Enable rewrites for quantized input models

If the input model for rewriting is quantized:

- Record de-quantized TFRecords
  - enable writing de-quantized calibration data for the training
  - re-generate augmented training data, if needed
- Use quantization-aware training (QAT) to train the replacement models
- Check if replacement model is quantized:
  If source model is quantized, we make sure rewrite's output model
  is quantized too. Right now, only int8 is supported so raising
  an error if any other datatype is present in the output.

Resolves: MLIA-907, MLIA-908, MLIA-927
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
Change-Id: Icb4070a9e6f1fdb5ce36120d73823986e89ac955

1 files changed, 12 insertions, 0 deletions

diff --git a/tests/test_nn_tensorflow_config.py b/tests/test_nn_tensorflow_config.py
index 48aec0a..fff3857 100644
--- a/tests/test_nn_tensorflow_config.py
+++ b/tests/test_nn_tensorflow_config.py
@@ -111,3 +111,15 @@ def test_tflite_model_call(
     for named_input in data.as_numpy_iterator():
         res = model(named_input)
         assert res
+
+
+def test_tflite_model_is_tensor_quantized(test_tflite_model: Path) -> None:
+    """Test function TFLiteModel.is_tensor_quantized()."""
+    model = TFLiteModel(test_tflite_model)
+    input_details = model.input_details[0]
+    assert model.is_tensor_quantized(name=input_details["name"])
+    assert model.is_tensor_quantized(idx=input_details["index"])
+    with pytest.raises(ValueError):
+        assert model.is_tensor_quantized()
+    with pytest.raises(NameError):
+        assert model.is_tensor_quantized(name="NAME_DOES_NOT_EXIST")