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):