<gh_stars>100-1000
"""
 Copyright (c) 2022 Intel Corporation
 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.
"""

from tensorflow.python.keras import backend
from tensorflow.python.keras import layers
from tensorflow.python.keras.applications import imagenet_utils
from tensorflow.python.keras.engine import training

NUM_CLASSES = 1000


def dense_block(x, blocks, name):
    for i in range(blocks):
        x = conv_block(x, 32, name=name + '_block' + str(i + 1))
    return x


def transition_block(x, reduction, name):
    bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(
        x)
    x = layers.Activation('relu', name=name + '_relu')(x)
    x = layers.Conv2D(
        int(backend.int_shape(x)[bn_axis] * reduction),
        1,
        use_bias=False,
        name=name + '_conv')(
        x)
    x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x)
    return x


def conv_block(x, growth_rate, name):
    bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
    x1 = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(
        x)
    x1 = layers.Activation('relu', name=name + '_0_relu')(x1)
    x1 = layers.Conv2D(
        4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')(
        x1)
    x1 = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(
        x1)
    x1 = layers.Activation('relu', name=name + '_1_relu')(x1)
    x1 = layers.Conv2D(
        growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')(
        x1)
    x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1])
    return x


def DenseNet121(input_shape=None):
    input_shape = imagenet_utils.obtain_input_shape(
        input_shape,
        default_size=224,
        min_size=32,
        data_format=backend.image_data_format(),
        require_flatten=True)

    img_input = layers.Input(shape=input_shape)

    bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

    x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
    x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(
        x)
    x = layers.Activation('relu', name='conv1/relu')(x)
    x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
    x = layers.MaxPooling2D(3, strides=2, name='pool1')(x)

    x = dense_block(x, 6, name='conv2')
    x = transition_block(x, 0.5, name='pool2')
    x = dense_block(x, 12, name='conv3')
    x = transition_block(x, 0.5, name='pool3')
    x = dense_block(x, 24, name='conv4')
    x = transition_block(x, 0.5, name='pool4')
    x = dense_block(x, 16, name='conv5')

    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)
    x = layers.Activation('relu', name='relu')(x)

    x = layers.GlobalAveragePooling2D(name='avg_pool')(x)

    imagenet_utils.validate_activation('softmax', None)
    x = layers.Dense(NUM_CLASSES, activation='softmax',
                     name='predictions')(x)

    # Create model.
    model = training.Model(img_input, x, name='densenet121')

    return model

"""
 Copyright (c) 2022 Intel Corporation
 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.
"""

from tensorflow.python.keras import backend
from tensorflow.python.keras import layers
from tensorflow.python.keras.applications import imagenet_utils
from tensorflow.python.keras.engine import training

NUM_CLASSES = 1000


def dense_block(x, blocks, name):
    for i in range(blocks):
        x = conv_block(x, 32, name=name + '_block' + str(i + 1))
    return x


def transition_block(x, reduction, name):
    bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(
        x)
    x = layers.Activation('relu', name=name + '_relu')(x)
    x = layers.Conv2D(
        int(backend.int_shape(x)[bn_axis] * reduction),
        1,
        use_bias=False,
        name=name + '_conv')(
        x)
    x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x)
    return x


def conv_block(x, growth_rate, name):
    bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
    x1 = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(
        x)
    x1 = layers.Activation('relu', name=name + '_0_relu')(x1)
    x1 = layers.Conv2D(
        4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')(
        x1)
    x1 = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(
        x1)
    x1 = layers.Activation('relu', name=name + '_1_relu')(x1)
    x1 = layers.Conv2D(
        growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')(
        x1)
    x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1])
    return x


def DenseNet121(input_shape=None):
    input_shape = imagenet_utils.obtain_input_shape(
        input_shape,
        default_size=224,
        min_size=32,
        data_format=backend.image_data_format(),
        require_flatten=True)

    img_input = layers.Input(shape=input_shape)

    bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

    x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
    x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(
        x)
    x = layers.Activation('relu', name='conv1/relu')(x)
    x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
    x = layers.MaxPooling2D(3, strides=2, name='pool1')(x)

    x = dense_block(x, 6, name='conv2')
    x = transition_block(x, 0.5, name='pool2')
    x = dense_block(x, 12, name='conv3')
    x = transition_block(x, 0.5, name='pool3')
    x = dense_block(x, 24, name='conv4')
    x = transition_block(x, 0.5, name='pool4')
    x = dense_block(x, 16, name='conv5')

    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)
    x = layers.Activation('relu', name='relu')(x)

    x = layers.GlobalAveragePooling2D(name='avg_pool')(x)

    imagenet_utils.validate_activation('softmax', None)
    x = layers.Dense(NUM_CLASSES, activation='softmax',
                     name='predictions')(x)

    # Create model.
    model = training.Model(img_input, x, name='densenet121')

    return model
Copyright (c) 2022 Intel Corporation  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. # Create model.