chore: import upstream snapshot with attribution

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

@@ -0,0 +1,44 @@
+#
+# 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 os
+import sys
+import tensorflow as tf
+tf_models_path = os.path.realpath("./models")
+sys.path.insert(1, tf_models_path)
+try:
+    from official.legacy.image_classification.efficientnet import efficientnet_model
+except Exception:
+    print("Error importing TF official models codebase.")
+
+
+def create_efficientnet_model(model_version="b0"):
+    model_name = "efficientnet-" + model_version
+    model_configs = dict(efficientnet_model.MODEL_CONFIGS)
+    assert model_name in model_configs, "Model name is not valid!"
+    config = model_configs[model_name]
+
+    # Set the dataformat of the model to NCHW for training and inference
+    tf.keras.backend.set_image_data_format("channels_first")
+    # B0=(224, 224, 3); B3=(300, 300, 3)
+    image_input = tf.keras.layers.Input(
+        shape=(config.resolution, config.resolution, config.input_channels), name="image_input", dtype=tf.float32
+    )
+    outputs = efficientnet_model.efficientnet(image_input, config)
+    model = tf.keras.Model(inputs=image_input, outputs=outputs)
+
+    return model