Newmetric: ProcrustesDisparity

CHANGELOG.md

@@ -24,6 +24,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added multi-output support for MAE metric ([#2605](https://github.com/Lightning-AI/torchmetrics/pull/2605))
 
 
+- Added new metric `ProcrustesDistance` to new domain Shape ([#2723](https://github.com/Lightning-AI/torchmetrics/pull/2723)
+
+
 ### Changed
 
 - Tracker higher is better integration ([#2649](https://github.com/Lightning-AI/torchmetrics/pull/2649))

docs/source/index.rst

@@ -231,6 +231,14 @@ Or directly from conda
 
    segmentation/*
 
+.. toctree::
+   :maxdepth: 2
+   :name: shape
+   :caption: Shape
+   :glob:
+
+   shape/*
+
 .. toctree::
    :maxdepth: 2
    :name: text

docs/source/links.rst

@@ -172,3 +172,4 @@
 .. _averaging curve objects: https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
 .. _SCC: https://www.ingentaconnect.com/content/tandf/tres/1998/00000019/00000004/art00013
 .. _Generalized Dice Score: https://arxiv.org/abs/1707.03237
+.. _Procrustes Disparity: https://en.wikipedia.org/wiki/Procrustes_analysis

docs/source/segmentation/mean_iou.rst

@@ -1,7 +1,7 @@
 .. customcarditem::
    :header: Mean Intersection over Union (mIoU)
    :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/object_detection.svg
-   :tags: segmentation
+   :tags: Segmentation
 
 ###################################
 Mean Intersection over Union (mIoU)

docs/source/shape/procrustes.rst

@@ -0,0 +1,22 @@
+.. customcarditem::
+   :header: Procrustes Disparity
+   :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/tabular_classification.svg
+   :tags: shape
+
+.. include:: ../links.rst
+
+####################
+Procrustes Disparity
+####################
+
+Module Interface
+________________
+
+.. autoclass:: torchmetrics.shape.ProcrustesDisparity
+    :exclude-members: update, compute
+
+
+Functional Interface
+____________________
+
+.. autofunction:: torchmetrics.functional.shape.procrustes_disparity

src/torchmetrics/functional/shape/__init__.py

@@ -0,0 +1,16 @@
+# Copyright The 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 torchmetrics.functional.shape.procrustes import procrustes_disparity
+
+__all__ = ["procrustes_disparity"]

src/torchmetrics/functional/shape/procrustes.py

@@ -0,0 +1,64 @@
+# Copyright The 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 typing import Tuple, Union
+
+import torch
+from torch import Tensor, linalg
+
+from torchmetrics.utilities.checks import _check_same_shape
+from torchmetrics.utilities.prints import rank_zero_warn
+
+
+def procrustes_disparity(
+    dataset1: Tensor, dataset2: Tensor, return_all: bool = False
+) -> Union[Tensor, Tuple[Tensor, Tensor, Tensor]]:
+    """Runs procrustrus analysis on a batch of data points.
+
+    Works similar `scipy.spatial.procrustes` but for batches of data points.
+
+    Args:
+        dataset1: The first set of data points
+        dataset2: The second set of data points
+        return_all: If True, returns the scale and rotation matrices along with the disparity
+
+    """
+    _check_same_shape(dataset1, dataset2)
+    if dataset1.ndim != 3:
+        raise ValueError(
+            "Expected both datasets to be 3D tensors of shape (N, M, D), where N is the batch size, M is the number of "
+            f" data points and D is the dimensionality of the data points, but got {dataset1.ndim} dimensions."
+        )
+
+    dataset1 -= dataset1.mean(dim=1, keepdim=True)
+    dataset2 -= dataset2.mean(dim=1, keepdim=True)
+    dataset1 /= linalg.norm(dataset1, dim=[1, 2], keepdim=True)
+    dataset2 /= linalg.norm(dataset2, dim=[1, 2], keepdim=True)
+
+    try:
+        u, w, v = linalg.svd(torch.matmul(dataset2.transpose(1, 2), dataset1).transpose(1, 2), full_matrices=False)
+    except Exception as e:
+        rank_zero_warn(
+            f"SVD calculation in procrustes_disparity failed with exception {e}. Returning 0 disparity and identity"
+            " scale/rotation.",
+            UserWarning,
+        )
+        return torch.tensor(0.0), torch.ones(dataset1.shape[0]), torch.eye(dataset1.shape[2])
+
+    rotation = torch.matmul(u, v)
+    scale = w.sum(1, keepdim=True)
+    dataset2 = scale[:, None] * torch.matmul(dataset2, rotation.transpose(1, 2))
+    disparity = (dataset1 - dataset2).square().sum(dim=[1, 2])
+    if return_all:
+        return disparity, scale, rotation
+    return disparity

