第P7周-Pytorch实现马铃薯病害识别（VGG16复现）

• 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
• 🍖 原作者：K同学啊

目标

马铃薯病害数据集，该数据集包含表现出各种疾病的马铃薯植物的高分辨率图像，包括早期疫病、晚期疫病和健康叶子。它旨在帮助开发和测试图像识别模型，以实现准确的疾病检测和分类，从而促进农业诊断的进步。

具体实现

（一）环境

语言环境：Python 3.10
编 译 器: PyCharm
框 架: Pytorch

（二）具体步骤

1. Utils.py

**import torch  
import pathlib  
import matplotlib.pyplot as plt  
from torchvision.transforms import transforms  
  
  
# 第一步：设置GPU  
def USE_GPU():  
    if torch.cuda.is_available():  
        print('CUDA is available, will use GPU')  
        device = torch.device("cuda")  
    else:  
        print('CUDA is not available. Will use CPU')  
        device = torch.device("cpu")  
  
    return device  
  
temp_dict = dict()  
def recursive_iterate(path):  
    """  
    根据所提供的路径遍历该路径下的所有子目录，列出所有子目录下的文件  
    :param path: 路径  
    :return: 返回最后一级目录的数据  
    """    path = pathlib.Path(path)  
    for file in path.iterdir():  
        if file.is_file():  
            temp_key = str(file).split('\\')[-2]  
            if temp_key in temp_dict:  
                temp_dict.update({temp_key: temp_dict[temp_key] + 1})  
            else:  
                temp_dict.update({temp_key: 1})  
            # print(file)  
        elif file.is_dir():  
            recursive_iterate(file)  
  
    return temp_dict  
  
  
def data_from_directory(directory, train_dir=None, test_dir=None, show=False):  
    """  
    提供是的数据集是文件形式的，提供目录方式导入数据，简单分析数据并返回数据分类  
    :param test_dir: 是否设置了测试集目录  
    :param train_dir: 是否设置了训练集目录  
    :param directory: 数据集所在目录  
    :param show: 是否需要以柱状图形式显示数据分类情况,默认显示  
    :return: 数据分类列表,类型: list  
    """    global total_image  
    print("数据目录：{}".format(directory))  
    data_dir = pathlib.Path(directory)  
  
    # for d in data_dir.glob('**/*'): # **/*通配符可以遍历所有子目录  
    #     if d.is_dir():  
    #         print(d)    class_name = []  
    total_image = 0  
    # temp_sum = 0  
  
    if train_dir is None or test_dir is None:  
        data_path = list(data_dir.glob('*'))  
        class_name = [str(path).split('\\')[-1] for path in data_path]  
        print("数据分类: {}, 类别数量：{}".format(class_name, len(list(data_dir.glob('*')))))  
        total_image = len(list(data_dir.glob('*/*')))  
        print("图片数据总数: {}".format(total_image))  
    else:  
        temp_dict.clear()  
        train_data_path = directory + '/' + train_dir  
        train_data_info = recursive_iterate(train_data_path)  
        print("{}目录:{},{}".format(train_dir, train_data_path, train_data_info))  
  
        temp_dict.clear()  
        test_data_path = directory + '/' + test_dir  
        print("{}目录:{},{}".format(test_dir,  test_data_path, recursive_iterate(test_data_path)))  
        class_name = temp_dict.keys()  
  
    if show:  
        # 隐藏警告  
        import warnings  
        warnings.filterwarnings("ignore")  # 忽略警告信息  
        plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签  
        plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号  
        plt.rcParams['figure.dpi'] = 100  # 分辨率  
  
        for i in class_name:  
            data = len(list(pathlib.Path((directory + '\\' + i + '\\')).glob('*')))  
            plt.title('数据分类情况')  
            plt.grid(ls='--', alpha=0.5)  
            plt.bar(i, data)  
            plt.text(i, data, str(data), ha='center', va='bottom')  
            print("类别-{}:{}".format(i, data))  
            # temp_sum += data  
        plt.show()  
  
