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import torch
import torch.nn.functional as F
class KernelPredictor(torch.nn.Module):
"""Kernel predictor for the location-variable convolutions"""
def __init__( # pylint: disable=dangerous-default-value
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):
kpnet_
"""
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
l_w = conv_in_channels * conv_out_channels * conv_kernel_size * conv_layers
l_b = conv_out_channels * conv_layers
padding = (kpnet_conv_size - 1) // 2
self.input_conv = torch.nn.Sequential(
torch.nn.Conv1d(cond_channels, kpnet_hidden_channels, 5, padding=(5 - 1) // 2, bias=True),
getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
)
self.residual_conv = torch.nn.Sequential(
torch.nn.Dropout(kpnet_dropout),
torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True),
getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True),
getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
torch.nn.Dropout(kpnet_dropout),
torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True),
getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True),
getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
torch.nn.Dropout(kpnet_dropout),
torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True),
getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
torch.nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, bias=True),
getattr(torch.nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
)
self.kernel_conv = torch.nn.Conv1d(kpnet_hidden_channels, l_w, kpnet_conv_size, padding=padding, bias=True)
self.bias_conv = torch.nn.Conv1d(kpnet_hidden_channels, l_b, kpnet_conv_size, padding=padding, bias=True)
def forward(self, c):
"""
Args:
c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)
Returns:
"""
batch, _, cond_length = c.shape
c = self.input_conv(c)
c = c + self.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
class LVCBlock(torch.nn.Module):
"""the location-variable convolutions"""
def __init__(
self,
in_channels,
cond_channels,
upsample_ratio,
conv_layers=4,
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 = conv_layers
self.conv_kernel_size = conv_kernel_size
self.convs = torch.nn.ModuleList()
self.upsample = torch.nn.ConvTranspose1d(
in_channels,
in_channels,
kernel_size=upsample_ratio * 2,
stride=upsample_ratio,
padding=upsample_ratio // 2 + upsample_ratio % 2,
output_padding=upsample_ratio % 2,
)
self.kernel_predictor = KernelPredictor(
cond_channels=cond_channels,
conv_in_channels=in_channels,
conv_out_channels=2 * in_channels,
conv_layers=conv_layers,
conv_kernel_size=conv_kernel_size,
kpnet_hidden_channels=kpnet_hidden_channels,
kpnet_conv_size=kpnet_conv_size,
kpnet_dropout=kpnet_dropout,
)
for i in range(conv_layers):
padding = (3**i) * int((conv_kernel_size - 1) / 2)
conv = torch.nn.Conv1d(
in_channels, in_channels, kernel_size=conv_kernel_size, padding=padding, dilation=3**i
)
self.convs.append(conv)
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[1]
kernels, bias = self.kernel_predictor(c)
x = F.leaky_relu(x, 0.2)
x = self.upsample(x)
for i in range(self.conv_layers):
y = F.leaky_relu(x, 0.2)
y = self.convs[i](y)
y = F.leaky_relu(y, 0.2)
k = kernels[:, i, :, :, :, :]
b = bias[:, i, :, :]
y = self.location_variable_convolution(y, k, b, 1, self.cond_hop_length)
x = x + torch.sigmoid(y[:, :in_channels, :]) * torch.tanh(y[:, in_channels:, :])
return x
@staticmethod
def location_variable_convolution(x, kernel, bias, dilation, hop_size):
"""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
), f"length of (x, kernel) is not matched, {in_length} vs {kernel_length * hop_size}"
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 + bias.unsqueeze(-1).unsqueeze(-1)
o = o.contiguous().view(batch, out_channels, -1)
return o
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