# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Contains the definition of the Inception Resnet V2 architecture.

As described in http://arxiv.org/abs/1602.07261.

  Inception-v4, Inception-ResNet and the Impact of Residual Connections
    on Learning
  <NAME>, <NAME>, <NAME>, <NAME>
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

slim = tf.contrib.slim


def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
    """Builds the 35x35 resnet block.

    Args:
        net (Network): .
        scale (float, optional): [description]. Defaults to 1.0.
        activation_fn ([type], optional): [description]. Defaults to tf.nn.relu.
        scope ([type], optional): [description]. Defaults to None.
        reuse ([type], optional): [description]. Defaults to None.

    Returns:
        Network: Network.

    """
    with tf.variable_scope(scope, "Block35", [net], reuse=reuse):
        with tf.variable_scope("Branch_0"):
            tower_conv = slim.conv2d(net, 32, 1, scope="Conv2d_1x1")
        with tf.variable_scope("Branch_1"):
            tower_conv1_0 = slim.conv2d(net, 32, 1, scope="Conv2d_0a_1x1")
            tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope="Conv2d_0b_3x3")
        with tf.variable_scope("Branch_2"):
            tower_conv2_0 = slim.conv2d(net, 32, 1, scope="Conv2d_0a_1x1")
            tower_conv2_1 = slim.conv2d(tower_conv2_0, 48, 3, scope="Conv2d_0b_3x3")
            tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope="Conv2d_0c_3x3")
        mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_1, tower_conv2_2])
        up = slim.conv2d(
            mixed,
            net.get_shape()[3],
            1,
            normalizer_fn=None,
            activation_fn=None,
            scope="Conv2d_1x1",
        )
        net += scale * up
        if activation_fn:
            net = activation_fn(net)
    return net


def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
    """Builds the 17x17 resnet block.

    Args:
        net ([type]): [description]
        scale (float, optional): [description]. Defaults to 1.0.
        activation_fn ([type], optional): [description]. Defaults to tf.nn.relu.
        scope ([type], optional): [description]. Defaults to None.
        reuse ([type], optional): [description]. Defaults to None.

    Returns:
        [type]: [description]

    """
    with tf.variable_scope(scope, "Block17", [net], reuse=reuse):
        with tf.variable_scope("Branch_0"):
            tower_conv = slim.conv2d(net, 192, 1, scope="Conv2d_1x1")
        with tf.variable_scope("Branch_1"):
            tower_conv1_0 = slim.conv2d(net, 128, 1, scope="Conv2d_0a_1x1")
            tower_conv1_1 = slim.conv2d(
                tower_conv1_0, 160, [1, 7], scope="Conv2d_0b_1x7"
            )
            tower_conv1_2 = slim.conv2d(
                tower_conv1_1, 192, [7, 1], scope="Conv2d_0c_7x1"
            )
        mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
        up = slim.conv2d(
            mixed,
            net.get_shape()[3],
            1,
            normalizer_fn=None,
            activation_fn=None,
            scope="Conv2d_1x1",
        )
        net += scale * up
        if activation_fn:
            net = activation_fn(net)
    return net


def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
    """Builds the 8x8 resnet block.

    Args:
        net ([type]): [description]
        scale (float, optional): [description]. Defaults to 1.0.
        activation_fn ([type], optional): [description]. Defaults to tf.nn.relu.
        scope ([type], optional): [description]. Defaults to None.
        reuse ([type], optional): [description]. Defaults to None.

