给出一个jetson使用pytorch训练图像模型的例子

以下是一个使用Jetson Nano运行PyTorch的图像分类器示例代码：

首先，安装必要的依赖项：

sudo apt-get install python3-pip libopenblas-dev libatlas-base-dev liblapack-dev libblas-dev liblapack-dev libjpeg-dev libpng-dev libtiff-dev libavcodec-dev libavformat-dev libswscale-dev libv4l-dev libxvidcore-dev libx264-dev libgtk-3-dev libhdf5-dev libprotobuf-dev protobuf-compiler libgflags-dev libgoogle-glog-dev liblmdb-dev libboost-all-dev libgphoto2-dev libeigen3-dev

然后，安装PyTorch：

sudo pip3 install torch torchvision

接下来，创建一个名为“image_classification.py”的文件，并将以下代码复制到其中：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
from torchvision import datasets, models, transforms
import time
import os
import copy

# 定义数据转换
data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

# 加载数据集
data_dir = 'data/hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
                                             shuffle=True, num_workers=4)
              for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

# 判断是否使用GPU
use_gpu = torch.cuda.is_available()

# 定义模型
model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2)

if use_gpu:
    model_ft = model_ft.cuda()

# 定义损失函数
criterion = nn.CrossEntropyLoss()

# 定义优化器
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)

# 定义学习率调整策略
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

# 定义训练函数
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model = model
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # 每个epoch都有训练和验证阶段
        for phase in ['train', 'val']:
            if phase == 'train':
                scheduler.step()
                model.train(True)
            else:
                model.train(False)

            running_loss = 0.0
            running_corrects = 0

            # 迭代数据
            for data in dataloaders[phase]:
                # 获取输入
                inputs, labels = data
                if use_gpu:
                    inputs = Variable(inputs.cuda())
                    labels = Variable(labels.cuda())
                else:
                    inputs, labels = Variable(inputs), Variable(labels)

                # 梯度清零
                optimizer.zero_grad()

                # 前向传播
                outputs = model(inputs)
                _, preds = torch.max(outputs.data, 1)
                loss = criterion(outputs, labels)

                # 反向传播 + 优化
                if phase == 'train':
                    loss.backward()
                    optimizer.step()

                # 统计
                running_loss += loss.item()
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

            # 保存最佳模型权重
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model = copy.deepcopy(model)

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

    return best_model

# 训练模型
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)

# 保存模型
torch.save(model_ft.state_dict(), 'model.pt')

此代码将加载一个预训练的ResNet-18模型，并使用Hymenoptera数据集对其进行微调。根据需要进行更改，例如更改数据集路径，更改迭代次数或更改模型架构。

运行代码：

python3 image_classification.py

代码将训练模型，并将最佳模型保存到“model.pt”文件中。