Bug-fixes and re-factoring for the rewrite module

- Fix input shape of rewrite replacement:
  During and after training of the replacement model for a rewrite the
  Keras model is converted and saved in TensorFlow Lite format. If the
  input shape does not match the teacher model exactly, e.g. if the
  batch size is undefined, the TFLiteConverter adds extra operators
  during conversion.
- Fix rewritten model output
  - Save the model output with the rewritten operator in the output dir
  - Log MAE and NRMSE of the rewrite
- Remove 'verbose' flag from rewrite module and rely on the logging
  mechanism to control verbose output.
- Re-factor utility classes for rewrites
  - Merge the two TFLiteModel classes
  - Move functionality to load/save TensorFlow Lite flatbuffers to
    nn/tensorflow/tflite_graph
- Fix issue with unknown shape in datasets
  After upgrading to TensorFlow 2.12 the unknown shape of the
  TFRecordDataset is causing problems when training the replacement models
  for rewrites. By explicitly setting the right shape of the tensors we
  can work around the issue.
- Adapt default parameters for rewrites. The training steps especially
  had to be increased significantly to be effective.

Resolves: MLIA-895, MLIA-907, MLIA-946, MLIA-979
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
Change-Id: I887ad165aed0f2c6e5a0041f64cec5e6c5ab5c5c

1 files changed, 27 insertions, 12 deletions

diff --git a/tests/conftest.py b/tests/conftest.py
index c42b8cb..bb2423f 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -5,6 +5,7 @@ import shutil
 from pathlib import Path
 from typing import Callable
 from typing import Generator
+from unittest.mock import MagicMock
 
 import numpy as np
 import pytest
@@ -17,6 +18,7 @@ from mlia.nn.tensorflow.utils import convert_to_tflite
 from mlia.nn.tensorflow.utils import save_keras_model
 from mlia.nn.tensorflow.utils import save_tflite_model
 from mlia.target.ethos_u.config import EthosUConfiguration
+from tests.utils.rewrite import TestTrainingParameters
 
 
 @pytest.fixture(scope="session", name="test_resources_path")
@@ -168,16 +170,12 @@ def _write_tfrecord(
             writer.write({input_name: data_generator()})
 
 
-@pytest.fixture(scope="session", name="test_tfrecord")
-def fixture_test_tfrecord(
-    tmp_path_factory: pytest.TempPathFactory,
+def create_tfrecord(
+    tmp_path_factory: pytest.TempPathFactory, random_data: Callable
 ) -> Generator[Path, None, None]:
     """Create a tfrecord with random data matching fixture 'test_tflite_model'."""
     tmp_path = tmp_path_factory.mktemp("tfrecords")
-    tfrecord_file = tmp_path / "test_int8.tfrecord"
-
-    def random_data() -> np.ndarray:
-        return np.random.randint(low=-127, high=128, size=(1, 28, 28, 1), dtype=np.int8)
+    tfrecord_file = tmp_path / "test.tfrecord"
 
     _write_tfrecord(tfrecord_file, random_data)
 
@@ -186,19 +184,36 @@ def fixture_test_tfrecord(
     shutil.rmtree(tmp_path)
 
 
+@pytest.fixture(scope="session", name="test_tfrecord")
+def fixture_test_tfrecord(
+    tmp_path_factory: pytest.TempPathFactory,
+) -> Generator[Path, None, None]:
+    """Create a tfrecord with random data matching fixture 'test_tflite_model'."""
+
+    def random_data() -> np.ndarray:
+        return np.random.randint(low=-127, high=128, size=(1, 28, 28, 1), dtype=np.int8)
+
+    yield from create_tfrecord(tmp_path_factory, random_data)
+
+
 @pytest.fixture(scope="session", name="test_tfrecord_fp32")
 def fixture_test_tfrecord_fp32(
     tmp_path_factory: pytest.TempPathFactory,
 ) -> Generator[Path, None, None]:
     """Create tfrecord with random data matching fixture 'test_tflite_model_fp32'."""
-    tmp_path = tmp_path_factory.mktemp("tfrecords")
-    tfrecord_file = tmp_path / "test_fp32.tfrecord"
 
     def random_data() -> np.ndarray:
         return np.random.rand(1, 28, 28, 1).astype(np.float32)
 
-    _write_tfrecord(tfrecord_file, random_data)
+    yield from create_tfrecord(tmp_path_factory, random_data)
 
-    yield tfrecord_file
 
-    shutil.rmtree(tmp_path)
+@pytest.fixture(scope="session", autouse=True)
+def set_training_steps() -> Generator[None, None, None]:
+    """Speed up tests by using TestTrainingParameters."""
+    with pytest.MonkeyPatch.context() as monkeypatch:
+        monkeypatch.setattr(
+            "mlia.nn.select._get_rewrite_train_params",
+            MagicMock(return_value=TestTrainingParameters()),
+        )
+        yield