    Returns:
        [type]: [description]

    """
    with tf.variable_scope(scope, "Block8", [net], reuse=reuse):
        with tf.variable_scope("Branch_0"):
            tower_conv = slim.conv2d(net, 192, 1, scope="Conv2d_1x1")
        with tf.variable_scope("Branch_1"):
            tower_conv1_0 = slim.conv2d(net, 192, 1, scope="Conv2d_0a_1x1")
            tower_conv1_1 = slim.conv2d(
                tower_conv1_0, 224, [1, 3], scope="Conv2d_0b_1x3"
            )
            tower_conv1_2 = slim.conv2d(
                tower_conv1_1, 256, [3, 1], scope="Conv2d_0c_3x1"
            )
        mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
        up = slim.conv2d(
            mixed,
            net.get_shape()[3],
            1,
            normalizer_fn=None,
            activation_fn=None,
            scope="Conv2d_1x1",
        )
        net += scale * up
        if activation_fn:
            net = activation_fn(net)
    return net


def inception_resnet_v2(
    inputs,
    num_classes=1001,
    is_training=True,
    dropout_keep_prob=0.8,
    reuse=None,
    scope="InceptionResnetV2",
):
    """Creates the Inception Resnet V2 model.

    Args:
        inputs ([type]): A 4-D tensor of size [batch_size, height, width, 3].
        num_classes (int, optional): Number of predicted classes. Defaults to 1001.
        is_training (bool, optional): Whether is training or not.. Defaults to True.
        dropout_keep_prob (float, optional): Float, the fraction to keep before final layer.. Defaults to 0.8.
        reuse ([type], optional): Whether or not the unfiltering and its variables should be reused. To be
                                    able to reuse 'scope' must be given. Defaults to None.
        scope (str, optional): Optional variable_scope. Defaults to 'InceptionResnetV2'.

    Returns:
        logits: the logits outputs of the model.
        end_points: the set of end_points from the inception model.

    """
    end_points = {}

    with tf.variable_scope(scope, "InceptionResnetV2", [inputs], reuse=reuse):
        with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training):
            with slim.arg_scope(
                [slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                stride=1,
                padding="SAME",
            ):
                # 149 x 149 x 32
                net = slim.conv2d(
                    inputs, 32, 3, stride=2, padding="VALID", scope="Conv2d_1a_3x3"
                )
                end_points["Conv2d_1a_3x3"] = net
                # 147 x 147 x 32
                net = slim.conv2d(net, 32, 3, padding="VALID", scope="Conv2d_2a_3x3")
                end_points["Conv2d_2a_3x3"] = net
                # 147 x 147 x 64
                net = slim.conv2d(net, 64, 3, scope="Conv2d_2b_3x3")
                end_points["Conv2d_2b_3x3"] = net
                # 73 x 73 x 64
                net = slim.max_pool2d(
                    net, 3, stride=2, padding="VALID", scope="MaxPool_3a_3x3"
                )
                end_points["MaxPool_3a_3x3"] = net
                # 73 x 73 x 80
                net = slim.conv2d(net, 80, 1, padding="VALID", scope="Conv2d_3b_1x1")
                end_points["Conv2d_3b_1x1"] = net
                # 71 x 71 x 192
                net = slim.conv2d(net, 192, 3, padding="VALID", scope="Conv2d_4a_3x3")
                end_points["Conv2d_4a_3x3"] = net
                # 35 x 35 x 192
                net = slim.max_pool2d(
                    net, 3, stride=2, padding="VALID", scope="MaxPool_5a_3x3"
                )
                end_points["MaxPool_5a_3x3"] = net

                # 35 x 35 x 320
                with tf.variable_scope("Mixed_5b"):
                    with tf.variable_scope("Branch_0"):
                        tower_conv = slim.conv2d(net, 96, 1, scope="Conv2d_1x1")
                    with tf.variable_scope("Branch_1"):
                        tower_conv1_0 = slim.conv2d(net, 48, 1, scope="Conv2d_0a_1x1")
                        tower_conv1_1 = slim.conv2d(
                            tower_conv1_0, 64, 5, scope="Conv2d_0b_5x5"
                        )
                    with tf.variable_scope("Branch_2"):
                        tower_conv2_0 = slim.conv2d(net, 64, 1, scope="Conv2d_0a_1x1")
                        tower_conv2_1 = slim.conv2d(
                            tower_conv2_0, 96, 3, scope="Conv2d_0b_3x3"
                        )
                        tower_conv2_2 = slim.conv2d(
                            tower_conv2_1, 96, 3, scope="Conv2d_0c_3x3"
                        )
                    with tf.variable_scope("Branch_3"):
                        tower_pool = slim.avg_pool2d(
                            net, 3, stride=1, padding="SAME", scope="AvgPool_0a_3x3"
                        )
                        tower_pool_1 = slim.conv2d(
                            tower_pool, 64, 1, scope="Conv2d_0b_1x1"
                        )
                    net = tf.concat(
                        axis=3,
                        values=[tower_conv, tower_conv1_1, tower_conv2_2, tower_pool_1],
                    )

