Create Test_model.py

Test_model.py

@@ -0,0 +1,22 @@
+# 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()