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pit.py
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pit.py
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# 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 typing import Any, Callable, Dict, Optional, Sequence, 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:
>>> import torch
>>> from torchmetrics.audio import PermutationInvariantTraining
>>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
>>> _ = torch.manual_seed(42)
>>> preds = torch.randn(3, 2, 5) # [batch, spk, time]
>>> target = torch.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)