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)
