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| import torch | |
| import torch.nn as nn | |
| from .constants import N_MELS | |
| class ConvBlockRes(nn.Module): | |
| def __init__(self, in_channels, out_channels, momentum=0.01): | |
| super(ConvBlockRes, self).__init__() | |
| self.conv = nn.Sequential( | |
| nn.Conv2d(in_channels=in_channels, | |
| out_channels=out_channels, | |
| kernel_size=(3, 3), | |
| stride=(1, 1), | |
| padding=(1, 1), | |
| bias=False), | |
| nn.BatchNorm2d(out_channels, momentum=momentum), | |
| nn.ReLU(), | |
| nn.Conv2d(in_channels=out_channels, | |
| out_channels=out_channels, | |
| kernel_size=(3, 3), | |
| stride=(1, 1), | |
| padding=(1, 1), | |
| bias=False), | |
| nn.BatchNorm2d(out_channels, momentum=momentum), | |
| nn.ReLU(), | |
| ) | |
| if in_channels != out_channels: | |
| self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1)) | |
| self.is_shortcut = True | |
| else: | |
| self.is_shortcut = False | |
| def forward(self, x): | |
| if self.is_shortcut: | |
| return self.conv(x) + self.shortcut(x) | |
| else: | |
| return self.conv(x) + x | |
| class ResEncoderBlock(nn.Module): | |
| def __init__(self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01): | |
| super(ResEncoderBlock, self).__init__() | |
| self.n_blocks = n_blocks | |
| self.conv = nn.ModuleList() | |
| self.conv.append(ConvBlockRes(in_channels, out_channels, momentum)) | |
| for i in range(n_blocks - 1): | |
| self.conv.append(ConvBlockRes(out_channels, out_channels, momentum)) | |
| self.kernel_size = kernel_size | |
| if self.kernel_size is not None: | |
| self.pool = nn.AvgPool2d(kernel_size=kernel_size) | |
| def forward(self, x): | |
| for i in range(self.n_blocks): | |
| x = self.conv[i](x) | |
| if self.kernel_size is not None: | |
| return x, self.pool(x) | |
| else: | |
| return x | |
| class ResDecoderBlock(nn.Module): | |
| def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01): | |
| super(ResDecoderBlock, self).__init__() | |
| out_padding = (0, 1) if stride == (1, 2) else (1, 1) | |
| self.n_blocks = n_blocks | |
| self.conv1 = nn.Sequential( | |
| nn.ConvTranspose2d(in_channels=in_channels, | |
| out_channels=out_channels, | |
| kernel_size=(3, 3), | |
| stride=stride, | |
| padding=(1, 1), | |
| output_padding=out_padding, | |
| bias=False), | |
| nn.BatchNorm2d(out_channels, momentum=momentum), | |
| nn.ReLU(), | |
| ) | |
| self.conv2 = nn.ModuleList() | |
| self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum)) | |
| for i in range(n_blocks-1): | |
| self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum)) | |
| def forward(self, x, concat_tensor): | |
| x = self.conv1(x) | |
| x = torch.cat((x, concat_tensor), dim=1) | |
| for i in range(self.n_blocks): | |
| x = self.conv2[i](x) | |
| return x | |
| class Encoder(nn.Module): | |
| def __init__(self, in_channels, in_size, n_encoders, kernel_size, n_blocks, out_channels=16, momentum=0.01): | |
| super(Encoder, self).__init__() | |
| self.n_encoders = n_encoders | |
| self.bn = nn.BatchNorm2d(in_channels, momentum=momentum) | |
| self.layers = nn.ModuleList() | |
| self.latent_channels = [] | |
| for i in range(self.n_encoders): | |
| self.layers.append(ResEncoderBlock(in_channels, out_channels, kernel_size, n_blocks, momentum=momentum)) | |
| self.latent_channels.append([out_channels, in_size]) | |
| in_channels = out_channels | |
| out_channels *= 2 | |
| in_size //= 2 | |
| self.out_size = in_size | |
| self.out_channel = out_channels | |
| def forward(self, x): | |
| concat_tensors = [] | |
| x = self.bn(x) | |
| for i in range(self.n_encoders): | |
| _, x = self.layers[i](x) | |
| concat_tensors.append(_) | |
| return x, concat_tensors | |
| class Intermediate(nn.Module): | |
| def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01): | |
| super(Intermediate, self).__init__() | |
| self.n_inters = n_inters | |
| self.layers = nn.ModuleList() | |
| self.layers.append(ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)) | |
| for i in range(self.n_inters-1): | |
| self.layers.append(ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)) | |
| def forward(self, x): | |
| for i in range(self.n_inters): | |
| x = self.layers[i](x) | |
| return x | |
| class Decoder(nn.Module): | |
| def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01): | |
| super(Decoder, self).__init__() | |
| self.layers = nn.ModuleList() | |
| self.n_decoders = n_decoders | |
| for i in range(self.n_decoders): | |
| out_channels = in_channels // 2 | |
| self.layers.append(ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)) | |
| in_channels = out_channels | |
| def forward(self, x, concat_tensors): | |
| for i in range(self.n_decoders): | |
| x = self.layers[i](x, concat_tensors[-1-i]) | |
| return x | |
| class TimbreFilter(nn.Module): | |
| def __init__(self, latent_rep_channels): | |
| super(TimbreFilter, self).__init__() | |
| self.layers = nn.ModuleList() | |
| for latent_rep in latent_rep_channels: | |
| self.layers.append(ConvBlockRes(latent_rep[0], latent_rep[0])) | |
| def forward(self, x_tensors): | |
| out_tensors = [] | |
| for i, layer in enumerate(self.layers): | |
| out_tensors.append(layer(x_tensors[i])) | |
| return out_tensors | |
| class DeepUnet(nn.Module): | |
| def __init__(self, kernel_size, n_blocks, en_de_layers=5, inter_layers=4, in_channels=1, en_out_channels=16): | |
| super(DeepUnet, self).__init__() | |
| self.encoder = Encoder(in_channels, N_MELS, en_de_layers, kernel_size, n_blocks, en_out_channels) | |
| self.intermediate = Intermediate(self.encoder.out_channel // 2, self.encoder.out_channel, inter_layers, n_blocks) | |
| self.tf = TimbreFilter(self.encoder.latent_channels) | |
| self.decoder = Decoder(self.encoder.out_channel, en_de_layers, kernel_size, n_blocks) | |
| def forward(self, x): | |
| x, concat_tensors = self.encoder(x) | |
| x = self.intermediate(x) | |
| concat_tensors = self.tf(concat_tensors) | |
| x = self.decoder(x, concat_tensors) | |
| return x | |
| class DeepUnet0(nn.Module): | |
| def __init__(self, kernel_size, n_blocks, en_de_layers=5, inter_layers=4, in_channels=1, en_out_channels=16): | |
| super(DeepUnet0, self).__init__() | |
| self.encoder = Encoder(in_channels, N_MELS, en_de_layers, kernel_size, n_blocks, en_out_channels) | |
| self.intermediate = Intermediate(self.encoder.out_channel // 2, self.encoder.out_channel, inter_layers, n_blocks) | |
| self.tf = TimbreFilter(self.encoder.latent_channels) | |
| self.decoder = Decoder(self.encoder.out_channel, en_de_layers, kernel_size, n_blocks) | |
| def forward(self, x): | |
| x, concat_tensors = self.encoder(x) | |
| x = self.intermediate(x) | |
| x = self.decoder(x, concat_tensors) | |
| return x | |