# magic line #!/usr/bin/env python3 # debugger import pdb; pdb.set_trace() # parallel debugger class ForkedPdb(pdb.Pdb): """A Pdb subclass that may be used  from a forked multiprocessing child   """ def interaction(self, *args, **kwargs): _stdin = sys.stdin try: sys.stdin = open('/dev/stdin') pdb.Pdb.interaction(self, *args, **kwargs) finally: sys.stdin = _stdin ForkedPdb().set_trace() # filepath os.path.dirname(os.path.realpath(__file__)) # parallel running from multiprocessing import Pool import multiprocessing def run_parallel(items, function, arguments, num_cpus=8): """  divides items to num_cpus groups and  starts function with each group + arguments in parallel threads  """ items = sorted(items) # build group of items that are matching number of cpus groups = [] length = len(items) // multiprocessing.cpu_count() for x in range(0, len(items), length): groups.append(items[x: x + length]) # add for each group the shared arguments params = [tuple([item_group] + list(arguments)) for item_group in groups] # run parallel pool = Pool(processes=num_cpus) return pool.starmap(function, params) # ipython %matplotlib inline from IPython.display import display as d import pandas as pd import numpy as np import seaborn as sns sns.set(rc={'figure.figsize':(12,12)}) # argument parsing import argparse import sys def main(_): # argument parsing parser = argparse.ArgumentParser(description='whatever', epilog="stg7 2018", formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("database", type=str, help="training database csv file (consists of video segment and rating value) or restricted yaml file") parser.add_argument("--feature_folder", type=str, default="features", help="folder for storing the features") parser.add_argument("--skip_feature_extraction", action="store_true", help="skip feature extraction step") parser.add_argument("--feaure_backtrack", action="store_true", help="backtrack all feature sets") parser.add_argument("--train_repetitions", type=int, default=1, help="number of repeatitions for training") parser.add_argument("--use_features_subset", action="store_true", help="use only a defined subset of features ({})".format(features_subset())) parser.add_argument("--model", type=str, default="models/hyfu.npz", help="output model") parser.add_argument("--mode", choices=[0,1], type=int, default=0, help="mode of model") parser.add_argument('--cpu_count', type=int, default=multiprocessing.cpu_count() // 2, help='thread/cpu count') parser.add_argument("--validation_database", type=str, help="database that is used for validation") parser.add_argument("--feature_folder_validation", type=str, default="features", help="folder where validation features are stored") a = vars(parser.parse_args()) if __name__ == "__main__": sys.exit(main(sys.argv[1:]))