src/torchmetrics/shape/__init__.py

@@ -0,0 +1,16 @@
+# Copyright The 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 torchmetrics.shape.procrustes import ProcrustesDisparity
+
+__all__ = ["ProcrustesDisparity"]

src/torchmetrics/shape/procrustes.py

@@ -0,0 +1,137 @@
+# Copyright The 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 typing import Any, Optional, Sequence, Union
+
+import torch
+from torch import Tensor
+from typing_extensions import Literal
+
+from torchmetrics import Metric
+from torchmetrics.functional.shape.procrustes import procrustes_disparity
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+    __doctest_skip__ = ["ProcrustesDisparity.plot"]
+
+
+class ProcrustesDisparity(Metric):
+    r"""Compute the `Procrustes Disparity`_.
+
+    The Procrustes Disparity is defined as the sum of the squared differences between two datasets after
+    applying a Procrustes transformation. The Procrustes Disparity is useful to compare two datasets
+    that are similar but not aligned.
+
+    The metric works similar to `scipy.spatial.procrustes` but for batches of data points. The disparity is
+    aggregated over the batch, thus to get the individual disparities please use the functional version of this
+    metric: ``torchmetrics.functional.shape.procrustes.procrustes_disparity``.
+
+    As input to ``forward`` and ``update`` the metric accepts the following input:
+
+        - ``dataset1`` (torch.Tensor): A tensor of shape ``(N, M, D)`` with ``N`` being the batch size,
+          ``M`` the number of data points and ``D`` the dimensionality of the data points.
+        - ``dataset2`` (torch.Tensor): A tensor of shape ``(N, M, D)`` with ``N`` being the batch size,
+          ``M`` the number of data points and ``D`` the dimensionality of the data points.
+
+
+    As output to ``forward`` and ``compute`` the metric returns the following output:
+
+        - ``gds`` (:class:`~torch.Tensor`): A scalar tensor with the Procrustes Disparity.
+
+    Args:
+        reduction: Determines whether to return the mean disparity or the sum of the disparities.
+            Can be one of ``"mean"`` or ``"sum"``.
+        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
+
+    Raises:
+        ValueError: If ``average`` is not one of ``"mean"`` or ``"sum"``.
+
+    Example:
+        >>> from torch import randn
+        >>> from torchmetrics.shape import ProcrustesDisparity
+        >>> metric = ProcrustesDisparity()
+        >>> dataset1 = randn(10, 50, 2)
+        >>> dataset2 = randn(10, 50, 2)
+        >>> metric(dataset1, dataset2)
+        tensor(0.9770)
+
+    """
+
+    disparity: Tensor
+    total: Tensor
+    full_state_update: bool = False
+    is_differentiable: bool = False
+    higher_is_better: bool = False
+    plot_lower_bound: float = 0.0
+    plot_upper_bound: float = 1.0
+
+    def __init__(self, reduction: Literal["mean", "sum"] = "mean", **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+        if reduction not in ("mean", "sum"):
+            raise ValueError(f"Argument `reduction` must be one of ['mean', 'sum'], got {reduction}")
+        self.reduction = reduction
+        self.add_state("disparity", default=torch.tensor(0.0), dist_reduce_fx="sum")
+        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
+
+    def update(self, dataset1: torch.Tensor, dataset2: torch.Tensor) -> None:
+        """Update the Procrustes Disparity with the given datasets."""
+        disparity: Tensor = procrustes_disparity(dataset1, dataset2)  # type: ignore[assignment]
+        self.disparity += disparity.sum()
+        self.total += disparity.numel()
+
+    def compute(self) -> torch.Tensor:
+        """Computes the Procrustes Disparity."""
+        if self.reduction == "mean":
+            return self.disparity / self.total
+        return self.disparity
+
+    def plot(self, val: Union[Tensor, Sequence[Tensor], None] = 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 and Axes object
+
+        Raises:
+            ModuleNotFoundError:
+                If `matplotlib` is not installed
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting a single value
+            >>> import torch
+            >>> from torchmetrics.shape import ProcrustesDisparity
+            >>> metric = ProcrustesDisparity()
+            >>> metric.update(torch.randn(10, 50, 2), torch.randn(10, 50, 2))
+            >>> fig_, ax_ = metric.plot()
+
+        .. plot::
+            :scale: 75
+
+            >>> # Example plotting multiple values
+            >>> import torch
+            >>> from torchmetrics.shape import ProcrustesDisparity
+            >>> metric = ProcrustesDisparity()
+            >>> values = [ ]
+            >>> for _ in range(10):
+            ...     values.append(metric(torch.randn(10, 50, 2), torch.randn(10, 50, 2)))
+            >>> fig_, ax_ = metric.plot(values)
+
+        """
+        return self._plot(val, ax)

tests/unittests/shape/__init__.py

@@ -0,0 +1,13 @@
+# Copyright The 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.