    # if temp_sum == total_image:  
    #     print("图片数据总数检查一致")  
    # else:    #     print("数据数据总数检查不一致，请检查数据集是否正确！")  
    return class_name  
  
  
def get_transforms_setting(size):  
    """  
    获取transforms的初始设置  
    :param size: 图片大小  
    :return: transforms.compose设置  
    """    transform_setting = {  
        'train': transforms.Compose([  
            transforms.Resize(size),  
            transforms.ToTensor(),  
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])  
        ]),  
        'test': transforms.Compose([  
            transforms.Resize(size),  
            transforms.ToTensor(),  
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])  
        ])  
    }  
  
    return transform_setting  
  
  
# 训练循环  
def train(dataloader, device, model, loss_fn, optimizer):  
    size = len(dataloader.dataset)  # 训练集的大小  
    num_batches = len(dataloader)  # 批次数目, (size/batch_size，向上取整)  
  
    train_loss, train_acc = 0, 0  # 初始化训练损失和正确率  
  
    for X, y in dataloader:  # 获取图片及其标签  
        X, y = X.to(device), y.to(device)  
  
        # 计算预测误差  
        pred = model(X)  # 网络输出  
        loss = loss_fn(pred, y)  # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失  
  
        # 反向传播  
        optimizer.zero_grad()  # grad属性归零  
        loss.backward()  # 反向传播  
        optimizer.step()  # 每一步自动更新  
  
        # 记录acc与loss  
        train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()  
        train_loss += loss.item()  
  
    train_acc /= size  
    train_loss /= num_batches  
  
    return train_acc, train_loss  
  
  
def test(dataloader, device, model, loss_fn):  
    size = len(dataloader.dataset)  # 测试集的大小  
    num_batches = len(dataloader)  # 批次数目, (size/batch_size，向上取整)  
    test_loss, test_acc = 0, 0  
  
    # 当不进行训练时，停止梯度更新，节省计算内存消耗  
    with torch.no_grad():  
        for imgs, target in dataloader:  
            imgs, target = imgs.to(device), target.to(device)  
  
            # 计算loss  
            target_pred = model(imgs)  
            loss = loss_fn(target_pred, target)  
  
            test_loss += loss.item()  
            test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()  
  
    test_acc /= size  
    test_loss /= num_batches  
  
    return test_acc, test_loss  
  
  
from PIL import Image  
  
def predict_one_image(image_path, device, model, transform, classes):  
    """  
    预测单张图片  
    :param image_path: 图片路径  
    :param device: CPU or GPU    :param model: cnn模型  
    :param transform:    :param classes:    :return:   
    """  
    test_img = Image.open(image_path).convert('RGB')  
    plt.imshow(test_img)  # 展示预测的图片  
  
    test_img = transform(test_img)  
    img = test_img.to(device).unsqueeze(0)  
  
    model.eval()  
    output = model(img)  
  
    _, pred = torch.max(output, 1)  
    pred_class = classes[pred]  
    print(f'预测结果是：{pred_class}')**

2. model.py

import torch.nn as nn  
import torch
import torch.nn.functional as F

  
class VGG16(nn.Module):  
    def __init__(self, num_classes):  
        super(VGG16, self).__init__()  
        # 卷积块1  
        self.block1 = nn.Sequential(  
            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),  
            nn.ReLU(),  
            nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),  
            nn.ReLU(),  
            nn.MaxPool2d(kernel_size=2, stride=2)  
        )  
        # 卷积块2  
        self.block2 = nn.Sequential(  
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),  
            nn.ReLU(),  
            nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),  
            nn.ReLU(),  
            nn.MaxPool2d(kernel_size=2, stride=2)  
        )  
        # 卷积块3  
        self.block3 = nn.Sequential(  
            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),  
            nn.ReLU(),  
            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),  
            nn.ReLU(),  
            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),  
            nn.ReLU(),  
            nn.MaxPool2d(kernel_size=2, stride=2)  
        )  
        # 卷积块4  
        self.block4 = nn.Sequential(  
            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1),  
            nn.ReLU(),  
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),  
            nn.ReLU(),  
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),  
            nn.ReLU(),  
            nn.MaxPool2d(kernel_size=2, stride=2)  
        )  
        # 卷积块5  
        self.block5 = nn.Sequential(  
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),  
            nn.ReLU(),  
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),  
            nn.ReLU(),  
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),  
            nn.ReLU(),  
            nn.MaxPool2d(kernel_size=2, stride=2)  
        )  
        # 全连接网络层，用于分类  
        self.classifier = nn.Sequential(  
            nn.Linear( in_features=512 * 7 * 7, out_features=4096),  
            nn.ReLU(),  
            nn.Linear(in_features=4096, out_features=4096),  
            nn.ReLU(),  
            nn.Linear(in_features=4096, out_features=num_classes)  
        )  
  
