pytorch实现好莱坞明星识别的示例代码

01-03 70阅读 0评论

?=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 = './hlw/' data_dir = pathlib.Path(data_dir)   data_paths = list(data_dir.glob('*')) classNames = [str(path).split('\\')[1] for path in data_paths] classNames
['Angelina Jolie',  'Brad Pitt',  'Denzel Washington',  'Hugh Jackman', 'Jennifer Lawrence',  'Johnny Depp',  'Kate Winslet',  'Leonardo DiCaprio',  'Megan Fox',  'Natalie Portman', 'Nicole KIDMan',  'Robert Downey Jr', 'Sandra Bullock',  'Scarlett Johansson', 'Tom CrUIse', 'Tom Hanks', 'Will Smith']
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]) # 从数据集随机抽样计算得到 ])   total_data = dataseTS.ImageFolder(data_dir,transform=train_transforms) total_data
Dataset ImageFolder Number of datapoints: 1800 Root location: hlw 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])    )

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

(<torch.utils.data.dataset.Subset at 0x12f8aceda00>, <torch.utils.data.dataset.Subset at 0x12f8acedac0>)

train_size,test_size

(1440, 360)

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)

4. 数据可视化

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')

pytorch实现好莱坞明星识别的示例代码

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

from torchvision.models import vgg16  model = vgg16(pretrained = True).to(device) for param in model.parameters(): 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=17, bias=True)   ) )
# 查看训练的层 params_to_update = model.parameters() # params_to_update = [] for name,param in model.named_parameters(): if param.requires_grad == True: # params_to_update.append(param) print("\t",name)

三、训练模型

1.优化器设置

# 优化器设置 optimizer = torch.optim.Adam(params_to_update, 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()

2.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

3.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

4、正式训练

训练输出层

epochs = 20 train_loss = [] train_acc  = [] test_loss  = [] test_acc   = [] best_acc = 0 filename='checkpoint.pth'   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(), } torch.save(state, filename)   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: 1, Train_acc:12.2%, Train_loss:2.701, Test_acc:13.9%,Test_loss:2.544

Epoch: 2, Train_acc:20.8%, Train_loss:2.386, Test_acc:20.6%,Test_loss:2.377

Epoch: 3, Train_acc:26.1%, Train_loss:2.228, Test_acc:22.5%,Test_loss:2.274...

Epoch:19, Train_acc:51.6%, Train_loss:1.528, Test_acc:35.8%,Test_loss:1.864

Epoch:20, Train_acc:53.9%, Train_loss:1.499, Test_acc:35.3%,Test_loss:1.852

Done

best_acc: 0.37430555555555556

继续训练所有层

for param in model.parameters(): param.requires_grad = True   # 再继续训练所有的参数,学习率调小一点 optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.92)   # 损失函数 criterion = nn.CrossEntropyLoss()
# 加载之前训练好的权重参数 checkpoint = torch.load(filename) best_acc = checkpoint['best_acc'] model.load_state_dict(checkpoint['state_dict'])
epochs = 20 train_loss = [] train_acc  = [] test_loss  = [] test_acc   = [] best_acc = 0 filename='best_vgg16.pth'   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_test_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(), } torch.save(state, filename)   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: 1, Train_acc:41.0%, Train_loss:1.654, Test_acc:57.5%,Test_loss:1.301

Epoch: 2, Train_acc:72.3%, Train_loss:0.781, Test_acc:58.9%,Test_loss:1.139

Epoch: 3, Train_acc:87.0%, Train_loss:0.381, Test_acc:67.8%,Test_loss:1.079

...

Epoch:19, Train_acc:99.3%, Train_loss:0.033, Test_acc:74.2%,Test_loss:0.895

Epoch:20, Train_acc:99.9%, Train_loss:0.003, Test_acc:74.4%,Test_loss:1.001

Done

best_acc: 0.7666666666666667

四、结果可视化

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()

pytorch实现好莱坞明星识别的示例代码

到此这篇关于PyTorch实现好莱坞明星识别的文章就介绍到这了,更多相关pytorch实现好莱坞明星识别内容请搜索云初冀北以前的文章或继续浏览下面的相关文章希望大家以后多多支持云初冀北!

免责声明
本站提供的资源,都来自网络,版权争议与本站无关,所有内容及软件的文章仅限用于学习和研究目的。不得将上述内容用于商业或者非法用途,否则,一切后果请用户自负,我们不保证内容的长久可用性,通过使用本站内容随之而来的风险与本站无关,您必须在下载后的24个小时之内,从您的电脑/手机中彻底删除上述内容。如果您喜欢该程序,请支持正版软件,购买注册,得到更好的正版服务。侵删请致信E-mail:Goliszhou@gmail.com
$

发表评论

表情:
评论列表 (暂无评论,70人围观)

还没有评论,来说两句吧...