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Panoptic Quality

Module Interface

class torchmetrics.detection.PanopticQuality(things, stuffs, allow_unknown_preds_category=False, return_sq_and_rq=False, return_per_class=False, **kwargs)[source]

Compute the Panoptic Quality for panoptic segmentations.

\[PQ = \frac{IOU}{TP + 0.5 FP + 0.5 FN}\]

where IOU, TP, FP and FN are respectively the sum of the intersection over union for true positives, the number of true positives, false positives and false negatives. This metric is inspired by the PQ implementation of panopticapi, a standard implementation for the PQ metric for panoptic segmentation.

As input to forward and update the metric accepts the following input:

  • preds (Tensor): An int tensor of shape (B, *spatial_dims, 2), where there needs to be at least one spatial dimension.

  • target (Tensor): An int tensor of shape (B, *spatial_dims, 2), where there needs to be at least one spatial dimension.

As output to forward and compute the metric returns the following output:

  • quality (Tensor): If return_sq_and_rq=False and return_per_class=False then a single scalar tensor is returned with average panoptic quality over all classes. If return_sq_and_rq=True and return_per_class=False a tensor of length 3 is returned with panoptic, segmentation and recognition quality (in that order). If If return_sq_and_rq=False and return_per_class=True a tensor of length equal to the number of classes are returned, with panoptic quality for each class. Finally, if both arguments are True a tensor of shape (3, C) is returned with individual panoptic, segmentation and recognition quality for each class.

Parameters:

  • things (Collection[int]) – Set of category_id for countable things.

  • stuffs (Collection[int]) – Set of category_id for uncountable stuffs.

  • allow_unknown_preds_category (bool) – Boolean flag to specify if unknown categories in the predictions are to be ignored in the metric computation or raise an exception when found.

  • return_sq_and_rq (bool) – Boolean flag to specify if Segmentation Quality and Recognition Quality should be also returned.

  • return_per_class (bool) – Boolean flag to specify if the per-class values should be returned or the class average.

Raises:

  • ValueError – If things, stuffs have at least one common category_id.

  • TypeError – If things, stuffs contain non-integer category_id.

Example

>>> from torch import tensor
>>> from torchmetrics.detection import PanopticQuality
>>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [7, 0], [6, 0], [1, 0]],
...                  [[0, 0], [7, 0], [7, 0], [7, 0]]]])
>>> target = tensor([[[[6, 0], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [1, 0]],
...                   [[0, 1], [7, 0], [1, 0], [1, 0]],
...                   [[0, 1], [7, 0], [7, 0], [7, 0]]]])
>>> panoptic_quality = PanopticQuality(things = {0, 1}, stuffs = {6, 7})
>>> panoptic_quality(preds, target)
tensor(0.5463, dtype=torch.float64)
You can also return the segmentation and recognition quality alognside the PQ
>>> from torch import tensor
>>> from torchmetrics.detection import PanopticQuality
>>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [7, 0], [6, 0], [1, 0]],
...                  [[0, 0], [7, 0], [7, 0], [7, 0]]]])
>>> target = tensor([[[[6, 0], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [1, 0]],
...                   [[0, 1], [7, 0], [1, 0], [1, 0]],
...                   [[0, 1], [7, 0], [7, 0], [7, 0]]]])
>>> panoptic_quality = PanopticQuality(things = {0, 1}, stuffs = {6, 7}, return_sq_and_rq=True)
>>> panoptic_quality(preds, target)
tensor([0.5463, 0.6111, 0.6667], dtype=torch.float64)
You can also specify to return the per-class metrics
>>> from torch import tensor
>>> from torchmetrics.detection import PanopticQuality
>>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [7, 0], [6, 0], [1, 0]],
...                  [[0, 0], [7, 0], [7, 0], [7, 0]]]])
>>> target = tensor([[[[6, 0], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [1, 0]],
...                   [[0, 1], [7, 0], [1, 0], [1, 0]],
...                   [[0, 1], [7, 0], [7, 0], [7, 0]]]])
>>> panoptic_quality = PanopticQuality(things = {0, 1}, stuffs = {6, 7}, return_per_class=True)
>>> panoptic_quality(preds, target)
tensor([[0.5185, 0.0000, 0.6667, 1.0000]], dtype=torch.float64)
plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:

  • val (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.

  • ax (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

Tuple[Figure, Union[Axes, ndarray]]

Returns:

Figure object and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> from torch import tensor
>>> from torchmetrics.detection import PanopticQuality
>>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [7, 0], [6, 0], [1, 0]],
...                  [[0, 0], [7, 0], [7, 0], [7, 0]]]])
>>> target = tensor([[[[6, 0], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [1, 0]],
...                   [[0, 1], [7, 0], [1, 0], [1, 0]],
...                   [[0, 1], [7, 0], [7, 0], [7, 0]]]])
>>> metric = PanopticQuality(things = {0, 1}, stuffs = {6, 7})
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
../_images/panoptic_quality-1.png 
>>> # Example plotting multiple values
>>> from torch import tensor
>>> from torchmetrics.detection import PanopticQuality
>>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [7, 0], [6, 0], [1, 0]],
...                  [[0, 0], [7, 0], [7, 0], [7, 0]]]])
>>> target = tensor([[[[6, 0], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [1, 0]],
...                   [[0, 1], [7, 0], [1, 0], [1, 0]],
...                   [[0, 1], [7, 0], [7, 0], [7, 0]]]])
>>> metric = PanopticQuality(things = {0, 1}, stuffs = {6, 7})
>>> vals = []
>>> for _ in range(20):
...     vals.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(vals)
../_images/panoptic_quality-2.png