                end_points["Mixed_5b"] = net
                net = slim.repeat(net, 10, block35, scale=0.17)

                # 17 x 17 x 1088
                with tf.variable_scope("Mixed_6a"):
                    with tf.variable_scope("Branch_0"):
                        tower_conv = slim.conv2d(
                            net,
                            384,
                            3,
                            stride=2,
                            padding="VALID",
                            scope="Conv2d_1a_3x3",
                        )
                    with tf.variable_scope("Branch_1"):
                        tower_conv1_0 = slim.conv2d(net, 256, 1, scope="Conv2d_0a_1x1")
                        tower_conv1_1 = slim.conv2d(
                            tower_conv1_0, 256, 3, scope="Conv2d_0b_3x3"
                        )
                        tower_conv1_2 = slim.conv2d(
                            tower_conv1_1,
                            384,
                            3,
                            stride=2,
                            padding="VALID",
                            scope="Conv2d_1a_3x3",
                        )
                    with tf.variable_scope("Branch_2"):
                        tower_pool = slim.max_pool2d(
                            net, 3, stride=2, padding="VALID", scope="MaxPool_1a_3x3"
                        )
                    net = tf.concat(
                        axis=3, values=[tower_conv, tower_conv1_2, tower_pool]
                    )

                end_points["Mixed_6a"] = net
                net = slim.repeat(net, 20, block17, scale=0.10)

                # Auxiliary tower
                with tf.variable_scope("AuxLogits"):
                    aux = slim.avg_pool2d(
                        net, 5, stride=3, padding="VALID", scope="Conv2d_1a_3x3"
                    )
                    aux = slim.conv2d(aux, 128, 1, scope="Conv2d_1b_1x1")
                    aux = slim.conv2d(
                        aux,
                        768,
                        aux.get_shape()[1:3],
                        padding="VALID",
                        scope="Conv2d_2a_5x5",
                    )
                    aux = slim.flatten(aux)
                    aux = slim.fully_connected(
                        aux, num_classes, activation_fn=None, scope="Logits"
                    )
                    end_points["AuxLogits"] = aux

                with tf.variable_scope("Mixed_7a"):
                    with tf.variable_scope("Branch_0"):
                        tower_conv = slim.conv2d(net, 256, 1, scope="Conv2d_0a_1x1")
                        tower_conv_1 = slim.conv2d(
                            tower_conv,
                            384,
                            3,
                            stride=2,
                            padding="VALID",
                            scope="Conv2d_1a_3x3",
                        )
                    with tf.variable_scope("Branch_1"):
                        tower_conv1 = slim.conv2d(net, 256, 1, scope="Conv2d_0a_1x1")
                        tower_conv1_1 = slim.conv2d(
                            tower_conv1,
                            288,
                            3,
                            stride=2,
                            padding="VALID",
                            scope="Conv2d_1a_3x3",
                        )
                    with tf.variable_scope("Branch_2"):
                        tower_conv2 = slim.conv2d(net, 256, 1, scope="Conv2d_0a_1x1")
                        tower_conv2_1 = slim.conv2d(
                            tower_conv2, 288, 3, scope="Conv2d_0b_3x3"
                        )
                        tower_conv2_2 = slim.conv2d(
                            tower_conv2_1,
                            320,
                            3,
                            stride=2,
                            padding="VALID",
                            scope="Conv2d_1a_3x3",
                        )
                    with tf.variable_scope("Branch_3"):
                        tower_pool = slim.max_pool2d(
                            net, 3, stride=2, padding="VALID", scope="MaxPool_1a_3x3"
                        )
                    net = tf.concat(
                        axis=3,
                        values=[tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool],
                    )

