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import torch
import torch.nn as nn
class CvnxtBlock(nn.Module):
def __init__(
self,
dim,
kernel_size=7,
layer_scale=1.0,
expansion=4,
dilation=1,
padding_mode: str = "zeros",
):
super().__init__()
self.dwconv = nn.Conv2d(
dim,
dim,
kernel_size=kernel_size,
padding=dilation * (kernel_size - 1) // 2,
groups=dim,
dilation=dilation,
padding_mode=padding_mode,
) # depthwise conv
self.norm = nn.LayerNorm(dim)
self.pwconv1 = nn.Linear(dim, expansion * dim)
self.act = nn.GELU()
self.pwconv2 = nn.Linear(expansion * dim, dim)
self.gamma = (
nn.Parameter(layer_scale * torch.ones((dim))) if layer_scale > 0.0 else 1.0
)
def forward(self, x):
input = x
x = self.dwconv(x)
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
x = self.norm(x)
x = self.pwconv1(x)
x = self.act(x)
x = self.pwconv2(x)
x = self.gamma * x
x = input + x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
return x
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