翻译class Block2(nn.Module): def __init__(self): super(Block2, self).__init__() self.block = nn.Sequential( nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(num_features=16), nn.ReLU(), nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(num_features=16), ) def forward(self, inputs): ans = torch.add(inputs, self.block(inputs)) # print('ans shape: ', ans.shape) return inputs + ans

这里定义了一个名为Block2的类，继承自nn.Module。它有一个构造函数__init__()和一个前向传播函数forward()。

在构造函数中，首先调用了nn.Module的构造函数，然后定义了一个nn.Sequential对象，其中包含了两个卷积层以及对应的批归一化层和ReLU激活函数。这两个卷积层的输入和输出通道数均为16，卷积核大小为3x3，填充为1，步长为1。

在前向传播函数中，先对输入进行卷积操作，然后将输入和卷积结果相加，最后返回相加后的结果。

该类的作用是实现一个残差块，用于搭建深度神经网络时进行堆叠。

相关问题

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

class BasicBlock(nn.Module): def __init__(self, net_depth, dim, kernel_size=3, conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=nn.Sigmoid): super().__init__() self.norm = norm_layer(dim) self.conv = conv_layer(net_depth, dim, kernel_size, gate_act) def forward(self, x): identity = x x = self.norm(x) x = self.conv(x) x = identity + x return x转化为Paddle框架写法

class BasicBlock(fluid.dygraph.Layer):
def __init__(self, net_depth, dim, kernel_size=3, conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=fluid.dygraph.nn.functional.sigmoid):
super(BasicBlock, self).__init__()
self.norm = norm_layer(dim)
self.conv = conv_layer(net_depth, dim, kernel_size, gate_act)
def forward(self, x):
identity = x
x = self.norm(x)
x = self.conv(x)
x = identity + x
return x