Resnet101网络结构

可以使用Resnet 101网络结构替换Resnet 50网络结构，这样可以增加网络深度并且提升模型的性能。Resnet 101相比于Resnet 50，增加了更多的卷积层和残差块，可以更好地提取高级特征，提高模型的准确率。当然，使用更深的网络结构也会增加计算和存储开销，需要根据具体场景和硬件条件进行选择。

以下是ResNet101网络结构的代码，使用PyTorch实现： ```python import torch.nn as nn class Bottleneck(nn.Module): expansion = 4 def __init__(self, in_planes, planes, stride=1): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * self.expansion) self.shortcut = nn.Sequential() if stride != 1 or in_planes != planes * self.expansion: self.shortcut = nn.Sequential( nn.Conv2d(in_planes, planes * self.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * self.expansion) ) def forward(self, x): out = nn.ReLU()(self.bn1(self.conv1(x))) out = nn.ReLU()(self.bn2(self.conv2(out))) out = self.bn3(self.conv3(out)) out += self.shortcut(x) out = nn.ReLU()(out) return out class ResNet(nn.Module): def __init__(self, block, num_blocks, num_classes=1000): super(ResNet, self).__init__() self.in_planes = 64 self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) self.linear = nn.Linear(512 * block.expansion, num_classes) def _make_layer(self, block, planes, num_blocks, stride): strides = [stride] + [1] * (num_blocks - 1) layers = [] for stride in strides: layers.append(block(self.in_planes, planes, stride)) self.in_planes = planes * block.expansion return nn.Sequential(*layers) def forward(self, x): out = nn.ReLU()(self.bn1(self.conv1(x))) out = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)(out) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = nn.AvgPool2d(kernel_size=7, stride=1)(out) out = out.view(out.size(0), -1) out = self.linear(out) return out def ResNet101(): return ResNet(Bottleneck, [3, 4, 23, 3]) ``` 这个代码实现了ResNet101的网络结构。其中Bottleneck类定义了ResNet中的一个基本块，ResNet类定义了整个网络结构，包括多个Bottleneck块和全连接层。最后的ResNet101函数用于创建ResNet101网络。