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