                end_points["Mixed_7a"] = net

                net = slim.repeat(net, 9, block8, scale=0.20)
                net = block8(net, activation_fn=None)

                net = slim.conv2d(net, 1536, 1, scope="Conv2d_7b_1x1")
                end_points["Conv2d_7b_1x1"] = net

                with tf.variable_scope("Logits"):
                    end_points["PrePool"] = net
                    net = slim.avg_pool2d(
                        net,
                        net.get_shape()[1:3],
                        padding="VALID",
                        scope="AvgPool_1a_8x8",
                    )
                    net = slim.flatten(net)

                    net = slim.dropout(
                        net, dropout_keep_prob, is_training=is_training, scope="Dropout"
                    )

                    end_points["PreLogitsFlatten"] = net
                    logits = slim.fully_connected(
                        net, num_classes, activation_fn=None, scope="Logits"
                    )
                    end_points["Logits"] = logits
                    end_points["Predictions"] = tf.nn.softmax(
                        logits, name="Predictions"
                    )

        return logits, end_points


inception_resnet_v2.default_image_size = 299


def inception_resnet_v2_arg_scope(
    weight_decay=0.00004, batch_norm_decay=0.9997, batch_norm_epsilon=0.001
):
    """Yields the scope with the default parameters for inception_resnet_v2.

    Args:
        weight_decay (float, optional): The weight decay for weights variables.. Defaults to 0.00004.
        batch_norm_decay (float, optional): Decay for the moving average of batch_norm momentums. 
                                            Defaults to 0.9997.
        batch_norm_epsilon (float, optional): Small float added to variance to avoid dividing by zero. 
                                                Defaults to 0.001.

    Returns:
        arg_scope with the parameters needed for inception_resnet_v2.
    """
    # Set weight_decay for weights in conv2d and fully_connected layers.
    with slim.arg_scope(
        [slim.conv2d, slim.fully_connected],
        weights_regularizer=slim.l2_regularizer(weight_decay),
        biases_regularizer=slim.l2_regularizer(weight_decay),
    ):
        batch_norm_params = {
            "decay": batch_norm_decay,
            "epsilon": batch_norm_epsilon,
        }
        # Set activation_fn and parameters for batch_norm.
        with slim.arg_scope(
            [slim.conv2d],
            activation_fn=tf.nn.relu,
            normalizer_fn=slim.batch_norm,
            normalizer_params=batch_norm_params,
        ) as scope:
            return scope

# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Contains the definition of the Inception Resnet V2 architecture.

As described in http://arxiv.org/abs/1602.07261.

  Inception-v4, Inception-ResNet and the Impact of Residual Connections
    on Learning
  <NAME>, <NAME>, <NAME>, <NAME>
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

slim = tf.contrib.slim


def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
    """Builds the 35x35 resnet block.

    Args:
        net (Network): .
        scale (float, optional): [description]. Defaults to 1.0.
        activation_fn ([type], optional): [description]. Defaults to tf.nn.relu.
        scope ([type], optional): [description]. Defaults to None.
        reuse ([type], optional): [description]. Defaults to None.

    Returns:
        Network: Network.

    """
    with tf.variable_scope(scope, "Block35", [net], reuse=reuse):
        with tf.variable_scope("Branch_0"):
            tower_conv = slim.conv2d(net, 32, 1, scope="Conv2d_1x1")
        with tf.variable_scope("Branch_1"):
            tower_conv1_0 = slim.conv2d(net, 32, 1, scope="Conv2d_0a_1x1")
            tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope="Conv2d_0b_3x3")
        with tf.variable_scope("Branch_2"):
            tower_conv2_0 = slim.conv2d(net, 32, 1, scope="Conv2d_0a_1x1")
            tower_conv2_1 = slim.conv2d(tower_conv2_0, 48, 3, scope="Conv2d_0b_3x3")
            tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope="Conv2d_0c_3x3")
        mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_1, tower_conv2_2])
        up = slim.conv2d(
            mixed,
            net.get_shape()[3],
            1,
            normalizer_fn=None,
            activation_fn=None,
            scope="Conv2d_1x1",
        )
        net += scale * up
        if activation_fn:
            net = activation_fn(net)
    return net


def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
    """Builds the 17x17 resnet block.

