- 🍨 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
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')device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device
2.导入数据
data_dir='../data/PotatoPlants/PotatoPlants'
data_dir=pathlib.Path(data_dir)data_paths=list(data_dir.glob('*'))
classeNames=[str(path).split('\\')[4] for path in data_paths]
classeNames
train_transforms=transforms.Compose([transforms.Resize([224,224]),transforms.ToTensor(),transforms.Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225])
])
test_transforms=transforms.Compose([transforms.Resize([224,224]),transforms.ToTensor(),transforms.Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225])
])
total_data=datasets.ImageFolder('../data/PotatoPlants/PotatoPlants',transform=train_transforms)
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=32train_dl=torch.utils.data.DataLoader(train_dataset,batch_size=batch_size,shuffle=True) test_dl=torch.utils.data.DataLoader(test_dataset,batch_size=batch_size,shuffle=True)
for x,y in test_dl:print('shape of [N,C,W,H]',x.shape)print('shape of y',y.shape,y.dtype)break
二.VGG-16模型
1.搭建模型
import torch.nn.functional as Fclass vgg16(nn.Module):def __init__(self):super(vgg16, self).__init__()# 卷积块1self.block1 = nn.Sequential(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.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.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.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 xdevice = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))model = vgg16().to(device)
model
2.查看模型详情
import torchsummary as summarysummary.summary(model,(3,224,224))
三.训练模型
1.编写训练函数
# 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_loss
2.编写测试函数
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
3.正式训练
import copyoptimizer = torch.optim.Adam(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')
四.结果可视化
1.Loss与Accuracy图
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()
2.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}')
3.模型评估
# 预测训练集中的某张照片 predict_one_image(image_path=r'D:\AI_Learning\deep_learning\data\PotatoPlants\PotatoPlants\Early_blight\0a8a68ee-f587-4dea-beec-79d02e7d3fa4___RS_Early.B 8461.JPG',model=model,transform=train_transforms,classes=classes)
基于 VGG16 的马铃薯病害分类模型，完成了数据预处理、模型训练、测试评估和可视化过程，并最终保存了最优模型，实现了单张图片的病害预测。