text: # 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.
  # =============================================================================
  """MobileNet v1.
  
  MobileNet is a general architecture and can be used for multiple use cases.
  Depending on the use case, it can use different input layer size and different
  head (for example: embeddings, localization and classification).
  
  As described in https://arxiv.org/abs/1704.04861.
  
    MobileNets: Efficient Convolutional Neural Networks for
      Mobile Vision Applications
    Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang,
      Tobias Weyand, Marco Andreetto, Hartwig Adam
  
  100% Mobilenet V1 (base) with input size 224x224:
  
  See mobilenet_v1()
  
  Layer                                                     params           macs
  --------------------------------------------------------------------------------
  MobilenetV1/Conv2d_0/Conv2D:                                 864      10,838,016
  MobilenetV1/Conv2d_1_depthwise/depthwise:                    288       3,612,672
  MobilenetV1/Conv2d_1_pointwise/Conv2D:                     2,048      25,690,112
  MobilenetV1/Conv2d_2_depthwise/depthwise:                    576       1,806,336
  MobilenetV1/Conv2d_2_pointwise/Conv2D:                     8,192      25,690,112
  MobilenetV1/Conv2d_3_depthwise/depthwise:                  1,152       3,612,672
  MobilenetV1/Conv2d_3_pointwise/Conv2D:                    16,384      51,380,224
  MobilenetV1/Conv2d_4_depthwise/depthwise:                  1,152         903,168
  MobilenetV1/Conv2d_4_pointwise/Conv2D:                    32,768      25,690,112
  MobilenetV1/Conv2d_5_depthwise/depthwise:                  2,304       1,806,336
  MobilenetV1/Conv2d_5_pointwise/Conv2D:                    65,536      51,380,224
  MobilenetV1/Conv2d_6_depthwise/depthwise:                  2,304         451,584
  MobilenetV1/Conv2d_6_pointwise/Conv2D:                   131,072      25,690,112
  MobilenetV1/Conv2d_7_depthwise/depthwise:                  4,608         903,168
  MobilenetV1/Conv2d_7_pointwise/Conv2D:                   262,144      51,380,224
  MobilenetV1/Conv2d_8_depthwise/depthwise:                  4,608         903,168
  MobilenetV1/Conv2d_8_pointwise/Conv2D:                   262,144      51,380,224
  MobilenetV1/Conv2d_9_depthwise/depthwise:                  4,608         903,168
  MobilenetV1/Conv2d_9_pointwise/Conv2D:                   262,144      51,380,224
  MobilenetV1/Conv2d_10_depthwise/depthwise:                 4,608         903,168
  MobilenetV1/Conv2d_10_pointwise/Conv2D:                  262,144      51,380,224
  MobilenetV1/Conv2d_11_depthwise/depthwise:                 4,608         903,168
  MobilenetV1/Conv2d_11_pointwise/Conv2D:                  262,144      51,380,224
  MobilenetV1/Conv2d_12_depthwise/depthwise:                 4,608         225,792
  MobilenetV1/Conv2d_12_pointwise/Conv2D:                  524,288      25,690,112
  MobilenetV1/Conv2d_13_depthwise/depthwise:                 9,216         451,584
  MobilenetV1/Conv2d_13_pointwise/Conv2D:                1,048,576      51,380,224
  --------------------------------------------------------------------------------
  Total:                                                 3,185,088     567,716,352
  
  
  75% Mobilenet V1 (base) with input size 128x128:
  
  See mobilenet_v1_075()
  
