image-match/python/py/Lib/site-packages/torchmetrics/detection/panoptic_qualities.py

# 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)