image-match/python/py/Lib/site-packages/torchmetrics/audio/pit.py

# 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, Callable, Optional, Union

from torch import Tensor, tensor
from typing_extensions import Literal

from torchmetrics.functional.audio.pit import permutation_invariant_training
from torchmetrics.metric import Metric
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE

__doctest_requires__ = {"PermutationInvariantTraining": ["pit"]}

if not _MATPLOTLIB_AVAILABLE:
    __doctest_skip__ = ["PermutationInvariantTraining.plot"]


class PermutationInvariantTraining(Metric):
    """Calculate `Permutation invariant training`_ (PIT).

    This metric can evaluate models for speaker independent multi-talker speech separation in a permutation
    invariant way.

    As input to ``forward`` and ``update`` the metric accepts the following input

    - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(batch_size,num_speakers,...)``
    - ``target`` (:class:`~torch.Tensor`): float tensor with shape ``(batch_size,num_speakers,...)``

    As output of `forward` and `compute` the metric returns the following output

    - ``pesq`` (:class:`~torch.Tensor`): float scalar tensor with average PESQ value over samples

    Args:
        metric_func:
            a metric function accept a batch of target and estimate.

            if `mode`==`'speaker-wise'`, then ``metric_func(preds[:, i, ...], target[:, j, ...])`` is called
            and expected to return a batch of metric tensors ``(batch,)``;

            if `mode`==`'permutation-wise'`, then ``metric_func(preds[:, p, ...], target[:, :, ...])`` is called,
            where `p` is one possible permutation, e.g. [0,1] or [1,0] for 2-speaker case, and expected to return
            a batch of metric tensors ``(batch,)``;
        mode:
            can be `'speaker-wise'` or `'permutation-wise'`.
        eval_func:
            the function to find the best permutation, can be 'min' or 'max', i.e. the smaller the better
            or the larger the better.
        kwargs: Additional keyword arguments for either the ``metric_func`` or distributed communication,
            see :ref:`Metric kwargs` for more info.

    Example:
        >>> from torch import randn
        >>> from torchmetrics.audio import PermutationInvariantTraining
        >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
        >>> preds = randn(3, 2, 5) # [batch, spk, time]
        >>> target = randn(3, 2, 5) # [batch, spk, time]
        >>> pit = PermutationInvariantTraining(scale_invariant_signal_noise_ratio,
        ...     mode="speaker-wise", eval_func="max")
        >>> pit(preds, target)
        tensor(-2.1065)

    """

    full_state_update: bool = False
    is_differentiable: bool = True
    sum_pit_metric: Tensor
    total: Tensor
    plot_lower_bound: Optional[float] = None
    plot_upper_bound: Optional[float] = None

    def __init__(
        self,
        metric_func: Callable,
        mode: Literal["speaker-wise", "permutation-wise"] = "speaker-wise",
        eval_func: Literal["max", "min"] = "max",
        **kwargs: Any,
    ) -> None:
        base_kwargs: dict[str, Any] = {
            "dist_sync_on_step": kwargs.pop("dist_sync_on_step", False),
            "process_group": kwargs.pop("process_group", None),
            "dist_sync_fn": kwargs.pop("dist_sync_fn", None),
        }
        super().__init__(**base_kwargs)
        self.metric_func = metric_func
        self.mode = mode
        self.eval_func = eval_func
        self.kwargs = kwargs

        self.add_state("sum_pit_metric", default=tensor(0.0), dist_reduce_fx="sum")
        self.add_state("total", default=tensor(0), dist_reduce_fx="sum")

    def update(self, preds: Tensor, target: Tensor) -> None:
        """Update state with predictions and targets."""
        pit_metric = permutation_invariant_training(
            preds, target, self.metric_func, self.mode, self.eval_func, **self.kwargs
        )[0]

        self.sum_pit_metric += pit_metric.sum()
        self.total += pit_metric.numel()

    def compute(self) -> Tensor:
        """Compute metric."""
        return self.sum_pit_metric / self.total

    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.audio import PermutationInvariantTraining
            >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
            >>> preds = torch.randn(3, 2, 5) # [batch, spk, time]
            >>> target = torch.randn(3, 2, 5) # [batch, spk, time]
            >>> metric = PermutationInvariantTraining(scale_invariant_signal_noise_ratio,
            ...     mode="speaker-wise", eval_func="max")
            >>> metric.update(preds, target)
            >>> fig_, ax_ = metric.plot()

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> import torch
            >>> from torchmetrics.audio import PermutationInvariantTraining
            >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
            >>> preds = torch.randn(3, 2, 5) # [batch, spk, time]
            >>> target = torch.randn(3, 2, 5) # [batch, spk, time]
            >>> metric = PermutationInvariantTraining(scale_invariant_signal_noise_ratio,
            ...     mode="speaker-wise", eval_func="max")
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(preds, target))
            >>> fig_, ax_ = metric.plot(values)

        """
        return self._plot(val, ax)