# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections.abc import Collection, Sequence from typing import Any, Optional, Union import torch from torch import Tensor from torchmetrics.functional.detection._panoptic_quality_common import ( _get_category_id_to_continuous_id, _get_void_color, _panoptic_quality_compute, _panoptic_quality_update, _parse_categories, _prepocess_inputs, _validate_inputs, ) from torchmetrics.metric import Metric from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = ["PanopticQuality.plot", "ModifiedPanopticQuality.plot"] class PanopticQuality(Metric): r"""Compute the `Panoptic Quality`_ for panoptic segmentations. .. math:: 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. .. note: Points in the target tensor that do not map to a known category ID are automatically ignored in the metric computation. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): An int tensor of shape ``(B, *spatial_dims, 2)`` containing the pair ``(category_id, instance_id)`` for each point, where there needs to be at least one spatial dimension. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(B, *spatial_dims, 2)`` containing the pair ``(category_id, instance_id)`` for each point, where there needs to be at least one spatial dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``quality`` (:class:`~torch.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. The order of classes is ``things`` first and then ``stuffs``, and numerically sorted within each. (ex. with ``things=[4, 1], stuffs=[3, 2]``, the output classes are ordered by ``[1, 4, 2, 3]``) Finally, if both arguments are ``True`` a tensor of shape ``(3, C)`` is returned with individual panoptic, segmentation and recognition quality for each class. Args: things: Set of ``category_id`` for countable things. stuffs: Set of ``category_id`` for uncountable stuffs. allow_unknown_preds_category: 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: Boolean flag to specify if Segmentation Quality and Recognition Quality should be also returned. return_per_class: 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) """ is_differentiable: bool = False higher_is_better: bool = True full_state_update: bool = False plot_lower_bound: float = 0.0 plot_upper_bound: float = 1.0 iou_sum: Tensor true_positives: Tensor false_positives: Tensor false_negatives: Tensor def __init__( self, things: Collection[int], stuffs: Collection[int], allow_unknown_preds_category: bool = False, return_sq_and_rq: bool = False, return_per_class: bool = False, **kwargs: Any, ) -> None: super().__init__(**kwargs) things, stuffs = _parse_categories(things, stuffs) self.things = things self.stuffs = stuffs self.void_color = _get_void_color(things, stuffs) self.cat_id_to_continuous_id = _get_category_id_to_continuous_id(things, stuffs) self.allow_unknown_preds_category = allow_unknown_preds_category self.return_sq_and_rq = return_sq_and_rq self.return_per_class = return_per_class # per category intermediate metrics num_categories = len(things) + len(stuffs) self.add_state("iou_sum", default=torch.zeros(num_categories, dtype=torch.double), dist_reduce_fx="sum") self.add_state("true_positives", default=torch.zeros(num_categories, dtype=torch.int), dist_reduce_fx="sum") self.add_state("false_positives", default=torch.zeros(num_categories, dtype=torch.int), dist_reduce_fx="sum") self.add_state("false_negatives", default=torch.zeros(num_categories, dtype=torch.int), dist_reduce_fx="sum") def update(self, preds: Tensor, target: Tensor) -> None: r"""Update state with predictions and targets. Args: preds: panoptic detection of shape ``[batch, *spatial_dims, 2]`` containing the pair ``(category_id, instance_id)`` for each point. If the ``category_id`` refer to a stuff, the instance_id is ignored. target: ground truth of shape ``[batch, *spatial_dims, 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. Raises: TypeError: If ``preds`` or ``target`` is not an ``torch.Tensor``. ValueError: If ``preds`` and ``target`` have different shape. ValueError: If ``preds`` has less than 3 dimensions. ValueError: If the final dimension of ``preds`` has size != 2. """ _validate_inputs(preds, target) flatten_preds = _prepocess_inputs( self.things, self.stuffs, preds, self.void_color, self.allow_unknown_preds_category ) flatten_target = _prepocess_inputs(self.things, self.stuffs, target, self.void_color, True) iou_sum, true_positives, false_positives, false_negatives = _panoptic_quality_update( flatten_preds, flatten_target, self.cat_id_to_continuous_id, self.void_color ) self.iou_sum += iou_sum self.true_positives += true_positives self.false_positives += false_positives self.false_negatives += false_negatives def compute(self) -> Tensor: """Compute panoptic quality based on inputs passed in to ``update`` previously.""" pq, sq, rq, pq_avg, sq_avg, rq_avg = _panoptic_quality_compute( self.iou_sum, self.true_positives, self.false_positives, self.false_negatives ) if self.return_per_class: if self.return_sq_and_rq: return torch.stack((pq, sq, rq), dim=-1) return pq.view(1, -1) if self.return_sq_and_rq: return torch.stack((pq_avg, sq_avg, rq_avg), dim=0) return pq_avg def plot( self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None ) -> _PLOT_OUT_TYPE: """Plot a single or multiple values from the metric. Args: val: 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: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> 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() .. plot:: :scale: 75 >>> # 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) """ return self._plot(val, ax) class ModifiedPanopticQuality(Metric): r"""Compute `Modified Panoptic Quality`_ for panoptic segmentations. The metric was introduced in `Seamless Scene Segmentation paper`_, and is an adaptation of the original `Panoptic Quality`_ where the metric for a stuff class is computed as .. math:: PQ^{\dagger}_c = \frac{IOU_c}{|S_c|} where :math:`IOU_c` is the sum of the intersection over union of all matching segments for a given class, and :math:`|S_c|` is the overall number of segments in the ground truth for that class. .. note: Points in the target tensor that do not map to a known category ID are automatically ignored in the metric computation. Args: things: Set of ``category_id`` for countable things. stuffs: Set of ``category_id`` for uncountable stuffs. allow_unknown_preds_category: Boolean flag to specify if unknown categories in the predictions are to be ignored in the metric computation or raise an exception when found. 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 ModifiedPanopticQuality >>> preds = tensor([[[0, 0], [0, 1], [6, 0], [7, 0], [0, 2], [1, 0]]]) >>> target = tensor([[[0, 1], [0, 0], [6, 0], [7, 0], [6, 0], [255, 0]]]) >>> pq_modified = ModifiedPanopticQuality(things = {0, 1}, stuffs = {6, 7}) >>> pq_modified(preds, target) tensor(0.7667, dtype=torch.float64) """ is_differentiable: bool = False higher_is_better: bool = True full_state_update: bool = False plot_lower_bound: float = 0.0 plot_upper_bound: float = 1.0 iou_sum: Tensor true_positives: Tensor false_positives: Tensor false_negatives: Tensor def __init__( self, things: Collection[int], stuffs: Collection[int], allow_unknown_preds_category: bool = False, **kwargs: Any, ) -> None: super().__init__(**kwargs) things, stuffs = _parse_categories(things, stuffs) self.things = things self.stuffs = stuffs self.void_color = _get_void_color(things, stuffs) self.cat_id_to_continuous_id = _get_category_id_to_continuous_id(things, stuffs) self.allow_unknown_preds_category = allow_unknown_preds_category # per category intermediate metrics num_categories = len(things) + len(stuffs) self.add_state("iou_sum", default=torch.zeros(num_categories, dtype=torch.double), dist_reduce_fx="sum") self.add_state("true_positives", default=torch.zeros(num_categories, dtype=torch.int), dist_reduce_fx="sum") self.add_state("false_positives", default=torch.zeros(num_categories, dtype=torch.int), dist_reduce_fx="sum") self.add_state("false_negatives", default=torch.zeros(num_categories, dtype=torch.int), dist_reduce_fx="sum") def update(self, preds: Tensor, target: Tensor) -> None: r"""Update state with predictions and targets. Args: preds: panoptic detection of shape ``[batch, *spatial_dims, 2]`` containing the pair ``(category_id, instance_id)`` for each point. If the ``category_id`` refer to a stuff, the instance_id is ignored. target: ground truth of shape ``[batch, *spatial_dims, 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. Raises: TypeError: If ``preds`` or ``target`` is not an ``torch.Tensor``. ValueError: If ``preds`` and ``target`` have different shape. ValueError: If ``preds`` has less than 3 dimensions. ValueError: If the final dimension of ``preds`` has size != 2. """ _validate_inputs(preds, target) flatten_preds = _prepocess_inputs( self.things, self.stuffs, preds, self.void_color, self.allow_unknown_preds_category ) flatten_target = _prepocess_inputs(self.things, self.stuffs, target, self.void_color, True) iou_sum, true_positives, false_positives, false_negatives = _panoptic_quality_update( flatten_preds, flatten_target, self.cat_id_to_continuous_id, self.void_color, modified_metric_stuffs=self.stuffs, ) self.iou_sum += iou_sum self.true_positives += true_positives self.false_positives += false_positives self.false_negatives += false_negatives def compute(self) -> Tensor: """Compute panoptic quality based on inputs passed in to ``update`` previously.""" _, _, _, pq_avg, _, _ = _panoptic_quality_compute( self.iou_sum, self.true_positives, self.false_positives, self.false_negatives ) return pq_avg def plot( self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None ) -> _PLOT_OUT_TYPE: """Plot a single or multiple values from the metric. Args: val: 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: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import tensor >>> from torchmetrics.detection import ModifiedPanopticQuality >>> 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 = ModifiedPanopticQuality(things = {0, 1}, stuffs = {6, 7}) >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import tensor >>> from torchmetrics.detection import ModifiedPanopticQuality >>> 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 = ModifiedPanopticQuality(things = {0, 1}, stuffs = {6, 7}) >>> vals = [] >>> for _ in range(20): ... vals.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(vals) """ return self._plot(val, ax)