pit.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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# limitations under the License.
from typing import Any, Callable, Dict, Optional

from deprecate import deprecated, void
from torch import Tensor, tensor

from torchmetrics.functional.audio.pit import permutation_invariant_training
from torchmetrics.metric import Metric
from torchmetrics.utilities import _future_warning


class PermutationInvariantTraining(Metric):
    """Permutation invariant training (PermutationInvariantTraining). The PermutationInvariantTraining implements
    the famous Permutation Invariant Training method.

    [1] in speech separation field in order to calculate audio metrics in a permutation invariant way.

    Forward accepts

    - ``preds``: ``shape [batch, spk, ...]``
    - ``target``: ``shape [batch, spk, ...]``

    Args:
        metric_func:
            a metric function accept a batch of target and estimate, i.e. metric_func(preds[:, i, ...],
            target[:, j, ...]), and returns a batch of metric tensors [batch]
        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.
        compute_on_step:
            Forward only calls ``update()`` and returns None if this is set to False.
        dist_sync_on_step:
            Synchronize metric state across processes at each ``forward()``
            before returning the value at the step.
        process_group:
            Specify the process group on which synchronization is called.
        dist_sync_fn:
            Callback that performs the allgather operation on the metric state. When `None`, DDP
            will be used to perform the allgather.
        kwargs:
            additional args for metric_func

    Returns:
        average PermutationInvariantTraining metric

    Example:
        >>> import torch
        >>> from torchmetrics import PermutationInvariantTraining
        >>> from torchmetrics.functional import si_snr
        >>> _ = torch.manual_seed(42)
        >>> preds = torch.randn(3, 2, 5) # [batch, spk, time]
        >>> target = torch.randn(3, 2, 5) # [batch, spk, time]
        >>> pit = PermutationInvariantTraining(si_snr, 'max')
        >>> pit(preds, target)
        tensor(-2.1065)

    Reference:
        [1]	D. Yu, M. Kolbaek, Z.-H. Tan, J. Jensen, Permutation invariant training of deep models for
        speaker-independent multi-talker speech separation, in: 2017 IEEE Int. Conf. Acoust. Speech
        Signal Process. ICASSP, IEEE, New Orleans, LA, 2017: pp. 241–245. https://doi.org/10.1109/ICASSP.2017.7952154.
    """

    is_differentiable = True
    sum_pit_metric: Tensor
    total: Tensor

    def __init__(
        self,
        metric_func: Callable,
        eval_func: str = "max",
        compute_on_step: bool = True,
        dist_sync_on_step: bool = False,
        process_group: Optional[Any] = None,
        dist_sync_fn: Optional[Callable[[Tensor], Tensor]] = None,
        **kwargs: Dict[str, Any],
    ) -> None:
        super().__init__(
            compute_on_step=compute_on_step,
            dist_sync_on_step=dist_sync_on_step,
            process_group=process_group,
            dist_sync_fn=dist_sync_fn,
        )
        self.metric_func = metric_func
        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:  # type: ignore
        """Update state with predictions and targets.

        Args:
            preds: Predictions from model
            target: Ground truth values
        """
        pit_metric = permutation_invariant_training(preds, target, self.metric_func, self.eval_func, **self.kwargs)[0]

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

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


class PIT(PermutationInvariantTraining):
    """Permutation invariant training (PIT). The PIT implements the famous Permutation Invariant Training method.

    [1] in speech separation field in order to calculate audio metrics in a permutation invariant way.

    .. deprecated:: v0.7
        Use :class:`torchmetrics.audio.PermutationInvariantTraining`. Will be removed in v0.8.

    Example:
        >>> import torch
        >>> from torchmetrics.functional import si_snr
        >>> _ = torch.manual_seed(42)
        >>> pit = PIT(si_snr, 'max')
        >>> pit(torch.randn(3, 2, 5), torch.randn(3, 2, 5))
        tensor(-2.1065)

    Reference:
        [1]	D. Yu, M. Kolbaek, Z.-H. Tan, J. Jensen, Permutation invariant training of deep models for
        speaker-independent multi-talker speech separation, in: 2017 IEEE Int. Conf. Acoust. Speech
        Signal Process. ICASSP, IEEE, New Orleans, LA, 2017: pp. 241–245. https://doi.org/10.1109/ICASSP.2017.7952154.
    """

    @deprecated(target=PermutationInvariantTraining, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
    def __init__(
        self,
        metric_func: Callable,
        eval_func: str = "max",
        compute_on_step: bool = True,
        dist_sync_on_step: bool = False,
        process_group: Optional[Any] = None,
        dist_sync_fn: Optional[Callable[[Tensor], Tensor]] = None,
        **kwargs: Dict[str, Any],
    ) -> None:
        void(metric_func, eval_func, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn, **kwargs)