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Program for complete evaluation of source code similarity analyzer
using sequence or Tokens technique by 2-way siamese type DNN
Program analyzes all pairs of source code samples
Program operated in in multi-GPU mode
"""
import sys
import os
import argparse
import pickle
import tensorflow as tf
main_dir = os.path.dirname(
os.path.dirname(os.path.realpath(__file__)))
sys.path.extend([f"{main_dir}/Dataset",
f"{main_dir}/CommonFunctions",
f"{main_dir}/PostProcess"])
from SeqTokSim2WayComplDS import SeqTokSim2WayComplDS
from ProgramArguments import parseArguments
from Utilities import *
from SimilConfusion import SimilConfusAnalysis
def main(args):
"""
Main function of program for predicting similarity of
two source code samples
Parameters:
- args -- Parsed command line arguments
as object returned by ArgumentParser
"""
resetSeeds()
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir)
latest_checkpoint = getCheckpoint(args.ckpt_dir, args.ckpt)
_ds = SeqTokSim2WayComplDS(args.dataset,
short_code_th = 1,
max_seq_length = args.seq_len,
seq_len_to_pad = args.pad_len,
labels01 = not args.symmetric_labels)
#Indices of problems corresponding to subsequences of samples
_sample_probl_indices = _ds.sample_probl_indices
with open(f"{args.out_dir}/problem_indices.pcl", "wb") as _f:
pickle.dump(_sample_probl_indices, _f)
#Create parallelization strategy for multi GPU mode
#It also can be either MirroredStrategy or MultiWorkerMirroredStrategy
#But MultiWorkerMirroredStrategy works better
strategy = tf.distribute.MultiWorkerMirroredStrategy()
#distribute.MirroredStrategy()
print("Number of devices: {}".format(strategy.num_replicas_in_sync))
#Construct DNN in the scope of parallelization strategy.
with strategy.scope():
# Everything that creates variables should be under the strategy scope.
# In general this is only model construction & `compile()`.
print("Restoring from", latest_checkpoint)
_dnn = tf.keras.models.load_model(latest_checkpoint)
_test_ds = _ds.testDataset(batch = args.batch)
if args.evaluate:
_eval_loss, _eval_acc = _dnn.evaluate(_test_ds, verbose = args.progress)
print("\n")
print("Evaluation accuracy is {:5.2f}%".format(_eval_acc * 100))
print("Evaluation loss is {:5.2f}".format(_eval_loss))
_prob = _dnn.predict(_test_ds, verbose = args.progress)
_prob = _prob[:,0]
with open(f"{args.out_dir}/similarity_probabilities.pcl", "wb") as _f:
pickle.dump(_prob, _f)
"""
_confusion = SimilConfusAnalysis(_prob, _labels,
_ds.solution_names,
_ds.problems, _annotations)
"""
#_confusion.writeReport()
##############################################################################
# Args are described below
##############################################################################
if __name__ == '__main__':
print("\nEVALUATION OF SOURCE CODE SIMILARITY ANALYZER")
#Handle command-line arguments
parser = argparse.ArgumentParser(description =
"Evaluation of source code similarity analyzer")
parser.add_argument("dataset",
type=str, help="dataset directory")
parser.add_argument("ckpt_dir",
type=str, help="checkpoint directory")
parser.add_argument("out_dir",
type=str, help="directory for results")
parser.add_argument("--ckpt", default = None,
type=str, help="checkpoint file")
parser.add_argument("--batch", default=32, type=int,
help="batch size")
parser.add_argument('--seq_len', default=None, type=int,
help='maximum lengths of token sequence')
parser.add_argument('--pad_len', default=None, type=int,
help='sequence lengths to pad to')
parser.add_argument('--symmetric_labels', action='store_true',
default=False,
help="use symmetric labels: -1 and +1")
parser.add_argument('--evaluate', action='store_true',
default=False,
help="run evaluation")
parser.add_argument('--progress', default=2, type=int,
choices=[0, 1, 2],
help="mode of keras training progress bar")
args = parseArguments(parser)
main(args)
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