Spaces:
Running
Running
| # Adapted from https://github.com/ubisoft/ubisoft-laforge-daft-exprt Apache License Version 2.0 | |
| # Unsupervised Domain Adaptation by Backpropagation | |
| import torch | |
| import torch.nn as nn | |
| from torch.autograd import Function | |
| from torch.nn.utils import weight_norm | |
| class GradientReversalFunction(Function): | |
| def forward(ctx, x, lambda_): | |
| ctx.lambda_ = lambda_ | |
| return x.clone() | |
| def backward(ctx, grads): | |
| lambda_ = ctx.lambda_ | |
| lambda_ = grads.new_tensor(lambda_) | |
| dx = -lambda_ * grads | |
| return dx, None | |
| class GradientReversal(torch.nn.Module): | |
| ''' Gradient Reversal Layer | |
| Y. Ganin, V. Lempitsky, | |
| "Unsupervised Domain Adaptation by Backpropagation", | |
| in ICML, 2015. | |
| Forward pass is the identity function | |
| In the backward pass, upstream gradients are multiplied by -lambda (i.e. gradient are reversed) | |
| ''' | |
| def __init__(self, lambda_reversal=1): | |
| super(GradientReversal, self).__init__() | |
| self.lambda_ = lambda_reversal | |
| def forward(self, x): | |
| return GradientReversalFunction.apply(x, self.lambda_) | |
| class SpeakerClassifier(nn.Module): | |
| def __init__(self, embed_dim, spk_dim): | |
| super(SpeakerClassifier, self).__init__() | |
| self.classifier = nn.Sequential( | |
| GradientReversal(lambda_reversal=1), | |
| weight_norm(nn.Conv1d(embed_dim, embed_dim, kernel_size=5, padding=2)), | |
| nn.ReLU(), | |
| weight_norm(nn.Conv1d(embed_dim, embed_dim, kernel_size=5, padding=2)), | |
| nn.ReLU(), | |
| weight_norm(nn.Conv1d(embed_dim, spk_dim, kernel_size=5, padding=2)) | |
| ) | |
| def forward(self, x): | |
| ''' Forward function of Speaker Classifier: | |
| x = (B, embed_dim, len) | |
| ''' | |
| # pass through classifier | |
| outputs = self.classifier(x) # (B, nb_speakers) | |
| outputs = torch.mean(outputs, dim=-1) | |
| return outputs | |