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ocl.metrics.masks

Metrics related to the evaluation of masks.

ARIMetric

Bases: torchmetrics.Metric

Computes ARI metric.

Source code in ocl/metrics/masks.py 
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class ARIMetric(torchmetrics.Metric):
    """Computes ARI metric."""

    def __init__(
        self,
        foreground: bool = True,
        convert_target_one_hot: bool = False,
        ignore_overlaps: bool = False,
    ):
        super().__init__()
        self.foreground = foreground
        self.convert_target_one_hot = convert_target_one_hot
        self.ignore_overlaps = ignore_overlaps
        self.add_state(
            "values", default=torch.tensor(0.0, dtype=torch.float64), dist_reduce_fx="sum"
        )
        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")

    def update(
        self, prediction: torch.Tensor, target: torch.Tensor, ignore: Optional[torch.Tensor] = None
    ):
        """Update this metric.

        Args:
            prediction: Predicted mask of shape (B, C, H, W) or (B, F, C, H, W), where C is the
                number of classes.
            target: Ground truth mask of shape (B, K, H, W) or (B, F, K, H, W), where K is the
                number of classes.
            ignore: Ignore mask of shape (B, 1, H, W) or (B, 1, K, H, W)
        """
        if prediction.ndim == 5:
            # Merge frames, height and width to single dimension.
            prediction = prediction.transpose(1, 2).flatten(-3, -1)
            target = target.transpose(1, 2).flatten(-3, -1)
            if ignore is not None:
                ignore = ignore.to(torch.bool).transpose(1, 2).flatten(-3, -1)
        elif prediction.ndim == 4:
            # Merge height and width to single dimension.
            prediction = prediction.flatten(-2, -1)
            target = target.flatten(-2, -1)
            if ignore is not None:
                ignore = ignore.to(torch.bool).flatten(-2, -1)
        else:
            raise ValueError(f"Incorrect input shape: f{prediction.shape}")

        if self.ignore_overlaps:
            overlaps = (target > 0).sum(1, keepdim=True) > 1
            if ignore is None:
                ignore = overlaps
            else:
                ignore = ignore | overlaps

        if ignore is not None:
            assert ignore.ndim == 3 and ignore.shape[1] == 1
            prediction = prediction.clone()
            prediction[ignore.expand_as(prediction)] = 0
            target = target.clone()
            target[ignore.expand_as(target)] = 0

        # Make channels / gt labels the last dimension.
        prediction = prediction.transpose(-2, -1)
        target = target.transpose(-2, -1)

        if self.convert_target_one_hot:
            target_oh = tensor_to_one_hot(target, dim=2)
            # For empty pixels (all values zero), one-hot assigns 1 to the first class, correct for
            # this (then it is technically not one-hot anymore).
            target_oh[:, :, 0][target.sum(dim=2) == 0] = 0
            target = target_oh

        # Should be either 0 (empty, padding) or 1 (single object).
        assert torch.all(target.sum(dim=-1) < 2), "Issues with target format, mask non-exclusive"

        if self.foreground:
            ari = fg_adjusted_rand_index(prediction, target)
        else:
            ari = adjusted_rand_index(prediction, target)

        self.values += ari.sum()
        self.total += len(ari)

    def compute(self) -> torch.Tensor:
        return self.values / self.total

update

Update this metric.

Parameters:

Name Type Description Default
prediction torch.Tensor

Predicted mask of shape (B, C, H, W) or (B, F, C, H, W), where C is the number of classes.

 required
target torch.Tensor

Ground truth mask of shape (B, K, H, W) or (B, F, K, H, W), where K is the number of classes.

 required
ignore Optional[torch.Tensor]

Ignore mask of shape (B, 1, H, W) or (B, 1, K, H, W)

 None
Source code in ocl/metrics/masks.py 
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def update(
    self, prediction: torch.Tensor, target: torch.Tensor, ignore: Optional[torch.Tensor] = None
):
    """Update this metric.

