validate.py

import argparse
import sys
import os
import os.path
import numpy as np
import re
from scipy import spatial
from scipy import stats

# 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 format_output_name(name):
    return re.sub('[^0-9a-zA-Z]+', '_', name)


def compare_output(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', FLAGS.platform.upper(
        ), 'similarity: ', similarity
        if (FLAGS.mace_runtime == "cpu" and similarity > 0.999) or \
            (FLAGS.mace_runtime == "neon" and similarity > 0.999) or \
            (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=================='
            sys.exit(-1)
    else:
        print '=======================Skip empty node==================='
        sys.exit(-1)


def validate_tf_model(input_names, input_shapes, output_names):
    import tensorflow as tf
    if not os.path.isfile(FLAGS.model_file):
        print("Input graph file '" + FLAGS.model_file + "' does not exist!")
        sys.exit(-1)

    input_graph_def = tf.GraphDef()
    with 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_dict = {}
                for i in range(len(input_names)):
                    input_value = load_data(
                        FLAGS.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 = FLAGS.mace_out_file + "_" + \
                            format_output_name(output_names[i])
                    mace_out_value = load_data(output_file_name)
                    compare_output(output_names[i], mace_out_value,
                                   output_values[i])


def validate_caffe_model(input_names, input_shapes, output_names,
                         output_shapes):
    os.environ['GLOG_minloglevel'] = '1'  # suprress Caffe verbose prints
    import caffe
    if not os.path.isfile(FLAGS.model_file):
        print("Input graph file '" + FLAGS.model_file + "' does not exist!")
        sys.exit(-1)
    if not os.path.isfile(FLAGS.weight_file):
        print("Input weight file '" + FLAGS.weight_file + "' does not exist!")
        sys.exit(-1)

    caffe.set_mode_cpu()

    net = caffe.Net(FLAGS.model_file, caffe.TEST, weights=FLAGS.weight_file)

    for i in range(len(input_names)):
        input_value = load_data(FLAGS.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 = FLAGS.mace_out_file + "_" + format_output_name(
            output_names[i])
        mace_out_value = load_data(output_file_name)
        compare_output(output_names[i], mace_out_value, value)


def main(unused_args):
    input_names = [name for name in FLAGS.input_node.split(',')]
    input_shape_strs = [shape for shape in FLAGS.input_shape.split(':')]
    input_shapes = [[int(x) for x in shape.split(',')]
                    for shape in input_shape_strs]
    output_names = [name for name in FLAGS.output_node.split(',')]
    assert len(input_names) == len(input_shapes)

    if FLAGS.platform == 'tensorflow':
        validate_tf_model(input_names, input_shapes, output_names)
    elif FLAGS.platform == 'caffe':
        output_shape_strs = [shape for shape in FLAGS.output_shape.split(':')]
        output_shapes = [[int(x) for x in shape.split(',')]
                         for shape in output_shape_strs]
        validate_caffe_model(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()
    main(unused_args=[sys.argv[0]] + unparsed)