model/VAE_torchV.py

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