Required request body is missing: public com.frtech.frog.common.core.util.R com.frtech.frog.pro.controller.CkSoDdController.save(com.frtech.frog.pro.entity.CkSoDdEntity,java.util.List<com.frtech.frog.pro.entity.CkSoDdSubEntity>)报错含义
时间: 2023-09-16 16:09:06 浏览: 50
这个报错的含义是请求体缺失。在调用 com.frtech.frog.pro.controller.CkSoDdController.save 方法时,需要传入一个 CkSoDdEntity 类型的对象和一个 CkSoDdSubEntity 类型的列表,但是请求中没有包含这些数据。可能原因是在发送请求时未正确设置请求体或者请求体中缺少必要的数据。您需要检查请求的代码和数据,确保请求体中包含所需的数据。
相关问题
torchvision.datasets.CIFAR10
torchvision.datasets.CIFAR10 is a dataset class in PyTorch that provides access to the CIFAR-10 image classification dataset. CIFAR-10 consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. The dataset is divided into 50,000 training images and 10,000 testing images.
This dataset is commonly used in image classification tasks as a benchmark dataset for evaluating the performance of deep learning models. The classes in CIFAR-10 are airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.
To use the CIFAR-10 dataset in PyTorch, you can create an instance of the CIFAR10 class and pass in the location of the data and whether you want to use the training or testing dataset. For example:
```python
import torchvision.datasets as datasets
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True)
```
This will download the CIFAR-10 dataset to the './data' directory and create a CIFAR10 object for the training and testing datasets.
通过./cifar_net.pth文件生成32*32图像的标签
如果你使用`torch.save`保存了一个已经训练好的CIFAR-10分类模型,并将其保存在`cifar_net.pth`文件中,你可以使用以下代码加载模型并对图像进行分类:
```python
import torch
import torchvision.transforms as transforms
from models import Net # 自定义的模型
# 加载CIFAR-10数据集
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# 实例化模型
model = Net()
model.load_state_dict(torch.load('./cifar_net.pth'))
model.eval()
# 对测试集中的图像进行分类
dataiter = iter(testloader)
images, labels = dataiter.next()
outputs = model(images)
# 输出预测结果
_, predicted = torch.max(outputs, 1)
print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
for j in range(4)))
```
在这个示例代码中,我们从`cifar_net.pth`文件中加载了一个已经训练好的CIFAR-10分类模型,并使用`model.eval()`将模型切换到评估模式。然后,我们从测试集中加载了一批图像,将它们输入模型,得到模型的输出,并使用`torch.max`函数找到每张图像的预测类别。最后,我们将预测结果输出到控制台中。
需要注意的是,这里的`models.Net()`是一个自定义的模型,你需要根据你自己的模型进行相应的修改。同时,需要确保你的模型能够处理32*32像素的图像。