• 本文为365天深度学习训练营中的学习记录博客
  • 参考文章地址:365天深度学习训练营-第P6周:好莱坞明星识别
  • 作者:K同学啊

一、前期准备

1.设置GPU

import torchfrom torch import nnimport torchvisionfrom torchvision import transforms,datasets,modelsimport matplotlib.pyplot as pltimport 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    StandardTransformTransform: 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_sizetrain_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 = 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)
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 = Falsemodel.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 summarysummary.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]               0AdaptiveAvgPool2d-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: 134,276,932Trainable params: 16,388Non-trainable params: 134,260,544----------------------------------------------------------------Input size (MB): 0.57Forward/backward pass size (MB): 218.77Params size (MB): 512.23Estimated 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1/10loss_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     = 20train_loss = []train_acc  = []test_loss  = []test_acc   = []best_acc = 0for 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:0.223Epoch:19, Train_acc:94.5%, Train_loss:0.241, Test_acc:95.8%,Test_loss:0.223Epoch:20, Train_acc:94.4%, Train_loss:0.243, Test_acc:96.2%,Test_loss:0.207Donebest_acc: 0.94375

四、结果可视化

1.Loss与Accuracy图

import matplotlib.pyplot as plt#隐藏警告import warningswarnings.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 Imageclasses = 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