parameters: (self, model_file, pretrained_file, mean=None,
                   input_scale=None, raw_scale=None, channel_swap=None,
                   context_pad=None)
docstring: Take
              mean, input_scale, raw_scale, channel_swap: params for
              preprocessing options.
          context_pad: amount of surrounding context to take s.t. a `context_pad`
              sized border of pixels in the network input image is context, as in
              R-CNN feature extraction.
docstring_summary: Take
              mean, input_scale, raw_scale, channel_swap: params for
              preprocessing options.
          context_pad: amount of surrounding context to take s.t. a `context_pad`
              sized border of pixels in the network input image is context, as in
              R-CNN feature extraction.
docstring_tokens: Take mean input_scale raw_scale channel_swap : params for preprocessing options . context_pad : amount of surrounding context to take s . t . a context_pad sized border of pixels in the network input image is context as in R - CNN feature extraction .
function: def __init__(self, model_file, pretrained_file, mean=None,
                   input_scale=None, raw_scale=None, channel_swap=None,
                   context_pad=None):
          """
          Take
              mean, input_scale, raw_scale, channel_swap: params for
              preprocessing options.
          context_pad: amount of surrounding context to take s.t. a `context_pad`
              sized border of pixels in the network input image is context, as in
              R-CNN feature extraction.
          """
          caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
  
          # configure pre-processing
          in_ = self.inputs[0]
          self.transformer = caffe.io.Transformer(
              {in_: self.blobs[in_].data.shape})
          self.transformer.set_transpose(in_, (2,0,1))
          if mean is not None:
              self.transformer.set_mean(in_, mean)
          if input_scale is not None:
              self.transformer.set_input_scale(in_, input_scale)
          if raw_scale is not None:
              self.transformer.set_raw_scale(in_, raw_scale)
          if channel_swap is not None:
              self.transformer.set_channel_swap(in_, channel_swap)
  
          self.configure_crop(context_pad)
function_tokens: def __init__ ( self , model_file , pretrained_file , mean = None , input_scale = None , raw_scale = None , channel_swap = None , context_pad = None ) : caffe . Net . __init__ ( self , model_file , pretrained_file , caffe . TEST ) # configure pre-processing in_ = self . inputs [ 0 ] self . transformer = caffe . io . Transformer ( { in_ : self . blobs [ in_ ] . data . shape } ) self . transformer . set_transpose ( in_ , ( 2 , 0 , 1 ) ) if mean is not None : self . transformer . set_mean ( in_ , mean ) if input_scale is not None : self . transformer . set_input_scale ( in_ , input_scale ) if raw_scale is not None : self . transformer . set_raw_scale ( in_ , raw_scale ) if channel_swap is not None : self . transformer . set_channel_swap ( in_ , channel_swap ) self . configure_crop ( context_pad )