import torch
|
from typing import Tuple
|
from typing import Union
|
from funasr.modules.nets_utils import make_non_pad_mask
|
|
|
class StatisticPooling(torch.nn.Module):
|
def __init__(self, pooling_dim: Union[int, Tuple] = 2, eps=1e-12):
|
super(StatisticPooling, self).__init__()
|
if isinstance(pooling_dim, int):
|
pooling_dim = (pooling_dim, )
|
self.pooling_dim = pooling_dim
|
self.eps = eps
|
|
def forward(self, xs_pad, ilens=None):
|
# xs_pad in (Batch, Channel, Time, Frequency)
|
|
if ilens is None:
|
masks = torch.ones_like(xs_pad).to(xs_pad)
|
else:
|
masks = make_non_pad_mask(ilens, xs_pad, length_dim=2).to(xs_pad)
|
mean = (torch.sum(xs_pad, dim=self.pooling_dim, keepdim=True) /
|
torch.sum(masks, dim=self.pooling_dim, keepdim=True))
|
squared_difference = torch.pow(xs_pad - mean, 2.0)
|
variance = (torch.sum(squared_difference, dim=self.pooling_dim, keepdim=True) /
|
torch.sum(masks, dim=self.pooling_dim, keepdim=True))
|
for i in reversed(self.pooling_dim):
|
mean, variance = torch.squeeze(mean, dim=i), torch.squeeze(variance, dim=i)
|
|
mask = torch.less_equal(variance, self.eps).float()
|
variance = (1.0 - mask) * variance + mask * self.eps
|
stddev = torch.sqrt(variance)
|
|
stat_pooling = torch.cat([mean, stddev], dim=1)
|
|
return stat_pooling
|
