1Score 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.

  • 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.

  • zero_division (float, default: 0 ) –

    Value to return when there is a zero division. Defaults to 0.

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

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

Source code in darts-segmentation/src/darts_segmentation/metrics/binary_instance_stat_scores.py 
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
def __init__(
    self,
    threshold: float = 0.5,
    multidim_average: Literal["global", "samplewise"] = "global",
    ignore_index: int | None = None,
    validate_args: bool = True,
    zero_division: float = 0,
    **kwargs: Any,
) -> None:
    """Create a new instance of the BinaryInstanceF1Score metric.

    Args:
        threshold (float, optional): Threshold for binarizing the prediction.
            Has no effect if the prediction is already binarized. 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.
        zero_division (float, optional): Value to return when there is a zero division. Defaults to 0.
        kwargs: Additional arguments for the Metric class, regarding compute-methods.
            Please refer to torchmetrics for more examples.

    """
    super().__init__(
        beta=1.0,
        threshold=threshold,
        multidim_average=multidim_average,
        ignore_index=ignore_index,
        validate_args=validate_args,
        zero_division=zero_division,
        **kwargs,
    )

beta instance-attribute 

beta = darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceFBetaScore(
    beta
)

full_state_update class-attribute instance-attribute 

full_state_update: bool = False

higher_is_better class-attribute instance-attribute 

higher_is_better: bool | None = True

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
)

plot_lower_bound class-attribute instance-attribute 

plot_lower_bound: float = 0.0

plot_upper_bound class-attribute instance-attribute 

plot_upper_bound: float = 1.0

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.BinaryInstanceFBetaScore(
    validate_args
)

zero_division instance-attribute 

zero_division = darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceFBetaScore(
    zero_division
)

compute 

compute() -> torch.Tensor
Source code in darts-segmentation/src/darts_segmentation/metrics/binary_instance_stat_scores.py 
272
273
274
275
276
277
278
279
280
281
282
283
def compute(self) -> Tensor:  # noqa: D102
    tp, fp, tn, fn = self._final_state()
    return _fbeta_reduce(
        tp,
        fp,
        tn,
        fn,
        self.beta,
        average="binary",
        multidim_average=self.multidim_average,
        zero_division=self.zero_division,
    )

plot 

plot(
    val: torch.Tensor
    | collections.abc.Sequence[torch.Tensor]
    | None = None,
    ax: torchmetrics.utilities.plot._AX_TYPE | None = None,
) -> torchmetrics.utilities.plot._PLOT_OUT_TYPE
Source code in darts-segmentation/src/darts_segmentation/metrics/binary_instance_stat_scores.py 
329
330
331
332
333
334
def plot(  # noqa: D102
    self,
    val: Tensor | Sequence[Tensor] | None = None,
    ax: _AX_TYPE | None = None,  # type: ignore
) -> _PLOT_OUT_TYPE:  # type: ignore
    return self._plot(val, ax)

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)

BinaryInstanceFBetaScore 

BinaryInstanceFBetaScore(
    beta: float,
    threshold: float = 0.5,
    multidim_average: typing.Literal[
        "global", "samplewise"
    ] = "global",
    ignore_index: int | None = None,
    validate_args: bool = True,
    zero_division: float = 0,
    **kwargs: typing.Any,
)

Bases: darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceStatScores

Binary instance F-beta score metric.

Create a new instance of the BinaryInstanceFBetaScore metric.

Parameters:

  • beta (float) –

    The beta parameter for the F-beta score.

  • threshold (float, default: 0.5 ) –

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

  • zero_division (float, default: 0 ) –

    Value to return when there is a zero division. Defaults to 0.

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

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

Source code in darts-segmentation/src/darts_segmentation/metrics/binary_instance_stat_scores.py 
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
def __init__(
    self,
    beta: float,
    threshold: float = 0.5,
    multidim_average: Literal["global", "samplewise"] = "global",
    ignore_index: int | None = None,
    validate_args: bool = True,
    zero_division: float = 0,
    **kwargs: Any,
) -> None:
    """Create a new instance of the BinaryInstanceFBetaScore metric.

    Args:
        beta (float): The beta parameter for the F-beta score.
        threshold (float, optional): Threshold for binarizing the prediction.
            Has no effect if the prediction is already binarized. 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.
        zero_division (float, optional): Value to return when there is a zero division. Defaults to 0.
        kwargs: Additional arguments for the Metric class, regarding compute-methods.
            Please refer to torchmetrics for more examples.

