P7使用pytorch实现马铃薯病害识别

• 🍨 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
  我的环境
  语言环境：python 3.7.12
  编译器：pycharm
  深度学习环境：tensorflow 2.7.0
  数据：本地数据集

这次我们使用的是马铃薯病害数据集，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。

一、代码

#一、前期准备
#1.设置GPU
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import os,PIL,pathlib,warnings

warnings.filterwarnings("ignore")             #忽略警告信息

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)

#2.导入数据
import os,PIL,random,pathlib

data_dir1 = './data_PotatoPlants/'
data_dir = pathlib.Path(data_dir1)

data_paths  = list(data_dir.glob('*'))
classeNames = [str(path).split("/")[1] for path in data_paths]
classeNames

# 关于transforms.Compose的更多介绍可以参考：https://blog.youkuaiyun.com/qq_38251616/article/details/124878863
train_transforms = transforms.Compose([
    transforms.Resize([224, 224]),  # 将输入图片resize成统一尺寸
    # transforms.RandomHorizontalFlip(), # 随机水平翻转
    transforms.ToTensor(),          # 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
    transforms.Normalize(           # 标准化处理-->转换为标准正太分布（高斯分布），使模型更容易收敛
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225])  # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])

test_transform = transforms.Compose([
    transforms.Resize([224, 224]),  # 将输入图片resize成统一尺寸
    transforms.ToTensor(),          # 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
    transforms.Normalize(           # 标准化处理-->转换为标准正太分布（高斯分布），使模型更容易收敛
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225])  # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])

total_data = datasets.ImageFolder(data_dir1,transform=train_transforms)
print(total_data)

print(total_data.class_to_idx)

#3.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])
train_dataset, test_dataset

batch_size = 32

train_dl = torch.utils.data.DataLoader(train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True,
                                           num_workers=1)
test_dl = torch.utils.data.DataLoader(test_dataset,
                                          batch_size=batch_size,
                                          shuffle=True,
                                          num_workers=1)

for X, y in test_dl:
    print("Shape of X [N, C, H, W]: ", X.shape)
    print("Shape of y: ", y.shape, y.dtype)
    break

#二、搭建模型
#1.手动搭建VGG-16
import torch.nn.functional as F

class vgg16(nn.Module):
    def __init__(self):
        super(vgg16,self).__init__()
        #卷积快1
        self.block1=nn.Sequential(
            #输入通道数3，输出通道数64，卷积核3*3，步幅1，填充1像素
            nn.Conv2d(3,64,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
            nn.ReLU(),
            nn.Conv2d(64,64,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=(2,2),stride=(2,2))
        )
        #卷积块2
        self.block2 = nn.Sequential(
            nn.Conv2d(64,128,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
            nn.ReLU(),
            nn.Conv2d(128,128,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=(2,2),stride=(2,2))
        )
        #卷积块3
        self.block3 = nn.Sequential(
            nn.Conv2d(128,256,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
            nn.ReLU(),
            nn.Conv2d(256,256,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=(2,2),stride=(2,2))
        )
        #卷积块4
        self.block4 = nn.Sequential(
            nn.Conv2d(256,512,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
            nn.ReLU(),
            nn.Conv2d(512,512,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=(2,2),stride=(2,2))
        )
        #卷积块5
        self.block5 = nn.Sequential(
            nn.Conv2d(512,512,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
            nn.ReLU(),
            nn.Conv2d(512,512,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=(2,2),stride=(2,2))
        )

        #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=3)
        )
    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,start_dim=1)
        x = self.classifier(x)

        return x
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))

model = vgg16().to(device)
print(model)

#查看模型详情
import torchsummary as summary
summary.summary(model,(3,224,224))


# 训练循环
def train(dataloader, 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, 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

# 正式训练
#如果将优化器换成 SGD 会发生什么呢？请自行探索接下来发生的诡异事件的原因。
import copy

optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
# optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)
loss_fn = nn.CrossEntropyLoss()  # 创建损失函数

epochs = 40

train_loss = []
train_acc = []
test_loss = []
test_acc = []

best_acc = 0  # 设置一个最佳准确率，作为最佳模型的判别指标

for epoch in range(epochs):

    model.train()
    epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)

    model.eval()
    epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)

    # 保存最佳模型到 best_model
    if epoch_test_acc > best_acc:
        best_acc = epoch_test_acc
        best_model = copy.deepcopy(model)

    train_acc.append(epoch_train_acc)
    train_loss.append(epoch_train_loss)
    test_acc.append(epoch_test_acc)
    test_loss.append(epoch_test_loss)

    # 获取当前的学习率
    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 = './best_model.pth'  # 保存的参数文件名
torch.save(model.state_dict(), PATH)

print('Done')

# loss 与acc
import matplotlib.pyplot as plt
#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

from datetime import datetime
current_time = datetime.now() # 获取当前时间

epochs_range = range(epochs)

plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)

plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.xlabel(current_time) # 打卡请带上时间戳，否则代码截图无效

plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

# 指定图片进行预测
from PIL import Image

classes = list(total_data.class_to_idx)


def predict_one_image(image_path, model, transform, classes):
    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}')

# 模型评估
best_model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn)
print(epoch_test_acc,epoch_test_loss)

二、结果

三、总结

优化器换为SGD后

optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)

原因分析
先检查了自己搭建的VGG16，发现与官方的略有不同，classifier的结构不同，在每个Relu激活函数后没有dropout层，但是官方的有。
故改用官方的模型进行测试：

from torchvision.models import vgg16

# 加载预训练模型,并对模型进行微调
model = vgg16(pretrained = True).to(device)
for param in model.parameters():
    param.requires_grad = False#冻结模型参数，这样在训练时只训练最后一层的参数
# 修改classifier的最后一层
model.classifier._modules['6'] = nn.Linear(4096,3)

提精度
尝试1：动态损失，最高精度90.7%

lambda1 = lambda epoch: 0.92 ** (epoch // 4)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1) #选定调整方法

尝试2：优化器SGD，最高精度97%

如何查看模型的参数量以及相关指标

#查看模型详情
import torchsummary as summary
summary.summary(model,(3,224,224))