  Layer                                                     params           macs
  --------------------------------------------------------------------------------
  MobilenetV1/Conv2d_0/Conv2D:                                 648       2,654,208
  MobilenetV1/Conv2d_1_depthwise/depthwise:                    216         884,736
  MobilenetV1/Conv2d_1_pointwise/Conv2D:                     1,152       4,718,592
  MobilenetV1/Conv2d_2_depthwise/depthwise:                    432         442,368
  MobilenetV1/Conv2d_2_pointwise/Conv2D:                     4,608       4,718,592
  MobilenetV1/Conv2d_3_depthwise/depthwise:                    864         884,736
  MobilenetV1/Conv2d_3_pointwise/Conv2D:                     9,216       9,437,184
  MobilenetV1/Conv2d_4_depthwise/depthwise:                    864         221,184
  MobilenetV1/Conv2d_4_pointwise/Conv2D:                    18,432       4,718,592
  MobilenetV1/Conv2d_5_depthwise/depthwise:                  1,728         442,368
  MobilenetV1/Conv2d_5_pointwise/Conv2D:                    36,864       9,437,184
  MobilenetV1/Conv2d_6_depthwise/depthwise:                  1,728         110,592
  MobilenetV1/Conv2d_6_pointwise/Conv2D:                    73,728       4,718,592
  MobilenetV1/Conv2d_7_depthwise/depthwise:                  3,456         221,184
  MobilenetV1/Conv2d_7_pointwise/Conv2D:                   147,456       9,437,184
  MobilenetV1/Conv2d_8_depthwise/depthwise:                  3,456         221,184
  MobilenetV1/Conv2d_8_pointwise/Conv2D:                   147,456       9,437,184
  MobilenetV1/Conv2d_9_depthwise/depthwise:                  3,456         221,184
  MobilenetV1/Conv2d_9_pointwise/Conv2D:                   147,456       9,437,184
  MobilenetV1/Conv2d_10_depthwise/depthwise:                 3,456         221,184
  MobilenetV1/Conv2d_10_pointwise/Conv2D:                  147,456       9,437,184
  MobilenetV1/Conv2d_11_depthwise/depthwise:                 3,456         221,184
  MobilenetV1/Conv2d_11_pointwise/Conv2D:                  147,456       9,437,184
  MobilenetV1/Conv2d_12_depthwise/depthwise:                 3,456          55,296
  MobilenetV1/Conv2d_12_pointwise/Conv2D:                  294,912       4,718,592
  MobilenetV1/Conv2d_13_depthwise/depthwise:                 6,912         110,592
  MobilenetV1/Conv2d_13_pointwise/Conv2D:                  589,824       9,437,184
  --------------------------------------------------------------------------------
  Total:                                                 1,800,144     106,002,432
  
  """
  
  # Tensorflow mandates these.
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from collections import namedtuple
  import functools
  
  import tensorflow as tf
  
  slim = tf.contrib.slim
  
  # Conv and DepthSepConv namedtuple define layers of the MobileNet architecture
  # Conv defines 3x3 convolution layers
  # DepthSepConv defines 3x3 depthwise convolution followed by 1x1 convolution.
  # stride is the stride of the convolution
  # depth is the number of channels or filters in a layer
  Conv = namedtuple('Conv', ['kernel', 'stride', 'depth'])
  DepthSepConv = namedtuple('DepthSepConv', ['kernel', 'stride', 'depth'])
  
  # _CONV_DEFS specifies the MobileNet body
  _CONV_DEFS = [
      Conv(kernel=[3, 3], stride=2, depth=32),
      DepthSepConv(kernel=[3, 3], stride=1, depth=64),
      DepthSepConv(kernel=[3, 3], stride=2, depth=128),
      DepthSepConv(kernel=[3, 3], stride=1, depth=128),
      DepthSepConv(kernel=[3, 3], stride=2, depth=256),
      DepthSepConv(kernel=[3, 3], stride=1, depth=256),
      DepthSepConv(kernel=[3, 3], stride=2, depth=512),
      DepthSepConv(kernel=[3, 3], stride=1, depth=512),
      DepthSepConv(kernel=[3, 3], stride=1, depth=512),
      DepthSepConv(kernel=[3, 3], stride=1, depth=512),
      DepthSepConv(kernel=[3, 3], stride=1, depth=512),
      DepthSepConv(kernel=[3, 3], stride=1, depth=512),
      DepthSepConv(kernel=[3, 3], stride=2, depth=1024),
      DepthSepConv(kernel=[3, 3], stride=1, depth=1024)
  ]
  
  
  def mobilenet_v1_base(inputs,
                        final_endpoint='Conv2d_13_pointwise',
                        min_depth=8,
                        depth_multiplier=1.0,
                        conv_defs=None,
                        output_stride=None,
                        scope=None):
    """Mobilenet v1.
  
    Constructs a Mobilenet v1 network from inputs to the given final endpoint.
  
    Args:
      inputs: a tensor of shape [batch_size, height, width, channels].
      final_endpoint: specifies the endpoint to construct the network up to. It
        can be one of ['Conv2d_0', 'Conv2d_1_pointwise', 'Conv2d_2_pointwise',
        'Conv2d_3_pointwise', 'Conv2d_4_pointwise', 'Conv2d_5'_pointwise,
        'Conv2d_6_pointwise', 'Conv2d_7_pointwise', 'Conv2d_8_pointwise',
        'Conv2d_9_pointwise', 'Conv2d_10_pointwise', 'Conv2d_11_pointwise',
        'Conv2d_12_pointwise', 'Conv2d_13_pointwise'].
      min_depth: Minimum depth value (number of channels) for all convolution ops.
        Enforced when depth_multiplier < 1, and not an active constraint when
        depth_multiplier >= 1.
      depth_multiplier: Float multiplier for the depth (number of channels)
        for all convolution ops. The value must be greater than zero. Typical
        usage will be to set this value in (0, 1) to reduce the number of
        parameters or computation cost of the model.
      conv_defs: A list of ConvDef namedtuples specifying the net architecture.
      output_stride: An integer that specifies the requested ratio of input to
        output spatial resolution. If not None, then we invoke atrous convolution
        if necessary to prevent the network from reducing the spatial resolution
        of the activation maps. Allowed values are 8 (accurate fully convolutional
        mode), 16 (fast fully convolutional mode), 32 (classification mode).
      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 depth_multiplier <= 0, or the target output_stride is not
                  allowed.
    """
    depth = lambda d: max(int(d * depth_multiplier), min_depth)
    end_points = {}
  