    """
    super().__init__(
        threshold=threshold,
        multidim_average=multidim_average,
        ignore_index=ignore_index,
        validate_args=False,
        **kwargs,
    )
    if validate_args:
        _binary_fbeta_score_arg_validation(beta, threshold, multidim_average, ignore_index, zero_division)
    self.validate_args = validate_args
    self.zero_division = zero_division
    self.beta = beta

beta instance-attribute 

beta = darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceFBetaScore(
    beta
)

full_state_update class-attribute instance-attribute 

full_state_update: bool = False

higher_is_better class-attribute instance-attribute 

higher_is_better: bool | None = True

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
)

plot_lower_bound class-attribute instance-attribute 

plot_lower_bound: float = 0.0

plot_upper_bound class-attribute instance-attribute 

plot_upper_bound: float = 1.0

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.BinaryInstanceFBetaScore(
    validate_args
)

zero_division instance-attribute 

zero_division = darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceFBetaScore(
    zero_division
)

compute 

compute() -> torch.Tensor
Source code in darts-segmentation/src/darts_segmentation/metrics/binary_instance_stat_scores.py 
272
273
274
275
276
277
278
279
280
281
282
283
def compute(self) -> Tensor:  # noqa: D102
    tp, fp, tn, fn = self._final_state()
    return _fbeta_reduce(
        tp,
        fp,
        tn,
        fn,
        self.beta,
        average="binary",
        multidim_average=self.multidim_average,
        zero_division=self.zero_division,
    )

plot 

plot(
    val: torch.Tensor
    | collections.abc.Sequence[torch.Tensor]
    | None = None,
    ax: torchmetrics.utilities.plot._AX_TYPE | None = None,
) -> torchmetrics.utilities.plot._PLOT_OUT_TYPE
Source code in darts-segmentation/src/darts_segmentation/metrics/binary_instance_stat_scores.py 
285
286
287
288
289
290
def plot(  # noqa: D102
    self,
    val: Tensor | Sequence[Tensor] | None = None,
    ax: _AX_TYPE | None = None,  # type: ignore
) -> _PLOT_OUT_TYPE:  # type: ignore
    return self._plot(val, ax)

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)

BinaryInstancePrecision 

BinaryInstancePrecision(
    threshold: float = 0.5,
    matching_threshold: float = 0.5,
    multidim_average: typing.Literal[
        "global", "samplewise"
    ] = "global",
    ignore_index: int | None = None,
    validate_args: bool = True,
    **kwargs: typing.Any,
)

Bases: darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceStatScores

Binary instance precision metric.

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 = True

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
)

plot_lower_bound class-attribute instance-attribute 

plot_lower_bound: float = 0.0

plot_upper_bound class-attribute instance-attribute 

plot_upper_bound: float = 1.0

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 
176
177
178
179
180
181
182
183
184
185
186
187
def compute(self) -> Tensor:  # noqa: D102
    tp, fp, tn, fn = self._final_state()
    return _precision_recall_reduce(
        "precision",
        tp,
        fp,
        tn,
        fn,
        average="binary",
        multidim_average=self.multidim_average,
        zero_division=self.zero_division,
    )

plot 

plot(
    val: torch.Tensor
    | collections.abc.Sequence[torch.Tensor]
    | None = None,
    ax: torchmetrics.utilities.plot._AX_TYPE | None = None,
) -> torchmetrics.utilities.plot._PLOT_OUT_TYPE
Source code in darts-segmentation/src/darts_segmentation/metrics/binary_instance_stat_scores.py 
189
190
191
192
193
194
def plot(  # noqa: D102
    self,
    val: Tensor | Sequence[Tensor] | None = None,
    ax: _AX_TYPE | None = None,  # type: ignore
) -> _PLOT_OUT_TYPE:  # type: ignore
    return self._plot(val, ax)

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)

BinaryInstanceRecall 

BinaryInstanceRecall(
    threshold: float = 0.5,
    matching_threshold: float = 0.5,
    multidim_average: typing.Literal[
        "global", "samplewise"
    ] = "global",
    ignore_index: int | None = None,
    validate_args: bool = True,
    **kwargs: typing.Any,
)

Bases: darts_segmentation.metrics.binary_instance_stat_scores.BinaryInstanceStatScores

Binary instance recall metric.

