- 🍨 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,warningswarnings.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,pathlibdata_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.csdn.net/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_datasetbatch_size = 32train_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 Fclass vgg16(nn.Module):def __init__(self):super(vgg16,self).__init__()#卷积快1self.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2self.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3self.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4self.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5self.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losstrain_acc += (pred.argmax(1) == y).type(torch.float).sum().item()train_loss += loss.item()train_acc /= sizetrain_loss /= num_batchesreturn train_acc, train_lossdef 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losstarget_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 /= sizetest_loss /= num_batchesreturn 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 copyoptimizer = 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 = 40train_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_modelif epoch_test_acc > best_acc:best_acc = epoch_test_accbest_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 Imageclasses = 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))
