(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.
Mobilenet v1.
Mobilenet v1 .
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_base ( inputs , final_endpoint = 'Conv2d_13_pointwise' , min_depth = 8 , depth_multiplier = 1.0 , conv_defs = None , output_stride = None , scope = None ) : 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 )