project-codenet / model-experiments /token-based-similarity-classification /src /PostProcessor /ExpSimSeqTokEval.py
| """ | |
| Experimental program for evaluation of source code similarity analyzer | |
| using sequence or Tokens technique by 2-way Siamese type DNN | |
| Uses: | |
| - experimental Siameze CNN | |
| - custom metric | |
| runs in multi-GPU mode | |
| For correct evaluation of DNN model it is required that the test datset is | |
| generated with the same parameters | |
| """ | |
| import sys | |
| import os | |
| import argparse | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| main_dir = os.path.dirname( | |
| os.path.dirname(os.path.realpath(__file__))) | |
| sys.path.extend([f"{main_dir}/SeqOfTokens", | |
| f"{main_dir}/Dataset", | |
| f"{main_dir}/CommonFunctions", | |
| f"{main_dir}/PostProcess", | |
| f"{main_dir}/ModelMaker"]) | |
| from SeqTok2WaySimDsTF import SeqTok2WaySimDsTF | |
| from ProgramArguments import * | |
| from Utilities import * | |
| from DsUtilities import DataRand | |
| from SimilConfusion import SimilConfusAnalysis | |
| from ExpSiamModel import (getLossFunction, | |
| relaxedCrossEntropy, sinCrossEntropy, | |
| cappedCrossEntropy) | |
| 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() | |
| DataRand.setDsSeeds(args.seed_ds) | |
| latest_checkpoint = getCheckpoint(args.ckpt_dir, args.ckpt) | |
| _ds = SeqTok2WaySimDsTF(args.dataset, | |
| min_n_solutions = args.min_solutions, | |
| max_n_problems = args.problems, | |
| short_code_th = args.short_code, | |
| long_code_th = args.long_code, | |
| max_seq_length = args.seq_len, | |
| test = args.testpart, | |
| batch = args.batch, | |
| labels01 = not args.symmetric_labels) | |
| #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) | |
| #with tf.keras.utils.custom_object_scope({}): | |
| if args.loss: | |
| _loss = getLossFunction(args.loss) | |
| with tf.keras.utils.custom_object_scope({'loss_function': _loss}): | |
| _dnn = tf.keras.models.load_model(latest_checkpoint) | |
| else: | |
| _dnn = tf.keras.models.load_model(latest_checkpoint) | |
| _test_ds, _labels, _annotations = \ | |
| _ds.testDataset(args.valsize, args.similpart) | |
| _eval_loss, _eval_acc = _dnn.evaluate(_test_ds, | |
| verbose = args.progress) | |
| _prob = _dnn.predict(_test_ds, verbose = args.progress) | |
| _confusion = SimilConfusAnalysis(_prob, _labels, | |
| _ds.solution_names, | |
| _ds.problems, _annotations, | |
| labels01 = not args.symmetric_labels) | |
| print("\n") | |
| print("Evaluation accuracy is {:5.2f}%".format(_eval_acc * 100)) | |
| print("Evaluation loss is {:5.2f}".format(_eval_loss)) | |
| _confusion.writeReport() | |
| ############################################################################## | |
| # Args are described below | |
| ############################################################################## | |
| if __name__ == '__main__': | |
| print("\nEVALUATION OF EXPERIMENTAL SOURCE CODE SIMILARITY ANALYZER") | |
| #Handle command-line arguments | |
| parser = makeArgParserCodeML( | |
| "Evaluation of source code similarity analyzer", | |
| task = "similarity") | |
| parser.add_argument("--ckpt", default = None, | |
| type=str, help="checkpoint file") | |
| parser.add_argument('--seq_len', default=None, type=int, | |
| help='maximum lengths of token sequence') | |
| parser.add_argument('--symmetric_labels', action='store_true', | |
| default=False, | |
| help="use symmetric labels: -1 and +1") | |
| parser.add_argument("--loss", type=str, default=None, | |
| help="loss function to optimize") | |
| args = parseArguments(parser) | |
| main(args) | |