text: from __future__ import print_function
  import collections
  import os
  
  import numpy
  try:
      from PIL import Image
      available = True
  except ImportError as e:
      available = False
      _import_error = e
  
  from chainer.dataset.convert import concat_examples
  from chainer.dataset import download
  from chainer import flag
  from chainer.functions.activation.relu import relu
  from chainer.functions.activation.softmax import softmax
  from chainer.functions.array.reshape import reshape
  from chainer.functions.math.sum import sum
  from chainer.functions.pooling.average_pooling_2d import average_pooling_2d
  from chainer.functions.pooling.max_pooling_2d import max_pooling_2d
  from chainer.initializers import constant
  from chainer.initializers import normal
  from chainer import link
  from chainer.links.connection.convolution_2d import Convolution2D
  from chainer.links.connection.linear import Linear
  from chainer.links.normalization.batch_normalization import BatchNormalization
  from chainer.serializers import npz
  from chainer.utils import imgproc
  from chainer.variable import Variable
  
  
  class ResNetLayers(link.Chain):
  
      """A pre-trained CNN model provided by MSRA [1].
  
      When you specify the path of the pre-trained chainer model serialized as
      a ``.npz`` file in the constructor, this chain model automatically
      initializes all the parameters with it.
      This model would be useful when you want to extract a semantic feature
      vector per image, or fine-tune the model on a different dataset.
      Note that unlike ``VGG16Layers``, it does not automatically download a
      pre-trained caffemodel. This caffemodel can be downloaded at
      `GitHub <https://github.com/KaimingHe/deep-residual-networks>`_.
  
      If you want to manually convert the pre-trained caffemodel to a chainer
      model that can be specified in the constructor,
      please use ``convert_caffemodel_to_npz`` classmethod instead.
  
      .. [1] K. He et. al., `Deep Residual Learning for Image Recognition
          <https://arxiv.org/abs/1512.03385>`_
  
      Args:
          pretrained_model (str): the destination of the pre-trained
              chainer model serialized as a ``.npz`` file.
              If this argument is specified as ``auto``,
              it automatically loads and converts the caffemodel from
              ``$CHAINER_DATASET_ROOT/pfnet/chainer/models/ResNet-{n-layers}-model.caffemodel``,
              where ``$CHAINER_DATASET_ROOT`` is set as
              ``$HOME/.chainer/dataset`` unless you specify another value
              by modifying the environment variable and {n_layers} is replaced
              with the specified number of layers given as the first argment to
              this costructor. Note that in this case the converted chainer
              model is stored on the same directory and automatically used from
              the next time.
              If this argument is specified as ``None``, all the parameters
              are not initialized by the pre-trained model, but the default
              initializer used in the original paper, i.e.,
              ``chainer.initializers.HeNormal(scale=1.0)``.
          n_layers (int): The number of layers of this model. It should be either
              50, 101, or 152.
  
      Attributes:
          available_layers (list of str): The list of available layer names
              used by ``__call__`` and ``extract`` methods.
  
      """
  
      def __init__(self, pretrained_model, n_layers):
          if pretrained_model:
              # As a sampling process is time-consuming,
              # we employ a zero initializer for faster computation.
              kwargs = {'initialW': constant.Zero()}
          else:
              # employ default initializers used in the original paper
              kwargs = {'initialW': normal.HeNormal(scale=1.0)}
  
          if n_layers == 50:
              block = [3, 4, 6, 3]
          elif n_layers == 101:
              block = [3, 4, 23, 3]
          elif n_layers == 152:
              block = [3, 8, 36, 3]
          else:
              raise ValueError('The n_layers argument should be either 50, 101,'
                               ' or 152, but {} was given.'.format(n_layers))
  
