text: """
  Wrap the internal caffe C++ module (_caffe.so) with a clean, Pythonic
  interface.
  """
  
  from collections import OrderedDict
  try:
      from itertools import izip_longest
  except:
      from itertools import zip_longest as izip_longest
  import numpy as np
  
  from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \
          RMSPropSolver, AdaDeltaSolver, AdamSolver
  import caffe.io
  
  <<<<<<< HEAD
  import six
  
  =======
  >>>>>>> caffe-yolo/master
  # We directly update methods from Net here (rather than using composition or
  # inheritance) so that nets created by caffe (e.g., by SGDSolver) will
  # automatically have the improved interface.
  
  
  @property
  def _Net_blobs(self):
      """
      An OrderedDict (bottom to top, i.e., input to output) of network
      blobs indexed by name
      """
  <<<<<<< HEAD
      if not hasattr(self, '_blobs_dict'):
          self._blobs_dict = OrderedDict(zip(self._blob_names, self._blobs))
      return self._blobs_dict
  =======
      return OrderedDict(zip(self._blob_names, self._blobs))
  >>>>>>> caffe-yolo/master
  
  
  @property
  def _Net_blob_loss_weights(self):
      """
      An OrderedDict (bottom to top, i.e., input to output) of network
      blob loss weights indexed by name
      """
  <<<<<<< HEAD
      if not hasattr(self, '_blobs_loss_weights_dict'):
          self._blob_loss_weights_dict = OrderedDict(zip(self._blob_names,
                                                         self._blob_loss_weights))
      return self._blob_loss_weights_dict
  =======
      return OrderedDict(zip(self._blob_names, self._blob_loss_weights))
  >>>>>>> caffe-yolo/master
  
  
  @property
  def _Net_params(self):
      """
      An OrderedDict (bottom to top, i.e., input to output) of network
      parameters indexed by name; each is a list of multiple blobs (e.g.,
      weights and biases)
      """
  <<<<<<< HEAD
      if not hasattr(self, '_params_dict'):
          self._params_dict = OrderedDict([(name, lr.blobs)
                                          for name, lr in zip(
                                              self._layer_names, self.layers)
                                          if len(lr.blobs) > 0])
      return self._params_dict
  =======
      return OrderedDict([(name, lr.blobs)
                          for name, lr in zip(self._layer_names, self.layers)
                          if len(lr.blobs) > 0])
  >>>>>>> caffe-yolo/master
  
  
  @property
  def _Net_inputs(self):
  <<<<<<< HEAD
      if not hasattr(self, '_input_list'):
          keys = list(self.blobs.keys())
          self._input_list = [keys[i] for i in self._inputs]
      return self._input_list
  =======
      return [list(self.blobs.keys())[i] for i in self._inputs]
  >>>>>>> caffe-yolo/master
  
  
  @property
  def _Net_outputs(self):
  <<<<<<< HEAD
      if not hasattr(self, '_output_list'):
          keys = list(self.blobs.keys())
          self._output_list = [keys[i] for i in self._outputs]
      return self._output_list
  =======
      return [list(self.blobs.keys())[i] for i in self._outputs]
  >>>>>>> caffe-yolo/master
  
  
  def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
      """
      Forward pass: prepare inputs and run the net forward.
  
      Parameters
      ----------
      blobs : list of blobs to return in addition to output blobs.
      kwargs : Keys are input blob names and values are blob ndarrays.
               For formatting inputs for Caffe, see Net.preprocess().
               If None, input is taken from data layers.
      start : optional name of layer at which to begin the forward pass
      end : optional name of layer at which to finish the forward pass
            (inclusive)
  
      Returns
      -------
      outs : {blob name: blob ndarray} dict.
      """
      if blobs is None:
          blobs = []
  
      if start is not None:
          start_ind = list(self._layer_names).index(start)
      else:
          start_ind = 0
  
      if end is not None:
          end_ind = list(self._layer_names).index(end)
          outputs = set([end] + blobs)
      else:
          end_ind = len(self.layers) - 1
          outputs = set(self.outputs + blobs)
  
      if kwargs:
          if set(kwargs.keys()) != set(self.inputs):
              raise Exception('Input blob arguments do not match net inputs.')
          # Set input according to defined shapes and make arrays single and
          # C-contiguous as Caffe expects.
  <<<<<<< HEAD
          for in_, blob in six.iteritems(kwargs):
              if blob.shape[0] != self.blobs[in_].shape[0]:
  =======
          for in_, blob in kwargs.iteritems():
              if blob.shape[0] != self.blobs[in_].num:
  >>>>>>> caffe-yolo/master
                  raise Exception('Input is not batch sized')
              self.blobs[in_].data[...] = blob
  
      self._forward(start_ind, end_ind)
  
      # Unpack blobs to extract
      return {out: self.blobs[out].data for out in outputs}
  
  
  def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
      """
      Backward pass: prepare diffs and run the net backward.
  
