# 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
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# Unless required by applicable law or agreed to in writing, software
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# ==============================================================================
"""Contains the definition for inception v1 classification network."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

slim = tf.contrib.slim
trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)


def inception_v1_base(inputs,
                      final_endpoint='Mixed_5c',
                      scope='InceptionV1'):
  """Defines the Inception V1 base architecture.

  This architecture is defined in:
    Going deeper with convolutions
    Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
    Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
    http://arxiv.org/pdf/1409.4842v1.pdf.

  Args:
    inputs: a tensor of size [batch_size, height, width, channels].
    final_endpoint: specifies the endpoint to construct the network up to. It
      can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
      'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
      'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
      'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c']
    scope: Optional variable_scope.

  Returns:
    A dictionary from components of the network to the corresponding activation.

  Raises:
    ValueError: if final_endpoint is not set to one of the predefined values.
  """
  end_points = {}
  with tf.variable_scope(scope, 'InceptionV1', [inputs]):
    with slim.arg_scope(
        [slim.conv2d, slim.fully_connected],
        weights_initializer=trunc_normal(0.01)):
      with slim.arg_scope([slim.conv2d, slim.max_pool2d],
                          stride=1, padding='SAME'):
        end_point = 'Conv2d_1a_7x7'
        net = slim.conv2d(inputs, 64, [7, 7], stride=2, scope=end_point)
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points
        end_point = 'MaxPool_2a_3x3'
        net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points
        end_point = 'Conv2d_2b_1x1'
        net = slim.conv2d(net, 64, [1, 1], scope=end_point)
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points
        end_point = 'Conv2d_2c_3x3'
        net = slim.conv2d(net, 192, [3, 3], scope=end_point)
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points
        end_point = 'MaxPool_3a_3x3'
        net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_3b'
        with tf.variable_scope(end_point):
          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 32, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
          net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_3c'
        with tf.variable_scope(end_point):
          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 192, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')