一、前期准备
1.设置GPU
import torch from torch import nn import torchvision from torchvision import transforms,datasets,models import matplotlib.pyplot as plt import os,PIL,pathlib
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device
device(type='cuda')
2.导入数据
data_dir = './49-data/' data_dir = pathlib.Path(data_dir) data_paths = list(data_dir.glob('*')) classNames = [str(path).split('\\')[1] for path in data_paths] classNames
['Dark', 'Green', 'Light', 'Medium']
train_transforms = transforms.Compose([ transforms.Resize([224,224]),# resize输入图片 transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换成tensor transforms.Normalize( mean = [0.485, 0.456, 0.406], std = [0.229,0.224,0.225]) # 从数据集中随机抽样计算得到 ]) test_transforms = transforms.Compose([ transforms.Resize([224,224]),# resize输入图片 transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换成tensor transforms.Normalize( mean = [0.485, 0.456, 0.406], std = [0.229,0.224,0.225]) # 从数据集中随机抽样计算得到 ]) total_data = datasets.ImageFolder(data_dir,transform=train_transforms) total_data
Dataset ImageFolder Number of datapoints: 1200 Root location: 49-data StandardTransform Transform: Compose( Resize(size=[224, 224], interpolation=PIL.Image.BILINEAR) ToTensor() Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) )
total_data.class_to_idx
{'Dark': 0, 'Green': 1, 'Light': 2, 'Medium': 3}
3.数据集划分
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
train_size,test_size
(960, 240)
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)
imgs, labels = next(iter(train_dl)) imgs.shape
torch.Size([32, 3, 224, 224])
import numpy as np # 指定图片大小，图像大小为20宽、5高的绘图(单位为英寸inch) plt.figure(figsize=(20, 5)) for i, imgs in enumerate(imgs[:20]): npimg = imgs.numpy().transpose((1,2,0)) npimg = npimg * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406)) npimg = npimg.clip(0, 1) # 将整个figure分成2行10列，绘制第i+1个子图。 plt.subplot(2, 10, i+1) plt.imshow(npimg) plt.axis('off')
for X,y in test_dl: print('Shape of X [N, C, H, W]:', X.shape) print('Shape of y:', y.shape) break
Shape of X [N, C, H, W]: torch.Size([32, 3, 224, 224])
Shape of y: torch.Size([32])
二、构建简单的CNN网络
1. 搭建模型
import torch.nn.functional as F # class vgg16(nn.Module): # def __init__(self): # super(vgg16,self).__init__() # self.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)) # ) # 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)) # ) # 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.Conv2d(256,256,kernel_size=(3,3),stride=(1,1),padding=(1,1)), # nn.ReLU(), # nn.MaxPool2d(kernel_size=(2,2),stride=(2,2)) # ) # 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.Conv2d(512,512,kernel_size=(3,3),stride=(1,1),padding=(1,1)), # nn.ReLU(), # nn.MaxPool2d(kernel_size=(2,2),stride=(2,2)) # ) # 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.Conv2d(512,512,kernel_size=(3,3),stride=(1,1),padding=(1,1)), # nn.ReLU(), # nn.MaxPool2d(kernel_size=(2,2),stride=(2,2)) # ) # 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=4) # ) # 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 # model = vgg16().to(device) # model
from torchvision.models import vgg16 model = vgg16(pretrained = True).to(device) for param in model.parameters(): # 只训练输出层 param.requires_grad = False model.classifier._modules['6'] = nn.Linear(4096,len(classNames)) model.to(device) model
VGG( (features): Sequential( (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): ReLU(inplace=True) (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (6): ReLU(inplace=True) (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (8): ReLU(inplace=True) (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (11): ReLU(inplace=True) (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (13): ReLU(inplace=True) (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (15): ReLU(inplace=True) (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (18): ReLU(inplace=True) (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (20): ReLU(inplace=True) (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (22): ReLU(inplace=True) (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (25): ReLU(inplace=True) (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (27): ReLU(inplace=True) (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (29): ReLU(inplace=True) (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (avgpool): AdaptiveAvgPool2d(output_size=(7, 7)) (classifier): Sequential( (0): Linear(in_features=25088, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.5, inplace=False) (3): Linear(in_features=4096, out_features=4096, bias=True) (4): ReLU(inplace=True) (5): Dropout(p=0.5, inplace=False) (6): Linear(in_features=4096, out_features=4, bias=True) ) )
