resnet with bottleneck
时间: 2024-01-14 19:21:29 浏览: 26
ResNet with bottleneck是一种用于构建深度残差网络的瓶颈结构。它通过在每个残差块中引入一个瓶颈层来减少计算量,并提高网络的性能。瓶颈层由一个1x1的卷积层、一个3x3的卷积层和一个1x1的卷积层组成。这种结构可以有效地减少参数数量,并提高网络的表达能力。
以下是一个使用ResNet with bottleneck的示例代码:
```python
import torch
import torch.nn as nn
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_channels, out_channels, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = nn.Sequential(
nn.Conv2d(in_channels, out_channels * self.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(out_channels * self.expansion)
)
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
def _make_layer(self, block, out_channels, blocks, stride=1):
layers = []
layers.append(block(self.in_channels, out_channels, stride))
self.in_channels = out_channels * block.expansion
for _ in range(1, blocks):
layers.append(block(self.in_channels, out_channels))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
# 构建ResNet-50模型
model = ResNet(Bottleneck, [3, 4, 6, 3])
print(model)
```
这段代码演示了如何使用ResNet with bottleneck构建一个ResNet-50模型。该模型由多个残差块组成,每个残差块都使用了瓶颈结构。你可以根据需要修改模型的层数和输出类别数量。