    Args:
        prediction: Predicted mask of shape (B, C, H, W) or (B, F, C, H, W), where C is the
            number of classes.
        target: Ground truth mask of shape (B, K, H, W) or (B, F, K, H, W), where K is the
            number of classes.
        ignore: Ignore mask of shape (B, 1, H, W) or (B, 1, K, H, W)
    """
    if prediction.ndim == 5:
        # Merge frames, height and width to single dimension.
        prediction = prediction.transpose(1, 2).flatten(-3, -1)
        target = target.transpose(1, 2).flatten(-3, -1)
        if ignore is not None:
            ignore = ignore.to(torch.bool).transpose(1, 2).flatten(-3, -1)
    elif prediction.ndim == 4:
        # Merge height and width to single dimension.
        prediction = prediction.flatten(-2, -1)
        target = target.flatten(-2, -1)
        if ignore is not None:
            ignore = ignore.to(torch.bool).flatten(-2, -1)
    else:
        raise ValueError(f"Incorrect input shape: f{prediction.shape}")

    if self.ignore_overlaps:
        overlaps = (target > 0).sum(1, keepdim=True) > 1
        if ignore is None:
            ignore = overlaps
        else:
            ignore = ignore | overlaps

    if ignore is not None:
        assert ignore.ndim == 3 and ignore.shape[1] == 1
        prediction = prediction.clone()
        prediction[ignore.expand_as(prediction)] = 0
        target = target.clone()
        target[ignore.expand_as(target)] = 0

    # Make channels / gt labels the last dimension.
    prediction = prediction.transpose(-2, -1)
    target = target.transpose(-2, -1)

    if self.convert_target_one_hot:
        target_oh = tensor_to_one_hot(target, dim=2)
        # For empty pixels (all values zero), one-hot assigns 1 to the first class, correct for
        # this (then it is technically not one-hot anymore).
        target_oh[:, :, 0][target.sum(dim=2) == 0] = 0
        target = target_oh

    # Should be either 0 (empty, padding) or 1 (single object).
    assert torch.all(target.sum(dim=-1) < 2), "Issues with target format, mask non-exclusive"

    if self.foreground:
        ari = fg_adjusted_rand_index(prediction, target)
    else:
        ari = adjusted_rand_index(prediction, target)

    self.values += ari.sum()
    self.total += len(ari)

PatchARIMetric

Bases: ARIMetric

Computes ARI metric assuming patch masks as input.

Source code in ocl/metrics/masks.py 
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class PatchARIMetric(ARIMetric):
    """Computes ARI metric assuming patch masks as input."""

    def __init__(
        self,
        foreground=True,
        resize_masks_mode: str = "bilinear",
        **kwargs,
    ):
        super().__init__(foreground=foreground, **kwargs)
        self.resize_masks_mode = resize_masks_mode

    def update(self, prediction: torch.Tensor, target: torch.Tensor):
        """Update this metric.

        Args:
            prediction: Predicted mask of shape (B, C, P) or (B, F, C, P), where C is the
                number of classes and P the number of patches.
            target: Ground truth mask of shape (B, K, H, W) or (B, F, K, H, W), where K is the
                number of classes.
        """
        h, w = target.shape[-2:]
        assert h == w

        prediction_resized = resize_patches_to_image(
            prediction, size=h, resize_mode=self.resize_masks_mode
        )

        return super().update(prediction=prediction_resized, target=target)

update

Update this metric.

Parameters:

Name Type Description Default
prediction torch.Tensor

Predicted mask of shape (B, C, P) or (B, F, C, P), where C is the number of classes and P the number of patches.

 required
target torch.Tensor

Ground truth mask of shape (B, K, H, W) or (B, F, K, H, W), where K is the number of classes.

 required
Source code in ocl/metrics/masks.py 
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def update(self, prediction: torch.Tensor, target: torch.Tensor):
    """Update this metric.

    Args:
        prediction: Predicted mask of shape (B, C, P) or (B, F, C, P), where C is the
            number of classes and P the number of patches.
        target: Ground truth mask of shape (B, K, H, W) or (B, F, K, H, W), where K is the
            number of classes.
    """
    h, w = target.shape[-2:]
    assert h == w

    prediction_resized = resize_patches_to_image(
        prediction, size=h, resize_mode=self.resize_masks_mode
    )

    return super().update(prediction=prediction_resized, target=target)

UnsupervisedMaskIoUMetric

Bases: torchmetrics.Metric

Computes IoU metric for segmentation masks when correspondences to ground truth are not known.

Uses Hungarian matching to compute the assignment between predicted classes and ground truth classes.

Parameters:

Name Type Description Default
use_threshold bool

If True, convert predicted class probabilities to mask using a threshold. If False, class probabilities are turned into mask using a softmax instead.

 False
threshold float

Value to use for thresholding masks.

 0.5
matching str

Approach to match predicted to ground truth classes. For "hungarian", computes assignment that maximizes total IoU between all classes. For "best_overlap", uses the predicted class with maximum overlap for each ground truth class. Using "best_overlap" leads to the "average best overlap" metric.

 'hungarian'
compute_discovery_fraction bool

Instead of the IoU, compute the fraction of ground truth classes that were "discovered", meaning that they have an IoU greater than some threshold.

