#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2021/10/5 13:46
# @Author : SuenDanny
# @Site : 
# @File : nn_conv2d.py
# @Software: PyCharm

import torch
import torchvision
from torch import nn
from torch.nn import Conv2d

from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset = torchvision.datasets.CIFAR10("../data", train= False, transform= torchvision.transforms.ToTensor(),
                                       download= True)

dataloader = DataLoader(dataset, batch_size= 64)

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.conv1 = Conv2d(in_channels=3, out_channels=6, kernel_size=3,stride=1,padding=0)

    def forward(self, x):
        x = self.conv1(x)
        return x

tudui = Tudui()
print(tudui)

writer = SummaryWriter("./logs")

step = 0
for data in dataloader:
    imgs, targets =data
    output = tudui(imgs)
    print(imgs.shape)
    print(output.shape)
    #torch.Size([64, 3, 32, 32])
    writer.add_images("input", imgs, step)
    #torch.Size([64, 6, 30, 30])
    output = torch.reshape(output, (-1, 3, 30, 30))
    writer.add_images("output", output, step)
    step = step + 1


writer.close()


