File size: 3,553 Bytes
2493d72 |
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 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
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 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) |