 False
correct_localization bool

Instead of the IoU, compute the fraction of images on which at least one ground truth class was correctly localised, meaning that they have an IoU greater than some threshold.

 False
discovery_threshold float

Minimum IoU to count a class as discovered/correctly localized.

 0.5
ignore_background bool

If true, assume class at index 0 of ground truth masks is background class that is removed before computing IoU.

 False
ignore_overlaps bool

If true, remove points where ground truth masks has overlappign classes from predictions and ground truth masks.

 False
Source code in ocl/metrics/masks.py 
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class UnsupervisedMaskIoUMetric(torchmetrics.Metric):
    """Computes IoU metric for segmentation masks when correspondences to ground truth are not known.

    Uses Hungarian matching to compute the assignment between predicted classes and ground truth
    classes.

    Args:
        use_threshold: If `True`, convert predicted class probabilities to mask using a threshold.
            If `False`, class probabilities are turned into mask using a softmax instead.
        threshold: Value to use for thresholding masks.
        matching: Approach to match predicted to ground truth classes. For "hungarian", computes
            assignment that maximizes total IoU between all classes. For "best_overlap", uses the
            predicted class with maximum overlap for each ground truth class. Using "best_overlap"
            leads to the "average best overlap" metric.
        compute_discovery_fraction: Instead of the IoU, compute the fraction of ground truth classes
            that were "discovered", meaning that they have an IoU greater than some threshold.
        correct_localization: Instead of the IoU, compute the fraction of images on which at least
            one ground truth class was correctly localised, meaning that they have an IoU
            greater than some threshold.
        discovery_threshold: Minimum IoU to count a class as discovered/correctly localized.
        ignore_background: If true, assume class at index 0 of ground truth masks is background class
            that is removed before computing IoU.
        ignore_overlaps: If true, remove points where ground truth masks has overlappign classes from
            predictions and ground truth masks.
    """

    def __init__(
        self,
        use_threshold: bool = False,
        threshold: float = 0.5,
        matching: str = "hungarian",
        compute_discovery_fraction: bool = False,
        correct_localization: bool = False,
        discovery_threshold: float = 0.5,
        ignore_background: bool = False,
        ignore_overlaps: bool = False,
    ):
        super().__init__()
        self.use_threshold = use_threshold
        self.threshold = threshold
        self.discovery_threshold = discovery_threshold
        self.compute_discovery_fraction = compute_discovery_fraction
        self.correct_localization = correct_localization
        if compute_discovery_fraction and correct_localization:
            raise ValueError(
                "Only one of `compute_discovery_fraction` and `correct_localization` can be enabled."
            )

        matchings = ("hungarian", "best_overlap")
        if matching not in matchings:
            raise ValueError(f"Unknown matching type {matching}. Valid values are {matchings}.")
        self.matching = matching
        self.ignore_background = ignore_background
        self.ignore_overlaps = ignore_overlaps

        self.add_state(
            "values", default=torch.tensor(0.0, dtype=torch.float64), dist_reduce_fx="sum"
        )
        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")

    def update(
        self, prediction: torch.Tensor, target: torch.Tensor, ignore: Optional[torch.Tensor] = None
    ):
        """Update this metric.

        Args:
            prediction: Predicted mask of shape (B, C, H, W) or (B, F, C, H, W), where C is the
                number of classes. Assumes class probabilities as inputs.
            target: Ground truth mask of shape (B, K, H, W) or (B, F, K, H, W), where K is the
                number of classes.
            ignore: Ignore mask of shape (B, 1, H, W) or (B, 1, K, H, W)
        """
        if prediction.ndim == 5:
            # Merge frames, height and width to single dimension.
            predictions = prediction.transpose(1, 2).flatten(-3, -1)
            targets = target.transpose(1, 2).flatten(-3, -1)
            if ignore is not None:
                ignore = ignore.to(torch.bool).transpose(1, 2).flatten(-3, -1)
        elif prediction.ndim == 4:
            # Merge height and width to single dimension.
            predictions = prediction.flatten(-2, -1)
            targets = target.flatten(-2, -1)
            if ignore is not None:
                ignore = ignore.to(torch.bool).flatten(-2, -1)
        else:
            raise ValueError(f"Incorrect input shape: f{prediction.shape}")

        if self.use_threshold:
            predictions = predictions > self.threshold
        else:
            indices = torch.argmax(predictions, dim=1)
            predictions = torch.nn.functional.one_hot(indices, num_classes=predictions.shape[1])
            predictions = predictions.transpose(1, 2)

        if self.ignore_background:
            targets = targets[:, 1:]

        targets = targets > 0  # Ensure masks are binary

        if self.ignore_overlaps:
            overlaps = targets.sum(1, keepdim=True) > 1
            if ignore is None:
                ignore = overlaps
            else:
                ignore = ignore | overlaps

        if ignore is not None:
            assert ignore.ndim == 3 and ignore.shape[1] == 1
            predictions[ignore.expand_as(predictions)] = 0
            targets[ignore.expand_as(targets)] = 0

