Upload VAE_torchV.py

model/VAE_torchV.py

@@ -0,0 +1,171 @@
+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