    Args:
        net ([type]): [description]
        scale (float, optional): [description]. Defaults to 1.0.
        activation_fn ([type], optional): [description]. Defaults to tf.nn.relu.
        scope ([type], optional): [description]. Defaults to None.
        reuse ([type], optional): [description]. Defaults to None.

    Returns:
        [type]: [description]

    """
    with tf.variable_scope(scope, "Block17", [net], reuse=reuse):
        with tf.variable_scope("Branch_0"):
            tower_conv = slim.conv2d(net, 192, 1, scope="Conv2d_1x1")
        with tf.variable_scope("Branch_1"):
            tower_conv1_0 = slim.conv2d(net, 128, 1, scope="Conv2d_0a_1x1")
            tower_conv1_1 = slim.conv2d(
                tower_conv1_0, 160, [1, 7], scope="Conv2d_0b_1x7"
            )
            tower_conv1_2 = slim.conv2d(
                tower_conv1_1, 192, [7, 1], scope="Conv2d_0c_7x1"
            )
        mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
        up = slim.conv2d(
            mixed,
            net.get_shape()[3],
            1,
            normalizer_fn=None,
            activation_fn=None,
            scope="Conv2d_1x1",
        )
        net += scale * up
        if activation_fn:
            net = activation_fn(net)
    return net


def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
    """Builds the 8x8 resnet block.

    Args:
        net ([type]): [description]
        scale (float, optional): [description]. Defaults to 1.0.
        activation_fn ([type], optional): [description]. Defaults to tf.nn.relu.
        scope ([type], optional): [description]. Defaults to None.
        reuse ([type], optional): [description]. Defaults to None.

    Returns:
        [type]: [description]

    """
    with tf.variable_scope(scope, "Block8", [net], reuse=reuse):
        with tf.variable_scope("Branch_0"):
            tower_conv = slim.conv2d(net, 192, 1, scope="Conv2d_1x1")
        with tf.variable_scope("Branch_1"):
            tower_conv1_0 = slim.conv2d(net, 192, 1, scope="Conv2d_0a_1x1")
            tower_conv1_1 = slim.conv2d(
                tower_conv1_0, 224, [1, 3], scope="Conv2d_0b_1x3"
            )
            tower_conv1_2 = slim.conv2d(
                tower_conv1_1, 256, [3, 1], scope="Conv2d_0c_3x1"
            )
        mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
        up = slim.conv2d(
            mixed,
            net.get_shape()[3],
            1,
            normalizer_fn=None,
            activation_fn=None,
            scope="Conv2d_1x1",
        )
        net += scale * up
        if activation_fn:
            net = activation_fn(net)
    return net


def inception_resnet_v2(
    inputs,
    num_classes=1001,
    is_training=True,
    dropout_keep_prob=0.8,
    reuse=None,
    scope="InceptionResnetV2",
):
    """Creates the Inception Resnet V2 model.

    Args:
        inputs ([type]): A 4-D tensor of size [batch_size, height, width, 3].
        num_classes (int, optional): Number of predicted classes. Defaults to 1001.
        is_training (bool, optional): Whether is training or not.. Defaults to True.
        dropout_keep_prob (float, optional): Float, the fraction to keep before final layer.. Defaults to 0.8.
        reuse ([type], optional): Whether or not the unfiltering and its variables should be reused. To be
                                    able to reuse 'scope' must be given. Defaults to None.
        scope (str, optional): Optional variable_scope. Defaults to 'InceptionResnetV2'.

    Returns:
        logits: the logits outputs of the model.
        end_points: the set of end_points from the inception model.

