1 files changed, 52 insertions, 0 deletions
diff --git a/tests/test_nn_rewrite_core_graph_edit_record.py b/tests/test_nn_rewrite_core_graph_edit_record.py
new file mode 100644
index 0000000..39aeef5
--- /dev/null
+++ b/tests/test_nn_rewrite_core_graph_edit_record.py
@@ -0,0 +1,52 @@
+# SPDX-FileCopyrightText: Copyright 2023, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+"""Tests for module mlia.nn.rewrite.graph_edit.record."""
+from pathlib import Path
+
+import pytest
+import tensorflow as tf
+
+from mlia.nn.rewrite.core.graph_edit.record import record_model
+from mlia.nn.rewrite.core.utils.numpy_tfrecord import NumpyTFReader
+
+
+@pytest.mark.parametrize("batch_size", (None, 1, 2))
+def test_record_model(
+    test_tflite_model: Path,
+    tmp_path: Path,
+    test_tfrecord: Path,
+    batch_size: int,
+) -> None:
+    """Test the function record_model()."""
+    output_file = tmp_path / "out.tfrecord"
+    record_model(
+        input_filename=str(test_tfrecord),
+        model_filename=str(test_tflite_model),
+        output_filename=str(output_file),
+        batch_size=batch_size,
+    )
+    assert output_file.is_file()
+
+    def data_matches_outputs(name: str, tensor: tf.Tensor, model_outputs: list) -> bool:
+        """Check that the name and the tensor match any of the model outputs."""
+        for model_output in model_outputs:
+            if model_output["name"] == name:
+                # If the name is a match, tensor shape and type have to match!
+                tensor_shape = tensor.shape.as_list()
+                tensor_type = tensor.dtype.as_numpy_dtype
+                return all(
+                    (
+                        tensor_shape == model_output["shape"].tolist(),
+                        tensor_type == model_output["dtype"],
+                    )
+                )
+        return False
+
+    # Now load model and the data and make sure that the written data matches
+    # any of the model outputs
+    interpreter = tf.lite.Interpreter(str(test_tflite_model))
+    model_outputs = interpreter.get_output_details()
+    dataset = NumpyTFReader(str(output_file))
+    for data in dataset:
+        for name, tensor in data.items():
+            assert data_matches_outputs(name, tensor, model_outputs)