          super(ResNetLayers, self).__init__(
              conv1=Convolution2D(3, 64, 7, 2, 3, **kwargs),
              bn1=BatchNormalization(64),
              res2=BuildingBlock(block[0], 64, 64, 256, 1, **kwargs),
              res3=BuildingBlock(block[1], 256, 128, 512, 2, **kwargs),
              res4=BuildingBlock(block[2], 512, 256, 1024, 2, **kwargs),
              res5=BuildingBlock(block[3], 1024, 512, 2048, 2, **kwargs),
              fc6=Linear(2048, 1000),
          )
          if pretrained_model and pretrained_model.endswith('.caffemodel'):
              _retrieve(n_layers, 'ResNet-{}-model.npz'.format(n_layers),
                        pretrained_model, self)
          elif pretrained_model:
              npz.load_npz(pretrained_model, self)
          self.functions = collections.OrderedDict([
              ('conv1', [self.conv1, self.bn1, relu]),
              ('pool1', [lambda x: max_pooling_2d(x, ksize=3, stride=2)]),
              ('res2', [self.res2]),
              ('res3', [self.res3]),
              ('res4', [self.res4]),
              ('res5', [self.res5]),
              ('pool5', [_global_average_pooling_2d]),
              ('fc6', [self.fc6]),
              ('prob', [softmax]),
          ])
  
      @property
      def available_layers(self):
          return list(self.functions.keys())
  
      @classmethod
      def convert_caffemodel_to_npz(cls, path_caffemodel, path_npz, n_layers=50):
          """Converts a pre-trained caffemodel to a chainer model.
  
          Args:
              path_caffemodel (str): Path of the pre-trained caffemodel.
              path_npz (str): Path of the converted chainer model.
          """
  
          # As CaffeFunction uses shortcut symbols,
          # we import CaffeFunction here.
          from chainer.links.caffe.caffe_function import CaffeFunction
          caffemodel = CaffeFunction(path_caffemodel)
          chainermodel = cls(pretrained_model=None)
          if n_layers == 50:
              _transfer_resnet50(caffemodel, chainermodel)
          elif n_layers == 101:
              _transfer_resnet101(caffemodel, chainermodel)
          elif n_layers == 152:
              _transfer_resnet152(caffemodel, chainermodel)
          else:
              raise ValueError('The n_layers argument should be either 50, 101,'
                               ' or 152, but {} was given.'.format(n_layers))
          npz.save_npz(path_npz, chainermodel, compression=False)
  
      def __call__(self, x, layers=['prob'], test=True):
          """Computes all the feature maps specified by ``layers``.
  
          Args:
              x (~chainer.Variable): Input variable.
              layers (list of str): The list of layer names you want to extract.
              test (bool): If ``True``, BarchNormalization runs in test mode.
  
          Returns:
              Dictionary of ~chainer.Variable: A directory in which
              the key contains the layer name and the value contains
              the corresponding feature map variable.
  
          """
  
          h = x
          activations = {}
          target_layers = set(layers)
          for key, funcs in self.functions.items():
              if len(target_layers) == 0:
                  break
              for func in funcs:
                  if isinstance(func, BatchNormalization) or \
                          isinstance(func, BuildingBlock):
                      h = func(h, test=test)
                  else:
                      h = func(h)
              if key in target_layers:
                  activations[key] = h
                  target_layers.remove(key)
          return activations
  
      def extract(self, images, layers=['pool5'], size=(224, 224),
                  test=True, volatile=flag.OFF):
          """Extracts all the feature maps of given images.
  
          The difference of directly executing ``__call__`` is that
          it directly accepts images as an input and automatically
          transforms them to a proper variable. That is,
          it is also interpreted as a shortcut method that implicitly calls
          ``prepare`` and ``__call__`` functions.
  
          Args:
              images (iterable of PIL.Image or numpy.ndarray): Input images.
              layers (list of str): The list of layer names you want to extract.
              size (pair of ints): The resolution of resized images used as
                  an input of CNN. All the given images are not resized
                  if this argument is ``None``, but the resolutions of
                  all the images should be the same.
              test (bool): If ``True``, BatchNormalization runs in test mode.
              volatile (~chainer.Flag): Volatility flag used for input variables.
  
          Returns:
              Dictionary of ~chainer.Variable: A directory in which
              the key contains the layer name and the value contains
              the corresponding feature map variable.
  
