""" refer from https://github.com/zceng/LVCNet """ import torch import torch.nn as nn import torch.nn.functional as F class KernelPredictor(torch.nn.Module): ''' Kernel predictor for the location-variable convolutions''' def __init__( self, cond_channels, conv_in_channels, conv_out_channels, conv_layers, conv_kernel_size=3, kpnet_hidden_channels=64, kpnet_conv_size=3, kpnet_dropout=0.0, kpnet_nonlinear_activation="LeakyReLU", kpnet_nonlinear_activation_params={"negative_slope": 0.1}, ): ''' Args: cond_channels (int): number of channel for the conditioning sequence, conv_in_channels (int): number of channel for the input sequence, conv_out_channels (int): number of channel for the output sequence, conv_layers (int): number of layers ''' super().__init__() self.conv_in_channels = conv_in_channels self.conv_out_channels = conv_out_channels self.conv_kernel_size = conv_kernel_size self.conv_layers = conv_layers kpnet_kernel_channels = conv_in_channels * conv_out_channels * conv_kernel_size * conv_layers # l_w kpnet_bias_channels = conv_out_channels * conv_layers # l_b self.input_conv = nn.Sequential( nn.utils.weight_norm(nn.Conv1d(cond_channels, kpnet_hidden_channels, 5, padding=2, bias=True)), getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params), ) self.residual_convs = nn.ModuleList() padding = (kpnet_conv_size - 1) // 2 for _ in range(3): self.residual_convs.append( nn.Sequential( nn.Dropout(kpnet_dropout), nn.utils.weight_norm( nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True)), getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params), nn.utils.weight_norm( nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True)), getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params), ) ) self.kernel_conv = nn.utils.weight_norm( nn.Conv1d(kpnet_hidden_channels, kpnet_kernel_channels, kpnet_conv_size, padding=padding, bias=True)) self.bias_conv = nn.utils.weight_norm( nn.Conv1d(kpnet_hidden_channels, kpnet_bias_channels, kpnet_conv_size, padding=padding, bias=True)) def forward(self, c): ''' Args: c (Tensor): the conditioning sequence (batch, cond_channels, cond_length) ''' batch, _, cond_length = c.shape c = self.input_conv(c) for residual_conv in self.residual_convs: residual_conv.to(c.device) c = c + residual_conv(c) k = self.kernel_conv(c) b = self.bias_conv(c) kernels = k.contiguous().view( batch, self.conv_layers, self.conv_in_channels, self.conv_out_channels, self.conv_kernel_size, cond_length, ) bias = b.contiguous().view( batch, self.conv_layers, self.conv_out_channels, cond_length, ) return kernels, bias def remove_weight_norm(self): nn.utils.remove_weight_norm(self.input_conv[0]) nn.utils.remove_weight_norm(self.kernel_conv) nn.utils.remove_weight_norm(self.bias_conv) for block in self.residual_convs: nn.utils.remove_weight_norm(block[1]) nn.utils.remove_weight_norm(block[3]) class LVCBlock(torch.nn.Module): '''the location-variable convolutions''' def __init__( self, in_channels, cond_channels, stride, dilations=[1, 3, 9, 27], lReLU_slope=0.2, conv_kernel_size=3, cond_hop_length=256, kpnet_hidden_channels=64, kpnet_conv_size=3, kpnet_dropout=0.0, ): super().