| |
| import torch |
| from models.moe_model import MoEModel |
| from utils.data_loader import load_data |
| from utils.helper_functions import save_model, load_model |
|
|
| def test_model(): |
| model = MoEModel(input_dim=512, num_experts=3) |
| test_loader = load_data() |
|
|
| correct, total = 0, 0 |
| with torch.no_grad(): |
| for data in test_loader: |
| vision_input, audio_input, sensor_input, labels = data |
| outputs = model(vision_input, audio_input, sensor_input) |
| _, predicted = torch.max(outputs.data, 1) |
| total += labels.size(0) |
| correct += (predicted == labels).sum().item() |
| print(f"Accuracy: {100 * correct / total}%") |
|
|
| if __name__ == "__main__": |
| test_model() |
|
|