          """
  
          x = concat_examples([prepare(img, size=size) for img in images])
          x = Variable(self.xp.asarray(x), volatile=volatile)
          return self(x, layers=layers, test=test)
  
      def predict(self, images, oversample=True):
          """Computes all the probabilities of given images.
  
          Args:
              images (iterable of PIL.Image or numpy.ndarray): Input images.
              oversample (bool): If ``True``, it averages results across
                  center, corners, and mirrors. Otherwise, it uses only the
                  center.
  
          Returns:
              ~chainer.Variable: Output that contains the class probabilities
              of given images.
  
          """
  
          x = concat_examples([prepare(img, size=(256, 256)) for img in images])
          if oversample:
              x = imgproc.oversample(x, crop_dims=(224, 224))
          else:
              x = x[:, :, 16:240, 16:240]
          # Set volatile option to ON to reduce memory consumption
          x = Variable(self.xp.asarray(x), volatile=flag.ON)
          y = self(x, layers=['prob'])['prob']
          if oversample:
              n = y.data.shape[0] // 10
              y_shape = y.data.shape[1:]
              y = reshape(y, (n, 10) + y_shape)
              y = sum(y, axis=1) / 10
          return y
  
  
  class ResNet50Layers(ResNetLayers):
  
      """A pre-trained CNN model with 50 layers provided by MSRA [1].
  
      When you specify the path of the pre-trained chainer model serialized as
      a ``.npz`` file in the constructor, this chain model automatically
      initializes all the parameters with it.
      This model would be useful when you want to extract a semantic feature
      vector per image, or fine-tune the model on a different dataset.
      Note that unlike ``VGG16Layers``, it does not automatically download a
      pre-trained caffemodel. This caffemodel can be downloaded at
      `GitHub <https://github.com/KaimingHe/deep-residual-networks>`_.
  
      If you want to manually convert the pre-trained caffemodel to a chainer
      model that can be specified in the constructor,
      please use ``convert_caffemodel_to_npz`` classmethod instead.
  
      ResNet50 has 25,557,096 trainable parameters, and it's 58% and 43% fewer
      than ResNet101 and ResNet152, respectively. On the other hand, the top-5
      classification accuracy on ImageNet dataset drops only 0.7% and 1.1% from
      ResNet101 and ResNet152, respectively. Therefore, ResNet50 may have the
      best balance between the accuracy and the model size. It would be basically
      just enough for many cases, but some advanced models for object detection
      or semantic segmentation use deeper ones as their building blocks, so these
      deeper ResNets are here for making reproduction work easier.
  
      .. [1] K. He et. al., `Deep Residual Learning for Image Recognition
          <https://arxiv.org/abs/1512.03385>`_
  
      Args:
          pretrained_model (str): the destination of the pre-trained
              chainer model serialized as a ``.npz`` file.
              If this argument is specified as ``auto``,
              it automatically loads and converts the caffemodel from
              ``$CHAINER_DATASET_ROOT/pfnet/chainer/models/ResNet-50-model.caffemodel``,
              where ``$CHAINER_DATASET_ROOT`` is set as
              ``$HOME/.chainer/dataset`` unless you specify another value
              by modifying the environment variable. Note that in this case the
              converted chainer model is stored on the same directory and
              automatically used from the next time.
              If this argument is specified as ``None``, all the parameters
              are not initialized by the pre-trained model, but the default
              initializer used in the original paper, i.e.,
              ``chainer.initializers.HeNormal(scale=1.0)``.
  
      Attributes:
          available_layers (list of str): The list of available layer names
              used by ``__call__`` and ``extract`` methods.
  
      """
  
      def __init__(self, pretrained_model='auto'):
          if pretrained_model == 'auto':
              pretrained_model = 'ResNet-50-model.caffemodel'
          super(ResNet50Layers, self).__init__(pretrained_model, 50)
  
  
  class ResNet101Layers(ResNetLayers):
  
      """A pre-trained CNN model with 101 layers provided by MSRA [1].
  
      When you specify the path of the pre-trained chainer model serialized as
      a ``.npz`` file in the constructor, this chain model automatically
      initializes all the parameters with it.
      This model would be useful when you want to extract a semantic feature
      vector per image, or fine-tune the model on a different dataset.
      Note that unlike ``VGG16Layers``, it does not automatically download a
      pre-trained caffemodel. This caffemodel can be downloaded at
      `GitHub <https://github.com/KaimingHe/deep-residual-networks>`_.
  
      If you want to manually convert the pre-trained caffemodel to a chainer
      model that can be specified in the constructor,
      please use ``convert_caffemodel_to_npz`` classmethod instead.
  
      ResNet101 has 44,549,224 trainable parameters, and it's 43% fewer than
      ResNet152 model, while the top-5 classification accuracy on ImageNet
      dataset drops 1.1% from ResNet152. For many cases, ResNet50 may have the
      best balance between the accuracy and the model size.
  
      .. [1] K. He et. al., `Deep Residual Learning for Image Recognition
          <https://arxiv.org/abs/1512.03385>`_
  
      Args:
          pretrained_model (str): the destination of the pre-trained
              chainer model serialized as a ``.npz`` file.
              If this argument is specified as ``auto``,
              it automatically loads and converts the caffemodel from
              ``$CHAINER_DATASET_ROOT/pfnet/chainer/models/ResNet-101-model.caffemodel``,
              where ``$CHAINER_DATASET_ROOT`` is set as
              ``$HOME/.chainer/dataset`` unless you specify another value
              by modifying the environment variable. Note that in this case the
              converted chainer model is stored on the same directory and
              automatically used from the next time.
              If this argument is specified as ``None``, all the parameters
              are not initialized by the pre-trained model, but the default
              initializer used in the original paper, i.e.,
              ``chainer.initializers.HeNormal(scale=1.0)``.
  
      Attributes:
          available_layers (list of str): The list of available layer names
              used by ``__call__`` and ``extract`` methods.
  
      """
  
      def __init__(self, pretrained_model='auto'):
          if pretrained_model == 'auto':
              pretrained_model = 'ResNet-101-model.caffemodel'
          super(ResNet101Layers, self).__init__(pretrained_model, 101)
  
  
  class ResNet152Layers(ResNetLayers):
  
      """A pre-trained CNN model with 152 layers provided by MSRA [1].
  
      When you specify the path of the pre-trained chainer model serialized as
      a ``.npz`` file in the constructor, this chain model automatically
      initializes all the parameters with it.
      This model would be useful when you want to extract a semantic feature
      vector per image, or fine-tune the model on a different dataset.
      Note that unlike ``VGG16Layers``, it does not automatically download a
      pre-trained caffemodel. This caffemodel can be downloaded at
      `GitHub <https://github.com/KaimingHe/deep-residual-networks>`_.
  
      If you want to manually convert the pre-trained caffemodel to a chainer
      model that can be specified in the constructor,
      please use ``convert_caffemodel_to_npz`` classmethod instead.
  
      ResNet152 has 60,192,872 trainable parameters, and it's the deepest ResNet
      model and it achieves the best result on ImageNet classification task in
      `ILSVRC 2015 <http://image-net.org/challenges/LSVRC/2015/results#loc>`_.
  
      .. [1] K. He et. al., `Deep Residual Learning for Image Recognition
          <https://arxiv.org/abs/1512.03385>`_
  
      Args:
          pretrained_model (str): the destination of the pre-trained
              chainer model serialized as a ``.npz`` file.
              If this argument is specified as ``auto``,
              it automatically loads and converts the caffemodel from
              ``$CHAINER_DATASET_ROOT/pfnet/chainer/models/ResNet-152-model.caffemodel``,
              where ``$CHAINER_DATASET_ROOT`` is set as
              ``$HOME/.chainer/dataset`` unless you specify another value
              by modifying the environment variable. Note that in this case the
              converted chainer model is stored on the same directory and
              automatically used from the next time.
              If this argument is specified as ``None``, all the parameters
              are not initialized by the pre-trained model, but the default
              initializer used in the original paper, i.e.,
              ``chainer.initializers.HeNormal(scale=1.0)``.
  
      Attributes:
          available_layers (list of str): The list of available layer names
              used by ``__call__`` and ``extract`` methods.
  
      """
  
      def __init__(self, pretrained_model='auto'):
          if pretrained_model == 'auto':
              pretrained_model = 'ResNet-152-model.caffemodel'
          super(ResNet152Layers, self).__init__(pretrained_model, 152)
  
  
  def prepare(image, size=(224, 224)):
      """Converts the given image to the numpy array for ResNets.
  
      Note that you have to call this method before ``__call__``
      because the pre-trained resnet model requires to resize the given
      image, covert the RGB to the BGR, subtract the mean,
      and permute the dimensions before calling.
  
      Args:
          image (PIL.Image or numpy.ndarray): Input image.
              If an input is ``numpy.ndarray``, its shape must be
              ``(height, width)``, ``(height, width, channels)``,
              or ``(channels, height, width)``, and
              the order of the channels must be RGB.
          size (pair of ints): Size of converted images.
              If ``None``, the given image is not resized.
  
      Returns:
          numpy.ndarray: The converted output array.
  
      """
  
      if not available:
          raise ImportError('PIL cannot be loaded. Install Pillow!\n'
                            'The actual import error is as follows:\n' +
                            str(_import_error))
      if isinstance(image, numpy.ndarray):
          if image.ndim == 3:
              if image.shape[0] == 1:
                  image = image[0, :, :]
              elif image.shape[0] == 3:
                  image = image.transpose((1, 2, 0))
          image = Image.fromarray(image.astype(numpy.uint8))
      image = image.convert('RGB')
      if size:
          image = image.resize(size)
      image = numpy.asarray(image, dtype=numpy.float32)
      image = image[:, :, ::-1]
      # NOTE: in the original paper they subtract a fixed mean image,
      #       however, in order to support arbitrary size we instead use the
      #       mean pixel (rather than mean image) as with VGG team. The mean
      #       value used in ResNet is slightly different from that of VGG16.
      image -= numpy.array(
          [103.063,  115.903,  123.152], dtype=numpy.float32)
      image = image.transpose((2, 0, 1))
      return image
  
  
  class BuildingBlock(link.Chain):
  
      """A building block that consists of several Bottleneck layers.
  
      Args:
          n_layer (int): Number of layers used in the building block.
          in_channels (int): Number of channels of input arrays.
          mid_channels (int): Number of channels of intermediate arrays.
          out_channels (int): Number of channels of output arrays.
          stride (int or tuple of ints): Stride of filter application.
          initialW (4-D array): Initial weight value used in
              the convolutional layers.
      """
  
      def __init__(self, n_layer, in_channels, mid_channels,
                   out_channels, stride, initialW=None):
          links = [
              ('a', BottleneckA(
                  in_channels, mid_channels, out_channels, stride, initialW))
          ]
          for i in range(n_layer - 1):
              name = 'b{}'.format(i + 1)
              bottleneck = BottleneckB(out_channels, mid_channels, initialW)
              links.append((name, bottleneck))
          super(BuildingBlock, self).__init__(**dict(links))
          self.forward = links
  
      def __call__(self, x, test=True):
          for name, func in self.forward:
              x = func(x, test=test)
          return x
  
  
  class BottleneckA(link.Chain):
  
      """A bottleneck layer that reduces the resolution of the feature map.
  
      Args:
          in_channels (int): Number of channels of input arrays.
          mid_channels (int): Number of channels of intermediate arrays.
          out_channels (int): Number of channels of output arrays.
          stride (int or tuple of ints): Stride of filter application.
          initialW (4-D array): Initial weight value used in
              the convolutional layers.
      """
  
