Create script.py

script.py

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+mport torch
+import numpy as np
+from sklearn.metrics import accuracy_score  # Example metric
+
+# Load your hidden test set (adjust path and format to your data)
+TEST_DATA_PATH = "test_data.pt"  # Replace with the actual path
+TEST_LABELS_PATH = "test_labels.pt"
+
+test_data = torch.load(TEST_DATA_PATH)
+test_labels = torch.load(TEST_LABELS_PATH)
+
+# Evaluation script entry point
+def evaluate_submission(model_checkpoint_path: str):
+    """
+    Evaluates the submitted model on the hidden test set.
+    Args:
+        model_checkpoint_path (str): Path to the submitted model checkpoint.
+    
+    Returns:
+        dict: A dictionary containing the evaluation metrics.
+    """
+    # Load the participant's model
+    model = torch.load(model_checkpoint_path)
+    model.eval()
+
+    # Move model and data to the appropriate device (e.g., GPU if available)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = model.to(device)
+    test_data_tensor = test_data.to(device)
+
+    # Perform inference
+    with torch.no_grad():
+        predictions = model(test_data_tensor)
+        predictions = torch.argmax(predictions, axis=1).cpu().numpy()
+
+    # Calculate evaluation metric (e.g., accuracy)
+    accuracy = accuracy_score(test_labels, predictions)
+
+    return {"accuracy": accuracy}  # Replace with other metrics as needed
+
+if __name__ == "__main__":
+    # For local testing, you can pass a sample model path here
+    sample_model_path = "sample_submission.pt"  # Replace with a test checkpoint
+    result = evaluate_submission(sample_model_path)
+    print(result)