wehub-resource-sync
90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
from typing import Optional
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
from modules.commons.common_layers import AdamWConv1d
|
|
|
|
|
|
class ConvNeXtBlock(nn.Module):
|
|
"""ConvNeXt Block adapted from https://github.com/facebookresearch/ConvNeXt to 1D audio signal.
|
|
|
|
Args:
|
|
dim (int): Number of input channels.
|
|
intermediate_dim (int): Dimensionality of the intermediate layer.
|
|
layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling.
|
|
Defaults to None.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
intermediate_dim: int,
|
|
layer_scale_init_value: Optional[float] = None, drop_out: float = 0.0
|
|
|
|
):
|
|
super().__init__()
|
|
self.dwconv = nn.Conv1d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
|
|
|
|
self.norm = nn.LayerNorm(dim, eps=1e-6)
|
|
self.pwconv1 = nn.Linear(dim, intermediate_dim) # pointwise/1x1 convs, implemented with linear layers
|
|
self.act = nn.GELU()
|
|
self.pwconv2 = nn.Linear(intermediate_dim, dim)
|
|
self.gamma = (
|
|
nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True)
|
|
if layer_scale_init_value > 0
|
|
else None
|
|
)
|
|
# self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
|
self.drop_path = nn.Identity()
|
|
self.dropout = nn.Dropout(drop_out) if drop_out > 0. else nn.Identity()
|
|
|
|
def forward(self, x: torch.Tensor, ) -> torch.Tensor:
|
|
residual = x
|
|
x = self.dwconv(x)
|
|
x = x.transpose(1, 2) # (B, C, T) -> (B, T, C)
|
|
|
|
x = self.norm(x)
|
|
x = self.pwconv1(x)
|
|
x = self.act(x)
|
|
x = self.pwconv2(x)
|
|
if self.gamma is not None:
|
|
x = self.gamma * x
|
|
x = x.transpose(1, 2) # (B, T, C) -> (B, C, T)
|
|
x = self.dropout(x)
|
|
|
|
x = residual + self.drop_path(x)
|
|
return x
|
|
|
|
|
|
class ConvNeXtDecoder(nn.Module):
|
|
def __init__(
|
|
self, in_dims, out_dims, /, *,
|
|
num_channels=512, num_layers=6, kernel_size=7, dropout_rate=0.1
|
|
):
|
|
super().__init__()
|
|
self.inconv = nn.Conv1d(
|
|
in_dims, num_channels, kernel_size,
|
|
stride=1, padding=(kernel_size - 1) // 2
|
|
)
|
|
self.conv = nn.ModuleList(
|
|
ConvNeXtBlock(
|
|
dim=num_channels, intermediate_dim=num_channels * 4,
|
|
layer_scale_init_value=1e-6, drop_out=dropout_rate
|
|
) for _ in range(num_layers)
|
|
)
|
|
self.outconv = AdamWConv1d(
|
|
num_channels, out_dims, kernel_size,
|
|
stride=1, padding=(kernel_size - 1) // 2
|
|
)
|
|
|
|
# noinspection PyUnusedLocal
|
|
def forward(self, x, infer=False):
|
|
x = x.transpose(1, 2)
|
|
x = self.inconv(x)
|
|
for conv in self.conv:
|
|
x = conv(x)
|
|
x = self.outconv(x)
|
|
x = x.transpose(1, 2)
|
|
return x
|