食物图像分类代码实战

前言延续之前所讲基本上项目代码都是数据集的读入和处理模型定义、训练之前的各种准备设置以及训练流程那么接下来也是按照这个顺序进行。数据集读入和处理train_transform transforms.Compose( [ transforms.ToPILImage(), # Convert image to PIL format (224,224,3) - (3,224,224) transforms.RandomResizedCrop(224), # Random crop and resize to 224x224 transforms.RandomRotation(50), # Apply random rotation up to 50 degrees transforms.ToTensor() # Convert to tensor ] ) val_transform transforms.Compose( [ transforms.ToPILImage(), # Convert image to PIL format transforms.ToTensor() # Convert to tensor ] )图像数据集有点区别于前面的回归模型特征数据集通常在读入数据集阶段可以选用数据增强技术对图像处理这种技术对图片进行随机放大裁剪旋转等操作通过内置强化学习算法自动选择最优方式这个过程类似在分类任务中让模型见识不同角度各种各样的某类物体可以提高模型识别能力。另一方面也可以拓宽训练集抑制模型过拟合。但是在测试阶段不使用该技术测试集上数据模型都没见过可以检验模型泛化能力。class FoodDataset(Dataset): def __init__(self, path, modetrain): self.mode mode self.transform train_transform if mode train else val_transform self.X, self.Y self._load_data(path) def _load_data(self, path): X, Y None, None for class_idx in range(11): class_dir os.path.join(path, f{class_idx:02d}) img_files os.listdir(class_dir) class_images np.zeros((len(img_files), HW, HW, 3), dtypenp.uint8) class_labels np.full(len(img_files), class_idx, dtypenp.uint8) for idx, filename in enumerate(img_files): img_path os.path.join(class_dir, filename) img Image.open(img_path).resize((HW, HW)) class_images[idx] img if class_idx 0: X, Y class_images, class_labels else: X np.concatenate((X, class_images), axis0) Y np.concatenate((Y, class_labels), axis0) print(fLoaded {len(Y)} samples) return X, Y def __getitem__(self, index): return self.transform(self.X[index]), self.Y[index] def __len__(self): return len(self.X)这次项目训练集和测试集在不同文件因此不需要像之前一样拆分直接根据mode用读取不同的文件即可。不同项目的实现方式各有差异文件读取功能会根据数据集在本地的存储路径进行配置。Dataset类负责数据读取将原始数据转换为三维数值表示而Dataloader则用于处理这些数据集如批量加载数据支持数据打乱和多批次处理功能。模型定义class MyModel(nn.Module): def __init__(self, num_class): super(MyModel, self).__init__() # Input: 3x224x224 - Output: 512x7x7 - Flatten - Fully connected layers # Initial convolution block self.conv1 nn.Conv2d(3, 64, kernel_size3, stride1, padding1) # Output: 64x224x224 self.bn1 nn.BatchNorm2d(64) self.relu nn.ReLU() self.pool1 nn.MaxPool2d(2) # Output: 64x112x112 # Feature extraction layers self.layer1 nn.Sequential( nn.Conv2d(64, 128, kernel_size3, stride1, padding1), # 128x112x112 nn.BatchNorm2d(128), nn.ReLU(), nn.MaxPool2d(2) # 128x56x56 ) self.layer2 nn.Sequential( nn.Conv2d(128, 256, kernel_size3, stride1, padding1), # 256x56x56 nn.BatchNorm2d(256), nn.ReLU(), nn.MaxPool2d(2) # 256x28x28 ) self.layer3 nn.Sequential( nn.Conv2d(256, 512, kernel_size3, stride1, padding1), # 512x28x28 nn.BatchNorm2d(512), nn.ReLU(), nn.MaxPool2d(2) # 512x14x14 ) # Final pooling and classifier self.pool2 nn.MaxPool2d(2) # 512x7x7 self.fc1 nn.Linear(512*7*7, 1000) # 25088 - 1000 self.relu2 nn.ReLU() self.fc2 nn.Linear(1000, num_class) # 1000 - num_class def forward(self, x): x self.pool1(self.relu(self.bn1(self.conv1(x)))) x self.layer1(x) x self.layer2(x) x self.layer3(x) x self.pool2(x) x x.view(x.size(0), -1) # Flatten x self.relu2(self.fc1(x)) x self.fc2(x) return x模型定义相对简单且相似。值得一提的是数据作为新时代的石油资源我们训练的模型通常难以与投入数百万美元训练的大模型相媲美因此可以采用迁移学习策略。迁移学习主要分为微调和线性探测两种方式二者的核心区别在于是否冻结主干网络的参数。训练流程def train_val(model, train_loader, val_loader, no_label_loader, device, epochs, optimizer, loss, thres, save_path): model model.to(device) plt_train_loss [] plt_val_loss [] plt_train_acc [] plt_val_acc [] max_acc 0.0 for epoch in range(epochs): train_loss 0.0 val_loss 0.0 train_acc 0.0 val_acc 0.0 start_time time.time() # Training phase model.train() for batch_x, batch_y in train_loader: x, target batch_x.to(device), batch_y.to(device) pred model(x) train_bat_loss loss(pred, target) train_bat_loss.backward() optimizer.step() optimizer.zero_grad() train_loss train_bat_loss.item() train_acc (pred.argmax(dim1) target).sum().item() avg_train_loss train_loss / len(train_loader) avg_train_acc train_acc / len(train_loader.dataset) plt_train_loss.append(avg_train_loss) plt_train_acc.append(avg_train_acc) # Validation phase model.eval() with torch.no_grad(): for batch_x, batch_y in val_loader: x, target batch_x.to(device), batch_y.to(device) pred model(x) val_bat_loss loss(pred, target) val_loss val_bat_loss.item() val_acc (pred.argmax(dim1) target).sum().item() avg_val_loss val_loss / len(val_loader) avg_val_acc val_acc / len(val_loader.dataset) plt_val_loss.append(avg_val_loss) plt_val_acc.append(avg_val_acc) # Semi-supervised learning if epoch % 3 0 and avg_val_acc 0.6: semi_loader get_semi_loader(no_label_loader, model, device, thres) # Save best model if avg_val_acc max_acc: torch.save(model, save_path) max_acc avg_val_acc # Print progress elapsed time.time() - start_time print(f[{epoch:03d}/{epochs:03d}] {elapsed:.2f}s | fTrainLoss: {avg_train_loss:.6f} | ValLoss: {avg_val_loss:.6f} | fTrainAcc: {avg_train_acc:.6f} | ValAcc: {avg_val_acc:.6f}) # Plot training curves plt.figure(figsize(12, 4)) plt.subplot(1, 2, 1) plt.plot(plt_train_loss, labelTrain) plt.plot(plt_val_loss, labelVal) plt.title(Loss Curve) plt.legend() plt.subplot(1, 2, 2) plt.plot(plt_train_acc, labelTrain) plt.plot(plt_val_acc, labelVal) plt.title(Accuracy Curve) plt.legend() plt.show()主干训练流程也是大体相似这里和回归模型主要区别在于多了计算准确率识别正确图片占比因为分类任务输出 label 可以直接知道整体预测正确率。