    # Used to find thinned depths for each layer.
    if depth_multiplier <= 0:
      raise ValueError('depth_multiplier is not greater than zero.')
  
    if conv_defs is None:
      conv_defs = _CONV_DEFS
  
    if output_stride is not None and output_stride not in [8, 16, 32]:
      raise ValueError('Only allowed output_stride values are 8, 16, 32.')
  
    with tf.variable_scope(scope, 'MobilenetV1', [inputs]):
      with slim.arg_scope([slim.conv2d, slim.separable_conv2d], padding='SAME'):
        # The current_stride variable keeps track of the output stride of the
        # activations, i.e., the running product of convolution strides up to the
        # current network layer. This allows us to invoke atrous convolution
        # whenever applying the next convolution would result in the activations
        # having output stride larger than the target output_stride.
        current_stride = 1
  
        # The atrous convolution rate parameter.
        rate = 1
  
        net = inputs
        for i, conv_def in enumerate(conv_defs):
          end_point_base = 'Conv2d_%d' % i
  
          if output_stride is not None and current_stride == output_stride:
            # If we have reached the target output_stride, then we need to employ
            # atrous convolution with stride=1 and multiply the atrous rate by the
            # current unit's stride for use in subsequent layers.
            layer_stride = 1
            layer_rate = rate
            rate *= conv_def.stride
          else:
            layer_stride = conv_def.stride
            layer_rate = 1
            current_stride *= conv_def.stride
  
          if isinstance(conv_def, Conv):
            end_point = end_point_base
            net = slim.conv2d(net, depth(conv_def.depth), conv_def.kernel,
                              stride=conv_def.stride,
                              normalizer_fn=slim.batch_norm,
                              scope=end_point)
            end_points[end_point] = net
            if end_point == final_endpoint:
              return net, end_points
  
          elif isinstance(conv_def, DepthSepConv):
            end_point = end_point_base + '_depthwise'
  
            # By passing filters=None
            # separable_conv2d produces only a depthwise convolution layer
            net = slim.separable_conv2d(net, None, conv_def.kernel,
                                        depth_multiplier=1,
                                        stride=layer_stride,
                                        rate=layer_rate,
                                        normalizer_fn=slim.batch_norm,
                                        scope=end_point)
  
            end_points[end_point] = net
            if end_point == final_endpoint:
              return net, end_points
  
            end_point = end_point_base + '_pointwise'
  
            net = slim.conv2d(net, depth(conv_def.depth), [1, 1],
                              stride=1,
                              normalizer_fn=slim.batch_norm,
                              scope=end_point)
  
            end_points[end_point] = net
            if end_point == final_endpoint:
              return net, end_points
          else:
            raise ValueError('Unknown convolution type %s for layer %d'
                             % (conv_def.ltype, i))
    raise ValueError('Unknown final endpoint %s' % final_endpoint)
  
  
  def mobilenet_v1(inputs,
                   num_classes=1000,
                   dropout_keep_prob=0.999,
                   is_training=True,
                   min_depth=8,
                   depth_multiplier=1.0,
                   conv_defs=None,
                   prediction_fn=tf.contrib.layers.softmax,
                   spatial_squeeze=True,
                   reuse=None,
                   scope='MobilenetV1',
                   global_pool=False):
    """Mobilenet v1 model for classification.
  
    Args:
      inputs: a tensor of shape [batch_size, height, width, channels].
      num_classes: number of predicted classes. If 0 or None, the logits layer
        is omitted and the input features to the logits layer (before dropout)
        are returned instead.
      dropout_keep_prob: the percentage of activation values that are retained.
      is_training: whether is training or not.
      min_depth: Minimum depth value (number of channels) for all convolution ops.
        Enforced when depth_multiplier < 1, and not an active constraint when
        depth_multiplier >= 1.
      depth_multiplier: Float multiplier for the depth (number of channels)
        for all convolution ops. The value must be greater than zero. Typical
        usage will be to set this value in (0, 1) to reduce the number of
        parameters or computation cost of the model.
      conv_defs: A list of ConvDef namedtuples specifying the net architecture.
      prediction_fn: a function to get predictions out of logits.
      spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
          of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
      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.
      global_pool: Optional boolean flag to control the avgpooling before the
        logits layer. If false or unset, pooling is done with a fixed window
        that reduces default-sized inputs to 1x1, while larger inputs lead to
        larger outputs. If true, any input size is pooled down to 1x1.
  
