重构：将 Baseline.py 迁移至 baseline/ 目录，新增多模型对比脚本
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"""
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baseline/compare_models.py
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多模型对比：ROC 曲线 + 准确率柱状图
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添加新模型只需在 MODELS 列表加一行，无需修改绘图代码
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author: yukun-hh
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date: 2026-5-14
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"""
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import sys, os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib
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import torch
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from torch.utils.data import DataLoader
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from torchvision import transforms
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from tqdm import tqdm
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from sklearn.metrics import roc_curve, auc, accuracy_score
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from Model import Net
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from Dataloader import RobustImageFolder
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from baseline.VGG_KNN import VGGKNNBaseline
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matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
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matplotlib.rcParams['axes.unicode_minus'] = False
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# ============================================================
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# ★★★ 可配置参数 ★★★
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# ============================================================
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DATA_ROOT = '../trash_division_data/ultimate_4_class/'
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BATCH_SIZE = 32
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IMAGE_SIZE = 256
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NUM_WORKERS = 4
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K_KNN = 5
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# ============================================================
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CLASS_NAMES = ['厨余垃圾', '可回收物', '其他垃圾', '有害垃圾']
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NUM_CLASSES = 4
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# ============================================================
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# 预测函数 — 每个函数签名: (train_loader, val_loader, device) -> (y_true, y_preds, y_probs)
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# ============================================================
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def get_resnet34_preds(train_loader, val_loader, device):
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model = Net(num_classes=NUM_CLASSES)
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state_dict = torch.load('best_model.pth', map_location='cpu')
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if 'model_state_dict' in state_dict:
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state_dict = state_dict['model_state_dict']
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elif 'model' in state_dict:
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state_dict = state_dict['model']
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model.load_state_dict(state_dict)
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model = model.to(device).eval()
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y_true, y_preds, y_probs = [], [], []
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with torch.no_grad():
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for images, labels in tqdm(val_loader, desc='ResNet-34'):
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images, labels = images.to(device), labels
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logits = model(images)
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probs = torch.softmax(logits, dim=1)
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preds = probs.argmax(dim=1)
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y_true.append(labels.numpy())
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y_preds.append(preds.cpu().numpy())
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y_probs.append(probs.cpu().numpy())
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return np.concatenate(y_true), np.concatenate(y_preds), np.concatenate(y_probs)
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def get_vgg_knn_preds(train_loader, val_loader, device):
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baseline = VGGKNNBaseline(k=K_KNN, device=device)
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baseline.fit(train_loader)
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return baseline.predict(val_loader)
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# ============================================================
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# ★ 模型注册表 — 添加新模型只需在这里加一行 ★
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# ============================================================
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MODELS = [
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('ResNet-34', get_resnet34_preds),
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('VGG16 + KNN (K=5)', get_vgg_knn_preds),
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# 未来轻松扩展示例：
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# ('ResNet-18 (pretrained)', get_resnet18_preds),
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# ('ResNet-50 (pretrained)', get_resnet50_preds),
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# ('ResNet-34 (finetuned)', get_finetuned_preds),
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]
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# ============================================================
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# 调色板 (扩展时无需修改)
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# ============================================================
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COLORS = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b',
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'#e377c2', '#7f7f7f', '#bcbd22', '#17becf']
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def compute_macro_roc(y_true, y_probs):
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one_hot = np.eye(NUM_CLASSES)[y_true]
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fpr_dict, tpr_dict = {}, {}
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for c in range(NUM_CLASSES):
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fpr_dict[c], tpr_dict[c], _ = roc_curve(one_hot[:, c], y_probs[:, c])
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all_fpr = np.unique(np.concatenate([fpr_dict[c] for c in range(NUM_CLASSES)]))
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mean_tpr = np.zeros_like(all_fpr)
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for c in range(NUM_CLASSES):
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mean_tpr += np.interp(all_fpr, fpr_dict[c], tpr_dict[c])
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mean_tpr /= NUM_CLASSES
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macro_auc = auc(all_fpr, mean_tpr)
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return all_fpr, mean_tpr, macro_auc
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if __name__ == '__main__':
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out_dir = os.path.dirname(os.path.abspath(__file__))
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device = torch.device('cuda' if torch.cuda.is_available()
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else 'xpu' if hasattr(torch, 'xpu') and torch.xpu.is_available()
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else 'cpu')
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print(f"Device: {device}")
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val_transform = transforms.Compose([
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transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225]),
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])
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train_dataset = RobustImageFolder(root=os.path.join(DATA_ROOT, 'train'),
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transform=val_transform)
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val_dataset = RobustImageFolder(root=os.path.join(DATA_ROOT, 'val'),
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transform=val_transform)
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print(f"训练集: {len(train_dataset)} 验证集: {len(val_dataset)}")
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train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=False,
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num_workers=NUM_WORKERS, pin_memory=True, drop_last=False)
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val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False,
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num_workers=NUM_WORKERS, pin_memory=True, drop_last=False)
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# ———— 评估所有模型 ————
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results = {}
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for name, func in MODELS:
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print(f"\n{'='*50}")
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print(f"评估: {name}")
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y_true, y_preds, y_probs = func(train_loader, val_loader, device)
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acc = accuracy_score(y_true, y_preds)
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fpr, tpr, roc_auc = compute_macro_roc(y_true, y_probs)
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results[name] = {'y_true': y_true, 'y_preds': y_preds, 'y_probs': y_probs,
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'acc': acc, 'fpr': fpr, 'tpr': tpr, 'auc': roc_auc}
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print(f" Accuracy: {acc:.4f} | Macro-AUC: {roc_auc:.4f}")
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# ———— ROC 对比图 ————
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fig, ax = plt.subplots(figsize=(8, 7))
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for i, (name, r) in enumerate(results.items()):
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color = COLORS[i % len(COLORS)]
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ax.plot(r['fpr'], r['tpr'], color=color, lw=2,
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label=f"{name} (AUC={r['auc']:.4f})")
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ax.plot([0, 1], [0, 1], 'k--', lw=1, alpha=0.5)
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ax.set_xlim(0, 1); ax.set_ylim(0, 1.05)
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ax.set_xlabel('False Positive Rate'); ax.set_ylabel('True Positive Rate')
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ax.set_title('ROC Curve Comparison (Macro-Average)', fontsize=14)
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ax.legend(loc='lower right'); ax.grid(True, alpha=0.3)
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plt.tight_layout()
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roc_path = os.path.join(out_dir, 'roc_comparison.png')
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plt.savefig(roc_path, dpi=150, bbox_inches='tight')
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plt.show()
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print(f"\nROC 对比图已保存: {roc_path}")
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# ———— 准确率柱状图 ————
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names = list(results.keys())
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accs = [results[n]['acc'] for n in names]
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fig, ax = plt.subplots(figsize=(8, 5))
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bar_colors = [COLORS[i % len(COLORS)] for i in range(len(names))]
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bars = ax.bar(names, accs, color=bar_colors, edgecolor='white', linewidth=1.2)
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for bar, acc in zip(bars, accs):
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ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.005,
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f'{acc:.4f}', ha='center', va='bottom', fontsize=12, fontweight='bold')
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ax.set_ylim(0, max(accs) * 1.15)
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ax.set_ylabel('Accuracy'); ax.set_title('Accuracy Comparison', fontsize=14)
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ax.grid(True, alpha=0.3, axis='y')
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plt.tight_layout()
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bar_path = os.path.join(out_dir, 'accuracy_bar.png')
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plt.savefig(bar_path, dpi=150, bbox_inches='tight')
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plt.show()
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print(f"准确率柱状图已保存: {bar_path}")
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