if args.model == 'GAN': model = GAN(args) elif args.model == 'DCGAN': model = DCGAN_MODEL(args) elif args.model == 'WGAN-CP': model = WGAN_CP(args) elif args.model == 'WGAN-GP': model = WGAN_GP(args) else: print("Model type non-existing. Try again.") exit(-1) # Load datasets to train and test loaders train_loader, test_loader = get_data_loader(args) # feature_extraction = FeatureExtractionTest(train_loader, test_loader, args.cuda, args.batch_size) # Start model training if args.is_train == 'True': model.train(train_loader) # start evaluating on test data else: model.evaluate(test_loader, args.load_D, args.load_G) # for i in range(50): # model.generate_latent_walk(i) if __name__ == '__main__': args = parse_args() main(args)
# just top-1 result will be returned for the final if scores[0][0] >= self.config["IndexProcess"]["score_thres"]: preds["rec_docs"] = self.id_map[docs[0][0]].split()[1] preds["rec_scores"] = scores[0][0] output.append(preds) # st5: nms to the final results to avoid fetching duplicate results output = self.nms_to_rec_results( output, self.config["Global"]["rec_nms_thresold"]) return output def main(config): system_predictor = SystemPredictor(config) image_list = get_image_list(config["Global"]["infer_imgs"]) assert config["Global"]["batch_size"] == 1 for idx, image_file in enumerate(image_list): img = cv2.imread(image_file)[:, :, ::-1] output = system_predictor.predict(img) draw_bbox_results(img, output, image_file) print(output) return if __name__ == "__main__": args = config.parse_args() config = config.get_config(args.config, overrides=args.override, show=True) main(config)
command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True, universal_newlines=True) for line in proc.stdout: sys.stdout.write(line) log_file.write(line) proc.wait() log_file.close() def main(_): begin = time.time() tf.gfile.MakeDirs(FLAGS.model_dir) # redirects tf logs to file log_file = logging.init_logger(FLAGS.model_dir, FLAGS.do_debug) config.display_args(FLAGS) if FLAGS.model == "bert": run_bert_classifier(log_file) else: E = DRSCExperiment(FLAGS) E.run() tf.logging.info("Execution Time: {:.2f}s".format(time.time() - begin)) if __name__ == '__main__': FLAGS = config.parse_args() tf.app.run()
optimizer.step() for i in range(normalized_data.shape[1]): if normalized_data[0][i] > cfg.data.normalized_max_value[i]: normalized_data[0][i] = cfg.data.normalized_max_value[i] if normalized_data[0][i] < cfg.data.normalized_min_value[i]: normalized_data[0][i] = cfg.data.normalized_min_value[i] normalized_data = normalized_data.detach().clone() normalized_data.requires_grad = True denormalized_data = denormalize(normalized_data, cfg) rounded_data = denormalized_data.copy() for i in range(rounded_data.shape[0]): rounded_data[i] = _make_divisible(rounded_data[i], cfg.data.min_value[i]) flops, params = net2flops(list(rounded_data.astype(int)), device) pickle.dump(edit_net_set, open(f"{cfg.log_dir}/NCP.pkl", "wb")) if __name__ == '__main__': cfg = parse_args() # alias = f'epoch_{cfg.optimization.epoch}-bs_{cfg.optimization.batch_size}' \ # f'-{cfg.optimization.optimizer}-{cfg.optimization.scheduler}' # cfg.timestamp = time.strftime('{}-%Y%m%d-%H%M%S-{}'.format(cfg.data.dataset, alias)) cfg.log_dir = '{}/{}'.format(cfg.log_dir, cfg.data.dataset) setup_logging(cfg.log_dir, file_name='NCP.log') logging.info(cfg) main(cfg)
""" @author: tompx-nobug """ from utils.config import parse_args from utils.data_loader import get_data_loader from models.nk_model import nkModel import pandas as pd def main(args): train_loader, val_loader, test_loader = get_data_loader(args) # test_loader 만들어야 한다 model = nkModel(args, train_loader, val_loader, test_loader) if args.is_train: model.train() else: temp_list = model.test() print(temp_list) my_df = pd.DataFrame(temp_list) my_df.to_csv('my_csv.csv', index=False, header=False) if __name__ == '__main__': config = parse_args() main(config)