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#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@Project ：Pytorch_learn 
@File    ：Sequential与模型搭建实战.py
@IDE     ：PyCharm 
@Author  ：咋
@Date    ：2023/7/4 15:25 
"""
import torch
from torch.utils.data import DataLoader,Dataset
from torch.nn import Module,Conv2d,MaxPool2d,Sequential,Flatten,Linear
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
import torchvision

dataset = torchvision.datasets.CIFAR10("CIFAR10",train=True,transform=transforms.ToTensor(),download=True)
dataloader = DataLoader(dataset=dataset,batch_size=64)
from tensorboardX import SummaryWriter
'''
普通定义
class MyModule(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()
        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

'''


# Sequential定义
class MyModule(Module):
    def __init__(self):
        super(MyModule, self).__init__()
        self.model = 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.model(x)
        return x

model = MyModule()
print(model)


write = SummaryWriter("log_4")
# write.add_graph(model)
# input = torch.ones((64,3,32,32))
# write.add_graph(model,input)
for i,data in enumerate(dataloader):
    image,label = data
    print(image.shape)
    write.add_graph(model,image)
    output = model(image)
    print(output.shape)

write.close()
# MyModule(
#   (model): 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)
#   )
# )

png.png