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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt
from .diffusion import Mish
from utils.hparams import hparams
Linear = nn.Linear
ConvTranspose2d = nn.ConvTranspose2d
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def override(self, attrs):
if isinstance(attrs, dict):
self.__dict__.update(**attrs)
elif isinstance(attrs, (list, tuple, set)):
for attr in attrs:
self.override(attr)
elif attrs is not None:
raise NotImplementedError
return self
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = x[:, None] * emb[None, :]
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
def Conv1d(*args, **kwargs):
layer = nn.Conv1d(*args, **kwargs)
nn.init.kaiming_normal_(layer.weight)
return layer
@torch.jit.script
def silu(x):
return x * torch.sigmoid(x)
class ResidualBlock(nn.Module):
def __init__(self, encoder_hidden, residual_channels, dilation):
super().__init__()
self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation)
self.diffusion_projection = Linear(residual_channels, residual_channels)
self.conditioner_projection = Conv1d(encoder_hidden, 2 * residual_channels, 1)
self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1)
def forward(self, x, conditioner, diffusion_step):
diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
conditioner = self.conditioner_projection(conditioner)
y = x + diffusion_step
y = self.dilated_conv(y) + conditioner
gate, filter = torch.chunk(y, 2, dim=1)
y = torch.sigmoid(gate) * torch.tanh(filter)
y = self.output_projection(y)
residual, skip = torch.chunk(y, 2, dim=1)
return (x + residual) / sqrt(2.0), skip
class DiffNet(nn.Module):
def __init__(self, in_dims=80):
super().__init__()
self.params = params = AttrDict(
# Model params
encoder_hidden=hparams['hidden_size'],
residual_layers=hparams['residual_layers'],
residual_channels=hparams['residual_channels'],
dilation_cycle_length=hparams['dilation_cycle_length'],
)
self.input_projection = Conv1d(in_dims, params.residual_channels, 1)
self.diffusion_embedding = SinusoidalPosEmb(params.residual_channels)
dim = params.residual_channels
self.mlp = nn.Sequential(
nn.Linear(dim, dim * 4),
Mish(),
nn.Linear(dim * 4, dim)
)
self.residual_layers = nn.ModuleList([
ResidualBlock(params.encoder_hidden, params.residual_channels, 2 ** (i % params.dilation_cycle_length))
for i in range(params.residual_layers)
])
self.skip_projection = Conv1d(params.residual_channels, params.residual_channels, 1)
self.output_projection = Conv1d(params.residual_channels, in_dims, 1)
nn.init.zeros_(self.output_projection.weight)
def forward(self, spec, diffusion_step, cond):
"""
:param spec: [B, 1, M, T]
:param diffusion_step: [B, 1]
:param cond: [B, M, T]
:return:
"""
x = spec[:, 0]
x = self.input_projection(x) # x [B, residual_channel, T]
x = F.relu(x)
diffusion_step = self.diffusion_embedding(diffusion_step)
diffusion_step = self.mlp(diffusion_step)
skip = []
for layer_id, layer in enumerate(self.residual_layers):
x, skip_connection = layer(x, cond, diffusion_step)
skip.append(skip_connection)
x = torch.sum(torch.stack(skip), dim=0) / sqrt(len(self.residual_layers))
x = self.skip_projection(x)
x = F.relu(x)
x = self.output_projection(x) # [B, 80, T]
return x[:, None, :, :]
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