# 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
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# ==============================================================================
"""Contains a variant of the CIFAR-10 model definition."""

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(stddev=stddev)


def cifarnet(images, num_classes=10, is_training=False,
             dropout_keep_prob=0.5,
             prediction_fn=slim.softmax,
             scope='CifarNet'):
  """Creates a variant of the CifarNet model.

  Note that since the output is a set of 'logits', the values fall in the
  interval of (-infinity, infinity). Consequently, to convert the outputs to a
  probability distribution over the characters, one will need to convert them
  using the softmax function:

        logits = cifarnet.cifarnet(images, is_training=False)
        probabilities = tf.nn.softmax(logits)
        predictions = tf.argmax(logits, 1)

  Args:
    images: A batch of `Tensors` of size [batch_size, height, width, channels].
    num_classes: the number of classes in the dataset.
    is_training: specifies whether or not we're currently training the model.
      This variable will determine the behaviour of the dropout layer.
    dropout_keep_prob: the percentage of activation values that are retained.
    prediction_fn: a function to get predictions out of logits.
    scope: Optional variable_scope.

  Returns:
    logits: the pre-softmax activations, a tensor of size
      [batch_size, `num_classes`]
    end_points: a dictionary from components of the network to the corresponding
      activation.
  """
  end_points = {}

  with tf.variable_scope(scope, 'CifarNet', [images, num_classes]):
    net = slim.conv2d(images, 64, [5, 5], scope='conv1')
    end_points['conv1'] = net
    net = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
    end_points['pool1'] = net
    net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm1')
    net = slim.conv2d(net, 64, [5, 5], scope='conv2')
    end_points['conv2'] = net
    net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm2')
    net = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
    end_points['pool2'] = net
    net = slim.flatten(net)
    end_points['Flatten'] = net
    net = slim.fully_connected(net, 384, scope='fc3')
    end_points['fc3'] = net
    net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                       scope='dropout3')
    net = slim.fully_connected(net, 192, scope='fc4')
    end_points['fc4'] = net
    logits = slim.fully_connected(net, num_classes,
                                  biases_initializer=tf.zeros_initializer(),
                                  weights_initializer=trunc_normal(1/192.0),
                                  weights_regularizer=None,
                                  activation_fn=None,
                                  scope='logits')

    end_points['Logits'] = logits
    end_points['Predictions'] = prediction_fn(logits, scope='Predictions')

  return logits, end_points
cifarnet.default_image_size = 32


def cifarnet_arg_scope(weight_decay=0.004):
  """Defines the default cifarnet argument scope.

  Args:
    weight_decay: The weight decay to use for regularizing the model.

  Returns:
    An `arg_scope` to use for the inception v3 model.
  """
  with slim.arg_scope(
      [slim.conv2d],
      weights_initializer=tf.truncated_normal_initializer(stddev=5e-2),
      activation_fn=tf.nn.relu):
    with slim.arg_scope(
        [slim.fully_connected],
        biases_initializer=tf.constant_initializer(0.1),
        weights_initializer=trunc_normal(0.04),
        weights_regularizer=slim.l2_regularizer(weight_decay),
        activation_fn=tf.nn.relu) as sc:
      return sc
