chore: import upstream snapshot with attribution

tools/tensorflow-quantization/docs/source/notebooks/tiny_resnet.py

@@ -0,0 +1,78 @@
+#
+# 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.
+#
+
+"""
+A small resnet-like network for quick testing.
+"""
+
+import tensorflow as tf
+
+
+def identity_block(input_tensor):
+    """
+    Identity block with no shortcut convolution
+    """
+    y = tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), padding="same")(
+        input_tensor
+    )
+    y = tf.keras.layers.ReLU()(y)
+    y = tf.keras.layers.Conv2D(filters=24, kernel_size=(3, 3), padding="same")(y)
+    out = tf.keras.layers.Add()([y, input_tensor])
+    out = tf.keras.layers.ReLU()(out)
+    return out
+
+
+def identity_block_short_conv(input_tensor):
+    """
+    Identity block with shortcut convolution
+    """
+    y = tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), padding="same")(
+        input_tensor
+    )
+    y = tf.keras.layers.ReLU()(y)
+    y = tf.keras.layers.Conv2D(
+        filters=24, kernel_size=(3, 3), strides=(2, 2), padding="same"
+    )(y)
+    ds_input = tf.keras.layers.Conv2D(
+        filters=24, kernel_size=(3, 3), strides=(2, 2), padding="same"
+    )(input_tensor)
+    out = tf.keras.layers.Add()([y, ds_input])
+    out = tf.keras.layers.ReLU()(out)
+    return out
+
+
+def model():
+    """
+    Dummy network with resnet-like architecture.
+    """
+    input_img = tf.keras.layers.Input(shape=(32, 32, 3))
+    x = tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3))(input_img)
+    x = tf.keras.layers.ReLU()(x)
+    x = tf.keras.layers.Conv2D(filters=24, kernel_size=(3, 3))(x)
+    x = tf.keras.layers.ReLU()(x)
+    x = identity_block(x)
+    x = identity_block_short_conv(x)
+    x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
+    x = tf.keras.layers.Flatten()(x)
+    x = tf.keras.layers.Dense(100)(x)
+    x = tf.keras.layers.ReLU()(x)
+    x = tf.keras.layers.Dense(10)(x)
+    return tf.keras.Model(input_img, x, name="Dummy_Model")
+
+
+def optimizer(lr=0.001):
+    return tf.keras.optimizers.Adam(learning_rate=lr)