深度学习 Day29——利用Pytorch实现咖啡豆识别

深度学习 Day29——利用Pytorch实现咖啡豆识别

一、前言

本文为365天深度学习训练营 中的学习记录博客

参考文章：Pytorch实战 | 第P7周：咖啡豆识别(训练营内部成员可读)

原作者：K同学啊|接辅导、项目定制

本期博客我们将探索完成使用Pytorch框架搭建VGG16网络模型进行咖啡豆的识别任务。

二、我的环境

• 电脑系统：Windows 11
• 语言环境：Python 3.8.5
• 编译器：DataSpell
• 深度学习环境：
  • torch 1.12.1+cu113
  • torchvision 0.13.1+cu113
• 显卡及显存：RTX 3070 8G

三、前期工作

1、导入依赖项设置GPU

import torch 
import torch.nn as nn 
import torchvision.transforms as transforms 
import torchvision 
from torchvision import transforms, datasets 
import os,PIL,pathlib,warnings  
warnings.filterwarnings("ignore")             #忽略警告信息  
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
device

device(type='cuda')

2、导入数据

import os,PIL,random,pathlib  
data_dir = 'E:\深度学习\data\Day16' 
data_dir = pathlib.Path(data_dir)  
data_paths  = list(data_dir.glob('*')) 
classeNames = [str(path).split("\\")[4] for path in data_paths] 
classeNames

['Dark', 'Green', 'Light', 'Medium']

使用transforms.Compose对数据进行预处理的方法，包括将输入图片resize成统一尺寸、归一化处理等。其中，train_transforms和test_transform是训练集和测试集的预处理方法，total_data是处理后的数据集。

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。
])

total_data = datasets.ImageFolder(data_dir,transform=train_transforms)
total_data

Dataset ImageFolder
    Number of datapoints: 1200
    Root location: E:\深度学习\data\Day16
    StandardTransform
Transform: Compose(
               Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=None)
               ToTensor()
               Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
           )

total_data.class_to_idx     # 查看类别对应的索引

{'Dark': 0, 'Green': 1, 'Light': 2, 'Medium': 3}

3、划分数据集

将数据集分为训练集和测试集，首先通过数据总量的80%来计算训练集大小，然后用总量减去训练集大小得到测试集大小，最后使用PyTorch的random_split函数将数据集随机分为训练集和测试集。

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

(<torch.utils.data.dataset.Subset at 0x2168b369670>,
 <torch.utils.data.dataset.Subset at 0x2168b369df0>)

定义两个数据加载器，分别是train_dl和test_dl。每个加载器都有一个batch_size参数，用于指定每个批次的大小。此外，还有shuffle和num_workers参数，用于打乱数据集并指定使用的线程数。

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)

使用test_dl数据集进行迭代，输出X和y的形状和数据类型。其中X的形状为[N, C, H, W]，y的形状和数据类型则分别输出。

for x, y in test_dl:
    print(x.shape, y.shape)
    break

torch.Size([32, 3, 224, 224]) torch.Size([32])

四、手动搭建VGG16模型

1、模型搭建

import torch.nn.functional as F

class vgg16(nn.Module):
    def __init__(self):
        super(vgg16, self).__init__()
        # 卷积块1
        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))
        )
        # 卷积块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))
        )
        # 卷积块3
        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))
        )
        # 卷积块4
        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))
        )
        # 卷积块5
        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))
        )
        
        # 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=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

device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))
    
model = vgg16().to(device)
model

Using cuda device
Output exceeds the size limit. Open the full output data in a text editor
vgg16(
  (block1): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU()
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU()
    (4): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
  )
  (block2): Sequential(
    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU()
    (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU()
    (4): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
  )
  (block3): Sequential(
    (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU()
    (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU()
    (4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (5): ReLU()
    (6): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
  )
  (block4): Sequential(
...
    (2): Linear(in_features=4096, out_features=4096, bias=True)
    (3): ReLU()
    (4): Linear(in_features=4096, out_features=4, bias=True)
  )
)

这是一个使用PyTorch实现的VGG16模型，包括5个卷积块和1个全连接网络层，用于分类。模型输入为3通道的图像，输出为4个类别的概率分布。模型使用ReLU作为激活函数，最大池化作为下采样方式。模型已移植到GPU上。

