第P10周：Pytorch实现车牌识别
- 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
- 🍖 原作者：K同学啊
在之前的案例中，我们多是使用datasets.ImageFolder函数直接导入已经分类好的数据集形成Dataset，然后使用DataLoader加载Dataset，但是如果对无法分类的数据集，我们如何导入，并进行识别呢？
本周我将自定义一个MyDataset加载车牌数据集并完成车牌识别
🍺 基础要求：
- 学习并理解本文
🍺 拔高要求：
- 对单张车牌进行识别
🏡我的环境：
- 语言环境：Python3.8
- 编译器：Jupyter Lab
- 深度学习环境：
- torch==2.2.2
- torchvision==0.17.2
前期准备
- 如果设备上支持GPU就使用GPU,否则使用CPU
- Mac上的GPU使用mps
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
from torchvision import datasets
import torchvision.models as models
import torch.nn.functional as F
import torch.nn as nn
import torch,torchvisionimport os,PIL,pathlib,warningswarnings.filterwarnings("ignore") #忽略警告信息# this ensures that the current MacOS version is at least 12.3+
print(torch.backends.mps.is_available())# this ensures that the current current PyTorch installation was built with MPS activated.
print(torch.backends.mps.is_built())# 设置硬件设备，如果有GPU则使用，没有则使用cpu
device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
device # # 使用的是GPU
True
True
device(type='mps')
一、导入数据
1.1. 获取类别名
import os,PIL,random,pathlibdata_dir = './data/p10/015_licence_plate/'
data_dir = pathlib.Path(data_dir)data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("/")[-1].split("_")[1].split(".")[0] for path in data_paths]
classeNames[:3]
['沪G1CE81', '云G86LR6', '鄂U71R9F']
data_paths = list(data_dir.glob('*'))
data_paths_str = [str(path) for path in data_paths]
data_paths_str[:3]
['data/p10/015_licence_plate/000008250_沪G1CE81.jpg','data/p10/015_licence_plate/000015082_云G86LR6.jpg','data/p10/015_licence_plate/000004721_鄂U71R9F.jpg']
1.2. 数据可视化
import os,PIL,random,pathlib
import matplotlib.pyplot as pltplt.figure(figsize=(14,5))
plt.suptitle("数据示例",fontsize=15)for i in range(18):plt.subplot(3,6,i+1)# 显示图片images = plt.imread(data_paths_str[i])plt.imshow(images)plt.show()
1.3. 标签数字化
import numpy as npchar_enum = ["京","沪","津","渝","冀","晋","蒙","辽","吉","黑","苏","浙","皖","闽","赣","鲁",\"豫","鄂","湘","粤","桂","琼","川","贵","云","藏","陕","甘","青","宁","新","军","使"]number = [str(i) for i in range(0, 10)] # 0 到 9 的数字
alphabet = [chr(i) for i in range(65, 91)] # A 到 Z 的字母char_set = char_enum + number + alphabet
char_set_len = len(char_set)
label_name_len = len(classeNames[0])# 将字符串数字化
def text2vec(text):vector = np.zeros([label_name_len, char_set_len])for i, c in enumerate(text):idx = char_set.index(c)vector[i][idx] = 1.0return vectorall_labels = [text2vec(i) for i in classeNames]
1.4. 加载数据文件
import os
import pandas as pd
from torchvision.io import read_image
from torch.utils.data import Dataset
import torch.utils.data as data
from PIL import Imageclass MyDataset(data.Dataset):def __init__(self, all_labels, data_paths_str, transform):self.img_labels = all_labels # 获取标签信息self.img_dir = data_paths_str # 图像目录路径self.transform = transform # 目标转换函数def __len__(self):return len(self.img_labels)def __getitem__(self, index):image = Image.open(self.img_dir[index]).convert('RGB')#plt.imread(self.img_dir[index]) # 使用 torchvision.io.read_image 读取图像label = self.img_labels[index] # 获取图像对应的标签if self.transform:image = self.transform(image)return image, label # 返回图像和标签
