[人工智能]Pytorch学习笔记之卷积层处理图片并进行可视化

实战卷积层

import torch
from torch import nn
from torch.nn import Conv2d
from torch.utils.data import DataLoader
import torchvision
from tensorboardX import SummaryWriter

dataset = torchvision.datasets.CIFAR10('./data',
                                       train=False,
                                       transform=torchvision.transforms.ToTensor(),
                                       download=True)
dataloader = DataLoader(dataset, batch_size=64)


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


qingfeng = qingfeng()
print(qingfeng)
writer = SummaryWriter('./logs')
step = 0
for data in dataloader:
    imgs, targets = data
    print("输入图片形状",imgs.shape)
    output = qingfeng(imgs)
    print("输出图片形状",output.shape)
    writer.add_images("input", imgs, step)

    output = torch.reshape(output, (-1, 3, 30, 30))
    print("改变形状后的图像",output.shape)
    writer.add_images('output', output, step)
    step += 1
writer.close()

activate pytorch
tensorboard --logdir “logs”