"PyTorch Vision 模块未安装，请先安装后再进行相关操作"

在PyTorch中，为了更好地支持计算机视觉任务，PyTorch提供了一个名为torchvision的模块。该模块提供了许多常用的计算机视觉功能，如卷积神经网络、对象检测等。然而，很多程序员在初学PyTorch时，往往忽略了安装torchvision模块这一步骤。本文将针对这个问题进行详细的解答，帮助读者正确安装torchvision模块。

torchvision模块是PyTorch中一个重要的模块，提供了丰富的计算机视觉功能。通过使用torchvision模块，可以方便地搭建卷积神经网络、对象检测等模型。此外，torchvision模块还提供了许多常用的数据集，如ImageNet、COCO等，使得开发者可以轻松地预处理数据、训练模型。

对于许多开发者来说，搭建一个基本的卷积神经网络模型可能已经足够。然而，如果你想要更加复杂地搭建一个神经网络模型，或者想要尝试其他计算机视觉任务，那么torchvision模块就是一个必不可少的部分。

安装torchvision模块非常简单。如果你使用的是PyTorch 2.x版本，那么在创建一个新的PyTorch项目后，可以使用以下命令安装torchvision模块：

pip install torchvision

如果你使用的是PyTorch 3.x版本，那么需要使用以下命令来安装torchvision模块：

pip install torchvision==0.4.0

torchvision模块提供了许多常用的计算机视觉功能，如卷积神经网络、对象检测等。下面是一些示例，展示如何使用torchvision模块进行计算机视觉任务：

import torch
import torchvision

# 搭建卷积神经网络模型
class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = torch.nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.conv2 = torch.nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.conv3 = torch.nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv4 = torch.nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv5 = torch.nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv6 = torch.nn.Conv2d(512, 1024, kernel_size=3, padding=1)
        self.conv7 = torch.nn.Conv2d(1024, 2048, kernel_size=3, padding=1)
        self.conv8 = torch.nn.Conv2d(2048, 4096, kernel_size=3, padding=1)
        self.conv9 = torch.nn.Conv2d(4096, 8192, kernel_size=3, padding=1)
        self.conv10 = torch.nn.Conv2d(8192, 16384, kernel_size=3, padding=1)
        self.conv11 = torch.nn.Conv2d(16384, 3256, kernel_size=3, padding=1)
        self.conv12 = torch.nn.Conv2d(3256, 6500, kernel_size=3, padding=1)
        self.conv13 = torch.nn.Conv2d(6500, 13000, kernel_size=3, padding=1)
        self.conv14 = torch.nn.Conv2d(13000, 26000, kernel_size=3, padding=1)
        self.conv15 = torch.nn.Conv2d(26000, 52000, kernel_size=3, padding=1)
        self.conv16 = torch.nn.Conv2d(52000, 104000, kernel_size=3, padding=1)
        self.conv17 = torch.nn.Conv2d(104000, 168000, kernel_size=3, padding=1)
        self.conv18 = torch.nn.Conv2d(168000, 336000, kernel_size=3, padding=1)
        self.conv19 = torch.nn.Conv2d(336000, 672000, kernel_size=3, padding=1)
        self.conv20 = torch.nn.Conv2d(672000, 1344000, kernel_size=3, padding=1)
        self.conv21 = torch.nn.Conv2d(1344000, 2688000, kernel_size=3, padding=1)
        self.conv22 = torch.nn.Conv2d(2688000, 5376000, kernel_size=3, padding=1)
        self.conv23 = torch.nn.Conv2d(5376000, 10740000, kernel_size=3, padding=1)
        self.conv24 = torch.nn.Conv2d(10740000, 16160000, kernel_size=3, padding=1)
        self.conv25 = torch.nn.Conv2d(16160000, 32320000, kernel_size=3, padding=1)
        self.conv26 = torch.nn.Conv2d(32320000, 64640000, kernel_size=3, padding=1)
        self.conv27 = torch.nn.Conv2d(64640000, 129280000, kernel_size=3, padding=1)
        self.conv28 = torch.nn.Conv2d(129280000, 256000000, kernel_size=3, padding=1)
        self.conv29 = torch.nn.Conv2d(256000000, 512000000, kernel_size=3, padding=1)
        self.conv30 = torch.nn.Conv2d(512000000, 1024000000, kernel_size=3, padding=1)
        self.conv31 = torch.nn.Conv2d(1024000000, 2048000000, kernel_size=3, padding=1)
        self.conv32 = torch.nn.Conv2d(2048000000, 4096000000, kernel_size=3, padding=1)
        self.conv33 = torch.nn.Conv2d(4096000000, 8