# 关于transforms.Compose的更多介绍可以参考：https://blog.csdn.net/qq_38251616/article/details/124878863
train_transforms = 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 = MyDataset(all_labels, data_paths_str, train_transforms)
total_data
<__main__.MyDataset at 0x16cbaa430>
1.5. 划分数据
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_size,test_size
(10940, 2735)
train_loader = torch.utils.data.DataLoader(train_dataset,batch_size=16,shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset,batch_size=16,shuffle=True)print("The number of images in a training set is: ", len(train_loader)*16)
print("The number of images in a test set is: ", len(test_loader)*16)
print("The number of batches per epoch is: ", len(train_loader))
The number of images in a training set is: 10944
The number of images in a test set is: 2736
The number of batches per epoch is: 684
for X, y in test_loader: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([16, 3, 224, 224])
Shape of y: torch.Size([16, 7, 69]) torch.float64
二、自建模型
2.1. 搭建模型
class Network_bn(nn.Module):def __init__(self):super(Network_bn, self).__init__()"""nn.Conv2d()函数：第一个参数（in_channels）是输入的channel数量第二个参数（out_channels）是输出的channel数量第三个参数（kernel_size）是卷积核大小第四个参数（stride）是步长，默认为1第五个参数（padding）是填充大小，默认为0"""self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=5, stride=1, padding=0)self.bn1 = nn.BatchNorm2d(12)self.conv2 = nn.Conv2d(in_channels=12, out_channels=12, kernel_size=5, stride=1, padding=0)self.bn2 = nn.BatchNorm2d(12)self.pool = nn.MaxPool2d(2,2)self.conv4 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=5, stride=1, padding=0)self.bn4 = nn.BatchNorm2d(24)self.conv5 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=5, stride=1, padding=0)self.bn5 = nn.BatchNorm2d(24)self.fc1 = nn.Linear(24*50*50, label_name_len*char_set_len)self.reshape = Reshape([label_name_len,char_set_len])def forward(self, x):x = F.relu(self.bn1(self.conv1(x))) x = F.relu(self.bn2(self.conv2(x))) x = self.pool(x) x = F.relu(self.bn4(self.conv4(x))) x = F.relu(self.bn5(self.conv5(x))) x = self.pool(x) x = x.view(-1, 24*50*50)x = self.fc1(x)# 最终reshapex = self.reshape(x)return x# 定义Reshape层
class Reshape(nn.Module):def __init__(self, shape):super(Reshape, self).__init__()self.shape = shapedef forward(self, x):return x.view(x.size(0), *self.shape)print("Using {} device".format(device))model = Network_bn().to(device)
model
Using mps deviceNetwork_bn((conv1): Conv2d(3, 12, kernel_size=(5, 5), stride=(1, 1))(bn1): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(12, 12, kernel_size=(5, 5), stride=(1, 1))(bn2): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(conv4): Conv2d(12, 24, kernel_size=(5, 5), stride=(1, 1))(bn4): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv5): Conv2d(24, 24, kernel_size=(5, 5), stride=(1, 1))(bn5): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(fc1): Linear(in_features=60000, out_features=483, bias=True)(reshape): Reshape()
)
2.2. 查看模型详情
!pip install torchsummary
Looking in indexes: https://mirrors.aliyun.com/pypi/simple
Requirement already satisfied: torchsummary in /Users/henry/src/miniconda3/lib/python3.8/site-packages (1.5.1)
