| import torch, torch.nn as nn, torch.nn.functional as F |
|
|
| class GSP(nn.Module): |
| |
| 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): |
| |
| 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): |
| |
| 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 |
| |