      Parameters
      ----------
      diffs : list of diffs to return in addition to bottom diffs.
      kwargs : Keys are output blob names and values are diff ndarrays.
              If None, top diffs are taken from forward loss.
      start : optional name of layer at which to begin the backward pass
      end : optional name of layer at which to finish the backward pass
          (inclusive)
  
      Returns
      -------
      outs: {blob name: diff ndarray} dict.
      """
      if diffs is None:
          diffs = []
  
      if start is not None:
          start_ind = list(self._layer_names).index(start)
      else:
          start_ind = len(self.layers) - 1
  
      if end is not None:
          end_ind = list(self._layer_names).index(end)
          outputs = set([end] + diffs)
      else:
          end_ind = 0
          outputs = set(self.inputs + diffs)
  
      if kwargs:
          if set(kwargs.keys()) != set(self.outputs):
              raise Exception('Top diff arguments do not match net outputs.')
          # Set top diffs according to defined shapes and make arrays single and
          # C-contiguous as Caffe expects.
  <<<<<<< HEAD
          for top, diff in six.iteritems(kwargs):
              if diff.shape[0] != self.blobs[top].shape[0]:
  =======
          for top, diff in kwargs.iteritems():
              if diff.ndim != 4:
                  raise Exception('{} diff is not 4-d'.format(top))
              if diff.shape[0] != self.blobs[top].num:
  >>>>>>> caffe-yolo/master
                  raise Exception('Diff is not batch sized')
              self.blobs[top].diff[...] = diff
  
      self._backward(start_ind, end_ind)
  
      # Unpack diffs to extract
      return {out: self.blobs[out].diff for out in outputs}
  
  
  def _Net_forward_all(self, blobs=None, **kwargs):
      """
      Run net forward in batches.
  
      Parameters
      ----------
      blobs : list of blobs to extract as in forward()
      kwargs : Keys are input blob names and values are blob ndarrays.
               Refer to forward().
  
      Returns
      -------
      all_outs : {blob name: list of blobs} dict.
      """
      # Collect outputs from batches
      all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
      for batch in self._batch(kwargs):
          outs = self.forward(blobs=blobs, **batch)
  <<<<<<< HEAD
          for out, out_blob in six.iteritems(outs):
  =======
          for out, out_blob in outs.iteritems():
  >>>>>>> caffe-yolo/master
              all_outs[out].extend(out_blob.copy())
      # Package in ndarray.
      for out in all_outs:
          all_outs[out] = np.asarray(all_outs[out])
      # Discard padding.
  <<<<<<< HEAD
      pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
  =======
      pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next())
  >>>>>>> caffe-yolo/master
      if pad:
          for out in all_outs:
              all_outs[out] = all_outs[out][:-pad]
      return all_outs
  
  
  def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
      """
      Run net forward + backward in batches.
  
      Parameters
      ----------
      blobs: list of blobs to extract as in forward()
      diffs: list of diffs to extract as in backward()
      kwargs: Keys are input (for forward) and output (for backward) blob names
              and values are ndarrays. Refer to forward() and backward().
              Prefilled variants are called for lack of input or output blobs.
  