2.查看模型详情
import torchsummary as summary summary.summary(model,(3,224,224))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 224, 224] 1,792
ReLU-2 [-1, 64, 224, 224] 0
Conv2d-3 [-1, 64, 224, 224] 36,928
ReLU-4 [-1, 64, 224, 224] 0
MaxPool2d-5 [-1, 64, 112, 112] 0
Conv2d-6 [-1, 128, 112, 112] 73,856
ReLU-7 [-1, 128, 112, 112] 0
Conv2d-8 [-1, 128, 112, 112] 147,584
ReLU-9 [-1, 128, 112, 112] 0
MaxPool2d-10 [-1, 128, 56, 56] 0
Conv2d-11 [-1, 256, 56, 56] 295,168
ReLU-12 [-1, 256, 56, 56] 0
Conv2d-13 [-1, 256, 56, 56] 590,080
ReLU-14 [-1, 256, 56, 56] 0
Conv2d-15 [-1, 256, 56, 56] 590,080
ReLU-16 [-1, 256, 56, 56] 0
MaxPool2d-17 [-1, 256, 28, 28] 0
Conv2d-18 [-1, 512, 28, 28] 1,180,160
ReLU-19 [-1, 512, 28, 28] 0
Conv2d-20 [-1, 512, 28, 28] 2,359,808
ReLU-21 [-1, 512, 28, 28] 0
Conv2d-22 [-1, 512, 28, 28] 2,359,808
ReLU-23 [-1, 512, 28, 28] 0
MaxPool2d-24 [-1, 512, 14, 14] 0
Conv2d-25 [-1, 512, 14, 14] 2,359,808
ReLU-26 [-1, 512, 14, 14] 0
Conv2d-27 [-1, 512, 14, 14] 2,359,808
ReLU-28 [-1, 512, 14, 14] 0
Conv2d-29 [-1, 512, 14, 14] 2,359,808
ReLU-30 [-1, 512, 14, 14] 0
MaxPool2d-31 [-1, 512, 7, 7] 0
AdaptiveAvgPool2d-32 [-1, 512, 7, 7] 0
Linear-33 [-1, 4096] 102,764,544
ReLU-34 [-1, 4096] 0
Dropout-35 [-1, 4096] 0
Linear-36 [-1, 4096] 16,781,312
ReLU-37 [-1, 4096] 0
Dropout-38 [-1, 4096] 0
Linear-39 [-1, 4] 16,388
================================================================
Total params: 134,276,932
Trainable params: 16,388
Non-trainable params: 134,260,544
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 218.77
Params size (MB): 512.23
Estimated Total Size (MB): 731.57
----------------------------------------------------------------
三、训练模型
# 设置优化器 optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)#要训练什么参数/ scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.92)#学习率每5个epoch衰减成原来的1/10 loss_fn = nn.CrossEntropyLoss()
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，一共900张图片 num_batches = len(dataloader) # 批次数目，29（900/32） 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
2.编写测试函数
def test (dataloader, model, loss_fn): size = len(dataloader.dataset) # 测试集的大小，一共10000张图片 num_batches = len(dataloader) # 批次数目，8（255/32=8，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
3、正式训练
epochs = 20 train_loss = [] train_acc = [] test_loss = [] test_acc = [] best_acc = 0 for epoch in range(epochs): model.train() epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer) scheduler.step()#学习率衰减 model.eval() epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn) # 保存最优模型 if epoch_test_acc > best_acc: best_acc = epoch_train_acc state = { 'state_dict': model.state_dict(),#字典里key就是各层的名字，值就是训练好的权重 'best_acc': best_acc, 'optimizer' : optimizer.state_dict(), } train_acc.append(epoch_train_acc) train_loss.append(epoch_train_loss) test_acc.append(epoch_test_acc) test_loss.append(epoch_test_loss) template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}') print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss)) print('Done') print('best_acc：',best_acc)
Epoch:18, Train_acc:93.5%, Train_loss:0.270, Test_acc:95.4%，Test_loss:0.223
Epoch:19, Train_acc:94.5%, Train_loss:0.241, Test_acc:95.8%，Test_loss:0.223
Epoch:20, Train_acc:94.4%, Train_loss:0.243, Test_acc:96.2%，Test_loss:0.207
Done
best_acc： 0.94375
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 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.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 Image classes = list(total_data.class_to_idx) def predict_one_img(image_path,model,transform,classes): test_img = Image.open(image_path).convert('RGB') plt.imshow(test_img) 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}')
predict_one_img('./49-data/Dark/dark (1).png', model, train_transforms, classNames)
预测结果是：Dark
