#!/usr/bin/env python
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"""Contains the definition of the Inception V4 architecture.

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

  Inception-v4, Inception-ResNet and the Impact of Residual Connections
    on Learning
  Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

slim = tf.contrib.slim


def block_inception_a(inputs, scope=None, reuse=None):
    """Builds Inception-A block for Inception v4 network."""
    # By default use stride=1 and SAME padding
    with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
                        stride=1, padding='SAME'):
        with tf.variable_scope(scope, 'BlockInceptionA', [inputs], reuse=reuse):
            with tf.variable_scope('Branch_0'):
                branch_0 = slim.conv2d(inputs, 96, [1, 1], scope='Conv2d_0a_1x1')
            with tf.variable_scope('Branch_1'):
                branch_1 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1')
                branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_2'):
                branch_2 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1')
                branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
                branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
            with tf.variable_scope('Branch_3'):
                branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
                branch_3 = slim.conv2d(branch_3, 96, [1, 1], scope='Conv2d_0b_1x1')
            return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])


def block_reduction_a(inputs, scope=None, reuse=None):
    """Builds Reduction-A block for Inception v4 network."""
    # By default use stride=1 and SAME padding
    with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
                        stride=1, padding='SAME'):
        with tf.variable_scope(scope, 'BlockReductionA', [inputs], reuse=reuse):
            with tf.variable_scope('Branch_0'):
                branch_0 = slim.conv2d(inputs, 384, [3, 3], stride=2, padding='VALID',
                                       scope='Conv2d_1a_3x3')
            with tf.variable_scope('Branch_1'):
                branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
                branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
                branch_1 = slim.conv2d(branch_1, 256, [3, 3], stride=2,
                                       padding='VALID', scope='Conv2d_1a_3x3')
            with tf.variable_scope('Branch_2'):
                branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
                                           scope='MaxPool_1a_3x3')
            return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])


def block_inception_b(inputs, scope=None, reuse=None):
    """Builds Inception-B block for Inception v4 network."""
    # By default use stride=1 and SAME padding
    with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
                        stride=1, padding='SAME'):
        with tf.variable_scope(scope, 'BlockInceptionB', [inputs], reuse=reuse):
            with tf.variable_scope('Branch_0'):
                branch_0 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
            with tf.variable_scope('Branch_1'):
                branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
                branch_1 = slim.conv2d(branch_1, 224, [1, 7], scope='Conv2d_0b_1x7')
                branch_1 = slim.conv2d(branch_1, 256, [7, 1], scope='Conv2d_0c_7x1')
            with tf.variable_scope('Branch_2'):
                branch_2 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
                branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1')
                branch_2 = slim.conv2d(branch_2, 224, [1, 7], scope='Conv2d_0c_1x7')
                branch_2 = slim.conv2d(branch_2, 224, [7, 1], scope='Conv2d_0d_7x1')
                branch_2 = slim.conv2d(branch_2, 256, [1, 7], scope='Conv2d_0e_1x7')
            with tf.variable_scope('Branch_3'):
                branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
                branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
            return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])


def block_reduction_b(inputs, scope=None, reuse=None):
    """Builds Reduction-B block for Inception v4 network."""
    # By default use stride=1 and SAME padding
    with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
                        stride=1, padding='SAME'):
        with tf.variable_scope(scope, 'BlockReductionB', [inputs], reuse=reuse):
            with tf.variable_scope('Branch_0'):
                branch_0 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
                branch_0 = slim.conv2d(branch_0, 192, [3, 3], stride=2,
                                       padding='VALID', scope='Conv2d_1a_3x3')
            with tf.variable_scope('Branch_1'):
                branch_1 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1')
                branch_1 = slim.conv2d(branch_1, 256, [1, 7], scope='Conv2d_0b_1x7')
                branch_1 = slim.conv2d(branch_1, 320, [7, 1], scope='Conv2d_0c_7x1')
                branch_1 = slim.conv2d(branch_1, 320, [3, 3], stride=2,
                                       padding='VALID', scope='Conv2d_1a_3x3')
            with tf.variable_scope('Branch_2'):
                branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
                                           scope='MaxPool_1a_3x3')
            return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])


