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import torch
import torch.nn as nn
import torch.nn.functional as F
class ChannelAttention(nn.Module):
def __init__(self, in_planes, ratio=16):
super(ChannelAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False)
self.relu1 = nn.ReLU()
self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
y = avg_out + max_out
y = self.sigmoid(y)
return x * y.expand_as(x)
class ResCell(nn.Module):
def __init__(self, input_channel, output_channel, stride=1):
super(ResCell, self).__init__()
self.stride = stride
self.input_channel = input_channel
self.output_channel = output_channel
if self.stride == -1:
output_size = ()
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.skip = nn.Conv2d(self.input_channel, self.output_channel, kernel_size=1, stride=1, padding=0)
self.conv1 = nn.ConvTranspose2d(self.input_channel, self.output_channel, kernel_size=5, stride=2, padding=2, output_padding=1)
self.conv2 = nn.ConvTranspose2d(self.output_channel, self.output_channel, kernel_size=5, padding=2)
elif self.stride == 2:
self.skip = nn.Conv2d(self.input_channel, self.output_channel, kernel_size=1, stride=2, padding=0)
self.conv1 = nn.Conv2d(self.input_channel, self.output_channel, kernel_size=5, stride=self.stride, padding=2)
self.conv2 = nn.Conv2d(self.output_channel, self.output_channel, kernel_size=5, padding=2)
else:
self.conv1 = nn.Conv2d(self.input_channel, self.output_channel, kernel_size=5, stride=self.stride, padding=2)
self.conv2 = nn.Conv2d(self.output_channel, self.output_channel, kernel_size=5, padding=2)
self.bn1 = nn.BatchNorm2d(self.output_channel)
self.bn2 = nn.BatchNorm2d(self.output_channel)
# Please replace `CBAM` with the actual module and parameters
self.cbam = ChannelAttention(self.output_channel)
def forward(self, x):
if self.stride == -1:
upsampled_x = self.upsample(x)
skip = self.skip(upsampled_x)
x = F.elu(self.bn1(self.conv1(x)))
x = self.conv2(x)
elif self.stride == 2:
skip = self.skip(x)
x = F.elu(self.bn1(self.conv1(x)))
x = self.conv2(x)
else:
skip = x
x = F.elu(self.bn1(self.conv1(x)))
x = self.conv2(x)
x = self.bn2(x)
x = self.cbam(x)
x = x + skip
x = F.elu(x)
return x
class ResBlock(nn.Module):
def __init__(self, input_channel, output_channel, upsample=False, n_cells=2):
super(ResBlock, self).__init__()
stride = -1 if upsample else 2
self.cells = nn.ModuleList([ResCell(input_channel, output_channel, stride=stride)])
for _ in range(n_cells - 1):
self.cells.append(ResCell(input_channel, output_channel, stride=1))
def forward(self, x):
for cell in self.cells:
x = cell(x)
return x
class Encoder(nn.Module):
def __init__(self, input_shape, timbre_dim, N2=0, channel_sizes=None):
super(Encoder, self).__init__()
if channel_sizes is None:
channel_sizes = [32, 64, 64, 96, 96, 128, 160, 216]
self.input_shape = input_shape
self.timbre_dim = timbre_dim
self.blocks = nn.ModuleList()
self.blocks.append(ResBlock(input_channel=1, output_channel=channel_sizes[0], upsample=False, n_cells=1))
input_channel = channel_sizes[0]
for c in channel_sizes[1:]:
self.blocks.append(ResBlock(input_channel=input_channel, output_channel=c, upsample=False, n_cells=1 + N2))
input_channel = c
self.flatten = nn.Flatten()
self.mu_timbre = nn.Linear(self._get_flattened_dim(), timbre_dim)
self.sigma_timbre = nn.Linear(self._get_flattened_dim(), timbre_dim)
def _get_flattened_dim(self):
x = torch.zeros((1,) + self.input_shape)
for block in self.blocks:
x = block(x)
x = self.flatten(x)
return x.shape[1]
def reparameterize(self, mu, logvar):
std = torch.exp(0.5*logvar)
eps = torch.randn_like(std)
return mu + eps*std
def forward(self, x):
for block in self.blocks:
x = block(x)
x = self.flatten(x)
mu = self.mu_timbre(x)
logvar = self.sigma_timbre(x)
latent_vector = self.reparameterize(mu, logvar)
# kl_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=1)
# kl_loss = torch.mean(kl_loss)
return mu, logvar, latent_vector
class Decoder(nn.Module):
def __init__(self, timbre_dim, N2=0, N3=8, channel_sizes=None):
super(Decoder, self).__init__()
if channel_sizes is None:
channel_sizes = [32, 64, 64, 96, 96, 128, 160, 216]
self.conv_shape = [-1, channel_sizes[-1], 2 ** (9 - N3), 2 ** (8 - N3)]
self.dense = nn.Linear(timbre_dim, self.conv_shape[1] * self.conv_shape[2] * self.conv_shape[3])
self.blocks = nn.ModuleList()
input_channel = channel_sizes[-1]
for c in list(reversed(channel_sizes))[1:]:
self.blocks.append(ResBlock(input_channel=input_channel, output_channel=c, upsample=True, n_cells=1 + N2))
input_channel = c
self.decoder_conv = nn.ConvTranspose2d(channel_sizes[0], 1, kernel_size=5, stride=2, padding=2, output_padding=1)
def forward(self, x):
x = F.elu(self.dense(x))
x = x.view(-1, self.conv_shape[1], self.conv_shape[2], self.conv_shape[3])
for block in self.blocks:
x = block(x)
x = self.decoder_conv(x)
x = torch.sigmoid(x)
return x |