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test.py

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+import torch
+from torch.utils.data import DataLoader
+from torchvision import transforms
+from models.resnet import resnet18
+from models.openmax import OpenMax
+from models.metamax import MetaMax
+from train import GameDataset
+from utils.data_stats import load_dataset_stats
+from utils.eval_utils import evaluate_known_classes, evaluate_openmax, evaluate_metamax
+import os
+from pprint import pprint
+
+def test_models():
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    
+    # 加载数据集统计信息
+    mean, std = load_dataset_stats()
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize(mean=mean, std=std)
+    ])
+    
+    # 加载验证集
+    test_dataset = GameDataset('jk_zfls/round0_eval', num_labels=21, transform=transform)
+    test_loader = DataLoader(test_dataset, batch_size=400, shuffle=False, num_workers=4, pin_memory=True)
+    
+    # 加载基础模型
+    model = resnet18(num_classes=20)
+    checkpoint = torch.load('models/best_model_99.92_02.pth')
+    model.load_state_dict(checkpoint['model_state_dict'])
+    model = model.to(device)
+    model.eval()
+    
+    # 加载OpenMax和MetaMax模型
+    try:
+        openmax = torch.load('models/best_openmax_94.71_01.pth')
+        # metamax = torch.load('models/best_metamax.pth')
+        print("Successfully loaded OpenMax and MetaMax models")
+    except Exception as e:
+        print(f"Error loading models: {e}")
+        return
+    
+    # 测试基础ResNet
+    print("\n=== Testing ResNet (Known Classes Only) ===")
+    _, accuracy, errors = evaluate_known_classes(model, test_loader, torch.nn.CrossEntropyLoss(), device)
+    print(f"Known Classes Accuracy: {accuracy:.2f}%")
+    if errors:
+        print("\nErrors in known classes:")
+        pprint(errors)
+    
+    # 测试ResNet + OpenMax
+    print("\n=== Testing ResNet + OpenMax ===")
+    evaluate_openmax(openmax, model, test_loader, device, multiplier=0.5, fraction=0.2, verbose=True)
+    
+    # 测试ResNet + MetaMax
+    # print("\n=== Testing ResNet + MetaMax ===")
+    # evaluate_metamax(metamax, model, test_loader, device, threshold=0.5, verbose=True)
+
+if __name__ == '__main__':
+    test_models()