    def forward(self, x):  
        x = self.block1(x)  
        x = self.block2(x)  
        x = self.block3(x)  
        x = self.block4(x)  
        x = self.block5(x)  
        x = torch.flatten(x, 1)  
        x = self.classifier(x)  
  
        return x

3. main.py

import torch.utils.data  
from torchvision import datasets  
  
from Utils import USE_GPU, data_from_directory, get_transforms_setting, train, test  
from config import get_options  
from model import VGG16  
  
# 获取参数配置  
opt = get_options()  
  
# 设置GPU  
device = USE_GPU()  
  
DATA_DIR = "./data/PotatoPlants"  
  
# 导入数据  
classes_name = data_from_directory(DATA_DIR, show=True)  
  
transforms_setting = get_transforms_setting((224, 224))  
  
total_data = datasets.ImageFolder(DATA_DIR, transform=transforms_setting['train'])  
print(total_data)  
print(total_data.class_to_idx)  
  
# 划分数据集  
train_size = int(0.8 * len(total_data))  
test_size = len(total_data) - train_size  
  
train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])  
print(train_dataset, test_dataset)  
  
train_dl = torch.utils.data.DataLoader(train_dataset, batch_size=opt.batch_size, shuffle=True)  
test_dl = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size, shuffle=True)  
  
for X, y in train_dl:  
    print("Shape of X[N, C, H, W]：", X.shape)  
    print("Shape of y: ", y.shape, y.dtype)  
    break  

# 加载VGG16模型  
model = VGG16(len(classes_name)).to(device)  
print(model)  
  
# 统计模型参数量以及其他指标  
import torchsummary as summary  
summary.summary(model, (3, 224, 224))  

# 正式训练  
import copy  
optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr)  
loss_fn = torch.nn.CrossEntropyLoss() # 创建损失函数  
  
train_loss, train_acc, test_loss, test_acc = [], [], [], []  
best_acc = 0    # 设置了一个最佳准确率，来用作为最佳模型的判别标准  
  
for epoch in range(opt.epochs):  
    model.train()  
    epoch_train_acc, epoch_train_loss = train(train_dl, device, model, loss_fn, optimizer)  
  
    model.eval()  
    epoch_test_acc, epoch_test_loss = test(test_dl, device, model, loss_fn)  
  
    # 保存最佳模型  
    if epoch_test_acc > best_acc:  
        best_acc = epoch_test_acc  
        best_model = copy.deepcopy(model)  
  
    train_acc.append(epoch_train_acc)  
    test_acc.append(epoch_test_acc)  
    train_loss.append(epoch_train_loss)  
    test_loss.append(epoch_test_loss)  
  
    # 获取当前学习率  
    lr = optimizer.state_dict()['param_groups'][0]['lr']  
  
    template = 'Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}'  
    print(template.format(epoch + 1, epoch_train_acc * 100, epoch_train_loss,  
                          epoch_test_acc * 100, epoch_test_loss, lr))  
  
# 保存最佳模型到文件中  
PATH = './models/potato-model.pth'  
torch.save(model.state_dict(), PATH)  
  
print("完成")

4. predict.py 预测单张图片

import torch  
from PIL import Image  
from Utils import predict_one_image, USE_GPU, get_transforms_setting  
from model import VGG16  
  
classes = ['Early_blight','Late_blight', 'healthy']  
  
transforms = get_transforms_setting([224,224])  
  
device = USE_GPU()  
# 加载VGG16模型  
model = VGG16(3)  
model.load_state_dict(torch.load('./models/potato-model.pth', map_location=device))  
model.to(device)  
  
img_path = "./data/PotatoPlants/Early_blight/0c4f6f72-c7a2-42e1-9671-41ab3bf37fe7___RS_Early.B 6752.JPG"  
  
predict_one_image(img_path, device, model, transforms['train'], classes)