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linear_modulation.py
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linear_modulation.py
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import math
import torch
from model.base import BaseModule
from model.layers import Conv1dWithInitialization
LINEAR_SCALE=5000
class PositionalEncoding(BaseModule):
def __init__(self, n_channels):
super(PositionalEncoding, self).__init__()
self.n_channels = n_channels
def forward(self, noise_level):
if len(noise_level.shape) > 1:
noise_level = noise_level.squeeze(-1)
half_dim = self.n_channels // 2
exponents = torch.arange(half_dim, dtype=torch.float32).to(noise_level) / float(half_dim)
exponents = exponents ** 1e-4
exponents = LINEAR_SCALE * noise_level.unsqueeze(1) * exponents.unsqueeze(0)
return torch.cat([exponents.sin(), exponents.cos()], dim=-1)
class FeatureWiseLinearModulation(BaseModule):
def __init__(self, in_channels, out_channels, input_dscaled_by):
super(FeatureWiseLinearModulation, self).__init__()
self.signal_conv = torch.nn.Sequential(*[
Conv1dWithInitialization(
in_channels=in_channels,
out_channels=in_channels,
kernel_size=3,
stride=1,
padding=1
),
torch.nn.LeakyReLU(0.2)
])
self.positional_encoding = PositionalEncoding(in_channels)
self.scale_conv = Conv1dWithInitialization(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
stride=1,
padding=1
)
self.shift_conv = Conv1dWithInitialization(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
stride=1,
padding=1
)
def forward(self, x, noise_level):
outputs = self.signal_conv(x)
outputs = outputs + self.positional_encoding(noise_level).unsqueeze(-1)
scale, shift = self.scale_conv(outputs), self.shift_conv(outputs)
return scale, shift
class FeatureWiseAffine(BaseModule):
def __init__(self):
super(FeatureWiseAffine, self).__init__()
def forward(self, x, scale, shift):
outputs = scale * x + shift
return outputs