chore: import upstream snapshot with attribution

tools/tensorflow-quantization/examples/resnet/utils.py

@@ -0,0 +1,45 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import tensorflow as tf
+from examples.data.data_loader import _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS
+from examples.utils import get_tfkeras_model
+
+
+def get_resnet_model(resnet_depth: str = "50", resnet_version: str = "v1") -> tf.keras.Model:
+    """
+    Creates a native tf.keras ResNet model.
+
+    Args:
+        resnet_depth (str): ResNet depth. Options=[50 (default), 101, 152].
+        resnet_version (str): ResNet version. Options=[v1 (default), v2].
+
+    Returns:
+        model (tf.keras.Model): model corresponding to 'resnet_depth' and 'resnet_version'.
+    """
+
+    shape = (
+        _DEFAULT_IMAGE_SIZE["resnet_{}".format(resnet_version)],
+        _DEFAULT_IMAGE_SIZE["resnet_{}".format(resnet_version)],
+        _NUM_CHANNELS,
+    )
+
+    model_name = "resnet_" + resnet_depth + resnet_version
+    model = get_tfkeras_model(model_name=model_name, shape=shape)
+
+    return model