# 统计模型参数量以及其他指标
import torchsummary as summarysummary.summary(model.to("cpu"), (3, 224, 224))
----------------------------------------------------------------Layer (type) Output Shape Param #
================================================================Conv2d-1 [-1, 12, 220, 220] 912BatchNorm2d-2 [-1, 12, 220, 220] 24Conv2d-3 [-1, 12, 216, 216] 3,612BatchNorm2d-4 [-1, 12, 216, 216] 24MaxPool2d-5 [-1, 12, 108, 108] 0Conv2d-6 [-1, 24, 104, 104] 7,224BatchNorm2d-7 [-1, 24, 104, 104] 48Conv2d-8 [-1, 24, 100, 100] 14,424BatchNorm2d-9 [-1, 24, 100, 100] 48MaxPool2d-10 [-1, 24, 50, 50] 0Linear-11 [-1, 483] 28,980,483Reshape-12 [-1, 7, 69] 0
================================================================
Total params: 29,006,799
Trainable params: 29,006,799
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 26.56
Params size (MB): 110.65
Estimated Total Size (MB): 137.79
----------------------------------------------------------------
注意对比观察模型的输出[-1, 7, 69]，我们之前的网络结构输出都是[-1, 7]、[-1, 2]、[-1, 4]这样的二维数据，如果要求模型输出结果是多维数据，那么本案例将是很好的示例。
📮提问：[-1, 7, 69]中的-1是什么意思？
在神经网络中，如果我们不确定一个维度的大小，但是希望在计算中自动推断它，可以使用 -1。这个-1告诉 PyTorch 在计算中自动推断这个维度的大小，以确保其他维度的尺寸不变，并且能够保持张量的总大小不变。
例如，[-1, 7, 69]表示这个张量的形状是一个三维张量，其中第一个维度的大小是不确定的，第二维大小为7，第三大小分别为69。-1的作用是使得总的张量大小等于7 * 69，以适应实际的输入数据大小。
在实际的使用中，通常-1用在批处理维度上，因为在训练过程中，批处理大小可能会有所不同。使用-1可以使模型适应不同大小的批处理输入数据。
三、 训练模型
3.1. 优化器与损失函数
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=0.0001)loss_model = nn.CrossEntropyLoss()
本周任务之一：在下面的代码中我对loss进行了统计更新，请补充acc统计更新部分，即获取每一次测试的ACC值。
任务提示：pred.shape与y.shape是：[batch, 7, 69]，在进行acc计算时需注意～
from torch.autograd import Variabledef test(model, test_loader, loss_model):size = len(test_loader.dataset)num_batches = len(test_loader)model.eval()test_loss, correct = 0, 0with torch.no_grad():for X, y in test_loader:X, y = X.to(device), y.to(device)pred = model(X)test_loss += loss_model(pred, y).item()test_loss /= num_batchesprint(f"Avg loss: {test_loss:>8f} \n")return correct,test_lossdef train(model,train_loader,loss_model,optimizer):model=model.to(device)model.train()for i, (images, labels) in enumerate(train_loader, 0): #0是标起始位置的值。images = Variable(images.to(device))labels = Variable(labels.to(device))optimizer.zero_grad()outputs = model(images)loss = loss_model(outputs, labels)loss.backward()optimizer.step()if i % 1000 == 0: print('[%5d] loss: %.3f' % (i, loss))
3.2. 模型的训练
test_acc_list = []
test_loss_list = []
epochs = 30for t in range(epochs):print(f"Epoch {t+1}\n--device-----------------------------")train(model,train_loader,loss_model,optimizer)test_acc,test_loss = test(model, test_loader, loss_model)test_acc_list.append(test_acc)test_loss_list.append(test_loss)
print("Done!")
Epoch 1
-------------------------------
[ 0] loss: 0.213
Avg loss: 0.071063 Epoch 2
-------------------------------
[ 0] loss: 0.033
Avg loss: 0.057604 ......Epoch 30
-------------------------------
[ 0] loss: 0.014
Avg loss: 0.026364 Done!
四、 结果分析
import numpy as np
import matplotlib.pyplot as pltx = [i for i in range(1,31)]plt.plot(x, test_loss_list, label="Loss", alpha=0.8)plt.xlabel("Epoch")
plt.ylabel("Loss")plt.legend()
plt.show()
