可以帮我打印resnet50的结构吗
时间: 2023-11-19 09:05:09 浏览: 29
当然可以,你可以使用以下代码来打印resnet50的结构:
```python
import torch
import torchvision.models as models
resnet50 = models.resnet50()
print(resnet50)
```
这段代码将会打印出resnet50的结构信息。
相关问题
你可以直接把resnet50的网络结构打印输出吗
当然可以,ResNet50的网络结构如下所示:
Input --> Conv1 --> BatchNorm1 --> ReLU1 --> MaxPool --> Layer1 --> Layer2 --> Layer3 --> Layer4 --> GlobalAvgPool --> FC
其中,Layer1至Layer4为Residual Block,FC为全连接层。
打印resnet20网络结构
以下是resnet20网络结构的代码:
```python
import torch.nn as nn
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*planes)
)
def forward(self, x):
out = nn.ReLU()(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = nn.ReLU()(out)
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(ResNet, self).__init__()
self.in_planes = 16
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(16)
self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)
self.linear = nn.Linear(64*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 = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = nn.AvgPool2d(8)(out)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def ResNet20():
return ResNet(BasicBlock, [3,3,3])
```
这是一个基于PyTorch实现的ResNet20网络结构。