chore: import upstream snapshot with attribution
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wehub-resource-sync
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#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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This module contains tiny networks used for testing across different modules.
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They are named after famous Hobbits for obvious reasons.
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"""
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import tensorflow as tf
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##################################################
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###### Tiny, VGG like network ####################
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##################################################
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def bilbo_28_28():
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"""
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Network with VGG like architecture.
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"""
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input_img = tf.keras.layers.Input(shape=(28, 28), name="nn_input")
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x = tf.keras.layers.Reshape(target_shape=(28, 28, 1), name="reshape_0")(input_img)
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x = tf.keras.layers.Conv2D(filters=516, kernel_size=(3, 3), name="conv_0")(x)
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x = tf.keras.layers.ReLU(name="relu_0")(x)
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x = tf.keras.layers.Conv2D(filters=252, kernel_size=(3, 3), name="conv_1")(x)
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x = tf.keras.layers.ReLU(name="relu_1")(x)
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x = tf.keras.layers.Conv2D(filters=126, kernel_size=(3, 3), name="conv_2")(x)
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x = tf.keras.layers.ReLU(name="relu_2")(x)
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x = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), name="conv_3")(x)
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x = tf.keras.layers.ReLU(name="relu_3")(x)
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x = tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 3), name="conv_4")(x)
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x = tf.keras.layers.ReLU(name="relu_4")(x)
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x = tf.keras.layers.Conv2D(filters=16, kernel_size=(3, 3), name="conv_5")(x)
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x = tf.keras.layers.ReLU(name="relu_5")(x)
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x = tf.keras.layers.Conv2D(filters=8, kernel_size=(3, 3), name="conv_6")(x)
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x = tf.keras.layers.ReLU(name="relu_6")(x)
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x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), name="max_pool_0")(x)
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x = tf.keras.layers.Flatten(name="flatten_0")(x)
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x = tf.keras.layers.Dense(100, name="dense_0")(x)
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x = tf.keras.layers.ReLU(name="relu_7")(x)
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x = tf.keras.layers.Dense(10, name="dense_1")(x)
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return tf.keras.Model(input_img, x, name="Bilbo")
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#####################################################
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###### Tiny, ResNet like network ####################
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#####################################################
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def identity_block_plain(input_tensor):
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"""
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Identity block with no shortcut convolution
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"""
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y = tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), padding="same")(
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input_tensor
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)
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y = tf.keras.layers.ReLU()(y)
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y = tf.keras.layers.Conv2D(filters=24, kernel_size=(3, 3), padding="same")(y)
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out = tf.keras.layers.Add()([y, input_tensor])
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out = tf.keras.layers.ReLU()(out)
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return out
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def identity_block_short_conv_plain(input_tensor):
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"""
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Identity block with shortcut convolution
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"""
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y = tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), padding="same")(
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input_tensor
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)
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y = tf.keras.layers.ReLU()(y)
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y = tf.keras.layers.Conv2D(
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filters=24, kernel_size=(3, 3), strides=(2, 2), padding="same"
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)(y)
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ds_input = tf.keras.layers.Conv2D(
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filters=24, kernel_size=(3, 3), strides=(2, 2), padding="same"
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)(input_tensor)
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out = tf.keras.layers.Add()([y, ds_input])
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out = tf.keras.layers.ReLU()(out)
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return out
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def frodo_32_32():
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"""
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Dummy network with resnet like architecture.
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"""
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input_img = tf.keras.layers.Input(shape=(32, 32, 3))
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x = tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3))(input_img)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.Conv2D(filters=24, kernel_size=(3, 3))(x)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
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x = identity_block_plain(x)
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x = identity_block_short_conv_plain(x)
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x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
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x = tf.keras.layers.Flatten()(x)
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x = tf.keras.layers.Dense(100)(x)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.Dense(10)(x)
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return tf.keras.Model(input_img, x, name="Frodo")
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def sam_32_32():
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"""
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Dummy network with resnet like architecture.