def block_inception_c(inputs, scope=None, reuse=None):
    """Builds Inception-C block for Inception v4 network."""
    # By default use stride=1 and SAME padding
    with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
                        stride=1, padding='SAME'):
        with tf.variable_scope(scope, 'BlockInceptionC', [inputs], reuse=reuse):
            with tf.variable_scope('Branch_0'):
                branch_0 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1')
            with tf.variable_scope('Branch_1'):
                branch_1 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
                branch_1 = tf.concat(axis=3, values=[
                    slim.conv2d(branch_1, 256, [1, 3], scope='Conv2d_0b_1x3'),
                    slim.conv2d(branch_1, 256, [3, 1], scope='Conv2d_0c_3x1')])
            with tf.variable_scope('Branch_2'):
                branch_2 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
                branch_2 = slim.conv2d(branch_2, 448, [3, 1], scope='Conv2d_0b_3x1')
                branch_2 = slim.conv2d(branch_2, 512, [1, 3], scope='Conv2d_0c_1x3')
                branch_2 = tf.concat(axis=3, values=[
                    slim.conv2d(branch_2, 256, [1, 3], scope='Conv2d_0d_1x3'),
                    slim.conv2d(branch_2, 256, [3, 1], scope='Conv2d_0e_3x1')])
            with tf.variable_scope('Branch_3'):
                branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
                branch_3 = slim.conv2d(branch_3, 256, [1, 1], scope='Conv2d_0b_1x1')
            return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])


def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
    """Creates the Inception V4 network up to the given final endpoint.

    Args:
      inputs: a 4-D tensor of size [batch_size, height, width, 3].
      final_endpoint: specifies the endpoint to construct the network up to.
        It can be one of [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
        'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
        'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e',
        'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c',
        'Mixed_7d']
      scope: Optional variable_scope.

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

    Raises:
      ValueError: if final_endpoint is not set to one of the predefined values,
    """
    end_points = {}

    def add_and_check_final(name, net):
        end_points[name] = net
        return name == final_endpoint

    with tf.variable_scope(scope, 'InceptionV4', [inputs]):
        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                            stride=1, padding='SAME'):
            # 299 x 299 x 3
            net = slim.conv2d(inputs, 32, [3, 3], stride=2,
                              padding='VALID', scope='Conv2d_1a_3x3')
            if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points
            # 149 x 149 x 32
            net = slim.conv2d(net, 32, [3, 3], padding='VALID',
                              scope='Conv2d_2a_3x3')
            if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points
            # 147 x 147 x 32
            net = slim.conv2d(net, 64, [3, 3], scope='Conv2d_2b_3x3')
            if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points
            # 147 x 147 x 64
            with tf.variable_scope('Mixed_3a'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
                                               scope='MaxPool_0a_3x3')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 96, [3, 3], stride=2, padding='VALID',
                                           scope='Conv2d_0a_3x3')
                net = tf.concat(axis=3, values=[branch_0, branch_1])
                if add_and_check_final('Mixed_3a', net): return net, end_points

            # 73 x 73 x 160
            with tf.variable_scope('Mixed_4a'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_0 = slim.conv2d(branch_0, 96, [3, 3], padding='VALID',
                                           scope='Conv2d_1a_3x3')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 64, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 64, [7, 1], scope='Conv2d_0c_7x1')
                    branch_1 = slim.conv2d(branch_1, 96, [3, 3], padding='VALID',
                                           scope='Conv2d_1a_3x3')
                net = tf.concat(axis=3, values=[branch_0, branch_1])
                if add_and_check_final('Mixed_4a', net): return net, end_points

            # 71 x 71 x 192
            with tf.variable_scope('Mixed_5a'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [3, 3], stride=2, padding='VALID',
                                           scope='Conv2d_1a_3x3')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
                                               scope='MaxPool_1a_3x3')
                net = tf.concat(axis=3, values=[branch_0, branch_1])
                if add_and_check_final('Mixed_5a', net): return net, end_points

            # 35 x 35 x 384
            # 4 x Inception-A blocks
            for idx in range(4):
                block_scope = 'Mixed_5' + chr(ord('b') + idx)
                net = block_inception_a(net, block_scope)
                if add_and_check_final(block_scope, net): return net, end_points

            # 35 x 35 x 384
            # Reduction-A block
            net = block_reduction_a(net, 'Mixed_6a')
            if add_and_check_final('Mixed_6a', net): return net, end_points

            # 17 x 17 x 1024
            # 7 x Inception-B blocks
            for idx in range(7):
                block_scope = 'Mixed_6' + chr(ord('b') + idx)
                net = block_inception_b(net, block_scope)
                if add_and_check_final(block_scope, net): return net, end_points

