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基于pytorch实现运动鞋品牌识别功能

2024-02-01 10:10:29 作者：Tooii

这篇文章主要给大家介绍了关于如何基于pytorch实现运动鞋品牌识别功能,文中通过图文以及实例代码介绍的非常详细,对大家学习或者使用PyTorch具有一定的参考学习价值,需要的朋友可以参考下

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,random

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

device

2. 导入数据

data_dir = '../Data/运动鞋品牌识别数据/'
data_dir = pathlib.Path(data_dir)

data_paths = list(data_dir.glob('*/*'))
classeNames = sorted(item.name for item in data_dir.glob('*/') if item.is_dir())
classeNames

['test', 'train']

第一步：使用pathlib.Path()函数将字符串类型的文件夹路径转换为pathlib.Path对象。
第二步：使用glob()方法获取data_dir路径下的所有文件路径，并以列表形式存储在data_paths中。
第三步：通过split()函数对data_paths中的每个文件路径执行分割操作，获得各个文件所属的类别名称，并存储在classeNames中
第四步：打印classeNames列表，显示每个文件所属的类别名称。

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。
])

train_dataset = datasets.ImageFolder("../Data/运动鞋品牌识别数据/train/",transform=train_transforms)
test_dataset  = datasets.ImageFolder("../Data/运动鞋品牌识别数据/test/",transform=train_transforms)

train_dataset.class_to_idx

{'adidas': 0, 'nike': 1}

3. 划分数据集

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)

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

Shape of X [N, C, H, W]:  torch.Size([32, 3, 224, 224])
Shape of y:  torch.Size([32]) torch.int64

二、构建简单的CNN网络

对于一般的CNN网络来说，都是由特征提取网络和分类网络构成，其中特征提取网络用于提取图片的特征，分类网络用于将图片进行分类。

网络结构图

在这里插入图片描述

import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1=nn.Sequential(
            nn.Conv2d(3, 12, kernel_size=5, padding=0), # 12*220*220
            nn.BatchNorm2d(12),
            nn.ReLU())
        
        self.conv2=nn.Sequential(
            nn.Conv2d(12, 12, kernel_size=5, padding=0), # 12*216*216
            nn.BatchNorm2d(12),
            nn.ReLU())
        
        self.pool3=nn.Sequential(
            nn.MaxPool2d(2))                              # 12*108*108
        
        self.conv4=nn.Sequential(
            nn.Conv2d(12, 24, kernel_size=5, padding=0), # 24*104*104
            nn.BatchNorm2d(24),
            nn.ReLU())
        
        self.conv5=nn.Sequential(
            nn.Conv2d(24, 24, kernel_size=5, padding=0), # 24*100*100
            nn.BatchNorm2d(24),
            nn.ReLU())
        
        self.pool6=nn.Sequential(
            nn.MaxPool2d(2))                              # 24*50*50

        self.dropout = nn.Sequential(
            nn.Dropout(0.2))
        
        self.fc=nn.Sequential(
            nn.Linear(24*50*50, len(classeNames)))
        
    def forward(self, x):
        
        batch_size = x.size(0)
        x = self.conv1(x)  # 卷积-BN-激活
        x = self.conv2(x)  # 卷积-BN-激活
        x = self.pool3(x)  # 池化
        x = self.conv4(x)  # 卷积-BN-激活
        x = self.conv5(x)  # 卷积-BN-激活
        x = self.pool6(x)  # 池化
        x = self.dropout(x)
        x = x.view(batch_size, -1)  # flatten 变成全连接网络需要的输入 (batch, 24*50*50) ==> (batch, -1), -1 此处自动算出的是24*50*50
        x = self.fc(x)

        return x

device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))

model = Model().to(device)
model

Model(
  (conv1): Sequential(
    (0): Conv2d(3, 12, kernel_size=(5, 5), stride=(1, 1))
    (1): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
  )
  (conv2): Sequential(
    (0): Conv2d(12, 12, kernel_size=(5, 5), stride=(1, 1))
    (1): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
  )
  (pool3): Sequential(
    (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (conv4): Sequential(
    (0): Conv2d(12, 24, kernel_size=(5, 5), stride=(1, 1))
    (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
  )
  (conv5): Sequential(
    (0): Conv2d(24, 24, kernel_size=(5, 5), stride=(1, 1))
    (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
  )
  (pool6): Sequential(
    (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (dropout): Sequential(
    (0): Dropout(p=0.2, inplace=False)
  )
  (fc): Sequential(
    (0): Linear(in_features=60000, out_features=2, bias=True)
  )
)

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
    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. 设置动态学习率

def adjust_learning_rate(optimizer, epoch, start_lr):
    # 每 2 个epoch衰减到原来的 0.98
    lr = start_lr * (0.92 ** (epoch // 2))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

learn_rate = 1e-4 # 初始学习率
optimizer  = torch.optim.SGD(model.parameters(), lr=learn_rate)

