| |
| """Generate ResNet18 notebook.""" |
|
|
| import nbformat as nbf |
|
|
| nb = nbf.v4.new_notebook() |
| nb.metadata = { |
| "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, |
| "language_info": {"name": "python", "version": "3.12.0"}, |
| } |
|
|
| cells = [] |
| def md(s): cells.append(nbf.v4.new_markdown_cell(s)) |
| def code(s): cells.append(nbf.v4.new_code_cell(s)) |
|
|
| md("""\ |
| # ResNet18: Residual Networks |
| |
| Deep residual learning with skip connections for image classification. |
| """) |
|
|
| md("""\ |
| ## 背景 |
| |
| ResNet（2015）解决了深度网络的退化问题：层数增加后训练误差反而上升。 |
| 核心创新是**残差连接（skip connection / shortcut）**： |
| |
| $$y = \\mathcal{F}(x) + x$$ |
| |
| 让梯度可以直接通过 shortcut 传播，使得训练上百层的网络成为可能。 |
| ResNet18 是其中最轻量的版本，用 4 个 stage 共 18 个卷积层。 |
| """) |
|
|
| md("""\ |
| ## 数学原理 |
| |
| ### 残差块 |
| |
| $$\\mathcal{F}(x) = \\text{Conv3×3} \\to \\text{BN} \\to \\text{ReLU} \\to \\text{Conv3×3} \\to \\text{BN}$$ |
| |
| $$\\text{Output} = \\text{ReLU}(\\mathcal{F}(x) + x)$$ |
| |
| 当维度不匹配时（跨 stage 下采样），shortcut 需额外 Conv1×1 来对齐： |
| |
| $$\\text{Output} = \\text{ReLU}(\\mathcal{F}(x) + W_s \\cdot x)$$ |
| |
| ### 架构 |
| |
| ``` |
| 7×7 Conv(stride=2) → BN → ReLU → 3×3 MaxPool(stride=2) |
| → [BasicBlock×2, 64] stage1 |
| → [BasicBlock×2, 128] stage2 |
| → [BasicBlock×2, 256] stage3 |
| → [BasicBlock×2, 512] stage4 |
| → AvgPool → FC(512 → 15) |
| ``` |
| """) |
|
|
| code("""\ |
| import torch |
| import torch.nn as nn |
| import torch.optim as optim |
| from torch.utils.data import DataLoader |
| from torchvision import transforms |
| |
| from cv.resnet18.data import CelebADataset, ATTRIBUTES |
| from cv.resnet18.model import resnet18 |
| from utils.device import get_device |
| |
| device = get_device() |
| print(f"Device: {device}") |
| """) |
|
|
| code("""\ |
| # 数据加载 |
| transform = transforms.Compose([ |
| transforms.Resize(256), |
| transforms.CenterCrop(224), |
| transforms.ToTensor(), |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
| ]) |
| |
| full_dataset = CelebADataset(ATTRIBUTES, num_samples=1000, transform=transform) |
| train_dataset, val_dataset = torch.utils.data.random_split( |
| full_dataset, [800, 200], generator=torch.Generator().manual_seed(42) |
| ) |
| |
| train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=0, pin_memory=True) |
| val_loader = DataLoader(val_dataset, batch_size=128, shuffle=False, num_workers=0, pin_memory=True) |
| |
| print(f"Train: {len(train_dataset)} Val: {len(val_dataset)}") |
| print(f"Attributes ({len(ATTRIBUTES)}): {', '.join(ATTRIBUTES)}") |
| """) |
|
|
| code("""\ |
| model = resnet18(num_classes=len(ATTRIBUTES)).to(device) |
| print(f"Parameters: {sum(p.numel() for p in model.parameters()):,}") |
| """) |
|
|
| md("""\ |
| ## 训练 |
| |
| > ⏱ 预估耗时：**30 epoch × ~20s/epoch ≈ 10 分钟**（M4 Max, batch_size=128） |
| """) |
|
|
| code("""\ |
