可以调用任意框架，完成对tiny-imagenet的训练过程和分类预测,用jupyter notebook实现

好的，以下是使用PyTorch框架训练和分类预测tiny-imagenet的步骤：

## 数据准备

1. 下载tiny-imagenet数据集，可以从官网下载：http://cs231n.stanford.edu/tiny-imagenet-200.zip
2. 解压数据集到指定目录下，例如：`/data/tiny-imagenet-200/`
3. 创建训练集、验证集和测试集的目录，例如：`/data/tiny-imagenet-200/train/`、`/data/tiny-imagenet-200/val/`、`/data/tiny-imagenet-200/test/`
4. 在`/data/tiny-imagenet-200/train/`目录下，创建200个子目录，每个子目录的名称为对应类别的名称，例如：`/data/tiny-imagenet-200/train/n01443537/`、`/data/tiny-imagenet-200/train/n01629819/`，并将对应类别的图片放入其对应的子目录中
5. 在`/data/tiny-imagenet-200/val/`目录下，创建`images/`子目录，并将验证集图片放入其中，同时创建`val_annotations.txt`文件，其中每行包含一个图片的文件名和类别标签，例如：`val_0.JPEG n03837869`，其中`n03837869`为类别标签
6. 在`/data/tiny-imagenet-200/test/`目录下，创建`images/`子目录，并将测试集图片放入其中，同时创建`test_annotations.txt`文件，其中每行包含一个图片的文件名

## 数据预处理

使用PyTorch的`torchvision`模块可以方便地进行数据预处理，以下是数据预处理的代码：

import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder

# 定义训练集的数据预处理
train_transform = transforms.Compose([
    transforms.RandomRotation(degrees=15),
    transforms.RandomHorizontalFlip(p=0.5),
    transforms.CenterCrop(size=64),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

# 定义验证集和测试集的数据预处理
val_test_transform = transforms.Compose([
    transforms.CenterCrop(size=64),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

# 加载训练集、验证集和测试集
train_dataset = ImageFolder('/data/tiny-imagenet-200/train/', transform=train_transform)
val_dataset = ImageFolder('/data/tiny-imagenet-200/val/images/', transform=val_test_transform)
test_dataset = ImageFolder('/data/tiny-imagenet-200/test/images/', transform=val_test_transform)

# 定义训练集、验证集和测试集的DataLoader
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=4)
val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False, num_workers=4)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False, num_workers=4)

## 模型训练

使用PyTorch可以方便地定义模型和训练过程，以下是模型的定义和训练过程的代码：

import torch
import torch.nn as nn
import torch.optim as optim
from tqdm import tqdm

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.relu3 = nn.ReLU(inplace=True)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(128 * 8 * 8, 1024)
        self.relu4 = nn.ReLU(inplace=True)
        self.dropout = nn.Dropout(p=0.5)
        self.fc2 = nn.Linear(1024, 200)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)
        x = self.pool(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.relu4(x)
        x = self.dropout(x)
        x = self.fc2(x)
        return x

# 定义损失函数和优化器
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(tqdm(train_loader, desc=f'Epoch {epoch+1}/{10}')):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    print(f'Epoch {epoch+1} loss: {running_loss / len(train_loader)}')

## 模型评估

使用PyTorch可以方便地进行模型评估，以下是模型评估的代码：

import torch.nn.functional as F

correct = 0
total = 0
with torch.no_grad():
    for data in tqdm(val_loader, desc='Validation'):
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f'Validation accuracy: {100 * correct / total:.2f}%')

correct = 0
total = 0
with torch.no_grad():
    for data in tqdm(test_loader, desc='Testing'):
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f'Test accuracy: {100 * correct / total:.2f}%')

以上就是使用PyTorch框架训练和分类预测tiny-imagenet的完整步骤。