4. 正式训练

loss_fn    = nn.CrossEntropyLoss() # 创建损失函数
epochs     = 40

train_loss = []
train_acc  = []
test_loss  = []
test_acc   = []

for epoch in range(epochs):
    # 更新学习率（使用自定义学习率时使用）
    adjust_learning_rate(optimizer, epoch, learn_rate)
    
    model.train()
    epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)
    # scheduler.step() # 更新学习率（调用官方动态学习率接口时使用）
    
    model.eval()
    epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
    
    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))
print('Done')

Epoch: 1, Train_acc:52.6%, Train_loss:0.744, Test_acc:50.0%, Test_loss:0.716, Lr:1.00E-04
Epoch: 2, Train_acc:59.0%, Train_loss:0.690, Test_acc:67.1%, Test_loss:0.618, Lr:1.00E-04
Epoch: 3, Train_acc:64.1%, Train_loss:0.627, Test_acc:61.8%, Test_loss:0.637, Lr:9.20E-05
Epoch: 4, Train_acc:67.9%, Train_loss:0.588, Test_acc:77.6%, Test_loss:0.584, Lr:9.20E-05
Epoch: 5, Train_acc:74.7%, Train_loss:0.539, Test_acc:73.7%, Test_loss:0.553, Lr:8.46E-05
Epoch: 6, Train_acc:76.3%, Train_loss:0.516, Test_acc:76.3%, Test_loss:0.528, Lr:8.46E-05
Epoch: 7, Train_acc:77.1%, Train_loss:0.495, Test_acc:80.3%, Test_loss:0.533, Lr:7.79E-05
Epoch: 8, Train_acc:77.3%, Train_loss:0.491, Test_acc:76.3%, Test_loss:0.548, Lr:7.79E-05
Epoch: 9, Train_acc:78.1%, Train_loss:0.457, Test_acc:76.3%, Test_loss:0.516, Lr:7.16E-05
Epoch:10, Train_acc:83.1%, Train_loss:0.436, Test_acc:73.7%, Test_loss:0.513, Lr:7.16E-05
Epoch:11, Train_acc:81.5%, Train_loss:0.442, Test_acc:77.6%, Test_loss:0.525, Lr:6.59E-05
Epoch:12, Train_acc:83.3%, Train_loss:0.423, Test_acc:75.0%, Test_loss:0.552, Lr:6.59E-05
Epoch:13, Train_acc:82.3%, Train_loss:0.418, Test_acc:77.6%, Test_loss:0.477, Lr:6.06E-05
Epoch:14, Train_acc:85.3%, Train_loss:0.403, Test_acc:76.3%, Test_loss:0.513, Lr:6.06E-05
Epoch:15, Train_acc:86.1%, Train_loss:0.387, Test_acc:78.9%, Test_loss:0.509, Lr:5.58E-05
Epoch:16, Train_acc:87.5%, Train_loss:0.372, Test_acc:80.3%, Test_loss:0.486, Lr:5.58E-05
Epoch:17, Train_acc:88.2%, Train_loss:0.358, Test_acc:75.0%, Test_loss:0.460, Lr:5.13E-05
Epoch:18, Train_acc:88.2%, Train_loss:0.359, Test_acc:77.6%, Test_loss:0.469, Lr:5.13E-05
Epoch:19, Train_acc:88.6%, Train_loss:0.360, Test_acc:78.9%, Test_loss:0.504, Lr:4.72E-05
Epoch:20, Train_acc:89.4%, Train_loss:0.357, Test_acc:78.9%, Test_loss:0.480, Lr:4.72E-05
Epoch:21, Train_acc:90.4%, Train_loss:0.341, Test_acc:78.9%, Test_loss:0.475, Lr:4.34E-05
Epoch:22, Train_acc:90.2%, Train_loss:0.335, Test_acc:78.9%, Test_loss:0.481, Lr:4.34E-05
Epoch:23, Train_acc:89.4%, Train_loss:0.335, Test_acc:77.6%, Test_loss:0.491, Lr:4.00E-05
Epoch:24, Train_acc:91.4%, Train_loss:0.320, Test_acc:78.9%, Test_loss:0.469, Lr:4.00E-05
Epoch:25, Train_acc:92.6%, Train_loss:0.324, Test_acc:78.9%, Test_loss:0.485, Lr:3.68E-05
Epoch:26, Train_acc:92.4%, Train_loss:0.313, Test_acc:78.9%, Test_loss:0.478, Lr:3.68E-05
Epoch:27, Train_acc:91.8%, Train_loss:0.307, Test_acc:77.6%, Test_loss:0.436, Lr:3.38E-05
Epoch:28, Train_acc:90.4%, Train_loss:0.313, Test_acc:77.6%, Test_loss:0.480, Lr:3.38E-05
Epoch:29, Train_acc:93.0%, Train_loss:0.302, Test_acc:76.3%, Test_loss:0.485, Lr:3.11E-05
Epoch:30, Train_acc:92.2%, Train_loss:0.306, Test_acc:78.9%, Test_loss:0.438, Lr:3.11E-05
Epoch:31, Train_acc:92.4%, Train_loss:0.306, Test_acc:77.6%, Test_loss:0.455, Lr:2.86E-05
Epoch:32, Train_acc:92.6%, Train_loss:0.299, Test_acc:78.9%, Test_loss:0.425, Lr:2.86E-05
Epoch:33, Train_acc:91.6%, Train_loss:0.299, Test_acc:77.6%, Test_loss:0.524, Lr:2.63E-05
Epoch:34, Train_acc:93.6%, Train_loss:0.290, Test_acc:78.9%, Test_loss:0.477, Lr:2.63E-05
Epoch:35, Train_acc:94.0%, Train_loss:0.290, Test_acc:78.9%, Test_loss:0.455, Lr:2.42E-05
Epoch:36, Train_acc:93.4%, Train_loss:0.282, Test_acc:78.9%, Test_loss:0.453, Lr:2.42E-05
Epoch:37, Train_acc:94.2%, Train_loss:0.281, Test_acc:78.9%, Test_loss:0.457, Lr:2.23E-05
Epoch:38, Train_acc:94.0%, Train_loss:0.289, Test_acc:78.9%, Test_loss:0.449, Lr:2.23E-05
Epoch:39, Train_acc:94.2%, Train_loss:0.279, Test_acc:77.6%, Test_loss:0.435, Lr:2.05E-05
Epoch:40, Train_acc:93.8%, Train_loss:0.280, Test_acc:77.6%, Test_loss:0.425, Lr:2.05E-05
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

