def main(self):
# Generate Model: top structure of tflite model file
buf = self.tflite_file.read()
buf = bytearray(buf)
tf_model = tflite.Model.Model.GetRootAsModel(buf, 0)
stats = graph_stats.GraphStats()
# Model file can have many models
for subgraph_index in range(tf_model.SubgraphsLength()):
tf_subgraph = tf_model.Subgraphs(subgraph_index)
model_name = "#{0} {1}".format(subgraph_index, tf_subgraph.Name())
# 0th subgraph is main subgraph
if (subgraph_index == 0):
model_name += " (MAIN)"
# Parse Operators
op_parser = OperatorParser(tf_model, tf_subgraph)
op_parser.Parse()
stats += graph_stats.CalcGraphStats(op_parser)
if self.save == False:
# print all of operators or requested objects
self.PrintModel(model_name, op_parser)
else:
# save all of operators in this model
self.SaveModel(model_name, op_parser)
print('==== Model Stats ({} Subgraphs) ===='.format(
tf_model.SubgraphsLength()))
print('')
graph_stats.PrintGraphStats(stats, self.print_level)
def PrintInfo(self):
if self.print_all_tensor == True and self.print_all_operator == True:
self.PrintModelInfo()
self.PrintAllOperatorsInList()
graph_stats.PrintGraphStats(
graph_stats.CalcGraphStats(self.op_parser), self.verbose)
if self.print_all_tensor == False:
print('')
self.PrintSpecificTensors()
if self.print_all_operator == False:
print('')
self.PrintSpecificOperators()