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: