convnet_builder.py 文件源码

def inception_module(self, name, cols, input_layer=None, in_size=None):
    if input_layer is None:
      input_layer = self.top_layer
    if in_size is None:
      in_size = self.top_size
    name += str(self.counts[name])
    self.counts[name] += 1
    with tf.variable_scope(name):
      col_layers = []
      col_layer_sizes = []
      for c, col in enumerate(cols):
        col_layers.append([])
        col_layer_sizes.append([])
        for l, layer in enumerate(col):
          ltype, args = layer[0], layer[1:]
          kwargs = {
              'input_layer': input_layer,
              'num_channels_in': in_size
          } if l == 0 else {}
          if ltype == 'conv':
            self.conv(*args, **kwargs)
          elif ltype == 'mpool':
            self.mpool(*args, **kwargs)
          elif ltype == 'apool':
            self.apool(*args, **kwargs)
          elif ltype == 'share':  # Share matching layer from previous column
            self.top_layer = col_layers[c - 1][l]
            self.top_size = col_layer_sizes[c - 1][l]
          else:
            raise KeyError(
                'Invalid layer type for inception module: \'%s\'' % ltype)
          col_layers[c].append(self.top_layer)
          col_layer_sizes[c].append(self.top_size)
      catdim = 3 if self.data_format == 'NHWC' else 1
      self.top_layer = tf.concat([layers[-1] for layers in col_layers], catdim)
      self.top_size = sum([sizes[-1] for sizes in col_layer_sizes])
      return self.top_layer