inception_resnet_v2.py

# Copyright 2017 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
  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

from tensorflow.python.ops import init_ops

from tensorflow.python.framework import ops
from tensorflow.contrib.layers.python.layers.layers import _add_variable_to_collections, _build_variable_getter
from tensorflow.contrib.framework import add_arg_scope
from tensorflow.python.ops import variable_scope
from tensorflow.contrib.layers.python.layers import utils
from nets.bayesial_layer import ConvBayes

@add_arg_scope
def bayes_conv2d(inputs,
                 num_outputs,
                 kernel_size,
                 stride=1,
                 padding='SAME',
                 data_format=None,
                 rate=1,
                 activation_fn=tf.nn.relu,
                 normalizer_fn=None,
                 normalizer_params=None,
                 weights_initializer=tf.contrib.layers.xavier_initializer(),
                 weights_regularizer=None,
                 biases_initializer=init_ops.zeros_initializer(),
                 biases_regularizer=None,
                 reuse=None,
                 variables_collections=None,
                 outputs_collections=None,
                 trainable=True,
                 scope=None):
    if data_format not in [None, 'NWC', 'NCW', 'NHWC', 'NCHW', 'NDHWC', 'NCDHW']:
        raise ValueError('Invalid data_format: %r' % (data_format,))

    layer_variable_getter = _build_variable_getter(
        {'bias': 'biases', 'kernel': 'weights'})

    with variable_scope.variable_scope(
            scope, 'Conv', [inputs], reuse=reuse,
            custom_getter=layer_variable_getter) as sc:
        inputs = ops.convert_to_tensor(inputs)

        df = ('channels_first' if data_format and data_format.startswith('NC')
              else 'channels_last')

        df = 'channels_last'

        layer = ConvBayes(filters=num_outputs,
                          kernel_size=kernel_size,
                          strides=stride,
                          padding=padding,
                          data_format=df,
                          dilation_rate=rate,
                          activation=None,
                          use_bias=not normalizer_fn and biases_initializer,
                          kernel_initializer=weights_initializer,
                          bias_initializer=biases_initializer,
                          kernel_regularizer=weights_regularizer,
                          bias_regularizer=biases_regularizer,
                          activity_regularizer=None,
                          trainable=trainable,
                          name=sc.name,
                          dtype=inputs.dtype.base_dtype,
                          _scope=sc,
                          _reuse=reuse)
        outputs = layer.apply(inputs)

        # Add variables to collections.
        _add_variable_to_collections(layer.kernel, variables_collections, 'weights')
        if layer.use_bias:
            _add_variable_to_collections(layer.bias, variables_collections, 'biases')

        if normalizer_fn is not None:
            normalizer_params = normalizer_params or {}
            outputs = normalizer_fn(outputs, **normalizer_params)

        if activation_fn is not None:
            outputs = activation_fn(outputs)
        return utils.collect_named_outputs(outputs_collections,
                                           sc.original_name_scope, outputs)


def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
  """Builds the 35x35 resnet block."""
  with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
    with tf.variable_scope('Branch_0'):
      tower_conv = bayes_conv2d(net, 32, 1, scope='Conv2d_1x1')
    with tf.variable_scope('Branch_1'):
      tower_conv1_0 = bayes_conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
      tower_conv1_1 = bayes_conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3')
    with tf.variable_scope('Branch_2'):
      tower_conv2_0 = bayes_conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
      tower_conv2_1 = bayes_conv2d(tower_conv2_0, 48, 3, scope='Conv2d_0b_3x3')
      tower_conv2_2 = bayes_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 = bayes_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."""
  with tf.variable_scope(scope, 'Block17', [net], reuse=reuse):
    with tf.variable_scope('Branch_0'):
      tower_conv = bayes_conv2d(net, 192, 1, scope='Conv2d_1x1')
    with tf.variable_scope('Branch_1'):
      tower_conv1_0 = bayes_conv2d(net, 128, 1, scope='Conv2d_0a_1x1')
      tower_conv1_1 = bayes_conv2d(tower_conv1_0, 160, [1, 7],
                                  scope='Conv2d_0b_1x7')
      tower_conv1_2 = bayes_conv2d(tower_conv1_1, 192, [7, 1],
                                  scope='Conv2d_0c_7x1')
    mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
    up = bayes_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."""
  with tf.variable_scope(scope, 'Block8', [net], reuse=reuse):
    with tf.variable_scope('Branch_0'):
      tower_conv = bayes_conv2d(net, 192, 1, scope='Conv2d_1x1')
    with tf.variable_scope('Branch_1'):
      tower_conv1_0 = bayes_conv2d(net, 192, 1, scope='Conv2d_0a_1x1')
      tower_conv1_1 = bayes_conv2d(tower_conv1_0, 224, [1, 3],
                                  scope='Conv2d_0b_1x3')
      tower_conv1_2 = bayes_conv2d(tower_conv1_1, 256, [3, 1],
                                  scope='Conv2d_0c_3x1')
    mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
    up = bayes_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_base(inputs,
                             final_endpoint='Conv2d_7b_1x1',
                             output_stride=16,
                             align_feature_maps=False,
                             scope=None):
  """Inception model from  http://arxiv.org/abs/1602.07261.