        # Should be either 0 (empty, padding) or 1 (single object).
        assert torch.all(targets.sum(dim=1) < 2), "Issues with target format, mask non-exclusive"

        for pred, target in zip(predictions, targets):
            nonzero_classes = torch.sum(target, dim=-1) > 0
            target = target[nonzero_classes]  # Remove empty (e.g. padded) classes
            if len(target) == 0:
                continue  # Skip elements without any target mask

            iou_per_class = unsupervised_mask_iou(
                pred, target, matching=self.matching, reduction="none"
            )

            if self.compute_discovery_fraction:
                discovered = iou_per_class > self.discovery_threshold
                self.values += discovered.sum() / len(discovered)
            elif self.correct_localization:
                correctly_localized = torch.any(iou_per_class > self.discovery_threshold)
                self.values += correctly_localized.sum()
            else:
                self.values += iou_per_class.mean()
            self.total += 1

    def compute(self) -> torch.Tensor:
        if self.total == 0:
            return torch.zeros_like(self.values)
        else:
            return self.values / self.total

update

Update this metric.

Parameters:

Name Type Description Default
prediction torch.Tensor

Predicted mask of shape (B, C, H, W) or (B, F, C, H, W), where C is the number of classes. Assumes class probabilities as inputs.

 required
target torch.Tensor

Ground truth mask of shape (B, K, H, W) or (B, F, K, H, W), where K is the number of classes.

 required
ignore Optional[torch.Tensor]

Ignore mask of shape (B, 1, H, W) or (B, 1, K, H, W)

 None
Source code in ocl/metrics/masks.py 
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def update(
    self, prediction: torch.Tensor, target: torch.Tensor, ignore: Optional[torch.Tensor] = None
):
    """Update this metric.

    Args:
        prediction: Predicted mask of shape (B, C, H, W) or (B, F, C, H, W), where C is the
            number of classes. Assumes class probabilities as inputs.
        target: Ground truth mask of shape (B, K, H, W) or (B, F, K, H, W), where K is the
            number of classes.
        ignore: Ignore mask of shape (B, 1, H, W) or (B, 1, K, H, W)
    """
    if prediction.ndim == 5:
        # Merge frames, height and width to single dimension.
        predictions = prediction.transpose(1, 2).flatten(-3, -1)
        targets = target.transpose(1, 2).flatten(-3, -1)
        if ignore is not None:
            ignore = ignore.to(torch.bool).transpose(1, 2).flatten(-3, -1)
    elif prediction.ndim == 4:
        # Merge height and width to single dimension.
        predictions = prediction.flatten(-2, -1)
        targets = target.flatten(-2, -1)
        if ignore is not None:
            ignore = ignore.to(torch.bool).flatten(-2, -1)
    else:
        raise ValueError(f"Incorrect input shape: f{prediction.shape}")

    if self.use_threshold:
        predictions = predictions > self.threshold
    else:
        indices = torch.argmax(predictions, dim=1)
        predictions = torch.nn.functional.one_hot(indices, num_classes=predictions.shape[1])
        predictions = predictions.transpose(1, 2)

    if self.ignore_background:
        targets = targets[:, 1:]

    targets = targets > 0  # Ensure masks are binary

    if self.ignore_overlaps:
        overlaps = targets.sum(1, keepdim=True) > 1
        if ignore is None:
            ignore = overlaps
        else:
            ignore = ignore | overlaps

    if ignore is not None:
        assert ignore.ndim == 3 and ignore.shape[1] == 1
        predictions[ignore.expand_as(predictions)] = 0
        targets[ignore.expand_as(targets)] = 0

    # Should be either 0 (empty, padding) or 1 (single object).
    assert torch.all(targets.sum(dim=1) < 2), "Issues with target format, mask non-exclusive"

    for pred, target in zip(predictions, targets):
        nonzero_classes = torch.sum(target, dim=-1) > 0
        target = target[nonzero_classes]  # Remove empty (e.g. padded) classes
        if len(target) == 0:
            continue  # Skip elements without any target mask

        iou_per_class = unsupervised_mask_iou(
            pred, target, matching=self.matching, reduction="none"
        )

        if self.compute_discovery_fraction:
            discovered = iou_per_class > self.discovery_threshold
            self.values += discovered.sum() / len(discovered)
        elif self.correct_localization:
            correctly_localized = torch.any(iou_per_class > self.discovery_threshold)
            self.values += correctly_localized.sum()
        else:
            self.values += iou_per_class.mean()
        self.total += 1

unsupervised_mask_iou

Compute intersection-over-union (IoU) between masks with unknown class correspondences.

