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import torch
import torch.nn.functional as F
from torch import nn
from torch.nn.utils.parametrizations import weight_norm
from torch.nn.utils.parametrize import remove_parametrizations
class Conv1d(nn.Conv1d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
nn.init.orthogonal_(self.weight)
nn.init.zeros_(self.bias)
class PositionalEncoding(nn.Module):
"""Positional encoding with noise level conditioning"""
def __init__(self, n_channels, max_len=10000):
super().__init__()
self.n_channels = n_channels
self.max_len = max_len
self.C = 5000
self.pe = torch.zeros(0, 0)
def forward(self, x, noise_level):
if x.shape[2] > self.pe.shape[1]:
self.init_pe_matrix(x.shape[1], x.shape[2], x)
return x + noise_level[..., None, None] + self.pe[:, : x.size(2)].repeat(x.shape[0], 1, 1) / self.C
def init_pe_matrix(self, n_channels, max_len, x):
pe = torch.zeros(max_len, n_channels)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.pow(10000, torch.arange(0, n_channels, 2).float() / n_channels)
pe[:, 0::2] = torch.sin(position / div_term)
pe[:, 1::2] = torch.cos(position / div_term)
self.pe = pe.transpose(0, 1).to(x)
class FiLM(nn.Module):
def __init__(self, input_size, output_size):
super().__init__()
self.encoding = PositionalEncoding(input_size)
self.input_conv = nn.Conv1d(input_size, input_size, 3, padding=1)
self.output_conv = nn.Conv1d(input_size, output_size * 2, 3, padding=1)
nn.init.xavier_uniform_(self.input_conv.weight)
nn.init.xavier_uniform_(self.output_conv.weight)
nn.init.zeros_(self.input_conv.bias)
nn.init.zeros_(self.output_conv.bias)
def forward(self, x, noise_scale):
o = self.input_conv(x)
o = F.leaky_relu(o, 0.2)
o = self.encoding(o, noise_scale)
shift, scale = torch.chunk(self.output_conv(o), 2, dim=1)
return shift, scale
def remove_weight_norm(self):
remove_parametrizations(self.input_conv, "weight")
remove_parametrizations(self.output_conv, "weight")
def apply_weight_norm(self):
self.input_conv = weight_norm(self.input_conv)
self.output_conv = weight_norm(self.output_conv)
@torch.jit.script
def shif_and_scale(x, scale, shift):
o = shift + scale * x
return o
class UBlock(nn.Module):
def __init__(self, input_size, hidden_size, factor, dilation):
super().__init__()
assert isinstance(dilation, (list, tuple))
assert len(dilation) == 4
self.factor = factor
self.res_block = Conv1d(input_size, hidden_size, 1)
self.main_block = nn.ModuleList(
[
Conv1d(input_size, hidden_size, 3, dilation=dilation[0], padding=dilation[0]),
Conv1d(hidden_size, hidden_size, 3, dilation=dilation[1], padding=dilation[1]),
]
)
self.out_block = nn.ModuleList(
[
Conv1d(hidden_size, hidden_size, 3, dilation=dilation[2], padding=dilation[2]),
Conv1d(hidden_size, hidden_size, 3, dilation=dilation[3], padding=dilation[3]),
]
)
def forward(self, x, shift, scale):
x_inter = F.interpolate(x, size=x.shape[-1] * self.factor)
res = self.res_block(x_inter)
o = F.leaky_relu(x_inter, 0.2)
o = F.interpolate(o, size=x.shape[-1] * self.factor)
o = self.main_block[0](o)
o = shif_and_scale(o, scale, shift)
o = F.leaky_relu(o, 0.2)
o = self.main_block[1](o)
res2 = res + o
o = shif_and_scale(res2, scale, shift)
o = F.leaky_relu(o, 0.2)
o = self.out_block[0](o)
o = shif_and_scale(o, scale, shift)
o = F.leaky_relu(o, 0.2)
o = self.out_block[1](o)
o = o + res2
return o
def remove_weight_norm(self):
remove_parametrizations(self.res_block, "weight")
for _, layer in enumerate(self.main_block):
if len(layer.state_dict()) != 0:
remove_parametrizations(layer, "weight")
for _, layer in enumerate(self.out_block):
if len(layer.state_dict()) != 0:
remove_parametrizations(layer, "weight")
def apply_weight_norm(self):
self.res_block = weight_norm(self.res_block)
for idx, layer in enumerate(self.main_block):
if len(layer.state_dict()) != 0:
self.main_block[idx] = weight_norm(layer)
for idx, layer in enumerate(self.out_block):
if len(layer.state_dict()) != 0:
self.out_block[idx] = weight_norm(layer)
class DBlock(nn.Module):
def __init__(self, input_size, hidden_size, factor):
super().__init__()
self.factor = factor
self.res_block = Conv1d(input_size, hidden_size, 1)
self.main_block = nn.ModuleList(
[
Conv1d(input_size, hidden_size, 3, dilation=1, padding=1),
Conv1d(hidden_size, hidden_size, 3, dilation=2, padding=2),
Conv1d(hidden_size, hidden_size, 3, dilation=4, padding=4),
]
)
def forward(self, x):
size = x.shape[-1] // self.factor
res = self.res_block(x)
res = F.interpolate(res, size=size)
o = F.interpolate(x, size=size)
for layer in self.main_block:
o = F.leaky_relu(o, 0.2)
o = layer(o)
return o + res
def remove_weight_norm(self):
remove_parametrizations(self.res_block, "weight")
for _, layer in enumerate(self.main_block):
if len(layer.state_dict()) != 0:
remove_parametrizations(layer, "weight")
def apply_weight_norm(self):
self.res_block = weight_norm(self.res_block)
for idx, layer in enumerate(self.main_block):
if len(layer.state_dict()) != 0:
self.main_block[idx] = weight_norm(layer)
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