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| import torch | |
| from torch.utils.data import DataLoader | |
| from dataset import get_datasets | |
| from model import get_model | |
| # Paths | |
| data_root = r"C:\ASH_PROJECT\data\UCF101" | |
| annotation_path = r"C:\ASH_PROJECT\data\ucfTrainTestlist" | |
| # Dataset | |
| _, test_dataset = get_datasets(data_root, annotation_path) | |
| test_loader = DataLoader(test_dataset, batch_size=4, shuffle=False, num_workers=2) | |
| # Model | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = get_model(num_classes=101).to(device) | |
| model.load_state_dict(torch.load(r"C:\ASH_PROJECT\outputs\checkpoints\r3d18_ucf101.pth")) | |
| model.eval() | |
| # Evaluation | |
| correct, total = 0, 0 | |
| with torch.no_grad(): | |
| for clips, labels in test_loader: | |
| clips, labels = clips.to(device), labels.to(device) | |
| outputs = model(clips) | |
| _, predicted = torch.max(outputs, 1) | |
| total += labels.size(0) | |
| correct += (predicted == labels).sum().item() | |
| print(f"Test Accuracy: {100 * correct / total:.2f}%") | |