1 files changed, 46 insertions, 17 deletions

diff --git a/tests/test_nn_rewrite_core_graph_edit_record.py b/tests/test_nn_rewrite_core_graph_edit_record.py
index 41b9c50..422b53e 100644
--- a/tests/test_nn_rewrite_core_graph_edit_record.py
+++ b/tests/test_nn_rewrite_core_graph_edit_record.py
@@ -3,43 +3,57 @@
 """Tests for module mlia.nn.rewrite.graph_edit.record."""
 from pathlib import Path
 
+import numpy as np
+import pytest
 import tensorflow as tf
 
+from mlia.nn.rewrite.core.extract import ExtractPaths
 from mlia.nn.rewrite.core.graph_edit.record import record_model
 from mlia.nn.rewrite.core.utils.numpy_tfrecord import numpytf_read
 
 
+def data_matches_outputs(
+    name: str,
+    tensor: tf.Tensor,
+    model_outputs: list,
+    dequantized_output: bool,
+) -> 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 == np.float32
+                    if dequantized_output
+                    else model_output["dtype"],
+                )
+            )
+    return False
+
+
 def check_record_model(
     test_tflite_model: Path,
     tmp_path: Path,
     test_tfrecord: Path,
     batch_size: int,
+    dequantize_output: bool,
 ) -> None:
     """Test the function record_model()."""
-    output_file = tmp_path / "out.tfrecord"
+    output_file = ExtractPaths.tfrec.output(tmp_path)
     record_model(
         input_filename=str(test_tfrecord),
         model_filename=str(test_tflite_model),
         output_filename=str(output_file),
         batch_size=batch_size,
+        dequantize_output=dequantize_output,
     )
+    output_file = ExtractPaths.tfrec.output(tmp_path, dequantize_output)
     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))
@@ -47,4 +61,19 @@ def check_record_model(
     dataset = numpytf_read(str(output_file))
     for data in dataset:
         for name, tensor in data.items():
-            assert data_matches_outputs(name, tensor, model_outputs)
+            assert data_matches_outputs(name, tensor, model_outputs, dequantize_output)
+
+
+@pytest.mark.parametrize("batch_size", (None, 1, 2))
+@pytest.mark.parametrize("dequantize_output", (True, False))
+def test_record_model(
+    test_tflite_model: Path,
+    tmp_path: Path,
+    test_tfrecord: Path,
+    batch_size: int,
+    dequantize_output: bool,
+) -> None:
+    """Test the function record_model()."""
+    check_record_model(
+        test_tflite_model, tmp_path, test_tfrecord, batch_size, dequantize_output
+    )