def run_robustness(id): from runs import robustness run_opt = experiments.get_experiments(output_path, dataset_path)[id] if os.path.exists(run_opt.log_dir_base + run_opt.name): robustness.run(run_opt) else: print(run_opt.log_dir_base + run_opt.name + " NOT TRAINED OR OUTPUT PATH INVALID")
from __future__ import print_function import csv import os.path import pickle import sys import numpy as np from experiments import experiments output_path = '/om/user/xboix/share/robustness/imagenet/' dataset_path = '/om/user/xboix/data/ImageNet/' run_opts = experiments.get_experiments(output_path, dataset_path) range_len = 7 perturbation_range = np.linspace(0.0, 1.0, num=range_len, endpoint=True) for opt in run_opts: header = ['model_name', 'evaluation_set', 'cross', 'perturbation_layer', 'size_factor', 'batch_size', 'perturbation_type', 'perturbation_amount', 'unchanged labels'] if not os.path.isfile(opt.results_dir + opt.name + '/robustness2.pkl'): print('Couldn\'t find files, skipped:', opt.name) sys.stdout.flush() continue with open(opt.csv_dir + opt.name + '_robustness.csv', 'w') as csvfile: filewriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL) filewriter.writerow(header) for cross in range(3):