    """
    end_points = {}

    with tf.variable_scope(scope, "InceptionResnetV2", [inputs], reuse=reuse):
        with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training):
            with slim.arg_scope(
                [slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                stride=1,
                padding="SAME",
            ):
                # 149 x 149 x 32
                net = slim.conv2d(
                    inputs, 32, 3, stride=2, padding="VALID", scope="Conv2d_1a_3x3"
                )
                end_points["Conv2d_1a_3x3"] = net
                # 147 x 147 x 32
                net = slim.conv2d(net, 32, 3, padding="VALID", scope="Conv2d_2a_3x3")
                end_points["Conv2d_2a_3x3"] = net
                # 147 x 147 x 64
                net = slim.conv2d(net, 64, 3, scope="Conv2d_2b_3x3")
                end_points["Conv2d_2b_3x3"] = net
                # 73 x 73 x 64
                net = slim.max_pool2d(
                    net, 3, stride=2, padding="VALID", scope="MaxPool_3a_3x3"
                )
                end_points["MaxPool_3a_3x3"] = net
                # 73 x 73 x 80
                net = slim.conv2d(net, 80, 1, padding="VALID", scope="Conv2d_3b_1x1")
                end_points["Conv2d_3b_1x1"] = net
                # 71 x 71 x 192
                net = slim.conv2d(net, 192, 3, padding="VALID", scope="Conv2d_4a_3x3")
                end_points["Conv2d_4a_3x3"] = net
                # 35 x 35 x 192
                net = slim.max_pool2d(
                    net, 3, stride=2, padding="VALID", scope="MaxPool_5a_3x3"
                )
                end_points["MaxPool_5a_3x3"] = net

                # 35 x 35 x 320
                with tf.variable_scope("Mixed_5b"):
                    with tf.variable_scope("Branch_0"):
                        tower_conv = slim.conv2d(net, 96, 1, scope="Conv2d_1x1")
                    with tf.variable_scope("Branch_1"):
                        tower_conv1_0 = slim.conv2d(net, 48, 1, scope="Conv2d_0a_1x1")
                        tower_conv1_1 = slim.conv2d(
                            tower_conv1_0, 64, 5, scope="Conv2d_0b_5x5"
                        )
                    with tf.variable_scope("Branch_2"):
                        tower_conv2_0 = slim.conv2d(net, 64, 1, scope="Conv2d_0a_1x1")
                        tower_conv2_1 = slim.conv2d(
                            tower_conv2_0, 96, 3, scope="Conv2d_0b_3x3"
                        )
                        tower_conv2_2 = slim.conv2d(
                            tower_conv2_1, 96, 3, scope="Conv2d_0c_3x3"
                        )
                    with tf.variable_scope("Branch_3"):
                        tower_pool = slim.avg_pool2d(
                            net, 3, stride=1, padding="SAME", scope="AvgPool_0a_3x3"
                        )
                        tower_pool_1 = slim.conv2d(
                            tower_pool, 64, 1, scope="Conv2d_0b_1x1"
                        )
                    net = tf.concat(
                        axis=3,
                        values=[tower_conv, tower_conv1_1, tower_conv2_2, tower_pool_1],
                    )

                end_points["Mixed_5b"] = net
                net = slim.repeat(net, 10, block35, scale=0.17)

                # 17 x 17 x 1088
                with tf.variable_scope("Mixed_6a"):
                    with tf.variable_scope("Branch_0"):
                        tower_conv = slim.conv2d(
                            net,
                            384,
                            3,
                            stride=2,
                            padding="VALID",
                            scope="Conv2d_1a_3x3",
                        )
                    with tf.variable_scope("Branch_1"):
                        tower_conv1_0 = slim.conv2d(net, 256, 1, scope="Conv2d_0a_1x1")
                        tower_conv1_1 = slim.conv2d(
                            tower_conv1_0, 256, 3, scope="Conv2d_0b_3x3"
                        )
                        tower_conv1_2 = slim.conv2d(
                            tower_conv1_1,
                            384,
                            3,
                            stride=2,
                            padding="VALID",
                            scope="Conv2d_1a_3x3",
                        )
                    with tf.variable_scope("Branch_2"):
                        tower_pool = slim.max_pool2d(
                            net, 3, stride=2, padding="VALID", scope="MaxPool_1a_3x3"
                        )
                    net = tf.concat(
                        axis=3, values=[tower_conv, tower_conv1_2, tower_pool]
                    )

                end_points["Mixed_6a"] = net
                net = slim.repeat(net, 20, block17, scale=0.10)

