99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
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import torch
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from typing import Tuple
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from typing import Union
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from funasr.models.transformer.utils.nets_utils import make_non_pad_mask
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from torch.nn import functional as F
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import math
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VAR2STD_EPSILON = 1e-12
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class StatisticPooling(torch.nn.Module):
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def __init__(self, pooling_dim: Union[int, Tuple] = 2, eps=1e-12):
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super(StatisticPooling, self).__init__()
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if isinstance(pooling_dim, int):
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pooling_dim = (pooling_dim,)
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self.pooling_dim = pooling_dim
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self.eps = eps
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def forward(self, xs_pad, ilens=None):
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# xs_pad in (Batch, Channel, Time, Frequency)
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if ilens is None:
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masks = torch.ones_like(xs_pad).to(xs_pad)
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else:
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masks = make_non_pad_mask(ilens, xs_pad, length_dim=2).to(xs_pad)
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mean = torch.sum(xs_pad, dim=self.pooling_dim, keepdim=True) / torch.sum(
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masks, dim=self.pooling_dim, keepdim=True
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)
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squared_difference = torch.pow(xs_pad - mean, 2.0)
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variance = torch.sum(squared_difference, dim=self.pooling_dim, keepdim=True) / torch.sum(
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masks, dim=self.pooling_dim, keepdim=True
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)
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for i in reversed(self.pooling_dim):
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mean, variance = torch.squeeze(mean, dim=i), torch.squeeze(variance, dim=i)
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mask = torch.less_equal(variance, self.eps).float()
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variance = (1.0 - mask) * variance + mask * self.eps
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stddev = torch.sqrt(variance)
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stat_pooling = torch.cat([mean, stddev], dim=1)
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return stat_pooling
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def statistic_pooling(
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xs_pad: torch.Tensor, ilens: torch.Tensor = None, pooling_dim: Tuple = (2, 3)
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) -> torch.Tensor:
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# xs_pad in (Batch, Channel, Time, Frequency)
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if ilens is None:
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seq_mask = torch.ones_like(xs_pad).to(xs_pad)
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else:
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seq_mask = make_non_pad_mask(ilens, xs_pad, length_dim=2).to(xs_pad)
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mean = torch.sum(xs_pad, dim=pooling_dim, keepdim=True) / torch.sum(
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seq_mask, dim=pooling_dim, keepdim=True
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)
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squared_difference = torch.pow(xs_pad - mean, 2.0)
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variance = torch.sum(squared_difference, dim=pooling_dim, keepdim=True) / torch.sum(
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seq_mask, dim=pooling_dim, keepdim=True
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)
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for i in reversed(pooling_dim):
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mean, variance = torch.squeeze(mean, dim=i), torch.squeeze(variance, dim=i)
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value_mask = torch.less_equal(variance, VAR2STD_EPSILON).float()
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variance = (1.0 - value_mask) * variance + value_mask * VAR2STD_EPSILON
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stddev = torch.sqrt(variance)
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stat_pooling = torch.cat([mean, stddev], dim=1)
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return stat_pooling
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def windowed_statistic_pooling(
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xs_pad: torch.Tensor,
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ilens: torch.Tensor = None,
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pooling_dim: Tuple = (2, 3),
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pooling_size: int = 20,
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pooling_stride: int = 1,
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) -> Tuple[torch.Tensor, int]:
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# xs_pad in (Batch, Channel, Time, Frequency)
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tt = xs_pad.shape[2]
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num_chunk = int(math.ceil(tt / pooling_stride))
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pad = pooling_size // 2
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if len(xs_pad.shape) == 4:
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features = F.pad(xs_pad, (0, 0, pad, pad), "replicate")
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else:
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features = F.pad(xs_pad, (pad, pad), "replicate")
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stat_list = []
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for i in range(num_chunk):
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# B x C
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st, ed = i * pooling_stride, i * pooling_stride + pooling_size
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stat = statistic_pooling(features[:, :, st:ed], pooling_dim=pooling_dim)
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stat_list.append(stat.unsqueeze(2))
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# B x C x T
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return torch.cat(stat_list, dim=2), ilens / pooling_stride
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