  Constructs an Inception Resnet v2 network from inputs to the given final
  endpoint. This method can construct the network up to the final inception
  block Conv2d_7b_1x1.

  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_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
      'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
      'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
    output_stride: A scalar that specifies the requested ratio of input to
      output spatial resolution. Only supports 8 and 16.
    align_feature_maps: When true, changes all the VALID paddings in the network
      to SAME padding so that the feature maps are aligned.
    scope: Optional variable_scope.

  Returns:
    tensor_out: output tensor corresponding to the final_endpoint.
    end_points: a set of activations for external use, for example summaries or
                losses.

  Raises:
    ValueError: if final_endpoint is not set to one of the predefined values,
      or if the output_stride is not 8 or 16, or if the output_stride is 8 and
      we request an end point after 'PreAuxLogits'.
  """
  if output_stride != 8 and output_stride != 16:
    raise ValueError('output_stride must be 8 or 16.')

  padding = 'SAME' if align_feature_maps else 'VALID'

  end_points = {}

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

  with tf.variable_scope(scope, 'InceptionResnetV2', [inputs]):
    with slim.arg_scope([bayes_conv2d, slim.max_pool2d, slim.avg_pool2d],
                        stride=1, padding='SAME'):
      # 149 x 149 x 32
      net = bayes_conv2d(inputs, 32, 3, stride=2, padding=padding,
                        scope='Conv2d_1a_3x3')
      if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points

      # 147 x 147 x 32
      net = bayes_conv2d(net, 32, 3, padding=padding,
                        scope='Conv2d_2a_3x3')
      if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points
      # 147 x 147 x 64
      net = bayes_conv2d(net, 64, 3, scope='Conv2d_2b_3x3')
      if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points
      # 73 x 73 x 64
      net = slim.max_pool2d(net, 3, stride=2, padding=padding,
                            scope='MaxPool_3a_3x3')
      if add_and_check_final('MaxPool_3a_3x3', net): return net, end_points
      # 73 x 73 x 80
      net = bayes_conv2d(net, 80, 1, padding=padding,
                        scope='Conv2d_3b_1x1')
      if add_and_check_final('Conv2d_3b_1x1', net): return net, end_points
      # 71 x 71 x 192
      net = bayes_conv2d(net, 192, 3, padding=padding,
                        scope='Conv2d_4a_3x3')
      if add_and_check_final('Conv2d_4a_3x3', net): return net, end_points
      # 35 x 35 x 192
      net = slim.max_pool2d(net, 3, stride=2, padding=padding,
                            scope='MaxPool_5a_3x3')
      if add_and_check_final('MaxPool_5a_3x3', net): return net, end_points

      # 35 x 35 x 320
      with tf.variable_scope('Mixed_5b'):
        with tf.variable_scope('Branch_0'):
          tower_conv = bayes_conv2d(net, 96, 1, scope='Conv2d_1x1')
        with tf.variable_scope('Branch_1'):
          tower_conv1_0 = bayes_conv2d(net, 48, 1, scope='Conv2d_0a_1x1')
          tower_conv1_1 = bayes_conv2d(tower_conv1_0, 64, 5,
                                      scope='Conv2d_0b_5x5')
        with tf.variable_scope('Branch_2'):
          tower_conv2_0 = bayes_conv2d(net, 64, 1, scope='Conv2d_0a_1x1')
          tower_conv2_1 = bayes_conv2d(tower_conv2_0, 96, 3,
                                      scope='Conv2d_0b_3x3')
          tower_conv2_2 = bayes_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 = bayes_conv2d(tower_pool, 64, 1,
                                     scope='Conv2d_0b_1x1')
        net = tf.concat(
            [tower_conv, tower_conv1_1, tower_conv2_2, tower_pool_1], 3)

      if add_and_check_final('Mixed_5b', net): return net, end_points
      # TODO(alemi): Register intermediate endpoints
      net = slim.repeat(net, 10, block35, scale=0.17)

