# Copyright 2018 Xiaomi, Inc. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import sys import os import os.path import numpy as np import re from scipy import spatial from scipy import stats import common # Validation Flow: # 1. Generate input data # 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 load_data(file): if os.path.isfile(file): return np.fromfile(file=file, dtype=np.float32) else: return np.empty([0]) def compare_output(platform, mace_runtime, output_name, mace_out_value, out_value): if mace_out_value.size != 0: out_value = out_value.reshape(-1) mace_out_value = mace_out_value.reshape(-1) assert len(out_value) == len(mace_out_value) similarity = (1 - spatial.distance.cosine(out_value, mace_out_value)) print output_name, 'MACE VS', platform.upper( ), 'similarity: ', similarity if (mace_runtime == "cpu" and similarity > 0.999) or \ (mace_runtime == "gpu" and similarity > 0.995) or \ (mace_runtime == "dsp" and similarity > 0.930): print '===================Similarity Test Passed==================' else: print '===================Similarity Test Failed==================' sys.exit(-1) else: print '=======================Skip empty node===================' sys.exit(-1) def validate_tf_model(platform, mace_runtime, model_file, input_file, mace_out_file, input_names, input_shapes, output_names): import tensorflow as tf if not os.path.isfile(model_file): print("Input graph file '" + model_file + "' does not exist!") sys.exit(-1) tf.reset_default_graph() input_graph_def = tf.GraphDef() with open(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_dict = {} for i in range(len(input_names)): input_value = load_data( common.formatted_file_name(input_file, input_names[i])) input_value = input_value.reshape(input_shapes[i]) input_node = graph.get_tensor_by_name( input_names[i] + ':0') input_dict[input_node] = input_value output_nodes = [] for name in output_names: output_nodes.extend( [graph.get_tensor_by_name(name + ':0')]) output_values = session.run(output_nodes, feed_dict=input_dict) for i in range(len(output_names)): output_file_name = common.formatted_file_name( mace_out_file, output_names[i]) mace_out_value = load_data(output_file_name) compare_output(platform, mace_runtime, output_names[i], mace_out_value, output_values[i]) def validate_caffe_model(platform, mace_runtime, model_file, input_file, mace_out_file, weight_file, input_names, input_shapes, output_names, output_shapes): os.environ['GLOG_minloglevel'] = '1' # suprress Caffe verbose prints import caffe if not os.path.isfile(model_file): print("Input graph file '" + model_file + "' does not exist!") sys.exit(-1) if not os.path.isfile(weight_file): print("Input weight file '" + weight_file + "' does not exist!") sys.exit(-1) caffe.set_mode_cpu() net = caffe.Net(model_file, caffe.TEST, weights=weight_file) for i in range(len(input_names)): input_value = load_data( common.formatted_file_name(input_file, input_names[i])) input_value = input_value.reshape(input_shapes[i]).transpose((0, 3, 1, 2)) input_blob_name = input_names[i] try: if input_names[i] in net.top_names: input_blob_name = net.top_names[input_names[i]][0] except ValueError: pass net.blobs[input_blob_name].data[0] = input_value net.forward() for i in range(len(output_names)): value = net.blobs[net.top_names[output_names[i]][0]].data out_shape = output_shapes[i] out_shape[1], out_shape[2], out_shape[3] = out_shape[3], out_shape[ 1], out_shape[2] value = value.reshape(out_shape).transpose((0, 2, 3, 1)) output_file_name = common.formatted_file_name( mace_out_file, output_names[i]) mace_out_value = load_data(output_file_name) compare_output(platform, mace_runtime, output_names[i], mace_out_value, value) def validate(platform, model_file, weight_file, input_file, mace_out_file, mace_runtime, input_shape, output_shape, input_node, output_node): input_names = [name for name in input_node.split(',')] input_shape_strs = [shape for shape in input_shape.split(':')] input_shapes = [[int(x) for x in shape.split(',')] for shape in input_shape_strs] output_names = [name for name in output_node.split(',')] assert len(input_names) == len(input_shapes) if platform == 'tensorflow': validate_tf_model(platform, mace_runtime, model_file, input_file, mace_out_file, input_names, input_shapes, output_names) elif platform == 'caffe': output_shape_strs = [shape for shape in output_shape.split(':')] output_shapes = [[int(x) for x in shape.split(',')] for shape in output_shape_strs] validate_caffe_model(platform, mace_runtime, model_file, input_file, mace_out_file, weight_file, input_names, input_shapes, output_names, output_shapes) def parse_args(): """Parses command line arguments.""" parser = argparse.ArgumentParser() parser.register("type", "bool", lambda v: v.lower() == "true") parser.add_argument( "--platform", type=str, default="", help="Tensorflow or Caffe.") parser.add_argument( "--model_file", type=str, default="", help="TensorFlow or Caffe \'GraphDef\' file to load.") parser.add_argument( "--weight_file", type=str, default="", help="caffe model 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") return parser.parse_known_args() if __name__ == '__main__': FLAGS, unparsed = parse_args() validate(FLAGS.platform, FLAGS.model_file, FLAGS.weight_file, FLAGS.input_file, FLAGS.mace_out_file, FLAGS.mace_runtime, FLAGS.input_shape, FLAGS.output_shape, FLAGS.input_node, FLAGS.output_node)