# 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)