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from typing import Optional
import torch
import torch.nn as nn
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 = nn.Conv1d(
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
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