Add MLIA codebase0.3.0-rc.1

Add MLIA codebase including sources and tests.

Change-Id: Id41707559bd721edd114793618d12ccd188d8dbd

3 files changed, 48 insertions, 0 deletions

diff --git a/tests/mlia/utils/__init__.py b/tests/mlia/utils/__init__.py
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+++ b/tests/mlia/utils/__init__.py
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+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+"""Test utils module."""
diff --git a/tests/mlia/utils/common.py b/tests/mlia/utils/common.py
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+++ b/tests/mlia/utils/common.py
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+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+"""Common test utils module."""
+from typing import Tuple
+
+import numpy as np
+import tensorflow as tf
+
+
+def get_dataset() -> Tuple[np.array, np.array]:
+    """Return sample dataset."""
+    mnist = tf.keras.datasets.mnist
+    (x_train, y_train), _ = mnist.load_data()
+    x_train = x_train / 255.0
+
+    # Use subset of 60000 examples to keep unit test speed fast.
+    x_train = x_train[0:1]
+    y_train = y_train[0:1]
+
+    return x_train, y_train
+
+
+def train_model(model: tf.keras.Model) -> None:
+    """Train model using sample dataset."""
+    num_epochs = 1
+
+    loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+    model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
+
+    x_train, y_train = get_dataset()
+
+    model.fit(x_train, y_train, epochs=num_epochs)
diff --git a/tests/mlia/utils/logging.py b/tests/mlia/utils/logging.py
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+++ b/tests/mlia/utils/logging.py
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+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+"""Utils for logging."""
+import logging
+
+
+def clear_loggers() -> None:
+    """Close the log handlers."""
+    for _, logger in logging.Logger.manager.loggerDict.items():
+        if not isinstance(logger, logging.PlaceHolder):
+            for handler in logger.handlers:
+                handler.close()
+                logger.removeHandler(handler)