project-codenet / model-experiments /token-based-similarity-classification /src /PostProcessor /SimSeqTokFullTest.py
| """ | |
| 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) | |