def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(channels)
self.prelu = nn.PReLU()
self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(channels)
def __init__(self, in_channels, up_scale):
super(UpsampleBLock, self).__init__()
self.conv = nn.Conv2d(in_channels,
in_channels * up_scale**2,
kernel_size=3,
padding=1)
self.pixel_shuffle = nn.PixelShuffle(up_scale)
self.prelu = nn.PReLU()
def __init__(self, scale_factor):
upsample_block_num = int(math.log(scale_factor, 2))
super(Generator, self).__init__()
self.block1 = nn.Sequential(nn.Conv2d(3, 64, kernel_size=9, padding=4),
nn.PReLU())
self.block2 = ResidualBlock(64)
self.block3 = ResidualBlock(64)
self.block4 = ResidualBlock(64)
self.block5 = ResidualBlock(64)
self.block6 = ResidualBlock(64)
self.block7 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.PReLU())
block8 = [UpsampleBLock(64, 2) for _ in range(upsample_block_num)]
block8.append(nn.Conv2d(64, 3, kernel_size=9, padding=4))
block8.append(nn.Tanh())
self.block8 = nn.Sequential(*block8)
def __init__(self):
super(ModuleDict, self).__init__()
self.choices = nn.ModuleDict(
{"conv": nn.Conv2d(10, 10, 3), "pool": nn.MaxPool2d(3)}
)
self.activations = nn.ModuleDict(
{"relu": nn.ReLU(), "prelu": nn.PReLU()}
)
def __init__(
self,
in_channels=256,
conv_channels=512,
kernel_size=3,
dilation=1,
norm="cLN",
causal=False,
):
super(Conv1DBlock, self).__init__()
# 1x1 conv
self.conv1x1 = Conv1D(in_channels, conv_channels, 1)
self.prelu1 = nn.PReLU()
self.lnorm1 = build_norm(norm, conv_channels)
dconv_pad = (
(dilation * (kernel_size - 1)) // 2
if not causal
else (dilation * (kernel_size - 1))
)
# depthwise conv
self.dconv = nn.Conv1d(
conv_channels,
conv_channels,
kernel_size,
groups=conv_channels,
padding=dconv_pad,
dilation=dilation,
bias=True,
)
self.prelu2 = nn.PReLU()
self.lnorm2 = build_norm(norm, conv_channels)
# 1x1 conv cross channel
self.sconv = nn.Conv1d(conv_channels, in_channels, 1, bias=True)
# different padding way
self.causal = causal
self.dconv_pad = dconv_pad