Spaces:
Runtime error
Runtime error
File size: 1,565 Bytes
1649667 |
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 |
import torch
from modules.base import BaseModule
from modules.layers import Conv1dWithInitialization
LINEAR_SCALE=5000
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):
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
|