      # 17 x 17 x 1088 if output_stride == 8,
      # 33 x 33 x 1088 if output_stride == 16
      use_atrous = output_stride == 8

      with tf.variable_scope('Mixed_6a'):
        with tf.variable_scope('Branch_0'):
          tower_conv = bayes_conv2d(net, 384, 3, stride=1 if use_atrous else 2,
                                   padding=padding,
                                   scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_1'):
          tower_conv1_0 = bayes_conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
          tower_conv1_1 = bayes_conv2d(tower_conv1_0, 256, 3,
                                      scope='Conv2d_0b_3x3')
          tower_conv1_2 = bayes_conv2d(tower_conv1_1, 384, 3,
                                      stride=1 if use_atrous else 2,
                                      padding=padding,
                                      scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_2'):
          tower_pool = slim.max_pool2d(net, 3, stride=1 if use_atrous else 2,
                                       padding=padding,
                                       scope='MaxPool_1a_3x3')
        net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)

      if add_and_check_final('Mixed_6a', net): return net, end_points

      # TODO(alemi): register intermediate endpoints
      with slim.arg_scope([bayes_conv2d], rate=2 if use_atrous else 1):
        net = slim.repeat(net, 20, block17, scale=0.10)
      if add_and_check_final('PreAuxLogits', net): return net, end_points

      if output_stride == 8:
        # TODO(gpapan): Properly support output_stride for the rest of the net.
        raise ValueError('output_stride==8 is only supported up to the '
                         'PreAuxlogits end_point for now.')

      # 8 x 8 x 2080
      with tf.variable_scope('Mixed_7a'):
        with tf.variable_scope('Branch_0'):
          tower_conv = bayes_conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
          tower_conv_1 = bayes_conv2d(tower_conv, 384, 3, stride=2,
                                     padding=padding,
                                     scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_1'):
          tower_conv1 = bayes_conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
          tower_conv1_1 = bayes_conv2d(tower_conv1, 288, 3, stride=2,
                                      padding=padding,
                                      scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_2'):
          tower_conv2 = bayes_conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
          tower_conv2_1 = bayes_conv2d(tower_conv2, 288, 3,
                                      scope='Conv2d_0b_3x3')
          tower_conv2_2 = bayes_conv2d(tower_conv2_1, 320, 3, stride=2,
                                      padding=padding,
                                      scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_3'):
          tower_pool = slim.max_pool2d(net, 3, stride=2,
                                       padding=padding,
                                       scope='MaxPool_1a_3x3')
        net = tf.concat(
            [tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3)

      if add_and_check_final('Mixed_7a', net): return net, end_points

      # TODO(alemi): register intermediate endpoints
      net = slim.repeat(net, 9, block8, scale=0.20)
      net = block8(net, activation_fn=None)

      # 8 x 8 x 1536
      net = bayes_conv2d(net, 1536, 1, scope='Conv2d_7b_1x1')
      if add_and_check_final('Conv2d_7b_1x1', net): return net, end_points

    raise ValueError('final_endpoint (%s) not recognized', final_endpoint)


def inception_resnet_v2(inputs, num_classes=1001, is_training=True,
                        dropout_keep_prob=0.8,
                        reuse=None,
                        scope='InceptionResnetV2',
                        create_aux_logits=True):
  """Creates the Inception Resnet V2 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 auxilliary 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, 'InceptionResnetV2', [inputs, num_classes],
                         reuse=reuse) as scope:
    with slim.arg_scope([slim.batch_norm, slim.dropout],
                        is_training=is_training):

      net, end_points = inception_resnet_v2_base(inputs, scope=scope)

      if create_aux_logits:
        with tf.variable_scope('AuxLogits'):
          aux = end_points['PreAuxLogits']
          aux = slim.avg_pool2d(aux, 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('Logits'):
        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):
  """Returns the scope with the default parameters for inception_resnet_v2.

  Args:
    weight_decay: the weight decay for weights variables.
    batch_norm_decay: decay for the moving average of batch_norm momentums.
    batch_norm_epsilon: small float added to variance to avoid dividing by zero.

  Returns:
    a 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([bayes_conv2d, 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([bayes_conv2d, slim.conv2d], activation_fn=tf.nn.relu,
                        normalizer_fn=slim.batch_norm,
                        normalizer_params=batch_norm_params) as scope:
      return scope