Implement first rewrite (proof of concept)

* Define replacement function fully_connected layer
* Define RewriteConfiguration and Rewriter to integrate
  rewrite module into mlia optimize command
* Fix a bug in the ethos_u/data_collection.py file
* Fix a bug in join.py
* Remove diff_stats and use diff instead, added related
  changes around this to ensure e2e tests passing
* Add unit tests for all changes
* Fix bug in diff_stats function
  * The bug was caused by a dividing by numpy array
    of all zeros. The previous way of handling it
    did not consider the all zeros case but only
    dealt with partially zeros
  * unit tests added.
* Fix the bug in rewrite/core/graph_edit/join.py
  * Remove the possibility of passing None to append_relabel
    function because it is immutable
  * The bug happened when empty dictionary was passed in the
    append_relabel function and the function overwrites the
    reference of operator_map which caused the dictionary
    was not updated after the function call

Resolves: MLIA-749, MLIA-864, MLIA-866
Change-Id: I1ab426996232f182345e6e98033d5dcb32aea08c
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>

1 files changed, 18 insertions, 0 deletions

diff --git a/src/mlia/nn/rewrite/library/fc_layer.py b/src/mlia/nn/rewrite/library/fc_layer.py
new file mode 100644
index 0000000..8704154
--- /dev/null
+++ b/src/mlia/nn/rewrite/library/fc_layer.py
@@ -0,0 +1,18 @@
+# SPDX-FileCopyrightText: Copyright 2023, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+"""Example rewrite with one fully connected layer."""
+from typing import Any
+
+import tensorflow as tf
+
+
+def get_keras_model(input_shape: Any, output_shape: Any) -> tf.keras.Model:
+    """Generate tflite model for rewrite."""
+    input_tensor = tf.keras.layers.Input(
+        shape=input_shape, name="MbileNet/avg_pool/AvgPool"
+    )
+    output_tensor = tf.keras.layers.Dense(output_shape, name="MobileNet/fc1/BiasAdd")(
+        input_tensor
+    )
+    model = tf.keras.Model(input_tensor, output_tensor)
+    return model