classification_results(Param, model, test_df, y_test, y_names,
'test', args.class_name)
else:
print("Use predict.py to create test predictions")
# to be save - free memory
del model
torch.cuda.empty_cache()
if __name__ == "__main__":
task_data_path = os.path.join(args.processed_data_path, 'c2_muse_topic')
transcription_path = os.path.join(task_data_path, 'transcription_segments')
Param = get_parameters(args)
# create working folders
if not os.path.exists(Param['output_dir']):
os.makedirs(Param['output_dir'])
if not os.path.exists(Param['cache_dir']):
os.makedirs(Param['cache_dir'])
if not os.path.exists(Param['best_model_dir']):
os.makedirs(Param['best_model_dir'])
with open(os.path.join(Param['output_dir'], 'parameter.json'), 'w') as pa:
json.dump(Param, pa, indent=' ')
data = prepare_data(task_data_path, transcription_path, args.class_name,
args.cont_emotions, args.evaluate_test, None)
if not args.predict_test:
self.imsize = opt.imsize
def sample(self):
im = Image.new("RGBA", (self.imsize, self.imsize))
anns = dict()
for bk in self.blocks:
bk.sample(self.imsize)
im.alpha_composite(bk.im)
for k, v in bk.annotations:
anns.setdefault(k, []).append(v)
return im, anns
if __name__ == "__main__":
opt = get_parameters()
os.makedirs(os.path.join(opt.save_to, "images"), exist_ok=True)
os.makedirs(os.path.join(opt.save_to, "annotations"), exist_ok=True)
rect = bk.Rectangle()
jpg = bk.Photo("/tf/CoordConv-pytorch/data/facebook")
text = bk.Text()
bg = bk.Background(
[bk.Rectangle()]
# [bk.Rectangle(), bk.Photo("/tf/CoordConv-pytorch/data/facebook")]
)
samplers = [
Sampler([bg, rect, text], opt),
Sampler([bg, rect, rect, text], opt),
'''
model = train(config)
# saving model weights
model.save_weights(config.model_save_path + 'model_weights')
else:
'''
Validate the CycleGAN Model
'''
validation_image_path = np.array([config.validate])
if config.subject == 0:
# dataset-loder used in case of sketch to colorize image
loader = GANDataGenerator(validation_image_path,
config.dataset,
1,
dim=(config.height, config.width))
else:
# dataset-loader used in case of gender-bender and glass to no-glass
loader = GANDataGeneratorXY(validation_image_path,
validation_image_path,
config.dataset,
1,
dim=(config.height, config.width))
source, destination = next(iter(loader))
testModel(source, destination, config)
if __name__ == '__main__':
config = get_parameters()
print(config)
main(config)
x1 = ECG_ele_add(ECG_k_point(s, Point(Gx, Gy)), ECG_k_point(t, PA)).x
# print("x1:", x1)
R = (e1 + x1) % n
#print("R:", R)
if R == r:
# print("wrong signature: R unequal r")
# return False
print("R等于r，验证通过")
else:
print("R不等于r，验证不通过")
return True
### test Signature ###
config.default_config()
parameters = config.get_parameters()
point_g = Point(config.get_Gx(), config.get_Gy())
n = config.get_n()
print("请输入待验证的文件:")
f1 = input()
f = open(f1, 'r')
M = f.read()
IDA = '*****@*****.**'
print("请输入需要验证的签名:")
f2 = input()
sign = open(f2, "r")
signature = sign.read().replace("[", "").replace("]",
"").replace("", "").split(",")
## standard library imports
import time, sys, sqlite3, re
## local imports
from url_handling import get_url_title
from irc import Irc
from database import Database
from config import get_parameters
from ddate import Ddate
# send_lag = 1 #depricated?
# by default ignore the other bot jamaal
ignorelist = [u"jamaal"]
# grab config
options = get_parameters()
# seed channels list with default from config
channels = ["#%s" % (options[u"CHANNEL"])]
# get 'default' database object
db = Database()
# get IRC object
laamaj = Irc(options["SERVER"], 6667, options["NICK"], options["IDENT"], options["REALNAME"])
DDATE = None
@laamaj.add_on_connected
def connectJoinChannels(connection, server):
""" Join channels when connecting. """
print(u"Connected to %s" % (server))
for channel in channels:
