def test_multiple_arguments(self): options = [ "@0:1:backprop=yes", "@0:2:backprop=no", "@0:3:backprop=heh" ] res = parse_options.parse_options(options) self.assertEqual(res.strip(), "--backprop=yes,no,heh") # args with extra syntactic sugar options = [ "@0:1:backprop=yes", "@0:2:sugar:backprop=no", "@0:4:somemoresugar:backprop=heh" ] res = parse_options.parse_options(options) self.assertEqual(res.strip(), "--backprop=yes,no,heh")
def test_skip(self): options = [ "@0:F:backprop=yes", "@1:S:deletion=yes", "@1:S:Geo:aryrestarts=2" ] res = parse_options.parse_options(options) self.assertEqual(res.strip(), "--backprop")
def __init__(self, predicate, callback, pause=5.): """ """ super(ClipboardWatcher, self).__init__() self._predicate = predicate self._callback = callback self._pause = 5 self._stopping = False self.options = parse_options.parse_options() print self.options
from call_cmd import call_cmd, make_call_in_dst import os, o_p def add_options(parser): parser.add_option("--bcp", action="store", dest="bcp", metavar='bcp_path', help="path to bcp utility", default='bcp') if __name__ == '__main__': from parse_options import parse_options options, (boost_src, boost_dst) = parse_options( "usage: %prog [options] boost_src boost_dst", add_options, num_args=2) bcp = options.bcp # политика: вроде как bcp сам не удаляет boost_dst, поэтому работаем по месту if os.path.exists(boost_dst): # удаляем все кроме своего lst = ['LICENSE_1_0.txt', 'README', 'SConscript', 'test_include'] for fname in os.listdir(boost_dst): if not fname in lst: fpath = os.path.join(boost_dst, fname) print 'rm', fpath o_p.del_any_fpath(fpath) cmd = '''%(bcp)s --boost=%(boost_src)s boost/smart_ptr.hpp boost/test boost/function.hpp boost/lambda boost/bind \ boost/filesystem system boost/regex format boost/foreach.hpp boost/iterator boost/cast.hpp boost/range/reference.hpp \ boost/assign/list_of.hpp boost/assign.hpp boost/mpl/print.hpp %(boost_dst)s''' % locals(
# # Скопировать часть библиотеки Boost для проекта Atom # # Пример: BCP=bcp tools/scripts/copy_boost.py /home/ilya/opt/programming/atom-project/boost_1_44_0 libs/boost-lib/ # from call_cmd import call_cmd, make_call_in_dst import os, o_p def add_options(parser): parser.add_option("--bcp", action="store", dest="bcp", metavar='bcp_path', help="path to bcp utility", default='bcp') if __name__ == '__main__': from parse_options import parse_options options, (boost_src, boost_dst) = parse_options("usage: %prog [options] boost_src boost_dst", add_options, num_args=2) bcp = options.bcp # политика: вроде как bcp сам не удаляет boost_dst, поэтому работаем по месту if os.path.exists(boost_dst): # удаляем все кроме своего lst = ['LICENSE_1_0.txt', 'README', 'SConscript', 'test_include'] for fname in os.listdir(boost_dst): if not fname in lst: fpath = os.path.join(boost_dst, fname) print 'rm', fpath o_p.del_any_fpath(fpath) cmd = '''%(bcp)s --boost=%(boost_src)s boost/smart_ptr.hpp boost/test boost/function.hpp boost/lambda boost/bind \ boost/filesystem system boost/regex format boost/foreach.hpp boost/iterator boost/cast.hpp boost/range/reference.hpp \ boost/assign/list_of.hpp boost/assign.hpp boost/mpl/print.hpp %(boost_dst)s''' % locals()
def test_flag(self): options = ["@0:F:backprop=yes"] res = parse_options.parse_options(options) self.assertEqual(res.strip(), "--backprop")
def test_special_cases(self): options = ["@0:No:vsids-progress=no"] res = parse_options.parse_options(options) self.assertEqual(res.strip(), "--vsids-progress=no")
def test_special_no_arguments(self): options = ["@0:no:eq=0", "@1:No:contraction=no"] res = parse_options.parse_options(options) self.assertEqual(res.strip(), "--eq=0 --no-contraction")
def test_argument(self): options = ["@0:backprop=yes", "@1:deletion=yes", "@1:aryrestarts=2"] res = parse_options.parse_options(options) self.assertEqual(res.strip(), "--backprop=yes --deletion=yes --aryrestarts=2")
import numpy as np from keras.preprocessing import image from keras import layers from keras import models from keras import optimizers from keras.applications import inception_v3, imagenet_utils from keras.preprocessing import image from keras import backend as K import parse_options options = parse_options.parse_options() def crop2square(img): short_side = min(img.size) x0 = (img.size[0] - short_side) / 2 y0 = (img.size[1] - short_side) / 2 x1 = img.size[0] - x0 y1 = img.size[1] - y0 return img.crop((x0, y0, x1, y1)) def dummy(value): if value == 1: return [0, 0] elif value == 2: return [1, 0] elif value == 3: return [1, 1] img = np.random.rand(224, 224, 3)