Functional Interface

torchmetrics.functional.detection.panoptic_quality(preds, target, things, stuffs, allow_unknown_preds_category=False, return_sq_and_rq=False, return_per_class=False)[source]

Compute Panoptic Quality for panoptic segmentations.

\[PQ = \frac{IOU}{TP + 0.5 FP + 0.5 FN}\]

where IOU, TP, FP and FN are respectively the sum of the intersection over union for true positives, the number of true positives, false positives and false negatives. This metric is inspired by the PQ implementation of panopticapi, a standard implementation for the PQ metric for object detection.

Parameters:

  • preds (Tensor) – torch tensor with panoptic detection of shape [height, width, 2] containing the pair (category_id, instance_id) for each pixel of the image. If the category_id refer to a stuff, the instance_id is ignored.

  • target (Tensor) – torch tensor with ground truth of shape [height, width, 2] containing the pair (category_id, instance_id) for each pixel of the image. If the category_id refer to a stuff, the instance_id is ignored.

  • things (Collection[int]) – Set of category_id for countable things.

  • stuffs (Collection[int]) – Set of category_id for uncountable stuffs.

  • allow_unknown_preds_category (bool) – Boolean flag to specify if unknown categories in the predictions are to be ignored in the metric computation or raise an exception when found.

  • return_sq_and_rq (bool) – Boolean flag to specify if Segmentation Quality and Recognition Quality should be also returned.

  • return_per_class (bool) – Boolean flag to specify if the per-class values should be returned or the class average.

Raises:

  • ValueError – If things, stuffs have at least one common category_id.

  • TypeError – If things, stuffs contain non-integer category_id.

  • TypeError – If preds or target is not an torch.Tensor.

  • ValueError – If preds or target has different shape.

  • ValueError – If preds has less than 3 dimensions.

  • ValueError – If the final dimension of preds has size != 2.

Return type:

Tensor

Example

>>> from torch import tensor
>>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [7, 0], [6, 0], [1, 0]],
...                  [[0, 0], [7, 0], [7, 0], [7, 0]]]])
>>> target = tensor([[[[6, 0], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [1, 0]],
...                   [[0, 1], [7, 0], [1, 0], [1, 0]],
...                   [[0, 1], [7, 0], [7, 0], [7, 0]]]])
>>> panoptic_quality(preds, target, things = {0, 1}, stuffs = {6, 7})
tensor(0.5463, dtype=torch.float64)
You can also return the segmentation and recognition quality alognside the PQ
>>> from torch import tensor
>>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [7, 0], [6, 0], [1, 0]],
...                  [[0, 0], [7, 0], [7, 0], [7, 0]]]])
>>> target = tensor([[[[6, 0], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [1, 0]],
...                   [[0, 1], [7, 0], [1, 0], [1, 0]],
...                   [[0, 1], [7, 0], [7, 0], [7, 0]]]])
>>> panoptic_quality(preds, target, things = {0, 1}, stuffs = {6, 7}, return_sq_and_rq=True)
tensor([0.5463, 0.6111, 0.6667], dtype=torch.float64)
You can also specify to return the per-class metrics
>>> from torch import tensor
>>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [7, 0], [6, 0], [1, 0]],
...                  [[0, 0], [7, 0], [7, 0], [7, 0]]]])
>>> target = tensor([[[[6, 0], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [1, 0]],
...                   [[0, 1], [7, 0], [1, 0], [1, 0]],
...                   [[0, 1], [7, 0], [7, 0], [7, 0]]]])
>>> panoptic_quality(preds, target, things = {0, 1}, stuffs = {6, 7}, return_per_class=True)
tensor([[0.5185, 0.0000, 0.6667, 1.0000]], dtype=torch.float64)
You can also specify to return the per-class metrics and the segmentation and recognition quality
>>> from torch import tensor
>>> preds = tensor([[[[6, 0], [0, 0], [6, 0], [6, 0]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [0, 0], [6, 0], [0, 1]],
...                  [[0, 0], [7, 0], [6, 0], [1, 0]],
...                  [[0, 0], [7, 0], [7, 0], [7, 0]]]])
>>> target = tensor([[[[6, 0], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [0, 1]],
...                   [[0, 1], [0, 1], [6, 0], [1, 0]],
...                   [[0, 1], [7, 0], [1, 0], [1, 0]],
...                   [[0, 1], [7, 0], [7, 0], [7, 0]]]])
>>> panoptic_quality(preds, target, things = {0, 1}, stuffs = {6, 7},
...                  return_per_class=True, return_sq_and_rq=True)
tensor([[0.5185, 0.7778, 0.6667],
        [0.0000, 0.0000, 0.0000],
        [0.6667, 0.6667, 1.0000],
        [1.0000, 1.0000, 1.0000]], dtype=torch.float64)

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