      def __init__(self, in_channels, mid_channels, out_channels,
                   stride=2, initialW=None):
          super(BottleneckA, self).__init__(
              conv1=Convolution2D(
                  in_channels, mid_channels, 1, stride, 0,
                  initialW=initialW, nobias=True),
              bn1=BatchNormalization(mid_channels),
              conv2=Convolution2D(
                  mid_channels, mid_channels, 3, 1, 1,
                  initialW=initialW, nobias=True),
              bn2=BatchNormalization(mid_channels),
              conv3=Convolution2D(
                  mid_channels, out_channels, 1, 1, 0,
                  initialW=initialW, nobias=True),
              bn3=BatchNormalization(out_channels),
              conv4=Convolution2D(
                  in_channels, out_channels, 1, stride, 0,
                  initialW=initialW, nobias=True),
              bn4=BatchNormalization(out_channels),
          )
  
      def __call__(self, x, test=True):
          h1 = relu(self.bn1(self.conv1(x), test=test))
          h1 = relu(self.bn2(self.conv2(h1), test=test))
          h1 = self.bn3(self.conv3(h1), test=test)
          h2 = self.bn4(self.conv4(x), test=test)
          return relu(h1 + h2)
  
  
  class BottleneckB(link.Chain):
  
      """A bottleneck layer that maintains the resolution of the feature map.
  
      Args:
          in_channels (int): Number of channels of input and output arrays.
          mid_channels (int): Number of channels of intermediate arrays.
          initialW (4-D array): Initial weight value used in
              the convolutional layers.
      """
  
      def __init__(self, in_channels, mid_channels, initialW=None):
          super(BottleneckB, self).__init__(
              conv1=Convolution2D(
                  in_channels, mid_channels, 1, 1, 0,
                  initialW=initialW, nobias=True),
              bn1=BatchNormalization(mid_channels),
              conv2=Convolution2D(
                  mid_channels, mid_channels, 3, 1, 1,
                  initialW=initialW, nobias=True),
              bn2=BatchNormalization(mid_channels),
              conv3=Convolution2D(
                  mid_channels, in_channels, 1, 1, 0,
                  initialW=initialW, nobias=True),
              bn3=BatchNormalization(in_channels),
          )
  
      def __call__(self, x, test=True):
          h = relu(self.bn1(self.conv1(x), test=test))
          h = relu(self.bn2(self.conv2(h), test=test))
          h = self.bn3(self.conv3(h), test=test)
          return relu(h + x)
  
  
  def _global_average_pooling_2d(x):
      n, channel, rows, cols = x.data.shape
      h = average_pooling_2d(x, (rows, cols), stride=1)
      h = reshape(h, (n, channel))
      return h
  
  
  def _transfer_components(src, dst_conv, dst_bn, bname, cname):
      src_conv = getattr(src, 'res{}_branch{}'.format(bname, cname))
      src_bn = getattr(src, 'bn{}_branch{}'.format(bname, cname))
      src_scale = getattr(src, 'scale{}_branch{}'.format(bname, cname))
      dst_conv.W.data[:] = src_conv.W.data
      dst_bn.avg_mean[:] = src_bn.avg_mean
      dst_bn.avg_var[:] = src_bn.avg_var
      dst_bn.gamma.data[:] = src_scale.W.data
      dst_bn.beta.data[:] = src_scale.bias.b.data
  
  
  def _transfer_bottleneckA(src, dst, name):
      _transfer_components(src, dst.conv1, dst.bn1, name, '2a')
      _transfer_components(src, dst.conv2, dst.bn2, name, '2b')
      _transfer_components(src, dst.conv3, dst.bn3, name, '2c')
      _transfer_components(src, dst.conv4, dst.bn4, name, '1')
  
  
  def _transfer_bottleneckB(src, dst, name):
      _transfer_components(src, dst.conv1, dst.bn1, name, '2a')
      _transfer_components(src, dst.conv2, dst.bn2, name, '2b')
      _transfer_components(src, dst.conv3, dst.bn3, name, '2c')
  
  
  def _transfer_block(src, dst, names):
      _transfer_bottleneckA(src, dst.a, names[0])
      for i, name in enumerate(names[1:]):
          dst_bottleneckB = getattr(dst, 'b{}'.format(i + 1))
          _transfer_bottleneckB(src, dst_bottleneckB, name)
  
  
  def _transfer_resnet50(src, dst):
      dst.conv1.W.data[:] = src.conv1.W.data
      dst.conv1.b.data[:] = src.conv1.b.data
      dst.bn1.avg_mean[:] = src.bn_conv1.avg_mean
      dst.bn1.avg_var[:] = src.bn_conv1.avg_var
      dst.bn1.gamma.data[:] = src.scale_conv1.W.data
      dst.bn1.beta.data[:] = src.scale_conv1.bias.b.data
  
