parameters: (self, model_file, pretrained_file, gpu=False, mean=None,
                   input_scale=None, raw_scale=None, channel_swap=None,
                   context_pad=None)
docstring: Take
          gpu, 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
          gpu, 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 gpu 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, gpu=False, mean=None,
                   input_scale=None, raw_scale=None, channel_swap=None,
                   context_pad=None):
          """
          Take
          gpu, 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)
          self.set_phase_test()
  
          if gpu:
              self.set_mode_gpu()
          else:
              self.set_mode_cpu()
  
          if mean is not None:
              self.set_mean(self.inputs[0], mean)
          if input_scale is not None:
              self.set_input_scale(self.inputs[0], input_scale)
          if raw_scale is not None:
              self.set_raw_scale(self.inputs[0], raw_scale)
          if channel_swap is not None:
              self.set_channel_swap(self.inputs[0], channel_swap)
  
          self.configure_crop(context_pad)
function_tokens: def __init__ ( self , model_file , pretrained_file , gpu = False , mean = None , input_scale = None , raw_scale = None , channel_swap = None , context_pad = None ) : caffe . Net . __init__ ( self , model_file , pretrained_file ) self . set_phase_test ( ) if gpu : self . set_mode_gpu ( ) else : self . set_mode_cpu ( ) if mean is not None : self . set_mean ( self . inputs [ 0 ] , mean ) if input_scale is not None : self . set_input_scale ( self . inputs [ 0 ] , input_scale ) if raw_scale is not None : self . set_raw_scale ( self . inputs [ 0 ] , raw_scale ) if channel_swap is not None : self . set_channel_swap ( self . inputs [ 0 ] , channel_swap ) self . configure_crop ( context_pad )