    Returns:
      net: a 2D Tensor with the logits (pre-softmax activations) if num_classes
        is a non-zero integer, or the non-dropped-out input to the logits layer
        if num_classes is 0 or None.
      end_points: a dictionary from components of the network to the corresponding
        activation.
  
    Raises:
      ValueError: Input rank is invalid.
    """
    input_shape = inputs.get_shape().as_list()
    if len(input_shape) != 4:
      raise ValueError('Invalid input tensor rank, expected 4, was: %d' %
                       len(input_shape))
  
    with tf.variable_scope(scope, 'MobilenetV1', [inputs], reuse=reuse) as scope:
      with slim.arg_scope([slim.batch_norm, slim.dropout],
                          is_training=is_training):
        net, end_points = mobilenet_v1_base(inputs, scope=scope,
                                            min_depth=min_depth,
                                            depth_multiplier=depth_multiplier,
                                            conv_defs=conv_defs)
        with tf.variable_scope('Logits'):
          if global_pool:
            # Global average pooling.
            net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
            end_points['global_pool'] = net
          else:
            # Pooling with a fixed kernel size.
            kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7])
            net = slim.avg_pool2d(net, kernel_size, padding='VALID',
                                  scope='AvgPool_1a')
            end_points['AvgPool_1a'] = net
          if not num_classes:
            return net, end_points
          # 1 x 1 x 1024
          net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
          logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
                               normalizer_fn=None, scope='Conv2d_1c_1x1')
          if spatial_squeeze:
            logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
        end_points['Logits'] = logits
        if prediction_fn:
          end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
    return logits, end_points
  
  mobilenet_v1.default_image_size = 224
  
  
  def wrapped_partial(func, *args, **kwargs):
    partial_func = functools.partial(func, *args, **kwargs)
    functools.update_wrapper(partial_func, func)
    return partial_func
  
  
  mobilenet_v1_075 = wrapped_partial(mobilenet_v1, depth_multiplier=0.75)
  mobilenet_v1_050 = wrapped_partial(mobilenet_v1, depth_multiplier=0.50)
  mobilenet_v1_025 = wrapped_partial(mobilenet_v1, depth_multiplier=0.25)
  
  
  def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
    """Define kernel size which is automatically reduced for small input.
  
    If the shape of the input images is unknown at graph construction time this
    function assumes that the input images are large enough.
  
    Args:
      input_tensor: input tensor of size [batch_size, height, width, channels].
      kernel_size: desired kernel size of length 2: [kernel_height, kernel_width]
  
    Returns:
      a tensor with the kernel size.
    """
    shape = input_tensor.get_shape().as_list()
    if shape[1] is None or shape[2] is None:
      kernel_size_out = kernel_size
    else:
      kernel_size_out = [min(shape[1], kernel_size[0]),
                         min(shape[2], kernel_size[1])]
    return kernel_size_out
  
  
  def mobilenet_v1_arg_scope(is_training=True,
                             weight_decay=0.00004,
                             stddev=0.09,
                             regularize_depthwise=False):
    """Defines the default MobilenetV1 arg scope.
  
    Args:
      is_training: Whether or not we're training the model.
      weight_decay: The weight decay to use for regularizing the model.
      stddev: The standard deviation of the trunctated normal weight initializer.
      regularize_depthwise: Whether or not apply regularization on depthwise.
  
    Returns:
      An `arg_scope` to use for the mobilenet v1 model.
    """
    batch_norm_params = {
        'is_training': is_training,
        'center': True,
        'scale': True,
        'decay': 0.9997,
        'epsilon': 0.001,
    }
  
    # Set weight_decay for weights in Conv and DepthSepConv layers.
    weights_init = tf.truncated_normal_initializer(stddev=stddev)
    regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
    if regularize_depthwise:
      depthwise_regularizer = regularizer
    else:
      depthwise_regularizer = None
    with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
                        weights_initializer=weights_init,
                        activation_fn=tf.nn.relu6, normalizer_fn=slim.batch_norm):
      with slim.arg_scope([slim.batch_norm], **batch_norm_params):
        with slim.arg_scope([slim.conv2d], weights_regularizer=regularizer):
          with slim.arg_scope([slim.separable_conv2d],
                              weights_regularizer=depthwise_regularizer) as sc:
            return sc
  