VAE.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

class VAE(nn.Module):
    def __init__(self, input_dim, hidden_dim, latent_dim, num_styles=2):
        super(VAE, self).__init__()
        self.input_dim = input_dim
        self.latent_dim = latent_dim
        self.hidden_dim = hidden_dim

        self.encode = Encoder(self.input_dim, self.hidden_dim, self.latent_dim)
        self.decode = Decoder(self.latent_dim, self.hidden_dim, self.input_dim)
        self.style_classifier = StyleClassifier(self.latent_dim, num_styles)

    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        return mu + eps * std

    def forward(self, x, right=None, left=None, check=False):
        mu, logvar, output = self.encode(x)
        z = self.reparameterize(mu, logvar)
        style_pred = self.style_classifier(z)
        decod = self.decode(z, output)
        return decod, mu, logvar, style_pred

class Encoder(nn.Module):
    def __init__(self, input_dim, hidden_dim, latent_dim):
        super(Encoder, self).__init__()
        self.hidden_dim = hidden_dim

        self.gru_piano_right = nn.GRU(input_dim, hidden_dim, batch_first=True)
        self.gru_piano_left = nn.GRU(input_dim, hidden_dim, batch_first=True)
        
        self.dense_layer = nn.Linear(hidden_dim * 2, hidden_dim, bias = True)

        self.fc_mu = nn.Linear(hidden_dim, latent_dim, bias = True)  
        self.fc_logvar = nn.Linear(hidden_dim, latent_dim, bias = True)  
    
    def forward(self, x):
        input_list = torch.chunk(x, 2, dim=1)
        right_input = input_list[0] # torch.Size([Batch Size, Sequence length, input length]) 
        left_input = input_list[1] 

        # initialize hidden state
        h0 = torch.zeros(1, right_input.size(0), self.hidden_dim, device=right_input.device)

        # Forward pass through GRU for each instrument
        o_r, h_r = self.gru_piano_right(right_input, h0)
        o_l, h_l = self.gru_piano_left(left_input, h0)
        
        output = torch.cat((o_r, o_l), dim=1)
        h = torch.cat((h_r[-1,], h_l[-1,]), dim=1)

        h = self.dense_layer(h)
        h = F.relu(h)
        mu = self.fc_mu(h)
        mu = F.relu(mu)
        logvar = self.fc_logvar(h)
        logvar = F.relu(logvar) + 1e-4
        return mu, logvar, output


class Decoder(nn.Module):
    def __init__(self, latent_dim, hidden_dim, output_dim):
        super(Decoder, self).__init__()
        self.latent_dim = latent_dim
        self.output_dim = output_dim

        self.latent_to_hidden = nn.Linear(latent_dim, latent_dim, bias = True)

        self.piano_right_layer = nn.Linear(latent_dim, hidden_dim, bias = True)
        self.piano_left_layer = nn.Linear(latent_dim, hidden_dim, bias = True)

        self.r_layer = nn.Linear(hidden_dim, output_dim, bias = True)
        self.l_layer = nn.Linear(hidden_dim, output_dim, bias = True)

        self.gru_piano_right_cell = nn.GRUCell(output_dim, hidden_dim)
        self.gru_piano_left_cell = nn.GRUCell(output_dim, hidden_dim)


        self.fr_layer = nn.Linear(hidden_dim, output_dim, bias = True)
        self.fl_layer = nn.Linear(hidden_dim , output_dim, bias = True) 

    def forward(self, z, output):
        
        h = self.latent_to_hidden(z)
        h = F.relu(h)
        
        right = torch.split(output, 150, dim=1)[0]
        left = torch.split(output, 150, dim=1)[1] 

        right = right.permute(1, 0, 2)
        left = left.permute(1, 0, 2)

        right = self.r_layer(right)
        right = F.tanh(right)
        left = self.l_layer(left)            
        left = F.tanh(left)


        piano_hidden = self.piano_right_layer(h) 
        left_hidden = self.piano_left_layer(h)

        right_outputs = []
        left_outputs = []

        for t in range(right.size(0)):
            piano_hidden = self.gru_piano_right_cell(right[t] , piano_hidden)
            left_hidden = self.gru_piano_left_cell(left[t], left_hidden)
            
            right_outputs.append(piano_hidden.unsqueeze(1))
            left_outputs.append(left_hidden.unsqueeze(1))

        # print(right_outputs[0].shape)
        right_outputs = torch.cat(right_outputs, dim=1)
        left_outputs = torch.cat(left_outputs, dim=1)

        right_outputs = self.fr_layer(right_outputs)
        left_outputs = self.fl_layer(left_outputs)
        
        right_outputs = F.sigmoid(right_outputs)
        left_outputs = F.sigmoid(left_outputs)

        output = torch.cat((right_outputs, left_outputs), dim=1)

        return output

class StyleClassifier(nn.Module):
    def __init__(self, latent_dim, num_styles):
        super(StyleClassifier, self).__init__()
        self.fc1 = nn.Linear(latent_dim, 128)
        self.fc2 = nn.Linear(128, num_styles)

    def forward(self, z):
        x = F.relu(self.fc1(z))
        x = self.fc2(x)
        return F.softmax(x, dim=-1)