                # Auxiliary tower
                with tf.variable_scope("AuxLogits"):
                    aux = slim.avg_pool2d(
                        net, 5, stride=3, padding="VALID", scope="Conv2d_1a_3x3"
                    )
                    aux = slim.conv2d(aux, 128, 1, scope="Conv2d_1b_1x1")
                    aux = slim.conv2d(
                        aux,
                        768,
                        aux.get_shape()[1:3],
                        padding="VALID",
                        scope="Conv2d_2a_5x5",
                    )
                    aux = slim.flatten(aux)
                    aux = slim.fully_connected(
                        aux, num_classes, activation_fn=None, scope="Logits"
                    )
                    end_points["AuxLogits"] = aux

                with tf.variable_scope("Mixed_7a"):
                    with tf.variable_scope("Branch_0"):
                        tower_conv = slim.conv2d(net, 256, 1, scope="Conv2d_0a_1x1")
                        tower_conv_1 = slim.conv2d(
                            tower_conv,
                            384,
                            3,
                            stride=2,
                            padding="VALID",
                            scope="Conv2d_1a_3x3",
                        )
                    with tf.variable_scope("Branch_1"):
                        tower_conv1 = slim.conv2d(net, 256, 1, scope="Conv2d_0a_1x1")
                        tower_conv1_1 = slim.conv2d(
                            tower_conv1,
                            288,
                            3,
                            stride=2,
                            padding="VALID",
                            scope="Conv2d_1a_3x3",
                        )
                    with tf.variable_scope("Branch_2"):
                        tower_conv2 = slim.conv2d(net, 256, 1, scope="Conv2d_0a_1x1")
                        tower_conv2_1 = slim.conv2d(
                            tower_conv2, 288, 3, scope="Conv2d_0b_3x3"
                        )
                        tower_conv2_2 = slim.conv2d(
                            tower_conv2_1,
                            320,
                            3,
                            stride=2,
                            padding="VALID",
                            scope="Conv2d_1a_3x3",
                        )
                    with tf.variable_scope("Branch_3"):
                        tower_pool = slim.max_pool2d(
                            net, 3, stride=2, padding="VALID", scope="MaxPool_1a_3x3"
                        )
                    net = tf.concat(
                        axis=3,
                        values=[tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool],
                    )

                end_points["Mixed_7a"] = net

                net = slim.repeat(net, 9, block8, scale=0.20)
                net = block8(net, activation_fn=None)

                net = slim.conv2d(net, 1536, 1, scope="Conv2d_7b_1x1")
                end_points["Conv2d_7b_1x1"] = net

                with tf.variable_scope("Logits"):
                    end_points["PrePool"] = net
                    net = slim.avg_pool2d(
                        net,
                        net.get_shape()[1:3],
                        padding="VALID",
                        scope="AvgPool_1a_8x8",
                    )
                    net = slim.flatten(net)

                    net = slim.dropout(
                        net, dropout_keep_prob, is_training=is_training, scope="Dropout"
                    )

                    end_points["PreLogitsFlatten"] = net
                    logits = slim.fully_connected(
                        net, num_classes, activation_fn=None, scope="Logits"
                    )
                    end_points["Logits"] = logits
                    end_points["Predictions"] = tf.nn.softmax(
                        logits, name="Predictions"
                    )

        return logits, end_points


inception_resnet_v2.default_image_size = 299


def inception_resnet_v2_arg_scope(
    weight_decay=0.00004, batch_norm_decay=0.9997, batch_norm_epsilon=0.001
):
    """Yields the scope with the default parameters for inception_resnet_v2.