            # 17 x 17 x 1024
            # Reduction-B block
            net = block_reduction_b(net, 'Mixed_7a')
            if add_and_check_final('Mixed_7a', net): return net, end_points

            # 8 x 8 x 1536
            # 3 x Inception-C blocks
            for idx in range(3):
                block_scope = 'Mixed_7' + chr(ord('b') + idx)
                net = block_inception_c(net, block_scope)
                if add_and_check_final(block_scope, net): return net, end_points
    raise ValueError('Unknown final endpoint %s' % final_endpoint)


def inception_v4(inputs, num_classes=1001, is_training=True,
                 dropout_keep_prob=0.8,
                 reuse=None,
                 scope='InceptionV4',
                 create_aux_logits=True):
    """Creates the Inception V4 model.

    Args:
      inputs: a 4-D tensor of size [batch_size, height, width, 3].
      num_classes: number of predicted classes.
      is_training: whether is training or not.
      dropout_keep_prob: float, the fraction to keep before final layer.
      reuse: whether or not the network and its variables should be reused. To be
        able to reuse 'scope' must be given.
      scope: Optional variable_scope.
      create_aux_logits: Whether to include the auxiliary logits.

    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, 'InceptionV4', [inputs], reuse=reuse) as scope:
        with slim.arg_scope([slim.batch_norm, slim.dropout],
                            is_training=is_training):
            net, end_points = inception_v4_base(inputs, scope=scope)

            with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                                stride=1, padding='SAME'):
                # Auxiliary Head logits
                if create_aux_logits:
                    with tf.variable_scope('AuxLogits'):
                        # 17 x 17 x 1024
                        aux_logits = end_points['Mixed_6h']
                        aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3,
                                                     padding='VALID',
                                                     scope='AvgPool_1a_5x5')
                        aux_logits = slim.conv2d(aux_logits, 128, [1, 1],
                                                 scope='Conv2d_1b_1x1')
                        aux_logits = slim.conv2d(aux_logits, 768,
                                                 aux_logits.get_shape()[1:3],
                                                 padding='VALID', scope='Conv2d_2a')
                        aux_logits = slim.flatten(aux_logits)
                        aux_logits = slim.fully_connected(aux_logits, num_classes,
                                                          activation_fn=None,
                                                          scope='Aux_logits')
                        end_points['AuxLogits'] = aux_logits

                # Final pooling and prediction
                with tf.variable_scope('Logits'):
                    # 8 x 8 x 1536
                    net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID',
                                          scope='AvgPool_1a')
                    # 1 x 1 x 1536
                    net = slim.dropout(net, dropout_keep_prob, scope='Dropout_1b')
                    net = slim.flatten(net, scope='PreLogitsFlatten')
                    end_points['PreLogitsFlatten'] = net
                    # 1536
                    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


def inception_v4_arg_scope(weight_decay=0.00004,
                           use_batch_norm=True,
                           batch_norm_decay=0.9997,
                           batch_norm_epsilon=0.001):
    """Defines the default arg scope for inception models.
    Args:
      weight_decay: The weight decay to use for regularizing the model.
      use_batch_norm: "If `True`, batch_norm is applied after each convolution.
      batch_norm_decay: Decay for batch norm moving average.
      batch_norm_epsilon: Small float added to variance to avoid dividing by zero
        in batch norm.
    Returns:
      An `arg_scope` to use for the inception models.
    """
    batch_norm_params = {
        # Decay for the moving averages.
        'decay': batch_norm_decay,
        # epsilon to prevent 0s in variance.
        'epsilon': batch_norm_epsilon,
        # collection containing update_ops.
        'updates_collections': tf.GraphKeys.UPDATE_OPS,
    }
    if use_batch_norm:
        normalizer_fn = slim.batch_norm
        normalizer_params = batch_norm_params
    else:
        normalizer_fn = None
        normalizer_params = {}
    # Set weight_decay for weights in Conv and FC layers.
    with slim.arg_scope([slim.conv2d, slim.fully_connected],
                        weights_regularizer=slim.l2_regularizer(weight_decay)):
        with slim.arg_scope(
                [slim.conv2d],
                weights_initializer=slim.variance_scaling_initializer(),
                activation_fn=tf.nn.relu,
                normalizer_fn=normalizer_fn,
                normalizer_params=normalizer_params) as sc:
            return sc


default_image_size = 299
