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#!/usr/bin/python
import os
import sys
import numpy
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tflite'))
flatbuffersPath = '../../externals/flatbuffers'
sys.path.append(
os.path.join(os.path.dirname(os.path.abspath(__file__)), flatbuffersPath + '/python'))
import flatbuffers
import tflite.Model
import tflite.SubGraph
import argparse
from operator_parser import OperatorParser
from perf_predictor import PerfPredictor
class TFLiteModelFileParser(object):
def __init__(self, args):
# Read flatbuffer file descriptor using argument
self.tflite_file = args.input_file
# Set print level (0 ~ 2)
# TODO: print information based on level
self.print_level = args.verbose
if (args.verbose > 2):
self.print_level = 2
if (args.verbose < 0):
self.print_level = 0
# Set tensor index list to print information
# TODO:
# Print tensors in list only
# Print all tensors if argument used and not specified index number
if (args.tensor != None):
if (len(args.tensor) == 0):
self.print_all_tensor = True
else:
self.print_all_tensor = False
self.print_tensor_index = []
for tensor_index in args.tensor:
self.print_tensor_index.append(int(tensor_index))
# Set operator index list to print information
# TODO:
# Print operators in list only
# Print all operators if argument used and not specified index number
if (args.operator != None):
if (len(args.operator) == 0):
self.print_all_oeprator = True
else:
self.print_all_oeprator = False
self.print_operator_index = []
for operator_index in args.operator:
self.print_operator_index.append(int(operator_index))
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)
# Model file can have many models
# 1st subgraph is main model
model_name = "Main model"
for subgraph_index in range(tf_model.SubgraphsLength()):
tf_subgraph = tf_model.Subgraphs(subgraph_index)
if (subgraph_index != 0):
model_name = "Model #" + str(subgraph_index)
print("[" + model_name + "]\n")
# Model inputs & outputs
model_inputs = tf_subgraph.InputsAsNumpy()
model_outputs = tf_subgraph.OutputsAsNumpy()
print(model_name + " input tensors: " + str(model_inputs))
print(model_name + " output tensors: " + str(model_outputs))
# Parse Operators and print all of operators
op_parser = OperatorParser(tf_model, tf_subgraph, PerfPredictor())
op_parser.Parse()
op_parser.PrintAll()
if __name__ == '__main__':
# Define argument and read
arg_parser = argparse.ArgumentParser()
arg_parser.add_argument(
"input_file", type=argparse.FileType('rb'), help="tflite file to read")
arg_parser.add_argument(
'-v',
'--verbose',
action='count',
default=0,
help="set print level (0~2, default: 0)")
arg_parser.add_argument(
'-t', '--tensor', nargs='*', help="tensor ID to print information (default: all)")
arg_parser.add_argument(
'-o',
'--operator',
nargs='*',
help="operator ID to print information (default: all)")
args = arg_parser.parse_args()
# Call main function
TFLiteModelFileParser(args).main()
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