    Args:
        weight_decay (float, optional): The weight decay for weights variables.. Defaults to 0.00004.
        batch_norm_decay (float, optional): Decay for the moving average of batch_norm momentums. 
                                            Defaults to 0.9997.
        batch_norm_epsilon (float, optional): Small float added to variance to avoid dividing by zero. 
                                                Defaults to 0.001.

    Returns:
        arg_scope with the parameters needed for inception_resnet_v2.
    """
    # Set weight_decay for weights in conv2d and fully_connected layers.
    with slim.arg_scope(
        [slim.conv2d, slim.fully_connected],
        weights_regularizer=slim.l2_regularizer(weight_decay),
        biases_regularizer=slim.l2_regularizer(weight_decay),
    ):
        batch_norm_params = {
            "decay": batch_norm_decay,
            "epsilon": batch_norm_epsilon,
        }
        # Set activation_fn and parameters for batch_norm.
        with slim.arg_scope(
            [slim.conv2d],
            activation_fn=tf.nn.relu,
            normalizer_fn=slim.batch_norm,
            normalizer_params=batch_norm_params,
        ) as scope:
            return scope

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # 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. # ============================================================================== Contains the definition of the Inception Resnet V2 architecture.  As described in http://arxiv.org/abs/1602.07261.    Inception-v4, Inception-ResNet and the Impact of Residual Connections     on Learning   <NAME>, <NAME>, <NAME>, <NAME> Builds the 35x35 resnet block.      Args:         net (Network): .         scale (float, optional): [description]. Defaults to 1.0.         activation_fn ([type], optional): [description]. Defaults to tf.nn.relu.         scope ([type], optional): [description]. Defaults to None.         reuse ([type], optional): [description]. Defaults to None.      Returns:         Network: Network. Builds the 17x17 resnet block.      Args:         net ([type]): [description]         scale (float, optional): [description]. Defaults to 1.0.         activation_fn ([type], optional): [description]. Defaults to tf.nn.relu.         scope ([type], optional): [description]. Defaults to None.         reuse ([type], optional): [description]. Defaults to None.      Returns:         [type]: [description] Builds the 8x8 resnet block.      Args:         net ([type]): [description]         scale (float, optional): [description]. Defaults to 1.0.         activation_fn ([type], optional): [description]. Defaults to tf.nn.relu.         scope ([type], optional): [description]. Defaults to None.         reuse ([type], optional): [description]. Defaults to None.      Returns:         [type]: [description] Creates the Inception Resnet V2 model.      Args:         inputs ([type]): A 4-D tensor of size [batch_size, height, width, 3].         num_classes (int, optional): Number of predicted classes. Defaults to 1001.         is_training (bool, optional): Whether is training or not.. Defaults to True.         dropout_keep_prob (float, optional): Float, the fraction to keep before final layer.. Defaults to 0.8.         reuse ([type], optional): Whether or not the unfiltering and its variables should be reused. To be                                     able to reuse 'scope' must be given. Defaults to None.         scope (str, optional): Optional variable_scope. Defaults to 'InceptionResnetV2'.      Returns:         logits: the logits outputs of the model.         end_points: the set of end_points from the inception model. # 149 x 149 x 32 # 147 x 147 x 32 # 147 x 147 x 64 # 73 x 73 x 64 # 73 x 73 x 80 # 71 x 71 x 192 # 35 x 35 x 192 # 35 x 35 x 320 # 17 x 17 x 1088 # Auxiliary tower Yields the scope with the default parameters for inception_resnet_v2.      Args:         weight_decay (float, optional): The weight decay for weights variables.. Defaults to 0.00004.         batch_norm_decay (float, optional): Decay for the moving average of batch_norm momentums.                                              Defaults to 0.9997.         batch_norm_epsilon (float, optional): Small float added to variance to avoid dividing by zero.                                                  Defaults to 0.001.      Returns:         arg_scope with the parameters needed for inception_resnet_v2. # Set weight_decay for weights in conv2d and fully_connected layers. # Set activation_fn and parameters for batch_norm.