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"""
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input_img = tf.keras.layers.Input(shape=(32, 32, 3))
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x = tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3))(input_img)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.Conv2D(filters=24, kernel_size=(3, 3))(x)
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x = tf.keras.layers.ReLU()(x)
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x = identity_block_plain(x)
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x = identity_block_plain(x)
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x = identity_block_short_conv_plain(x)
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x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
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x = tf.keras.layers.Flatten()(x)
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x = tf.keras.layers.Dense(100)(x)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.Dense(10)(x)
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return tf.keras.Model(input_img, x, name="Sam")
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##############################################
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###### Popular network blocks ################
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##############################################
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def relu_bn(input):
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"""
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Block with BN+ReLU
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"""
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bn = tf.keras.layers.BatchNormalization()(input)
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relu = tf.keras.layers.ReLU()(bn)
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return relu
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def bn(input):
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return tf.keras.layers.BatchNormalization()(input)
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def relu(input):
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return tf.keras.layers.ReLU()(input)
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def inception_block(input_tensor):
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"""
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Inception block from GoogleNet
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"""
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b1x1 = tf.keras.layers.Conv2D(filters=12, kernel_size=(1, 1), padding="same")(
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input_tensor
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)
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b1x1 = relu_bn(b1x1)
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b5x5 = tf.keras.layers.Conv2D(filters=12, kernel_size=(1, 1), padding="same")(
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input_tensor
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)
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b5x5 = tf.keras.layers.Conv2D(filters=24, kernel_size=(5, 5), padding="same")(b5x5)
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b5x5 = relu_bn(b5x5)
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b3x3 = tf.keras.layers.Conv2D(filters=12, kernel_size=(1, 1), padding="same")(
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input_tensor
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)
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b3x3 = tf.keras.layers.Conv2D(filters=20, kernel_size=(3, 3), padding="same")(b3x3)
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b3x3 = tf.keras.layers.Conv2D(filters=24, kernel_size=(3, 3), padding="same")(b3x3)
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b3x3 = relu_bn(b3x3)
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out = tf.keras.layers.Concatenate()([b1x1, b5x5])
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return out
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def identity_block_bn(input_tensor):
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"""
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Identity block with no shortcut convolution
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"""
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y = tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), padding="same")(
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input_tensor
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)
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y = relu_bn(y)
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y = tf.keras.layers.Conv2D(filters=24, kernel_size=(3, 3), padding="same")(y)
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y = bn(y)
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out = tf.keras.layers.Add()([y, input_tensor])
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out = relu(out)
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return out
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def identity_block_short_conv_bn(input_tensor):
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"""
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Identity block with shortcut convolution
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"""
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y = tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), padding="same")(
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input_tensor
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)
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y = relu_bn(y)
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y = tf.keras.layers.Conv2D(
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filters=24, kernel_size=(3, 3), strides=(2, 2), padding="same"
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)(y)
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y = bn(y)
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ds_input = tf.keras.layers.Conv2D(
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filters=24, kernel_size=(3, 3), strides=(2, 2), padding="same"
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)(input_tensor)
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ds_input = bn(ds_input)
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out = tf.keras.layers.Add()([y, ds_input])
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out = relu(out)
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return out
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def otho_28_28():
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input_img = tf.keras.layers.Input(shape=(28, 28), name="input_0")
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r = tf.keras.layers.Reshape(target_shape=(28, 28, 1), name="reshape_0")(input_img)
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x = tf.keras.layers.Conv2D(filters=2, kernel_size=(3, 3), name="conv_0")(r)
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x = tf.keras.layers.ReLU(name="relu_0")(x)
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x = tf.keras.layers.Conv2D(filters=2, kernel_size=(3, 3), name="conv_1")(x)
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x = tf.keras.layers.ReLU(name="relu_1")(x)
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x = tf.keras.layers.Flatten(name="flatten_0")(x)
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return tf.keras.Model(input_img, x, name="Otho")
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def lotho_28_28():
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input_img = tf.keras.layers.Input(shape=(28, 28), name="input_0")
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r = tf.keras.layers.Reshape(target_shape=(28, 28, 1), name="reshape_0")(input_img)
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x = tf.keras.layers.DepthwiseConv2D(kernel_size=(3, 3), name="dconv_0")(r)
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x = tf.keras.layers.ReLU(name="relu_0")(x)
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x = tf.keras.layers.DepthwiseConv2D(kernel_size=(3, 3), name="dconv_1")(x)
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x = tf.keras.layers.ReLU(name="relu_1")(x)
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x = tf.keras.layers.Flatten(name="flatten_0")(x)
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return tf.keras.Model(input_img, x, name="Lotho")
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def lobelia_28_28():
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input_img = tf.keras.layers.Input(shape=(28, 28), name="input_0")
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r = tf.keras.layers.Reshape(target_shape=(28, 28, 1), name="reshape_0")(input_img)
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x = tf.keras.layers.Flatten(name="flatten_0")(r)
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x = tf.keras.layers.Dense(100, name="dense_0")(x)
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x = tf.keras.layers.ReLU(name="relu_0")(x)
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x = tf.keras.layers.Dense(10, name="dense_1")(x)
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return tf.keras.Model(input_img, x, name="Lobelia")
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def merry_28_28():
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input_img = tf.keras.layers.Input(shape=(28, 28))
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x = tf.keras.layers.Reshape(target_shape=(28, 28, 1))(input_img)
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x = tf.keras.layers.Conv2D(filters=8, kernel_size=(3, 3))(x)
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x = relu_bn(x)
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x = tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3))(x)
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x = relu_bn(x)
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x = inception_block(x)
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x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
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x = tf.keras.layers.Flatten()(x)
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x = tf.keras.layers.Dense(100)(x)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.Dense(10)(x)
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return tf.keras.Model(input_img, x, name="Merry")
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def pippin_28_28():
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input_img = tf.keras.layers.Input(shape=(28, 28))
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x = tf.keras.layers.Reshape(target_shape=(28, 28, 1))(input_img)
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x = tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3))(x)
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x = relu_bn(x)
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x = tf.keras.layers.Conv2D(filters=24, kernel_size=(3, 3))(x)
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x = relu_bn(x)
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x = identity_block_bn(x)
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x = identity_block_short_conv_bn(x)
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x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
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x = tf.keras.layers.Flatten()(x)
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x = tf.keras.layers.Dense(100)(x)
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x = tf.keras.layers.ReLU()(x)
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x = tf.keras.layers.Dense(10)(x)
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return tf.keras.Model(input_img, x, name="Pippin")
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