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 = True

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
)

plot_lower_bound class-attribute instance-attribute 

plot_lower_bound: float = 0.0

plot_upper_bound class-attribute instance-attribute 

plot_upper_bound: float = 1.0

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 
146
147
148
149
150
151
152
153
154
155
156
157
def compute(self) -> Tensor:  # noqa: D102
    tp, fp, tn, fn = self._final_state()
    return _precision_recall_reduce(
        "recall",
        tp,
        fp,
        tn,
        fn,
        average="binary",
        multidim_average=self.multidim_average,
        zero_division=self.zero_division,
    )

plot 

plot(
    val: torch.Tensor
    | collections.abc.Sequence[torch.Tensor]
    | None = None,
    ax: torchmetrics.utilities.plot._AX_TYPE | None = None,
) -> torchmetrics.utilities.plot._PLOT_OUT_TYPE
Source code in darts-segmentation/src/darts_segmentation/metrics/binary_instance_stat_scores.py 
159
160
161
162
163
164
def plot(  # noqa: D102
    self,
    val: Tensor | Sequence[Tensor] | None = None,
    ax: _AX_TYPE | None = None,  # type: ignore
) -> _PLOT_OUT_TYPE:  # type: ignore
    return self._plot(val, ax)

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)

BinaryInstanceStatScores 

BinaryInstanceStatScores(
    threshold: float = 0.5,
    matching_threshold: float = 0.5,
    multidim_average: typing.Literal[
        "global", "samplewise"
    ] = "global",
    ignore_index: int | None = None,
    validate_args: bool = True,
    **kwargs: typing.Any,
)

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)

mask_to_instances 

mask_to_instances(
    x: torch.Tensor, validate_args: bool = False
) -> list[torch.Tensor]

Convert a binary segmentation mask into multiple instance masks. Expects a batched version of the input.

Currently only supports uint8 tensors, hence a maximum number of 255 instances per mask.

Parameters:

  • x (torch.Tensor) –

    The binary segmentation mask. Shape: (batch_size, height, width), dtype: torch.uint8

  • validate_args (bool, default: False ) –

    Whether to validate the input arguments. Defaults to False.

Returns:

  • list[torch.Tensor] –

    list[torch.Tensor]: The instance masks. Length of list: batch_size. Shape of a tensor: (height, width), dtype: torch.uint8

Source code in darts-segmentation/src/darts_segmentation/metrics/instance_helpers.py 
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
@torch.no_grad()
def mask_to_instances(x: torch.Tensor, validate_args: bool = False) -> list[torch.Tensor]:
    """Convert a binary segmentation mask into multiple instance masks. Expects a batched version of the input.

    Currently only supports uint8 tensors, hence a maximum number of 255 instances per mask.

    Args:
        x (torch.Tensor): The binary segmentation mask. Shape: (batch_size, height, width), dtype: torch.uint8
        validate_args (bool, optional): Whether to validate the input arguments. Defaults to False.

    Returns:
        list[torch.Tensor]: The instance masks. Length of list: batch_size.
            Shape of a tensor: (height, width), dtype: torch.uint8

    """
    if validate_args:
        assert x.dim() == 3, f"Expected 3 dimensions, got {x.dim()}"
        assert x.dtype == torch.uint8, f"Expected torch.uint8, got {x.dtype}"
        assert x.min() >= 0 and x.max() <= 1, f"Expected binary mask, got {x.min()} and {x.max()}"

    # A note on using lists as separation between instances instead of using a batched tensor:
    # Using a batched tensor with instance numbers (1, 2, 3, ...) would indicate that the instances of the samples
    # are identical. Using a list clearly separates the instances of the samples.

    if CUCIM_AVAILABLE:
        # Check if device is cuda
        assert x.device.type == "cuda", f"Expected device to be cuda, got {x.device.type}"
        x = cp.asarray(x).astype(cp.uint8)

        instances = []
        for x_i in x:
            instances_i = label_gpu(x_i)
            instances_i = torch.tensor(instances_i, dtype=torch.uint8)
            instances.append(instances_i)
        return instances

    else:
        instances = []
        for x_i in x:
            x_i = x_i.cpu().numpy()
            instances_i = label(x_i)
            instances_i = torch.tensor(instances_i, dtype=torch.uint8)
            instances.append(instances_i)
        return instances

match_instances 

match_instances(
    instances_target: torch.Tensor,
    instances_preds: torch.Tensor,
    match_threshold: float = 0.5,
    validate_args: bool = False,
) -> tuple[int, int, int]

Match instances between target and prediction masks. Expects non-batched input from skimage.measure.label.