      Returns
      -------
      all_blobs: {blob name: blob ndarray} dict.
      all_diffs: {blob name: diff ndarray} dict.
      """
      # Batch blobs and diffs.
      all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
      all_diffs = {diff: [] for diff in set(self.inputs + (diffs or []))}
      forward_batches = self._batch({in_: kwargs[in_]
                                     for in_ in self.inputs if in_ in kwargs})
      backward_batches = self._batch({out: kwargs[out]
                                      for out in self.outputs if out in kwargs})
      # Collect outputs from batches (and heed lack of forward/backward batches).
      for fb, bb in izip_longest(forward_batches, backward_batches, fillvalue={}):
          batch_blobs = self.forward(blobs=blobs, **fb)
          batch_diffs = self.backward(diffs=diffs, **bb)
  <<<<<<< HEAD
          for out, out_blobs in six.iteritems(batch_blobs):
              all_outs[out].extend(out_blobs.copy())
          for diff, out_diffs in six.iteritems(batch_diffs):
              all_diffs[diff].extend(out_diffs.copy())
  =======
          for out, out_blobs in batch_blobs.iteritems():
              all_outs[out].extend(out_blobs)
          for diff, out_diffs in batch_diffs.iteritems():
              all_diffs[diff].extend(out_diffs)
  >>>>>>> caffe-yolo/master
      # Package in ndarray.
      for out, diff in zip(all_outs, all_diffs):
          all_outs[out] = np.asarray(all_outs[out])
          all_diffs[diff] = np.asarray(all_diffs[diff])
      # Discard padding at the end and package in ndarray.
  <<<<<<< HEAD
      pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
  =======
      pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next())
  >>>>>>> caffe-yolo/master
      if pad:
          for out, diff in zip(all_outs, all_diffs):
              all_outs[out] = all_outs[out][:-pad]
              all_diffs[diff] = all_diffs[diff][:-pad]
      return all_outs, all_diffs
  
  
  def _Net_set_input_arrays(self, data, labels):
      """
      Set input arrays of the in-memory MemoryDataLayer.
      (Note: this is only for networks declared with the memory data layer.)
      """
      if labels.ndim == 1:
          labels = np.ascontiguousarray(labels[:, np.newaxis, np.newaxis,
                                               np.newaxis])
      return self._set_input_arrays(data, labels)
  
  
  def _Net_batch(self, blobs):
      """
      Batch blob lists according to net's batch size.
  
      Parameters
      ----------
      blobs: Keys blob names and values are lists of blobs (of any length).
             Naturally, all the lists should have the same length.
  
      Yields
      ------
      batch: {blob name: list of blobs} dict for a single batch.
      """
  <<<<<<< HEAD
      num = len(six.next(six.itervalues(blobs)))
      batch_size = six.next(six.itervalues(self.blobs)).shape[0]
      remainder = num % batch_size
      num_batches = num // batch_size
  =======
      num = len(blobs.itervalues().next())
      batch_size = self.blobs.itervalues().next().num
      remainder = num % batch_size
      num_batches = num / batch_size
  >>>>>>> caffe-yolo/master
  
      # Yield full batches.
      for b in range(num_batches):
          i = b * batch_size
          yield {name: blobs[name][i:i + batch_size] for name in blobs}
  
      # Yield last padded batch, if any.
      if remainder > 0:
          padded_batch = {}
          for name in blobs:
              padding = np.zeros((batch_size - remainder,)
                                 + blobs[name].shape[1:])
              padded_batch[name] = np.concatenate([blobs[name][-remainder:],
                                                   padding])
          yield padded_batch
  
  <<<<<<< HEAD
  def _Net_get_id_name(func, field):
      """
      Generic property that maps func to the layer names into an OrderedDict.
  
      Used for top_names and bottom_names.
  
      Parameters
      ----------
      func: function id -> [id]
      field: implementation field name (cache)
  
      Returns
      ------
      A one-parameter function that can be set as a property.
      """
      @property
      def get_id_name(self):
          if not hasattr(self, field):
              id_to_name = list(self.blobs)
              res = OrderedDict([(self._layer_names[i],
                                  [id_to_name[j] for j in func(self, i)])
                                  for i in range(len(self.layers))])
              setattr(self, field, res)
          return getattr(self, field)
      return get_id_name
  
  =======
  >>>>>>> caffe-yolo/master
  # Attach methods to Net.
  Net.blobs = _Net_blobs
  Net.blob_loss_weights = _Net_blob_loss_weights
  Net.params = _Net_params
  Net.forward = _Net_forward
  Net.backward = _Net_backward
  Net.forward_all = _Net_forward_all
  Net.forward_backward_all = _Net_forward_backward_all
  Net.set_input_arrays = _Net_set_input_arrays
  Net._batch = _Net_batch
  Net.inputs = _Net_inputs
  Net.outputs = _Net_outputs
  <<<<<<< HEAD
  Net.top_names = _Net_get_id_name(Net._top_ids, "_top_names")
  Net.bottom_names = _Net_get_id_name(Net._bottom_ids, "_bottom_names")
  =======
  >>>>>>> caffe-yolo/master
  