import torch
import torch.nn as nn
import torch.nn.functional as F
class VGG(nn.Module):
def __init__(self):
super(VGG,self).__init__()
self.conv1=nn.Sequential(
nn.Conv2d(3,64,3),
nn.Conv2d(64,64,3,padding=1)
)
self.max1=nn.MaxPool2d(2,2)
self.conv2=nn.Sequential(
nn.Conv2d(64,128,3),
nn.Conv2d(128,128,3,padding=1)
)
self.max2=nn.MaxPool2d(2,2)
self.conv3=nn.Sequential(
nn.Conv2d(128,256,3),
nn.Conv2d(256,256,3,padding=1),
nn.Conv2d(256,256,3,padding=1)
)
self.max3=nn.MaxPool2d(2,2)
self.conv4=nn.Sequential(
nn.Conv2d(256,512,3),
nn.Conv2d(512,512,3,padding=1),
nn.Conv2d(512,512,3,padding=1)
)
self.max4=nn.MaxPool2d(2,2)
self.conv5=nn.Sequential(
nn.Conv2d(512,512,3),
nn.Conv2d(512,512,3,padding=1),
nn.Conv2d(512,512,3,padding=1)
)
self.max5=nn.MaxPool2d(2,2)
self.fc1=nn.Linear(512*5*5,4096)
self.fc2=nn.Linear(4096,4096)
self.fc3=nn.Linear(4096,10)
self.softmax=nn.Softmax(10)
def forward(self, x):
x=self.conv1(x)
print('conv1:',x.size())
x=self.max1(x)
print('conv_max1:', x.size())
x=self.conv2(x)
print('conv2:', x.size())
x = self.max2(x)
x=self.conv3(x)
x = self.max3(x)
x = self.conv4(x)
x = self.max4(x)
x = self.conv5(x)
x = self.max5(x)
# print('conv_max5:', x.size())
# print('ccccccc',x.size(0))
x=x.view(x.size(0),-1)####
# print(x.size())
x=self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
x= F.log_softmax(x, dim=1)
return x
def test():
net = VGG()
print(net)
x = torch.randn(1, 3, 224, 224)
y = net(x)
print(y.size())
test()
复现VGG时出现如下错误:
RuntimeError: size mismatch, m1: [2560 x 5], m2: [12800 x 4096] at /pytorch/aten/src/TH/generic/THTe
一开始的代码:
x.view(x.size(0),-1)
出现如上的问题。后来将该代码改成如下形式:
x=x.view(x.size(0),-1)
代码运行成功