def _make_layer(self, block, input_channels, output_channels, num_blocks=1): layers = [] layers.append(block(input_channels, output_channels)) for i in range(num_blocks-1): layers.append(block(output_channels, output_channels)) return nn.Sequential(*layers)
时间: 2024-04-13 21:25:09 浏览: 15
这段代码是一个私有方法 `_make_layer`,它用于创建一个包含多个相同类型的块的层。它接受以下参数:
- `block`:块的类型
- `input_channels`:输入通道数
- `output_channels`:输出通道数
- `num_blocks`:块的数量
该方法首先创建一个空列表 `layers`,然后将一个输入通道数为 `input_channels`,输出通道数为 `output_channels` 的块添加到列表中。接下来,根据 `num_blocks` 的值,将剩余的块添加到列表中。最后,使用 `nn.Sequential` 将列表中的所有块组合成一个序列,并将其作为结果返回。
这段代码的作用是创建一个包含多个相同类型块的层,可以用于构建神经网络模型的结构。
相关问题
def _make_layer(self, block, input_channels, output_channels, num_blocks=1): layers = [] layers.append(block(input_channels, output_channels)) for i in range(num_blocks-1): layers.append(block(output_channels, output_channels)) return nn.Sequential(*layers)
这段代码定义了一个辅助函数_make_layer,用于创建一个由多个block组成的层。它接受四个参数:block表示卷积层的基本结构,input_channels表示输入通道数,output_channels表示输出通道数,num_blocks表示重复次数。
函数首先创建一个空的列表layers,然后将一个由block(input_channels, output_channels)构成的块添加到layers中。接下来,使用循环将block(output_channels, output_channels)添加到layers中,重复次数为num_blocks-1。最后,使用nn.Sequential将layers列表中的所有块连接起来,并返回该层。
这个函数的作用是根据指定的block、输入通道数、输出通道数和重复次数来构建一个由多个相同结构的块组成的层。在这个特定的代码行中,该函数被用于创建self.conv3_1层。
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
```