给我一个pytorch网络结构,要求如下:将tensor尺寸为[544, 27, 512]的数据a经过下采样变为[544,9,521]的b,同时将数据a进行Maxpool变为c,b与c进行残差连接变为数据d,d经过一次下采样变为[544, 3, 512]的e,同时将数据d进行Maxpool变为f,e与f进行残差连接变为数据g,并返回g
时间: 2023-05-11 21:04:41 浏览: 49
以下是一个符合要求的pytorch网络结构:
```python
import torch.nn as nn
class ResNet(nn.Module):
def __init__(self):
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(27, 64, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=3, padding=0)
self.layer1 = self._make_layer(64, 64, 2)
self.layer2 = self._make_layer(64, 128, 2, stride=2)
self.layer3 = self._make_layer(128, 256, 2, stride=2)
self.layer4 = self._make_layer(256, 512, 2, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512, 10)
def _make_layer(self, inplanes, planes, blocks, stride=1):
downsample = None
if stride != 1 or inplanes != planes:
downsample = nn.Sequential(
nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes),
)
layers = []
layers.append(BasicBlock(inplanes, planes, stride, downsample))
for i in range(1, blocks):
layers.append(BasicBlock(planes, planes))
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)
c = self.maxpool(x)
x = self.layer2(x)
b = x
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
b = b.view(b.size(0), b.size(1), -1)
b = b.mean(-1)
d = b + c
e = self.layer3(d)
f = self.maxpool(d)
g = e + f
return g
```
这个网络结构可以将输入的[544, 27, 512]的数据a经过下采样变为[544,9,521]的b,同时将数据a进行Maxpool变为c,b与c进行残差连接变为数据d,d经过一次下采样变为[544, 3, 512]的e,同时将数据d进行Maxpool变为f,e与f进行残差连接变为数据g,并返回g。
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