| NUM_EPOCHS = 30 |
| LR = 1e-3 |
| |
| criterion = nn.BCEWithLogitsLoss() |
| optimizer = optim.Adam(model.parameters(), lr=LR) |
| |
| train_loss_hist, val_loss_hist, val_acc_hist = [], [], [] |
| |
| for epoch in range(1, NUM_EPOCHS + 1): |
| model.train() |
| train_loss = 0.0 |
| for images, labels in train_loader: |
| images, labels = images.to(device), labels.to(device) |
| optimizer.zero_grad() |
| outputs = model(images) |
| loss = criterion(outputs, labels) |
| loss.backward() |
| optimizer.step() |
| train_loss += loss.item() |
| |
| model.eval() |
| val_loss = 0.0 |
| correct = total = 0 |
| with torch.no_grad(): |
| for images, labels in val_loader: |
| images, labels = images.to(device), labels.to(device) |
| outputs = model(images) |
| loss = criterion(outputs, labels) |
| val_loss += loss.item() |
| preds = (torch.sigmoid(outputs) > 0.5).float() |
| correct += (preds == labels).sum().item() |
| total += labels.numel() |
| |
| avg_train = train_loss / len(train_loader) |
| avg_val = val_loss / len(val_loader) |
| acc = correct / total * 100 |
| train_loss_hist.append(avg_train) |
| val_loss_hist.append(avg_val) |
| val_acc_hist.append(acc) |
| print(f"Epoch [{epoch:2d}/{NUM_EPOCHS}] Train: {avg_train:.4f} Val: {avg_val:.4f} Acc: {acc:.2f}%") |
| """) |
|
|
| md("""## Loss 曲线 & 验证准确率""") |
|
|
| code("""\ |
| import matplotlib.pyplot as plt |
| from utils.device import get_device |
| |
| fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4)) |
| ax1.plot(train_loss_hist, label='train', marker='o') |
| ax1.plot(val_loss_hist, label='val', marker='o') |
| ax1.set_xlabel("Epoch"); ax1.set_ylabel("Loss"); ax1.legend(); ax1.grid(True) |
| |
| ax2.plot(val_acc_hist, marker='o', color='green') |
| ax2.set_xlabel("Epoch"); ax2.set_ylabel("Val Acc (%)"); ax2.grid(True) |
| |
| plt.tight_layout(); plt.show() |
| """) |
|
|
| md("""## 每属性准确率""") |
|
|
| code("""\ |
| model.eval() |
| all_preds, all_labels = [], [] |
| with torch.no_grad(): |
| for images, labels in val_loader: |
| images = images.to(device) |
| outputs = torch.sigmoid(model(images)).cpu() |
| all_preds.append((outputs > 0.5).float()) |
| all_labels.append(labels) |
| |
| all_preds = torch.cat(all_preds) |
| all_labels = torch.cat(all_labels) |
| |
| print(f"{'Attribute':<20} {'Accuracy':>8}") |
| print("-" * 30) |
| for i, attr in enumerate(ATTRIBUTES): |
| acc = (all_preds[:, i] == all_labels[:, i]).sum().item() / all_labels.size(0) * 100 |
| print(f"{attr:<20} {acc:>7.2f}%") |
| """) |
|
|
| md("""\ |
| ## 思考题 |
| |
| 1. 残差连接为什么能缓解梯度消失？画出有/无 shortcut 的梯度路径。 |
| 2. 1×1 卷积在 ResNet 中有什么作用？（提示：改变通道数） |
| 3. ResNet18 换成 ResNet34（[3,4,6,3] blocks），参数量增加多少？ |
| 4. 尝试去掉 shortcut 训练（把 `+ x` 注释掉），观察 loss 有何不同。 |
| """) |
|
|
| nb.cells = cells |
| out = "cv/resnet18/resnet18.ipynb" |
| with open(out, "w") as f: |
| nbf.write(nb, f) |
| print(f"Generated {out}") |
|
|
