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""" refer from https://github.com/zceng/LVCNet """
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import nn
from torch.nn.utils.parametrizations import weight_norm
from .amp import AMPBlock
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(
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),
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),
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 = weight_norm(
nn.Conv1d(
kpnet_hidden_channels,
kpnet_kernel_channels,
kpnet_conv_size,
padding=padding,
bias=True,
)
)
self.bias_conv = 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
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,
add_extra_noise=False,
downsampling=False,
):
super().__init__()
self.add_extra_noise = add_extra_noise
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},
)
if downsampling:
self.convt_pre = nn.Sequential(
nn.LeakyReLU(lReLU_slope),
weight_norm(nn.Conv1d(in_channels, in_channels, 2 * stride + 1, padding="same")),
nn.AvgPool1d(stride, stride),
)
else:
if stride == 1:
self.convt_pre = nn.Sequential(
nn.LeakyReLU(lReLU_slope),
weight_norm(nn.Conv1d(in_channels, in_channels, 1)),
)
else:
self.convt_pre = nn.Sequential(
nn.LeakyReLU(lReLU_slope),
weight_norm(
nn.ConvTranspose1d(
in_channels,
in_channels,
2 * stride,
stride=stride,
padding=stride // 2 + stride % 2,
output_padding=stride % 2,
)
),
)
self.amp_block = AMPBlock(in_channels)
self.conv_blocks = nn.ModuleList()
for d in dilations:
self.conv_blocks.append(
nn.Sequential(
nn.LeakyReLU(lReLU_slope),
weight_norm(nn.Conv1d(in_channels, in_channels, conv_kernel_size, dilation=d, padding="same")),
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')
# Add one amp block just after the upsampling
x = self.amp_block(x) # (B, c_g, stride * L')
kernels, bias = self.kernel_predictor(c)
if self.add_extra_noise:
# Add extra noise to part of the feature
a, b = x.chunk(2, dim=1)
b = b + torch.randn_like(b) * 0.1
x = torch.cat([a, b], dim=1)
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
), f"length of (x, kernel) is not matched, {in_length} != {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.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
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