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308fdfc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | import torch, torch.nn as nn, torch.nn.functional as F
class GSP(nn.Module):
# GlobalStatsPool
def __init__(self,in_planes,acoustic_dim):
super(GSP, self).__init__()
self.out_dim = in_planes*8 * 2
def forward(self, x):
x = x.view(x.shape[0], x.shape[1], -1)
out = torch.cat([x.mean(dim=2), x.std(dim=2)], dim=1)
return out
class ASP(nn.Module):
# Attentive statistics pooling
def __init__(self,in_planes,acoustic_dim):
super(ASP, self).__init__()
outmap_size = int(acoustic_dim/8)
self.out_dim = in_planes*8 * outmap_size * 2
self.attention = nn.Sequential(
nn.Conv1d(in_planes*8 * outmap_size, 128, kernel_size=1),
nn.ReLU(),
nn.BatchNorm1d(128),
nn.Conv1d(128, in_planes*8 * outmap_size, kernel_size=1),
nn.Softmax(dim=2),
)
def forward(self, x):
x = x.reshape(x.size()[0],-1,x.size()[-1])
w = self.attention(x)
mu = torch.sum(x * w, dim=2)
sg = torch.sqrt( ( torch.sum((x**2) * w, dim=2) - mu**2 ).clamp(min=1e-5) )
x = torch.cat((mu,sg),1)
x = x.view(x.size()[0], -1)
return x
class TSP(nn.Module):
# TemporalStatsPool
def __init__(self,in_planes,acoustic_dim):
super(TSP, self).__init__()
outmap_size = int(acoustic_dim/8)
self.out_dim = in_planes * 8 * outmap_size * 2
def forward(self, x):
out_mean = x.mean(dim=-1)
out_std = torch.sqrt(torch.var(x, dim=-1) + 1e-7)
out_mean = out_mean.flatten(start_dim=1)
out_std = out_std.flatten(start_dim=1)
out = torch.cat((out_mean, out_std), 1)
return out
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