|
|
|
|
|
|
|
|
from typing import List, Tuple |
|
|
|
|
|
import torch |
|
|
import torch.nn as nn |
|
|
|
|
|
from toolbox.torchaudio.losses.vad_loss.base_vad_loss import BaseVadLoss |
|
|
|
|
|
|
|
|
class BCELoss(BaseVadLoss): |
|
|
""" |
|
|
Binary Cross-Entropy Loss, BCE Loss |
|
|
""" |
|
|
def __init__(self, |
|
|
reduction: str = "mean", |
|
|
): |
|
|
super(BCELoss, self).__init__() |
|
|
self.reduction = reduction |
|
|
|
|
|
self.bce_loss_fn = nn.BCELoss(reduction=reduction) |
|
|
|
|
|
def forward(self, inputs: torch.Tensor, targets: torch.Tensor): |
|
|
""" |
|
|
:param inputs: torch.Tensor, shape: [b, t, 1]. vad prob, after sigmoid activation. |
|
|
:param targets: shape as `inputs`. |
|
|
:return: |
|
|
""" |
|
|
loss = self.bce_loss_fn.forward(inputs, targets) |
|
|
return loss |
|
|
|
|
|
|
|
|
def main(): |
|
|
inputs = torch.zeros(size=(1, 198, 1), dtype=torch.float32) |
|
|
|
|
|
loss_fn = BCELoss() |
|
|
|
|
|
loss = loss_fn.forward(inputs, inputs) |
|
|
print(loss) |
|
|
return |
|
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
|
main() |
|
|
|
