- 🍨 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,torchvision device = 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 np char_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 Image class 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reshape x = self.reshape(x) return x # 定义Reshape层 class Reshape(nn.Module): def __init__(self, shape): super(Reshape, self).__init__() self.shape = shape def 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 torchsummary torchsummary.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 Variable def test(model, test_loader, loss_model): size = len(test_loader.dataset) num_batches = len(test_loader) model.eval() test_loss, correct = 0, 0 with 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_batches correct /=size print(f"Avg loss: {test_loss:>8f} \n") return correct,test_loss def 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 = labels outputs = 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 = 30 for 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 plt from 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