Parameters:

  • instances_target (torch.Tensor) –

    The instance mask of the target. Shape: (height, width), dtype: torch.uint8

  • instances_preds (torch.Tensor) –

    The instance mask of the prediction. Shape: (height, width), dtype: torch.uint8

  • match_threshold (float, default: 0.5 ) –

    The threshold for matching instances. Defaults to 0.5.

  • validate_args (bool, default: False ) –

    Whether to validate the input arguments. Defaults to False.

Returns:

  • tuple[int, int, int] –

    tuple[int, int, int]: True positives, false positives, false negatives

Source code in darts-segmentation/src/darts_segmentation/metrics/instance_helpers.py 
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 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
131
132
@torch.no_grad()
def match_instances(
    instances_target: torch.Tensor,
    instances_preds: torch.Tensor,
    match_threshold: float = 0.5,
    validate_args: bool = False,
) -> tuple[int, int, int]:
    """Match instances between target and prediction masks. Expects non-batched input from skimage.measure.label.

    Args:
        instances_target (torch.Tensor): The instance mask of the target. Shape: (height, width), dtype: torch.uint8
        instances_preds (torch.Tensor): The instance mask of the prediction. Shape: (height, width), dtype: torch.uint8
        match_threshold (float, optional): The threshold for matching instances. Defaults to 0.5.
        validate_args (bool, optional): Whether to validate the input arguments. Defaults to False.

    Returns:
        tuple[int, int, int]: True positives, false positives, false negatives

    """
    if validate_args:
        assert instances_target.dim() == 2, f"Expected 2 dimensions, got {instances_target.dim()}"
        assert instances_preds.dim() == 2, f"Expected 2 dimensions, got {instances_preds.dim()}"
        assert instances_target.dtype == torch.uint8, f"Expected torch.uint8, got {instances_target.dtype}"
        assert instances_preds.dtype == torch.uint8, f"Expected torch.uint8, got {instances_preds.dtype}"
        assert instances_target.shape == instances_preds.shape, (
            f"Shapes do not match: {instances_target.shape} and {instances_preds.shape}"
        )

    height, width = instances_target.shape
    ntargets = instances_target.max().item()
    npreds = instances_preds.max().item()
    # If target or predictions has no instances, return 0 for their respective metrics.
    # If none of them has instances, return 0 for all metrics. (This is implied)
    if ntargets == 0:
        return 0, npreds, 0
    if npreds == 0:
        return 0, 0, ntargets

    # TODO: These are old edge case filter that need revision.
    # They are probably not necessary, since the instance metrics are meaningless for noisy predictions.
    # If there are too many predictions, return all as false positives (this happens when the model is very noisy)
    # if npreds > ntargets * 5:
    #     return 0, npreds, ntargets
    # If there is only one prediction, return all as false negatives (this happens when the model is very noisy)
    # if npreds == 1 and ntargets > 1:
    #     return 0, 1, ntargets

    # Create one-hot encoding of instances, so that each instance is a channel
    instances_target_onehot = torch.zeros((ntargets, height, width), dtype=torch.uint8, device=instances_target.device)
    instances_preds_onehot = torch.zeros((npreds, height, width), dtype=torch.uint8, device=instances_target.device)
    for i in range(ntargets):
        instances_target_onehot[i, :, :] = instances_target == (i + 1)
    for i in range(npreds):
        instances_preds_onehot[i, :, :] = instances_preds == (i + 1)

    # Now the instances are channels, hence tensors of shape (num_instances, height, width)

    # Calculate IoU (we need to do a n-m intersection and union, therefore we need to broadcast)
    intersection = (instances_target_onehot.unsqueeze(1) & instances_preds_onehot.unsqueeze(0)).sum(
        dim=(2, 3)
    )  # Shape: (num_instances_target, num_instances_preds)
    union = (instances_target_onehot.unsqueeze(1) | instances_preds_onehot.unsqueeze(0)).sum(
        dim=(2, 3)
    )  # Shape: (num_instances_target, num_instances_preds)
    iou = intersection / union  # Shape: (num_instances_target, num_instances_preds)

    # Match instances based on IoU
    tp = (iou >= match_threshold).sum().item()
    fp = npreds - tp
    fn = ntargets - tp

    return tp, fp, fn