2、查看模型参数

# 查看模型参数
for name, param in model.named_parameters():
    print(name, '\t', param.shape)

block1.0.weight 	 torch.Size([64, 3, 3, 3])
block1.0.bias 	 torch.Size([64])
block1.2.weight 	 torch.Size([64, 64, 3, 3])
block1.2.bias 	 torch.Size([64])
block2.0.weight 	 torch.Size([128, 64, 3, 3])
block2.0.bias 	 torch.Size([128])
block2.2.weight 	 torch.Size([128, 128, 3, 3])
block2.2.bias 	 torch.Size([128])
block3.0.weight 	 torch.Size([256, 128, 3, 3])
block3.0.bias 	 torch.Size([256])
block3.2.weight 	 torch.Size([256, 256, 3, 3])
block3.2.bias 	 torch.Size([256])
block3.4.weight 	 torch.Size([256, 256, 3, 3])
block3.4.bias 	 torch.Size([256])
block4.0.weight 	 torch.Size([512, 256, 3, 3])
block4.0.bias 	 torch.Size([512])
block4.2.weight 	 torch.Size([512, 512, 3, 3])
block4.2.bias 	 torch.Size([512])
block4.4.weight 	 torch.Size([512, 512, 3, 3])
block4.4.bias 	 torch.Size([512])
block5.0.weight 	 torch.Size([512, 512, 3, 3])
block5.0.bias 	 torch.Size([512])
block5.2.weight 	 torch.Size([512, 512, 3, 3])
block5.2.bias 	 torch.Size([512])
block5.4.weight 	 torch.Size([512, 512, 3, 3])
...
classifier.2.weight 	 torch.Size([4096, 4096])
classifier.2.bias 	 torch.Size([4096])
classifier.4.weight 	 torch.Size([4, 4096])
classifier.4.bias 	 torch.Size([4])

3、调用官方的VGG16网络框架

可以使用 PyTorch 提供的 torchvision.models 模块中的 vgg16 函数来调用官方的 VGG16 网络框架。

import torch
import torch.nn as nn
import torchvision.models as models

# 加载预训练的 VGG16 模型
model = models.vgg16(pretrained=True)

# 将模型移动到 GPU 上
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(num_epochs):
    for images, labels in train_loader:
        images = images.to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy: {} %'.format(100 * correct / total))

我们首先加载了预训练的 VGG16 模型，然后将模型移动到 GPU 上，并定义了损失函数和优化器。在训练过程中，我们遍历训练集，计算损失并反向传播更新参数。在测试过程中，我们遍历测试集，计算模型的准确率。

五、训练模型

1、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

该函数为训练循环，输入数据集、模型、损失函数和优化器，返回训练损失和正确率。函数中进行了模型预测、损失计算、反向传播和参数更新等操作。最终计算并返回训练集的正确率和损失。

2、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

该函数用于测试模型在给定数据集上的准确率和损失。函数会遍历数据集并计算每个批次的损失和准确率，最后返回整个数据集的平均准确率和平均损失。

3、正式训练

import copy

optimizer = torch.optim.Adam(model.parameters(), lr= 1e-4)
loss_fn = nn.CrossEntropyLoss() # 创建损失函数

epochs = 40

train_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_model
    if epoch_test_acc > best_acc:
        best_acc = epoch_test_acc
        best_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')

