action='append', default=None)
group1.add_argument('--transfo-random', type=parse_transfo, nargs='+',
action='append', default=None)
group2 = parser.add_mutually_exclusive_group(required=True)
group2.add_argument('--lmdb', type=str, default=None)
group2.add_argument('--npz', type=str, default=None)
args = parser.parse_args()
print "Load model"
net = caffe.Net(args.proto, args.model, caffe.TEST)
caffe.set_mode_cpu()
print "args", vars(args)
if args.lmdb != None:
reader = lmdb_reader(args.lmdb)
if args.npz != None:
reader = npz_reader(args.npz)
if args.transfo_grid:
trs = utils.parse_transfo_grid(args.transfo_grid)
if args.transfo_random:
trs = utils.parse_transfo_random(args.transfo_random)
trs_len = len(trs)
print "Transformations: %s" % ("\n\t".join(map(repr, trs)))
count = 0
accuracies = defaultdict(list) # extreme disto value correctly classified
print "Test network against transformations"
def process( (i, image, label) ):
res = []
import numpy as np
import argparse
from collections import defaultdict
import multiprocessing
from utils import npz_reader, gen_adversial
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--in-npz', type=str, required=True)
parser.add_argument('--out-npz', type=str, required=True)
parser.add_argument('--proto', type=str, required=True)
parser.add_argument('--model', type=str, required=True)
args = parser.parse_args()
print "Load dataset and model"
reader = npz_reader(args.in_npz)
net = caffe.Net(args.proto, args.model, caffe.TEST)
caffe.set_mode_cpu()
labels = set(range(0, 10)) # FIXME: suppose MNIST
def process( (i, img, l) ):
mylabels = labels - set([ l ])
return [ gen_adversial(net, img, l, tl) for tl in mylabels ]
num_cores = multiprocessing.cpu_count()
pool = multiprocessing.Pool(num_cores)
res = defaultdict(list)
count = 0
try:
for i, xss in enumerate(pool.imap_unordered(process, reader)):