Spaces:
Sleeping
Sleeping
File size: 8,540 Bytes
9b2107c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 |
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
|