Epoch: 1, Train_acc:25.8%, Train_loss:1.378, Test_acc:21.2%, Test_loss:1.311, Lr:1.00E-04
Epoch: 2, Train_acc:52.6%, Train_loss:1.062, Test_acc:58.3%, Test_loss:0.798, Lr:1.00E-04
Epoch: 3, Train_acc:59.4%, Train_loss:0.829, Test_acc:71.2%, Test_loss:0.765, Lr:1.00E-04
Epoch: 4, Train_acc:65.8%, Train_loss:0.695, Test_acc:71.7%, Test_loss:0.652, Lr:1.00E-04
Epoch: 5, Train_acc:74.7%, Train_loss:0.576, Test_acc:66.7%, Test_loss:0.577, Lr:1.00E-04
Epoch: 6, Train_acc:74.7%, Train_loss:0.549, Test_acc:77.5%, Test_loss:0.539, Lr:1.00E-04
Epoch: 7, Train_acc:81.4%, Train_loss:0.425, Test_acc:82.5%, Test_loss:0.370, Lr:1.00E-04
Epoch: 8, Train_acc:82.3%, Train_loss:0.381, Test_acc:85.4%, Test_loss:0.315, Lr:1.00E-04
Epoch: 9, Train_acc:84.8%, Train_loss:0.336, Test_acc:85.8%, Test_loss:0.320, Lr:1.00E-04
Epoch:10, Train_acc:87.8%, Train_loss:0.303, Test_acc:91.7%, Test_loss:0.205, Lr:1.00E-04
Epoch:11, Train_acc:90.4%, Train_loss:0.247, Test_acc:89.6%, Test_loss:0.349, Lr:1.00E-04
Epoch:12, Train_acc:94.2%, Train_loss:0.163, Test_acc:97.1%, Test_loss:0.062, Lr:1.00E-04
Epoch:13, Train_acc:96.1%, Train_loss:0.139, Test_acc:86.2%, Test_loss:0.367, Lr:1.00E-04
Epoch:14, Train_acc:95.0%, Train_loss:0.154, Test_acc:93.3%, Test_loss:0.203, Lr:1.00E-04
Epoch:15, Train_acc:97.5%, Train_loss:0.066, Test_acc:98.3%, Test_loss:0.027, Lr:1.00E-04
Epoch:16, Train_acc:96.9%, Train_loss:0.072, Test_acc:97.9%, Test_loss:0.079, Lr:1.00E-04
Epoch:17, Train_acc:98.0%, Train_loss:0.055, Test_acc:98.3%, Test_loss:0.032, Lr:1.00E-04
Epoch:18, Train_acc:98.5%, Train_loss:0.031, Test_acc:100.0%, Test_loss:0.008, Lr:1.00E-04
Epoch:19, Train_acc:99.0%, Train_loss:0.025, Test_acc:99.6%, Test_loss:0.021, Lr:1.00E-04
Epoch:20, Train_acc:98.0%, Train_loss:0.065, Test_acc:98.3%, Test_loss:0.045, Lr:1.00E-04
Epoch:21, Train_acc:96.6%, Train_loss:0.099, Test_acc:96.2%, Test_loss:0.112, Lr:1.00E-04
Epoch:22, Train_acc:99.5%, Train_loss:0.025, Test_acc:99.2%, Test_loss:0.015, Lr:1.00E-04
Epoch:23, Train_acc:99.8%, Train_loss:0.006, Test_acc:99.6%, Test_loss:0.012, Lr:1.00E-04
Epoch:24, Train_acc:95.2%, Train_loss:0.156, Test_acc:96.7%, Test_loss:0.114, Lr:1.00E-04
Epoch:25, Train_acc:98.9%, Train_loss:0.039, Test_acc:98.8%, Test_loss:0.033, Lr:1.00E-04
...
Epoch:38, Train_acc:99.8%, Train_loss:0.005, Test_acc:99.6%, Test_loss:0.016, Lr:1.00E-04
Epoch:39, Train_acc:100.0%, Train_loss:0.000, Test_acc:99.6%, Test_loss:0.007, Lr:1.00E-04
Epoch:40, Train_acc:100.0%, Train_loss:0.000, Test_acc:99.6%, Test_loss:0.006, Lr:1.00E-04
Done

六、Loss-Accuracy图可视化

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()

七、预测

from PIL import Image 

classes = 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}')
 # 预测训练集中的某张照片
predict_one_image(image_path='E:\深度学习\data\Day16\Medium\medium (1).png', model=model, transform=train_transforms, classes=classes)

八、模型评估

best_model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn)
epoch_test_acc, epoch_test_loss

(1.0, 0.009578780613082927)

我这里训练之后准确率达到了100%。

九、使用官方MobileNetV2 模型

import torch
import torch.nn as nn
import torchvision.models as models
num_epochs = 20
# 加载预训练的 MobileNetV2 模型
model = models.mobilenet_v2(pretrained=True)

# 将最后一层替换为新的全连接层
model.classifier = nn.Sequential(
    nn.Dropout(0.2),
    nn.Linear(1280, 4),
)

# 将模型移动到 GPU 上
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(num_epochs):
    for images, labels in train_dl:
        images = images.to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_dl:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy: {} %'.format(100 * correct / total))

MobileNetV2 模型和 VGG16 模型在网络结构上有很大的不同。MobileNetV2 模型采用了深度可分离卷积的设计，可以在保持较高准确率的同时，大幅度减小模型的参数量和计算量，因此更加轻量级。而 VGG16 模型则是传统的卷积神经网络，参数量和计算量相对较大。