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import numpy as np
from torch import nn
class GBlock(nn.Module):
def __init__(self, in_channels, cond_channels, downsample_factor):
super().__init__()
self.in_channels = in_channels
self.cond_channels = cond_channels
self.downsample_factor = downsample_factor
self.start = nn.Sequential(
nn.AvgPool1d(downsample_factor, stride=downsample_factor),
nn.ReLU(),
nn.Conv1d(in_channels, in_channels * 2, kernel_size=3, padding=1),
)
self.lc_conv1d = nn.Conv1d(cond_channels, in_channels * 2, kernel_size=1)
self.end = nn.Sequential(
nn.ReLU(), nn.Conv1d(in_channels * 2, in_channels * 2, kernel_size=3, dilation=2, padding=2)
)
self.residual = nn.Sequential(
nn.Conv1d(in_channels, in_channels * 2, kernel_size=1),
nn.AvgPool1d(downsample_factor, stride=downsample_factor),
)
def forward(self, inputs, conditions):
outputs = self.start(inputs) + self.lc_conv1d(conditions)
outputs = self.end(outputs)
residual_outputs = self.residual(inputs)
outputs = outputs + residual_outputs
return outputs
class DBlock(nn.Module):
def __init__(self, in_channels, out_channels, downsample_factor):
super().__init__()
self.in_channels = in_channels
self.downsample_factor = downsample_factor
self.out_channels = out_channels
self.donwsample_layer = nn.AvgPool1d(downsample_factor, stride=downsample_factor)
self.layers = nn.Sequential(
nn.ReLU(),
nn.Conv1d(in_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv1d(out_channels, out_channels, kernel_size=3, dilation=2, padding=2),
)
self.residual = nn.Sequential(
nn.Conv1d(in_channels, out_channels, kernel_size=1),
)
def forward(self, inputs):
if self.downsample_factor > 1:
outputs = self.layers(self.donwsample_layer(inputs)) + self.donwsample_layer(self.residual(inputs))
else:
outputs = self.layers(inputs) + self.residual(inputs)
return outputs
class ConditionalDiscriminator(nn.Module):
def __init__(self, in_channels, cond_channels, downsample_factors=(2, 2, 2), out_channels=(128, 256)):
super().__init__()
assert len(downsample_factors) == len(out_channels) + 1
self.in_channels = in_channels
self.cond_channels = cond_channels
self.downsample_factors = downsample_factors
self.out_channels = out_channels
self.pre_cond_layers = nn.ModuleList()
self.post_cond_layers = nn.ModuleList()
# layers before condition features
self.pre_cond_layers += [DBlock(in_channels, 64, 1)]
in_channels = 64
for (i, channel) in enumerate(out_channels):
self.pre_cond_layers.append(DBlock(in_channels, channel, downsample_factors[i]))
in_channels = channel
# condition block
self.cond_block = GBlock(in_channels, cond_channels, downsample_factors[-1])
# layers after condition block
self.post_cond_layers += [
DBlock(in_channels * 2, in_channels * 2, 1),
DBlock(in_channels * 2, in_channels * 2, 1),
nn.AdaptiveAvgPool1d(1),
nn.Conv1d(in_channels * 2, 1, kernel_size=1),
]
def forward(self, inputs, conditions):
batch_size = inputs.size()[0]
outputs = inputs.view(batch_size, self.in_channels, -1)
for layer in self.pre_cond_layers:
outputs = layer(outputs)
outputs = self.cond_block(outputs, conditions)
for layer in self.post_cond_layers:
outputs = layer(outputs)
return outputs
class UnconditionalDiscriminator(nn.Module):
def __init__(self, in_channels, base_channels=64, downsample_factors=(8, 4), out_channels=(128, 256)):
super().__init__()
self.downsample_factors = downsample_factors
self.in_channels = in_channels
self.downsample_factors = downsample_factors
self.out_channels = out_channels
self.layers = nn.ModuleList()
self.layers += [DBlock(self.in_channels, base_channels, 1)]
in_channels = base_channels
for (i, factor) in enumerate(downsample_factors):
self.layers.append(DBlock(in_channels, out_channels[i], factor))
in_channels *= 2
self.layers += [
DBlock(in_channels, in_channels, 1),
DBlock(in_channels, in_channels, 1),
nn.AdaptiveAvgPool1d(1),
nn.Conv1d(in_channels, 1, kernel_size=1),
]
def forward(self, inputs):
batch_size = inputs.size()[0]
outputs = inputs.view(batch_size, self.in_channels, -1)
for layer in self.layers:
outputs = layer(outputs)
return outputs
class RandomWindowDiscriminator(nn.Module):
"""Random Window Discriminator as described in
http://arxiv.org/abs/1909.11646"""
def __init__(
self,
cond_channels,
hop_length,
uncond_disc_donwsample_factors=(8, 4),
cond_disc_downsample_factors=((8, 4, 2, 2, 2), (8, 4, 2, 2), (8, 4, 2), (8, 4), (4, 2, 2)),
cond_disc_out_channels=((128, 128, 256, 256), (128, 256, 256), (128, 256), (256,), (128, 256)),
window_sizes=(512, 1024, 2048, 4096, 8192),
):
super().__init__()
self.cond_channels = cond_channels
self.window_sizes = window_sizes
self.hop_length = hop_length
self.base_window_size = self.hop_length * 2
self.ks = [ws // self.base_window_size for ws in window_sizes]
# check arguments
assert len(cond_disc_downsample_factors) == len(cond_disc_out_channels) == len(window_sizes)
for ws in window_sizes:
assert ws % hop_length == 0
for idx, cf in enumerate(cond_disc_downsample_factors):
assert np.prod(cf) == hop_length // self.ks[idx]
# define layers
self.unconditional_discriminators = nn.ModuleList([])
for k in self.ks:
layer = UnconditionalDiscriminator(
in_channels=k, base_channels=64, downsample_factors=uncond_disc_donwsample_factors
)
self.unconditional_discriminators.append(layer)
self.conditional_discriminators = nn.ModuleList([])
for idx, k in enumerate(self.ks):
layer = ConditionalDiscriminator(
in_channels=k,
cond_channels=cond_channels,
downsample_factors=cond_disc_downsample_factors[idx],
out_channels=cond_disc_out_channels[idx],
)
self.conditional_discriminators.append(layer)
def forward(self, x, c):
scores = []
feats = []
# unconditional pass
for (window_size, layer) in zip(self.window_sizes, self.unconditional_discriminators):
index = np.random.randint(x.shape[-1] - window_size)
score = layer(x[:, :, index : index + window_size])
scores.append(score)
# conditional pass
for (window_size, layer) in zip(self.window_sizes, self.conditional_discriminators):
frame_size = window_size // self.hop_length
lc_index = np.random.randint(c.shape[-1] - frame_size)
sample_index = lc_index * self.hop_length
x_sub = x[:, :, sample_index : (lc_index + frame_size) * self.hop_length]
c_sub = c[:, :, lc_index : lc_index + frame_size]
score = layer(x_sub, c_sub)
scores.append(score)
return scores, feats
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