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| import torch | |
| from torch import nn | |
| class LayerNorm(nn.Module): | |
| def __init__(self, channels, eps=1e-4): | |
| """Layer norm for the 2nd dimension of the input. | |
| Args: | |
| channels (int): number of channels (2nd dimension) of the input. | |
| eps (float): to prevent 0 division | |
| Shapes: | |
| - input: (B, C, T) | |
| - output: (B, C, T) | |
| """ | |
| super().__init__() | |
| self.channels = channels | |
| self.eps = eps | |
| self.gamma = nn.Parameter(torch.ones(1, channels, 1) * 0.1) | |
| self.beta = nn.Parameter(torch.zeros(1, channels, 1)) | |
| def forward(self, x): | |
| mean = torch.mean(x, 1, keepdim=True) | |
| variance = torch.mean((x - mean) ** 2, 1, keepdim=True) | |
| x = (x - mean) * torch.rsqrt(variance + self.eps) | |
| x = x * self.gamma + self.beta | |
| return x | |
| class LayerNorm2(nn.Module): | |
| """Layer norm for the 2nd dimension of the input using torch primitive. | |
| Args: | |
| channels (int): number of channels (2nd dimension) of the input. | |
| eps (float): to prevent 0 division | |
| Shapes: | |
| - input: (B, C, T) | |
| - output: (B, C, T) | |
| """ | |
| def __init__(self, channels, eps=1e-5): | |
| super().__init__() | |
| self.channels = channels | |
| self.eps = eps | |
| self.gamma = nn.Parameter(torch.ones(channels)) | |
| self.beta = nn.Parameter(torch.zeros(channels)) | |
| def forward(self, x): | |
| x = x.transpose(1, -1) | |
| x = torch.nn.functional.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) | |
| return x.transpose(1, -1) | |
| class TemporalBatchNorm1d(nn.BatchNorm1d): | |
| """Normalize each channel separately over time and batch.""" | |
| def __init__(self, channels, affine=True, track_running_stats=True, momentum=0.1): | |
| super().__init__(channels, affine=affine, track_running_stats=track_running_stats, momentum=momentum) | |
| def forward(self, x): | |
| return super().forward(x.transpose(2, 1)).transpose(2, 1) | |
| class ActNorm(nn.Module): | |
| """Activation Normalization bijector as an alternative to Batch Norm. It computes | |
| mean and std from a sample data in advance and it uses these values | |
| for normalization at training. | |
| Args: | |
| channels (int): input channels. | |
| ddi (False): data depended initialization flag. | |
| Shapes: | |
| - inputs: (B, C, T) | |
| - outputs: (B, C, T) | |
| """ | |
| def __init__(self, channels, ddi=False, **kwargs): # pylint: disable=unused-argument | |
| super().__init__() | |
| self.channels = channels | |
| self.initialized = not ddi | |
| self.logs = nn.Parameter(torch.zeros(1, channels, 1)) | |
| self.bias = nn.Parameter(torch.zeros(1, channels, 1)) | |
| def forward(self, x, x_mask=None, reverse=False, **kwargs): # pylint: disable=unused-argument | |
| if x_mask is None: | |
| x_mask = torch.ones(x.size(0), 1, x.size(2)).to(device=x.device, dtype=x.dtype) | |
| x_len = torch.sum(x_mask, [1, 2]) | |
| if not self.initialized: | |
| self.initialize(x, x_mask) | |
| self.initialized = True | |
| if reverse: | |
| z = (x - self.bias) * torch.exp(-self.logs) * x_mask | |
| logdet = None | |
| else: | |
| z = (self.bias + torch.exp(self.logs) * x) * x_mask | |
| logdet = torch.sum(self.logs) * x_len # [b] | |
| return z, logdet | |
| def store_inverse(self): | |
| pass | |
| def set_ddi(self, ddi): | |
| self.initialized = not ddi | |
| def initialize(self, x, x_mask): | |
| with torch.no_grad(): | |
| denom = torch.sum(x_mask, [0, 2]) | |
| m = torch.sum(x * x_mask, [0, 2]) / denom | |
| m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom | |
| v = m_sq - (m**2) | |
| logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6)) | |
| bias_init = (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype) | |
| logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype) | |
| self.bias.data.copy_(bias_init) | |
| self.logs.data.copy_(logs_init) | |