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)
|