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()

在这里插入图片描述

2. 指定图片进行预测

from PIL import Image 

classes = list(train_dataset.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}')

# 预测训练集中的某张照片
predict_one_image(image_path='../Data/运动鞋品牌识别数据/test/nike/31.jpg', 
                    model=model, 
                    transform=train_transforms, 
                    classes=classes)

预测结果是：nike

五、保存并加载模型

# 模型保存
PATH = './model/shoeBrands_model.pth'  # 保存的参数文件名
torch.save(model.state_dict(), PATH)

# 将参数加载到model当中
model.load_state_dict(torch.load(PATH, map_location=device))

六、动态学习率

1.torch.optim.lr_scheduler.StepLR

等间隔动态调整方法，每经过step_size个epoch，做一次学习率decay，以gamma值为缩小倍数。

函数原型：torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=-1)
参数：
- optimizer(Optimizer)：要调整学习率的优化器
- step_size(int)：学习率调整的间隔epoch数
- gamma(float)：学习率调整的缩减比例
- last_epoch(int)：上一次调整学习率的时间点，默认为-1
代码示例：

optimizer = torch.optim.SGD(net.parameters(), lr=0.001 )

scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)

2.torch.optim.lr_scheduler.LambdaLR

根据给定的函数动态调整学习率。

函数原型：torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda, last_epoch=-1)
参数：
- optimizer(Optimizer)：要调整学习率的优化器
- lr_lambda(function)：根据epoch返回一个值，作为学习率的倍数
- last_epoch(int)：上一次调整学习率的时间点，默认为-1
代码示例：

lambda1 = lambda epoch: (0.92 ** (epoch // 2) # 第二组参数的调整方法

optimizer = torch.optim.SGD(model.parameters(), lr=learn_rate)

scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1) #选定调整方法

3.torch.optim.lr_scheduler.MultiStepLR

等间隔动态调整方法，在指定的epoch位置做一次学习率decay，以gamma值为缩小倍数。

函数原型：torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma=0.1, last_epoch=-1)
参数：
- optimizer(Optimizer)：要调整学习率的优化器
- milestones(list)：学习率调整的epoch位置
- gamma(float)：学习率调整的缩减比例
- last_epoch(int)：上一次调整学习率的时间点，默认为-1
代码示例：

optimizer = torch.optim.SGD(net.parameters(), lr=0.001 )

scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,milestones=[2,6,15],gamma=0.1)

七、个人收获

在这个项目中，我首先准备了数据，包括设置GPU环境、导入数据、划分数据集等。然后构建了一个简单的CNN网络，用于对运动鞋品牌进行识别。接着，我编写了训练函数和测试函数，用于训练模型和评估模型性能。在训练过程中，我还使用了动态学习率的方法，通过调整学习率来优化模型训练过程。最后，我展示了训练过程中的损失和准确率的变化情况，并对模型进行了保存和加载，以便后续的使用。

通过这个项目，我深入了解了深度学习模型的训练流程，包括数据准备、模型构建、训练和评估，以及模型的保存和加载。同时，动态学习率的应用也丰富了我的训练优化方法的知识储备。这些知识将对我未来的深度学习项目产生积极的影响。

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