import argparse import sys import os import os.path import tensorflow as tf import numpy as np from scipy import spatial from tensorflow import gfile # Validation Flow: # 1. Generate input data # python validate.py --generate_data true \ # --input_file input_file # --input_shape 1,64,64,3 # # 2. Use mace_run to run model on phone. # 3. adb pull the result. # 4. Compare output data of mace and tf # python validate.py --model_file tf_model_opt.pb \ # --input_file input_file \ # --mace_out_file output_file \ # --input_node input_node \ # --output_node output_node \ # --input_shape 1,64,64,3 \ # --output_shape 1,64,64,2 def generate_data(shape): np.random.seed() data = np.random.random(shape) * 2 - 1 print FLAGS.input_file data.astype(np.float32).tofile(FLAGS.input_file) print "Generate input file done." def load_data(file): if os.path.isfile(file): return np.fromfile(file=file, dtype=np.float32) else: return np.empty([0]) def valid_output(out_shape, mace_out_file, tf_out_value): mace_out_value = load_data(mace_out_file) if mace_out_value.size != 0: similarity = (1 - spatial.distance.cosine(tf_out_value.flat, mace_out_value)) print 'MACE VS TF similarity: ', similarity if (FLAGS.mace_runtime == "gpu" and similarity > 0.995) or \ (FLAGS.mace_runtime == "dsp" and similarity > 0.930): print '=======================Similarity Test Passed======================' else: print '=======================Similarity Test Failed======================' else: print '=======================Skip empty node===================' def run_model(input_shape): if not gfile.Exists(FLAGS.model_file): print("Input graph file '" + FLAGS.model_file + "' does not exist!") return -1 input_graph_def = tf.GraphDef() with gfile.Open(FLAGS.model_file, "rb") as f: data = f.read() input_graph_def.ParseFromString(data) tf.import_graph_def(input_graph_def, name="") with tf.Session() as session: with session.graph.as_default() as graph: tf.import_graph_def(input_graph_def, name="") input_node = graph.get_tensor_by_name(FLAGS.input_node + ':0') output_node = graph.get_tensor_by_name(FLAGS.output_node + ':0') input_value = load_data(FLAGS.input_file) input_value = input_value.reshape(input_shape) output_value = session.run(output_node, feed_dict={input_node: input_value}) output_value.astype(np.float32).tofile( os.path.dirname(FLAGS.input_file) + '/tf_out') return output_value def main(unused_args): input_shape = [int(x) for x in FLAGS.input_shape.split(',')] output_shape = [int(x) for x in FLAGS.output_shape.split(',')] if FLAGS.generate_data: generate_data(input_shape) else: output_value = run_model(input_shape) valid_output(output_shape, FLAGS.mace_out_file, output_value) def parse_args(): """Parses command line arguments.""" parser = argparse.ArgumentParser() parser.register("type", "bool", lambda v: v.lower() == "true") parser.add_argument( "--model_file", type=str, default="", help="TensorFlow \'GraphDef\' file to load.") parser.add_argument( "--input_file", type=str, default="", help="input file.") parser.add_argument( "--mace_out_file", type=str, default="", help="mace output file to load.") parser.add_argument( "--mace_runtime", type=str, default="gpu", help="mace runtime device.") parser.add_argument( "--input_shape", type=str, default="1,64,64,3", help="input shape.") parser.add_argument( "--output_shape", type=str, default="1,64,64,2", help="output shape.") parser.add_argument( "--input_node", type=str, default="input_node", help="input node") parser.add_argument( "--output_node", type=str, default="output_node", help="output node") parser.add_argument( "--generate_data", type='bool', default="false", help="Generate data or not.") return parser.parse_known_args() if __name__ == '__main__': FLAGS, unparsed = parse_args() main(unused_args=[sys.argv[0]] + unparsed)