self.b_module = B.sequential(*b_upsampler, b_HR_conv0, b_HR_conv1)
时间: 2024-06-02 11:09:50 浏览: 12
这段代码看起来是在定义一个类的属性 self.b_module,它是由 B.sequential 和一些其他操作构成的。B.sequential 可能是一个函数或类方法,用来将一系列操作组合成一个模块。其他操作可能包括升采样(upsampling)操作和卷积(convolution)操作,它们的目的可能是将低分辨率图像提升到高分辨率,或者进行特征提取等操作。
相关问题
class Partial_conv3(nn.Module): def __init__(self, dim, n_div, forward): super().__init__() self.dim_conv3 = dim // n_div self.dim_untouched = dim - self.dim_conv3 self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) self.global_pool = GlobalAvgPool2d() if forward == 'slicing': self.forward = self.forward_slicing elif forward == 'split_cat': self.forward = self.forward_split_cat else: raise NotImplementedError def forward_slicing(self, x: Tensor) -> Tensor: # only for inference x = x.clone() # !!! Keep the original input intact for the residual connection later x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) return x def forward_split_cat(self, x: Tensor) -> Tensor: x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1) x1 = self.partial_conv3(x1) x1 = self.global_pool(x1) x = torch.cat((x1, x2), 1) return x在这段代码中插入全局平均池化做通道增强的模块
你可以在forward_slicing和forward_split_cat的输出结果之前加入一个全局平均池化层进行通道增强,代码如下:
```
class Partial_conv3(nn.Module):
def __init__(self, dim, n_div, forward):
super().__init__()
self.dim_conv3 = dim // n_div
self.dim_untouched = dim - self.dim_conv3
self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False)
self.global_pool = GlobalAvgPool2d()
self.channel_enhance = nn.Sequential(
nn.Conv2d(dim, dim, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(dim),
nn.ReLU(inplace=True),
self.global_pool
)
if forward == 'slicing':
self.forward = self.forward_slicing
elif forward == 'split_cat':
self.forward = self.forward_split_cat
else:
raise NotImplementedError
def forward_slicing(self, x: Tensor) -> Tensor:
# only for inference
x = x.clone() # !!! Keep the original input intact for the residual connection later
x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :])
x = self.channel_enhance(x)
return x
def forward_split_cat(self, x: Tensor) -> Tensor:
x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1)
x1 = self.partial_conv3(x1)
x1 = self.channel_enhance(x1)
x = torch.cat((x1, x2), 1)
return x
```
这里使用了一个nn.Sequential模块,包含了一个1x1的卷积层、BatchNorm层、ReLU激活层和全局平均池化层,对输入的特征图进行通道增强,从而提高模型的性能。在forward_slicing和forward_split_cat的输出结果之前,将输入特征图通过这个通道增强模块之后再输出。
class conv_block(nn.Module): def __init__(self, ch_in, ch_out): super(conv_block, self).__init__() self.conv = nn.Sequential( nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1, bias=True), nn.BatchNorm2d(ch_out), nn.ReLU(inplace=True), nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1, bias=True), nn.BatchNorm2d(ch_out), nn.ReLU(inplace=True) ) def forward(self, x): x = self.conv(x) return x class SqueezeAttentionBlock(nn.Module): def __init__(self, ch_in, ch_out): super(SqueezeAttentionBlock, self).__init__() self.avg_pool = nn.AvgPool2d(kernel_size=2, stride=2) self.conv = conv_block(ch_in, ch_out) self.conv_atten = conv_block(ch_in, ch_out) self.upsample = nn.Upsample(scale_factor=2) def forward(self, x): # print(x.shape) x_res = self.conv(x) # print(x_res.shape) y = self.avg_pool(x) # print(y.shape) y = self.conv_atten(y) # print(y.shape) y = self.upsample(y) # print(y.shape, x_res.shape) return (y * x_res) + y为这段代码添加中文注释
# 定义卷积块模块
class conv_block(nn.Module):
def __init__(self, ch_in, ch_out):
super(conv_block, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1, bias=True), # 3x3卷积层,输入通道数为ch_in,输出通道数为ch_out
nn.BatchNorm2d(ch_out), # 批归一化层,对输出特征图进行归一化处理
nn.ReLU(inplace=True), # ReLU激活函数,将负数部分裁剪为0
nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1, bias=True), # 再次进行3x3卷积操作
nn.BatchNorm2d(ch_out), # 批归一化层
nn.ReLU(inplace=True) # ReLU激活函数
)
def forward(self, x):
x = self.conv(x) # 前向传播,进行卷积操作
return x
# 定义SqueezeAttentionBlock模块,用于对特征图进行注意力加权
class SqueezeAttentionBlock(nn.Module):
def __init__(self, ch_in, ch_out):
super(SqueezeAttentionBlock, self).__init__()
self.avg_pool = nn.AvgPool2d(kernel_size=2, stride=2) # 平均池化层,用于对特征图进行降采样
self.conv = conv_block(ch_in, ch_out) # 卷积块,用于对降采样后的特征图进行卷积操作
self.conv_atten = conv_block(ch_in, ch_out) # 卷积块,用于学习注意力权重
self.upsample = nn.Upsample(scale_factor=2) # 上采样层,用于将池化后的特征图恢复到原始尺寸
def forward(self, x):
x_res = self.conv(x) # 对原始特征图进行卷积操作
y = self.avg_pool(x) # 对特征图进行降采样
y = self.conv_atten(y) # 对降采样后的特征图进行卷积操作,得到注意力权重
y = self.upsample(y) # 将池化后的特征图恢复到原始尺寸
return (y * x_res) + y # 将注意力权重应用到原始特征图上,得到加权后的特征图
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