PyTorch_14_nn_Sequential

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


class TuDui(nn.Module):
    def __init__(self):
        super(TuDui, self).__init__()

        # self.conv1 = Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2)
        # self.maxpool1 = MaxPool2d(2)
        # self.conv2 = Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=2)
        # self.maxpool2 = MaxPool2d(2)
        # self.conv3 = Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=2)
        # self.maxpool3 = MaxPool2d(2)
        # self.flatten = Flatten()
        # self.linear1 = Linear(1024, 64)
        # self.linear2 = Linear(64, 10)

        self.model1 = Sequential(
            Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2),
            MaxPool2d(2),
            Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=2),
            MaxPool2d(2),
            Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            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)
        x = self.model1(x)
        return x


tudui = TuDui()
print(tudui)
input = torch.ones((64, 3, 32, 32))
output = tudui(input)
print(output.shape)

writer = SummaryWriter("logs")
writer.add_graph(tudui, input)
writer.flush()
writer.close()