      _transfer_block(src, dst.res2, ['2a', '2b', '2c'])
      _transfer_block(src, dst.res3, ['3a', '3b', '3c', '3d'])
      _transfer_block(src, dst.res4, ['4a', '4b', '4c', '4d', '4e', '4f'])
      _transfer_block(src, dst.res5, ['5a', '5b', '5c'])
  
      dst.fc6.W.data[:] = src.fc1000.W.data
      dst.fc6.b.data[:] = src.fc1000.b.data
  
  
  def _transfer_resnet101(src, dst):
      dst.conv1.W.data[:] = src.conv1.W.data
      dst.bn1.avg_mean[:] = src.bn_conv1.avg_mean
      dst.bn1.avg_var[:] = src.bn_conv1.avg_var
      dst.bn1.gamma.data[:] = src.scale_conv1.W.data
      dst.bn1.beta.data[:] = src.scale_conv1.bias.b.data
  
      _transfer_block(src, dst.res2, ['2a', '2b', '2c'])
      _transfer_block(src, dst.res3, ['3a', '3b1', '3b2', '3b3'])
      _transfer_block(src, dst.res4,
                      ['4a'] + ['4b{}'.format(i) for i in range(1, 23)])
      _transfer_block(src, dst.res5, ['5a', '5b', '5c'])
  
      dst.fc6.W.data[:] = src.fc1000.W.data
      dst.fc6.b.data[:] = src.fc1000.b.data
  
  
  def _transfer_resnet152(src, dst):
      dst.conv1.W.data[:] = src.conv1.W.data
      dst.bn1.avg_mean[:] = src.bn_conv1.avg_mean
      dst.bn1.avg_var[:] = src.bn_conv1.avg_var
      dst.bn1.gamma.data[:] = src.scale_conv1.W.data
      dst.bn1.beta.data[:] = src.scale_conv1.bias.b.data
  
      _transfer_block(src, dst.res2, ['2a', '2b', '2c'])
      _transfer_block(src, dst.res3,
                      ['3a'] + ['3b{}'.format(i) for i in range(1, 8)])
      _transfer_block(src, dst.res4,
                      ['4a'] + ['4b{}'.format(i) for i in range(1, 36)])
      _transfer_block(src, dst.res5, ['5a', '5b', '5c'])
  
      dst.fc6.W.data[:] = src.fc1000.W.data
      dst.fc6.b.data[:] = src.fc1000.b.data
  
  
  def _make_npz(path_npz, path_caffemodel, model, n_layers):
      print('Now loading caffemodel (usually it may take few minutes)')
      if not os.path.exists(path_caffemodel):
          raise IOError(
              'The pre-trained caffemodel does not exist. Please download it '
              'from \'https://github.com/KaimingHe/deep-residual-networks\', '
              'and place it on {}'.format(path_caffemodel))
  
      if n_layers == 50:
          ResNet50Layers.convert_caffemodel_to_npz(path_caffemodel, path_npz, 50)
      elif n_layers == 101:
          ResNet101Layers.convert_caffemodel_to_npz(
              path_caffemodel, path_npz, 101)
      elif n_layers == 152:
          ResNet152Layers.convert_caffemodel_to_npz(
              path_caffemodel, path_npz, 152)
      npz.load_npz(path_npz, model)
      return model
  
  
  def _retrieve(n_layers, name_npz, name_caffemodel, model):
      root = download.get_dataset_directory('pfnet/chainer/models/')
      path = os.path.join(root, name_npz)
      path_caffemodel = os.path.join(root, name_caffemodel)
      return download.cache_or_load_file(
          path, lambda path: _make_npz(path, path_caffemodel, model, n_layers),
          lambda path: npz.load_npz(path, model))
  