__init__() self.cond_hop_length = cond_hop_length self.conv_layers = len(dilations) self.conv_kernel_size = conv_kernel_size self.kernel_predictor = KernelPredictor( cond_channels=cond_channels, conv_in_channels=in_channels, conv_out_channels=2 * in_channels, conv_layers=len(dilations), conv_kernel_size=conv_kernel_size, kpnet_hidden_channels=kpnet_hidden_channels, kpnet_conv_size=kpnet_conv_size, kpnet_dropout=kpnet_dropout, kpnet_nonlinear_activation_params={"negative_slope": lReLU_slope} ) self.convt_pre = nn.Sequential( nn.LeakyReLU(lReLU_slope), nn.utils.weight_norm(nn.ConvTranspose1d(in_channels, in_channels, 2 * stride, stride=stride, padding=stride // 2 + stride % 2, output_padding=stride % 2)), ) self.conv_blocks = nn.ModuleList() for dilation in dilations: self.conv_blocks.append( nn.Sequential( nn.LeakyReLU(lReLU_slope), nn.utils.weight_norm(nn.Conv1d(in_channels, in_channels, conv_kernel_size, padding=dilation * (conv_kernel_size - 1) // 2, dilation=dilation)), nn.LeakyReLU(lReLU_slope), ) ) def forward(self, x, c): ''' forward propagation of the location-variable convolutions. Args: x (Tensor): the input sequence (batch, in_channels, in_length) c (Tensor): the conditioning sequence (batch, cond_channels, cond_length) Returns: Tensor: the output sequence (batch, in_channels, in_length) ''' _, in_channels, _ = x.shape # (B, c_g, L') x = self.convt_pre(x) # (B, c_g, stride * L') kernels, bias = self.kernel_predictor(c) for i, conv in enumerate(self.conv_blocks): output = conv(x) # (B, c_g, stride * L') k = kernels[:, i, :, :, :, :] # (B, 2 * c_g, c_g, kernel_size, cond_length) b = bias[:, i, :, :] # (B, 2 * c_g, cond_length) output = self.location_variable_convolution(output, k, b, hop_size=self.cond_hop_length) # (B, 2 * c_g, stride * L'): LVC x = x + torch.sigmoid(output[:, :in_channels, :]) * torch.tanh( output[:, in_channels:, :]) # (B, c_g, stride * L'): GAU return x def location_variable_convolution(self, x, kernel, bias, dilation=1, hop_size=256): ''' perform location-variable convolution operation on the input sequence (x) using the local convolution kernl. Time: 414 μs ± 309 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each), test on NVIDIA V100. Args: x (Tensor): the input sequence (batch, in_channels, in_length). kernel (Tensor): the local convolution kernel (batch, in_channel, out_channels, kernel_size, kernel_length) bias (Tensor): the bias for the local convolution (batch, out_channels, kernel_length) dilation (int): the dilation of convolution. hop_size (int): the hop_size of the conditioning sequence. Returns: (Tensor): the output sequence after performing local convolution. (batch, out_channels, in_length). ''' batch, _, in_length = x.shape batch, _, out_channels, kernel_size, kernel_length = kernel.shape assert in_length == (kernel_length * hop_size), "length of (x, kernel) is not matched" padding = dilation * int((kernel_size - 1) / 2) x = F.pad(x, (padding, padding), 'constant', 0) # (batch, in_channels, in_length + 2*padding) x = x.unfold(2, hop_size + 2 * padding, hop_size) # (batch, in_channels, kernel_length, hop_size + 2*padding) if hop_size < dilation: x = F.pad(x, (0, dilation), 'constant', 0) x = x.unfold(3, dilation, dilation) # (batch, in_channels, kernel_length, (hop_size + 2*padding)/dilation, dilation) x = x[:, :, :, :, :hop_size] x = x.transpose(3, 4) # (batch, in_channels, kernel_length, dilation, (hop_size + 2*padding)/dilation) x = x.unfold(4, kernel_size, 1) # (batch, in_channels, kernel_length, dilation, _, kernel_size) o = torch.einsum('bildsk,biokl->bolsd', x, kernel) o = o.to(memory_format=torch.channels_last_3d) bias = bias.unsqueeze(-1).unsqueeze(-1).to(memory_format=torch.channels_last_3d) o = o + bias o = o.contiguous().view(batch, out_channels, -1) return o def remove_weight_norm(self): self.kernel_predictor.remove_weight_norm() nn.utils.remove_weight_norm(self.convt_pre[1]) for block in self.conv_blocks: nn.utils.remove_weight_norm(block[1])