基于pytorch水果图像识别算法，数据集使用Fruit 360数据集，且数据集已在本地文件中，要求算法实现，数据增强，批量化归一，学习率策略，权重衰减，梯度裁剪，Adm优化，配置模型时要求设置精度函数和图像基类，具体代码实现

好的，这里是一个基于PyTorch实现的水果图像识别算法，使用Fruit 360数据集。代码中包含数据增强、批量化归一、学习率策略、权重衰减、梯度裁剪、Adm优化等内容，并配置了精度函数和图像基类。请注意，代码中的数据集路径需要根据本地文件路径进行修改。

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
import os

# 设置随机种子
torch.manual_seed(2021)

# 定义图像基类
class ImageDataset(Dataset):
    def __init__(self, data_dir, transform=None):
        self.data_dir = data_dir
        self.filenames = os.listdir(data_dir)
        self.transform = transform

    def __len__(self):
        return len(self.filenames)

    def __getitem__(self, idx):
        img_path = os.path.join(self.data_dir, self.filenames[idx])
        image = Image.open(img_path).convert('RGB')
        if self.transform:
            image = self.transform(image)
        label = int(self.filenames[idx].split('_')[0])
        return image, label

# 定义数据增强
train_transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomVerticalFlip(),
    transforms.RandomRotation(degrees=15),
    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

test_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

# 定义数据集路径
train_data_dir = 'train/'
test_data_dir = 'test/'

# 加载训练集和测试集
train_dataset = ImageDataset(train_data_dir, transform=train_transform)
test_dataset = ImageDataset(test_data_dir, transform=test_transform)

# 定义批量化归一
batch_size = 32
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义模型
class FruitClassification(nn.Module):
    def __init__(self):
        super(FruitClassification, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 16 * 16, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 15)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = x.view(-1, 64 * 16 * 16)
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 定义模型、损失函数、优化器
model = FruitClassification()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-4)

# 定义学习率策略
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=5)

# 定义训练函数
def train(model, dataloader, criterion, optimizer, scheduler):
    model.train()
    running_loss = 0.0
    correct = 0
    total = 0

    for i, data in enumerate(dataloader):
        inputs, labels = data
        inputs, labels = inputs.cuda(), labels.cuda()
        optimizer.zero_grad()

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

        # 定义梯度裁剪
        nn.utils.clip_grad_norm_(model.parameters(), max_norm=1)

        optimizer.step()

        running_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    train_loss = running_loss / len(dataloader)
    train_acc = correct / total

    # 更新学习率
    scheduler.step(train_loss)

    return train_loss, train_acc

# 定义测试函数
def test(model, dataloader, criterion):
    model.eval()
    running_loss = 0.0
    correct = 0
    total = 0

    with torch.no_grad():
        for i, data in enumerate(dataloader):
            inputs, labels = data
            inputs, labels = inputs.cuda(), labels.cuda()

            outputs = model(inputs)
            loss = criterion(outputs, labels)

            running_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    test_loss = running_loss / len(dataloader)
    test_acc = correct / total

    return test_loss, test_acc

# 开始训练
num_epochs = 50

for epoch in range(num_epochs):
    train_loss, train_acc = train(model, train_dataloader, criterion, optimizer, scheduler)
    test_loss, test_acc = test(model, test_dataloader, criterion)

    print('Epoch: {}, Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'.format(
        epoch+1, train_loss, train_acc, test_loss, test_acc))

这个代码实现可以作为一个基础模板，可以根据具体需求进行修改和优化。希望对您有所帮助！