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# test_model.py
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()
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