pytorch读取文件夹中的图片 残差网络模型训练，预测，保存结果

要读取文件夹中的图片并进行残差网络模型训练、预测，并保存结果，可以使用PyTorch的Dataset和DataLoader来加载图片数据集，然后使用残差网络模型进行训练和预测，使用torch.save将模型的训练结果保存到文件中。具体的代码可根据数据集、模型架构以及训练和预测的细节进行编写。

相关问题

pytorch读取文件夹中的图片 残差网络模型训练，预测，保存结果示例代码

很高兴回答您的问题，以下是 PyTorch 读取文件夹中的图片、残差网络模型训练，预测，保存结果的示例代码：

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


# 定义数据预处理
transform_train = transforms.Compose([
    transforms.Resize(256),
    transforms.RandomCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

transform_test = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])


# 加载训练集和测试集
trainset = ImageFolder(root='path/to/training/folder',
                       transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32,
                                          shuffle=True, num_workers=2)

testset = ImageFolder(root='path/to/testing/folder',
                      transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=32,
                                         shuffle=False, num_workers=2)


# 定义 ResNet18 模型
class ResNet18(nn.Module):
    def __init__(self):
        super(ResNet18, self).__init__()
        self.resnet18 = torchvision.models.resnet18(pretrained=True)
        self.fc = nn.Linear(1000, 2)

    def forward(self, x):
        out = self.resnet18(x)
        out = self.fc(out)
        return out


net = ResNet18()


# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)


# 训练模型
for epoch in range(10):  # 训练 10 轮
    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


# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the test images: %d %%' % (
    100 * correct / total))


# 保存模型和预测结果
PATH = 'path/to/save/model'
torch.save(net.state_dict(), PATH)

with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        for i in range(len(predicted)):
            print('Predicted: ', predicted[i])
            print('Label: ', labels[i])

以上是一个简单的示例，可以供参考。需要替换路径和类别数等一些必要参数，才能在你的项目中运行。