- 🍨 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
一.导入数据
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,torchvisiondevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
1.获取类别名
import os,PIL,random,pathlib
import matplotlib.pyplot as plt
# 支持中文
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data_dir = '../data/015_licence_plate/'
data_dir = pathlib.Path(data_dir)data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[3].split("_")[1].split(".")[0] for path in data_paths]
print(classeNames)
data_paths = list(data_dir.glob('*'))
data_paths_str = [str(path) for path in data_paths]
data_paths_str
2.数据可视化
plt.figure(figsize=(14,5))for i in range(18):plt.subplot(3,6,i+1)images = plt.imread(data_paths_str[i])plt.imshow(images)plt.show()
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):return [char_set.index(c) for c in text]all_labels = [text2vec(i) for i in classeNames]
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')label = self.img_labels[index]if self.transform:image = self.transform(image)return image, torch.tensor(label, dtype=torch.long)
total_datadir = './03_traffic_sign/'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]) ])total_data = MyDataset(all_labels, data_paths_str, train_transforms) total_data
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
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))
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
二.自建模型
class Network_bn(nn.Module):def __init__(self):super(Network_bn, self).__init__()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)device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))model = Network_bn().to(device)
model
import torchsummarytorchsummary.summary(model, (3, 224, 224))
三.模型训练
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()
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)pred=pred.view(-1, char_set_len)y=y.view(-1)test_loss += loss_model(pred, y).item()correct +=(pred.argmax(1)==y).type(torch.float).sum().item()test_loss /= num_batchescorrect /=sizeprint(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):images = Variable(images.to(device))labels = Variable(labels.to(device))optimizer.zero_grad()outputs = model(images)labels = labelsoutputs = outputs.view(-1, char_set_len)labels = labels.view(-1)loss = loss_model(outputs, labels)loss.backward()optimizer.step()if i % 1000 == 0:print('[%5d] loss: %.3f' % (i, loss))
test_acc_list = []
test_loss_list = []
epochs = 30for t in range(epochs):print(f"Epoch {t+1}\n-------------------------------")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!")
import numpy as np
import matplotlib.pyplot as pltfrom datetime import datetime
current_time = datetime.now() # 获取当前时间x = [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.title(current_time)plt.legend()
plt.show()
对无法使用 datasets.ImageFolder 导入的非预分类数据集，通过自定义 Dataset 类手动加载图像路径与标签，实现对原始数据的灵活读取与转换，使模型能够适用于更复杂、更自由的数据组织方式