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pit.py
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pit.py
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# 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
#
# 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
from torch import Tensor, tensor
from torchmetrics.functional.audio.pit import permutation_invariant_training
from torchmetrics.metric import Metric
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.
kwargs: Additional keyword arguments for either the ``metric_func`` or distributed communication,
see :ref:`Metric kwargs` for more info.
Returns:
average PermutationInvariantTraining metric
Example:
>>> import torch
>>> from torchmetrics import PermutationInvariantTraining
>>> from torchmetrics.functional 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, '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.
"""
full_state_update: bool = False
is_differentiable: bool = True
sum_pit_metric: Tensor
total: Tensor
def __init__(
self,
metric_func: Callable,
eval_func: str = "max",
**kwargs: Dict[str, 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.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