File size: 5,734 Bytes
9c6594c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
# 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 collections.abc import Sequence
from typing import Any, Optional, 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:

        - ``point_cloud1`` (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.
        - ``point_cloud2`` (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()
        >>> point_cloud1 = randn(10, 50, 2)
        >>> point_cloud2 = randn(10, 50, 2)
        >>> metric(point_cloud1, point_cloud2)
        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, point_cloud1: torch.Tensor, point_cloud2: torch.Tensor) -> None:
        """Update the Procrustes Disparity with the given datasets."""
        disparity: Tensor = procrustes_disparity(point_cloud1, point_cloud2)  # 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)