实现了一个基于卷积神经网络和 Swin Transformer 的图像处理模型。该模型主要用于对输入图像进行特征提取和处理，以实现特定的图像任务，如图像增强、去噪等。
项目完整代码下载链接：https://download.csdn.net/download/huanghm88/89909179

import torch
from torch import nn
import torch.nn.functional as F
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
##**项目完整代码下载链接：https://download.csdn.net/download/huanghm88/89909179**class Convlutioanl(nn.Module):def __init__(self,  in_channel, out_channel):super(Convlutioanl, self).__init__()self.padding=(2,2,2,2)self.conv=nn.Conv2d(in_channel,out_channel,kernel_size=5,padding=0,stride=1)self.bn=nn.BatchNorm2d(out_channel)self.relu=nn.ReLU(inplace=True)def forward(self, input):out=F.pad(input,self.padding,'replicate')out=self.conv(out)out=self.bn(out)out=self.relu(out)return outclass Convlutioanl_out(nn.Module):def __init__(self,  in_channel, out_channel):super(Convlutioanl_out, self).__init__()self.conv=nn.Conv2d(in_channel,out_channel,kernel_size=1,padding=0,stride=1)self.tanh=nn.Tanh()def forward(self, input):out=self.conv(input)out=self.tanh(out)return out
class Fem(nn.Module):def __init__(self,  in_channel, out_channel):super(Fem, self).__init__()self.padding = (1, 1, 1, 1)self.conv=nn.Conv2d(in_channel,out_channel,kernel_size=3,padding=0,stride=1)self.bn=nn.BatchNorm2d(out_channel)self.relu=nn.ReLU(inplace=True)def forward(self, input):out = F.pad(input, self.padding, 'replicate')out=self.conv(out)out=self.bn(out)out=self.relu(out)out = F.pad(out, self.padding, 'replicate')out=self.conv(out)out = self.bn(out)return outclass Channel_attention(nn.Module):def __init__(self,  channel, reduction=4):super(Channel_attention, self).__init__()self.avg_pool = nn.AdaptiveAvgPool2d(1)self.fc=nn.Sequential(nn.Conv2d(channel,channel//reduction,1),nn.ReLU(inplace=True),nn.Conv2d(channel//reduction,channel,1))self.sigmoid=nn.Sigmoid()def forward(self, input):out=self.avg_pool(input)out=self.fc(out)out=self.sigmoid(out)return outclass Spatial_attention(nn.Module):def __init__(self,  channel, reduction=4):super(Spatial_attention, self).__init__()self.body=nn.Sequential(nn.Conv2d(channel, channel//reduction,3,padding=1),nn.BatchNorm2d( channel//reduction),nn.ReLU(True),nn.Conv2d(channel // reduction, 1, 3, padding=1),nn.BatchNorm2d(1),nn.Sigmoid())def forward(self, input):return self.body(input)class WindowAttention(nn.Module):def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):super().__init__()self.dim = dimself.window_size = window_sizeself.num_heads = num_headshead_dim = dim // num_headsself.scale = qk_scale or head_dim ** -0.5self.relative_position_bias_table = nn