"""Contains a variant of the densenet model definition."""

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

import tensorflow as tf

slim = tf.contrib.slim


def trunc_normal(stddev): return tf.truncated_normal_initializer(stddev=stddev)


def bn_act_conv_drp(current, num_outputs, kernel_size, scope='block'):
    current = slim.batch_norm(current, scope=scope + '_bn')
    current = tf.nn.relu(current)
    current = slim.conv2d(current, num_outputs, kernel_size, scope=scope + '_conv')
    current = slim.dropout(current, scope=scope + '_dropout')
    return current


def block(net, layers, growth, scope='block'):
    for idx in range(layers):
        bottleneck = bn_act_conv_drp(net, 4 * growth, [1, 1],
                                     scope=scope + '_conv1x1' + str(idx))
        tmp = bn_act_conv_drp(bottleneck, growth, [3, 3],
                              scope=scope + '_conv3x3' + str(idx))
        net = tf.concat(axis=3, values=[net, tmp])
    return net


def densenet(images, num_classes=1001, is_training=False,
             dropout_keep_prob=0.8,
             scope='densenet'):
    """Creates a variant of the densenet model.

      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.
    """
    growth = 24
    compression_rate = 0.5

    def reduce_dim(input_feature):
        return int(int(input_feature.shape[-1]) * compression_rate)

    end_points = {}

    with tf.variable_scope(scope, 'DenseNet', [images, num_classes]):
        with slim.arg_scope(bn_drp_scope(is_training=is_training,
                                         keep_prob=dropout_keep_prob)) as ssc:
            
            ##########################
            # Put your code here.
            ##########################

            net = slim.conv2d(images,growth,[7,7],stride=2,scope='conv')
            end_points['conv']=net
            
            net=slim.max_pool2d(net,[3,3],padding='same',stride=2,scope='pooling')
            end_points['pooling']=net
            
            net=block(net,6,growth,scope='dense_block1')
            end_points['dense_block1']=net
            

            net = bn_act_conv_drp(net, reduce_dim(net), [1, 1], scope='transision_layer1')
            net=slim.avg_pool2d(net,[2,2],stride=2)
            end_points['transision_layer1']=net
            

            net=block(net,12,growth,scope='dense_block2')
            end_points['dense_block2']=net
            
            net = bn_act_conv_drp(net, reduce_dim(net), [1, 1], scope='transision_layer2')
            net=slim.avg_pool2d(net,[2,2],stride=2)
            end_points['transision_layer2']=net
            
            net=block(net,48,growth,scope='dense_block3')
            end_points['dense_block3']=net
            
            net = bn_act_conv_drp(net, reduce_dim(net), [1, 1], scope='transision_layer3')
            net=slim.avg_pool2d(net,[2,2],stride=2)
            end_points['transision_layer3']=net
            
            net=block(net,32,growth,scope='dense_block4')
            end_points['dense_block4']=net
            
            net=slim.batch_norm(net,scope='Classification_Layer' )
            net=slim.nn.relu(net)
            net=slim.avg_pool2d(net,net.shape[1:3],padding='Same')
            
            
            
            logits=slim.conv2d(net,num_classes,[1,1],biases_initializer=tf.zeros_initializer())
            end_points['Classification_Layer']=logits 
            return logits, end_points


def bn_drp_scope(is_training=True, keep_prob=0.8):
    keep_prob = keep_prob if is_training else 1
    with slim.arg_scope(
        [slim.batch_norm],
            scale=True, is_training=is_training, updates_collections=None):
        with slim.arg_scope(
            [slim.dropout],
                is_training=is_training, keep_prob=keep_prob) as bsc:
            return bsc


def densenet_arg_scope(weight_decay=0.004):
    """Defines the default densenet 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.contrib.layers.variance_scaling_initializer(
            factor=2.0, mode='FAN_IN', uniform=False),
        activation_fn=None, biases_initializer=None, padding='same',
            stride=1) as sc:
        return sc


densenet.default_image_size = 224