Python安装Pytorch最新图文教程

Pytorch 是一个由 Facebook 开源的深度学习框架，具有易于使用、动态计算图等特点。本文将详细讲解如何在 Python 上安装 Pytorch 最新版本。

步骤一：安装 Anaconda

首先需要在官网 https://www.anaconda.com/download/ 上下载对应系统的安装包，然后进行安装，安装过程中可以选择是否将 Anaconda 加入到系统 path，建议勾选此选项。

步骤二：创建虚拟环境

在命令行中运行以下命令来创建一个名为 pytorch 的虚拟环境：

conda create --name pytorch python=3

创建完成后，激活虚拟环境：

conda activate pytorch

步骤三：安装 Pytorch

在命令行中运行以下命令来安装最新版本的 Pytorch：

conda install pytorch torchvision torchaudio -c pytorch

如果需要安装特定版本的 Pytorch，可以在命令最后加上指定版本号，例如：

conda install pytorch==1.9.0 torchvision torchaudio -c pytorch

示例一：使用 Pytorch 进行 MNIST 手写数字识别

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 定义 transform
transform = transforms.Compose([
    transforms.ToTensor(),
])

# 加载数据集
trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
testset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)
testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False)

# 定义模型
class MNISTNet(nn.Module):
    def __init__(self):
        super(MNISTNet, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2)
        self.pool1 = nn.MaxPool2d(kernel_size=2)
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=2)
        self.pool2 = nn.MaxPool2d(kernel_size=2)
        self.fc1 = nn.Linear(in_features=32*7*7, out_features=128)
        self.fc2 = nn.Linear(in_features=128, out_features=10)

    def forward(self, x):
        x = self.conv1(x)
        x = torch.relu(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = torch.relu(x)
        x = self.pool2(x)
        x = x.view(-1, 32*7*7)
        x = self.fc1(x)
        x = torch.relu(x)
        x = self.fc2(x)
        return x

net = MNISTNet()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=1e-3)

# 开始训练
for epoch in range(5):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch+1, i+1, running_loss/100))
            running_loss = 0.0

示例二：使用 Pytorch 进行图像风格转换

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image


# 加载预训练的 VGG19 模型
vgg = models.vgg19(pretrained=True).features

# 选择需要用到的卷积层
conv_layers = [4, 9, 18, 27, 36]

# 定义 transform
transform = transforms.Compose([
    transforms.Resize(512),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# 加载内容图像和风格图像
content_image = Image.open('content.jpg').convert('RGB')
style_image = Image.open('style.jpg').convert('RGB')

# 对图像进行 transform 并转换为 Pytorch Tensor
content_tensor = transform(content_image).unsqueeze(0)
style_tensor = transform(style_image).unsqueeze(0)

# 将 content_tensor 和 style_tensor 送入 VGG19，提取对应的 feature
def get_features(tensor, model, layers):
    features = {}
    for name, layer in model._modules.items():
        tensor = layer(tensor)
        if int(name) in layers:
            features[name] = tensor
    return features

content_features = get_features(content_tensor, vgg, conv_layers)
style_features = get_features(style_tensor, vgg, conv_layers)

# 定义 Gram 矩阵
def gram_matrix(tensor):
    _, C, H, W = tensor.size()
    tensor = tensor.view(C, H*W)
    gram = torch.matmul(tensor, tensor.t())
    return gram

# 计算 content image 和 style image 的 Gram 矩阵
style_grams = {layer: gram_matrix(style_features[layer]) for layer in style_features}
content_grams = {layer: gram_matrix(content_features[layer]) for layer in content_features}

# 定义合成图像
target = content_tensor.clone().requires_grad_(True)

# 定义损失函数和优化器
content_weight = 1
style_weight = 100000
target_features = get_features(target, vgg, conv_layers)
optimizer = optim.Adam([target], lr=0.01)

# 开始训练
for i in range(1000):
    target_features = get_features(target, vgg, conv_layers)

    content_loss = 0.
    for layer in content_features:
        content_loss += torch.mean(torch.pow(target_features[layer] - content_features[layer], 2))

    style_loss = 0.
    for layer in style_features:
        style_loss += torch.mean(torch.pow(gram_matrix(target_features[layer]) - style_grams[layer], 2))

    total_loss = content_weight * content_loss + style_weight * style_loss

    optimizer.zero_grad()
    total_loss.backward()
    optimizer.step()

    if i % 100 == 0:
        print('iteration:', i, 'total loss:', total_loss.item())

# 保存合成图像
result_tensor = target.detach().squeeze().clamp_(0, 1)
result_image = transforms.ToPILImage()(result_tensor)
result_image.save('result.jpg')

至此，你已经成功安装了最新版本的 Pytorch，并了解了两个示例的使用。