This metric is also known as Jaccard index. Note that this is a non-batched implementation.

Parameters:

Name Type Description Default
pred_mask torch.Tensor

Predicted mask of shape (C, N), where C is the number of predicted classes and N is the number of points. Masks are assumed to be binary.

 required
true_mask torch.Tensor

Ground truth mask of shape (K, N), where K is the number of ground truth classes and N is the number of points. Masks are assumed to be binary.

 required
matching str

How to match predicted classes to ground truth classes. For "hungarian", computes assignment that maximizes total IoU between all classes. For "best_overlap", uses the predicted class with maximum overlap for each ground truth class (each predicted class can be assigned to multiple ground truth classes). Empty ground truth classes are assigned IoU of zero.

 'hungarian'
reduction str

If "mean", return IoU averaged over classes. If "none", return per-class IoU.

 'mean'
iou_empty float

IoU for the case when a class does not occur, but was also not predicted.

 0.0

Returns:

Type Description
torch.Tensor

Mean IoU over classes if reduction is mean, tensor of shape (K,) containing per-class IoU
torch.Tensor

otherwise.
Source code in ocl/metrics/masks.py 
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def unsupervised_mask_iou(
    pred_mask: torch.Tensor,
    true_mask: torch.Tensor,
    matching: str = "hungarian",
    reduction: str = "mean",
    iou_empty: float = 0.0,
) -> torch.Tensor:
    """Compute intersection-over-union (IoU) between masks with unknown class correspondences.

    This metric is also known as Jaccard index. Note that this is a non-batched implementation.

    Args:
        pred_mask: Predicted mask of shape (C, N), where C is the number of predicted classes and
            N is the number of points. Masks are assumed to be binary.
        true_mask: Ground truth mask of shape (K, N), where K is the number of ground truth
            classes and N is the number of points. Masks are assumed to be binary.
        matching: How to match predicted classes to ground truth classes. For "hungarian", computes
            assignment that maximizes total IoU between all classes. For "best_overlap", uses the
            predicted class with maximum overlap for each ground truth class (each predicted class
            can be assigned to multiple ground truth classes). Empty ground truth classes are
            assigned IoU of zero.
        reduction: If "mean", return IoU averaged over classes. If "none", return per-class IoU.
        iou_empty: IoU for the case when a class does not occur, but was also not predicted.

    Returns:
        Mean IoU over classes if reduction is `mean`, tensor of shape (K,) containing per-class IoU
        otherwise.
    """
    assert pred_mask.ndim == 2
    assert true_mask.ndim == 2
    n_gt_classes = len(true_mask)
    pred_mask = pred_mask.unsqueeze(1).to(torch.bool)
    true_mask = true_mask.unsqueeze(0).to(torch.bool)

    intersection = torch.sum(pred_mask & true_mask, dim=-1).to(torch.float64)
    union = torch.sum(pred_mask | true_mask, dim=-1).to(torch.float64)
    pairwise_iou = intersection / union

    # Remove NaN from divide-by-zero: class does not occur, and class was not predicted.
    pairwise_iou[union == 0] = iou_empty

    if matching == "hungarian":
        pred_idxs, true_idxs = scipy.optimize.linear_sum_assignment(
            pairwise_iou.cpu(), maximize=True
        )
        pred_idxs = torch.as_tensor(pred_idxs, dtype=torch.int64, device=pairwise_iou.device)
        true_idxs = torch.as_tensor(true_idxs, dtype=torch.int64, device=pairwise_iou.device)
    elif matching == "best_overlap":
        non_empty_gt = torch.sum(true_mask.squeeze(0), dim=1) > 0
        pred_idxs = torch.argmax(pairwise_iou, dim=0)[non_empty_gt]
        true_idxs = torch.arange(pairwise_iou.shape[1])[non_empty_gt]
    else:
        raise ValueError(f"Unknown matching {matching}")

    matched_iou = pairwise_iou[pred_idxs, true_idxs]
    iou = torch.zeros(n_gt_classes, dtype=torch.float64, device=pairwise_iou.device)
    iou[true_idxs] = matched_iou

    if reduction == "mean":
        return iou.mean()
    else:
        return iou