class定义class BasicBlock(layers.Layer): expansion = 1 def __init__(self, in_channels, out_channels, stride=1): super(BasicBlock, self).__init__() # 1. BasicBlock模块中的共有2个卷积;BasicBlock模块中的第1个卷积层; self.conv1 = regularized_padded_conv(out_channels, kernel_size=3, strides=stride) self.bn1 = layers.BatchNormalization() # 2. 第2个；第1个卷积如果做stride就会有一个下采样，在这个里面就不做下采样了。这一块始终保持size一致，把stride固定为1 self.conv2 = regularized_padded_conv(out_channels, kernel_size=3, strides=1) self.bn2 = layers.BatchNormalization() # 3. 判断stride是否等于1,如果为1就是没有降采样。 if stride != 1 or in_channels != self.expansion * out_channels: self.shortcut = Sequential([regularized_padded_conv(self.expansion * out_channels, kernel_size=1, strides=stride), layers.BatchNormalization()]) else: self.shortcut = lambda x, _: x self.max= layers.GlobalMaxPooling2D(kernel_size=2, stride=2) def call(self, inputs, training=False): out = self.conv1(inputs) out = self.bn1(out, training=training) out = tf.nn.relu(out) out = self.conv2(out) out = self.bn2(out, training=training) out = out + self.shortcut(inputs, training) out = self.pool(out) out = tf.nn.relu(out) return out 后如何调用该模块

要使用该模块，需要先实例化一个 BasicBlock 对象。例如，假设输入数据的通道数为 64，输出数据的通道数为 128，步幅为 1，则可以通过以下代码创建一个 BasicBlock 对象：

basic_block = BasicBlock(in_channels=64, out_channels=128, stride=1)

在使用该模块时，只需要将数据作为输入传递给该对象的 `call()` 方法即可。例如，如果输入数据为 `x`，则可以通过以下代码调用该模块并获得输出：

output = basic_block(x)

需要注意的是，在调用 `call()` 方法时，可以传递一个 `training` 参数，用于指定是否在训练模式下运行。如果需要在训练模式下运行，则将该参数设置为 `True`，否则将其设置为 `False`。例如，在训练模式下运行该模块可以通过以下代码实现：

output = basic_block(x, training=True)

相关问题

class BasicBlock1(layers.Layer): expansion = 1 def __init__(self, in_channels, out_channels, stride=1): super(BasicBlock, self).__init__() g\对不对

这段代码有一个问题：在 `super()` 函数中应该使用当前类的名称 `BasicBlock1` 而不是 `BasicBlock`，因为这个类的名称是 `BasicBlock1`。正确的代码应该如下所示：

class BasicBlock1(layers.Layer):
    expansion = 1
    def __init__(self, in_channels, out_channels, stride=1):
        super(BasicBlock1, self).__init__()
        # rest of the code

这样，`super()` 函数将会调用 `BasicBlock1` 的父类的构造函数，而不是 `BasicBlock` 的。

class BasicBlock2D(nn.Module): expansion = 1 def __init__(self, in_channels, out_channels, stride=1): super(BasicBlock2D, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channels) self.shortcut = nn.Sequential() if stride != 1 or in_channels != self.expansion * out_channels: self.shortcut = nn.Sequential( nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * out_channels) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(x) out = F.relu(out) return out # 定义二维ResNet-18模型 class ResNet18_2D(nn.Module): def __init__(self, num_classes=1000): super(ResNet18_2D, self).__init__() self.in_channels = 64 self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(BasicBlock2D, 64, 2, stride=1) self.layer2 = self._make_layer(BasicBlock2D, 128, 2, stride=2) self.layer3 = self._make_layer(BasicBlock2D, 256, 2, stride=2) self.layer4 = self._make_layer(BasicBlock2D, 512, 2, stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 , 512) def _make_layer(self, block, out_channels, num_blocks, stride): layers = [] layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion for _ in range(1, num_blocks): layers.append(block(self.in_channels, out_channels)) return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.maxpool(out) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.avgpool(out) # print(out.shape) out = out.view(out.size(0), -1) out = self.fc(out) return out改为用稀疏表示替换全连接层

为了用稀疏表示替换全连接层，需要先将特征图展平，然后使用稀疏矩阵与其进行乘法运算。

以下是修改后的代码：

import torch.sparse as sparse

class ResNet18_2D(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet18_2D, self).__init__()

        self.in_channels = 64

        self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(BasicBlock2D, 64, 2, stride=1)
        self.layer2 = self._make_layer(BasicBlock2D, 128, 2, stride=2)
        self.layer3 = self._make_layer(BasicBlock2D, 256, 2, stride=2)
        self.layer4 = self._make_layer(BasicBlock2D, 512, 2, stride=2)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

        # 替换全连接层
        self.fc = nn.Identity()

    def _make_layer(self, block, out_channels, num_blocks, stride):
        layers = []
        layers.append(block(self.in_channels, out_channels, stride))
        self.in_channels = out_channels * block.expansion
        for _ in range(1, num_blocks):
            layers.append(block(self.in_channels, out_channels))
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.maxpool(out)

        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)

        out = self.avgpool(out)
        out = out.view(out.size(0), -1)

        # 使用稀疏矩阵与展平后的特征图进行乘法运算
        out = sparse.mm(out, nn.Parameter(torch.randn(out.size(1), 512)))

        return out