class_docstr: Associates parameters loaded from a .caffemodel file with their corresponding nodes.
class_docstr_tok: ["Associates","parameters","loaded","from","a",".caffemodel","file","with","their","corresponding","nodes","."]
code: def __init__(self, def_path, data_path):
      # The .prototxt file defining the graph
      self.def_path = def_path
      # The .caffemodel file containing the learned parameters
      self.data_path = data_path
      # Set to true if the fallback protocol-buffer based backend was used
      self.did_use_pb = False
      # A list containing (layer name, parameters) tuples
      self.params = None
      # Load the parameters
      self.caffemodel = None
      if has_pycaffe() and self.def_path:
          self.load_using_caffe()
      else:
          self.load_using_pb()
  
imports: from __future__ import unicode_literals
  from google.protobuf import text_format
  import numpy
  from mmdnn.conversion.caffe.graph import GraphBuilder, NodeKind, LAYER_IN_TRAIN_PROTO
  from mmdnn.conversion.caffe.mapper import NodeMapper, get_handler_name
  from mmdnn.conversion.caffe.resolver import get_caffe_resolver, has_pycaffe
  from mmdnn.conversion.caffe.errors import print_stderr, ConversionError
  from mmdnn.conversion.caffe.common_graph import Graph
  from mmdnn.conversion.caffe.utils import get_lower_case, get_upper_case
label_desc: Use image node_id 1 to create a new DataInjector object from inherited base classes: object with example: obj = DataInjector(def_path, data_path)