Cifar-10用的模型结构： 

第一次卷积 (3,32,32) to (32,32,32) 卷积核（5×5），我们需要进行一下计算，看看padding和stride是多少:

未引入Sequential前：

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear

class MyModule(nn.Module):
    def __init__(self):
        super(MyModule, self).__init__()
        self.conv1 = Conv2d(3, 32, 5, padding=2)
        self.maxpool1 = MaxPool2d(2)
        self.conv2 = Conv2d(32, 32, 5, padding=2)
        self.maxpool2 = MaxPool2d(2)
        self.conv3 = Conv2d(32, 64, 5, padding=2)
        self.maxpool3 = MaxPool2d(2)
        self.flatten = Flatten()  # torch.Size([64,1024])
        self.linear1 = Linear(1024, 64)
        self.linear2 = Linear(64, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.maxpool2(x)
        x = self.conv3(x)
        x = self.maxpool3(x)
        x = self.flatten(x)
        x = self.linear1(x)
        x = self.linear2(x)
        return x

myMoudle1 = MyModule()
print(myMoudle1)
# 检测模型结构是否正确
input = torch.ones((64, 3, 32, 32))
output = myMoudle1(input)
print(output.shape)  # torch.Size([64, 10])--64张图片,每张对应10分类

引入Sequential：

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.tensorboard import SummaryWriter

class MyModule(nn.Module):
    def __init__(self):
        super(MyModule, self).__init__()
        self.model1 = Sequential(
            Conv2d(3, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 64, 5, padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)
        )

    def forward(self, x):
        x = self.model1(x)
        return x

myMoudle1 = MyModule()
print(myMoudle1)
# 检测模型结构是否正确
input = torch.ones((64, 3, 32, 32))
output = myMoudle1(input)
# print(output.shape)  # torch.Size([64, 10])--64张图片,每张对应10分类

writer = SummaryWriter('logs')
writer.add_graph(myMoudle1, input)
writer.close()

​​​​​​​MyModule(
  (model1): Sequential(
    (0): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (4): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (6): Flatten(start_dim=1, end_dim=-1)
    (7): Linear(in_features=1024, out_features=64, bias=True)
    (8): Linear(in_features=64, out_features=10, bias=True)
  )
)