def get_opt(): parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("--name", default = "GMM") parser.add_argument("--gpu_ids", default = "") parser.add_argument('-j', '--workers', type=int, default=1) parser.add_argument('-b', '--batch-size', type=int, default=4) parser.add_argument("--dataroot", default = "data") parser.add_argument("--vvt_dataroot", default="/data_hdd/vvt_competition") parser.add_argument("--mpv_dataroot", default="/data_hdd/mpv_competition") parser.add_argument("--datamode", default = "train") parser.add_argument( "--dataset", choices=DATASETS.keys(), default="cp" ) parser.add_argument("--stage", default = "GMM") parser.add_argument("--data_list", default = "train_pairs.txt") parser.add_argument("--fine_width", type=int, default = 192) parser.add_argument("--fine_height", type=int, default = 256) parser.add_argument("--radius", type=int, default = 5) parser.add_argument("--grid_size", type=int, default = 5) parser.add_argument('--tensorboard_dir', type=str, help='save tensorboard infos') parser.add_argument('--result_dir', type=str, default='result', help='save result infos') parser.add_argument('--checkpoint', type=str, default='', help='model checkpoint for test') parser.add_argument("--display_count", type=int, default = 1) parser.add_argument("--shuffle", action='store_true', help='shuffle input data') opt = parser.parse_args() return opt
def parse_args(): parser = argparse.ArgumentParser(description='Model training') # params of model parser.add_argument( '--model_name', dest='model_name', help='Model type for testing, which is one of {}'.format(str(list(MODELS.keys()))), type=str, default='FarNet') # params of infer parser.add_argument( '--dataset', dest='dataset', help="The dataset you want to test, which is one of {}".format(str(list(DATASETS.keys()))), type=str, default='ImageFolder') parser.add_argument( '--infer_root', dest='infer_root', help="dataset root directory", type=str, default=None) parser.add_argument( '--num_workers', dest='num_workers', help="number works of data loader", type=int, default=0) # params of prediction parser.add_argument( '--batch_size', dest='batch_size', help='Mini batch size', type=int, default=32) parser.add_argument( '--model_file', dest='model_file', help='The path of model for evaluation', type=str, required=True) parser.add_argument( '--save_dir', dest='save_dir', help='The directory for saving the inference results', type=str, default='./outputs/result') parser.add_argument( '--device', dest='device', help='device for training', type=str, default="cuda") return parser.parse_args()
def parse_args(): parser = argparse.ArgumentParser(description='Model evaluation') # params of model parser.add_argument( '--model_name', dest='model_name', help='Model type for evaluation, which is one of {}'.format( str(list(MODELS.keys()))), type=str, default='FarNet') # params of dataset parser.add_argument( '--dataset', dest='dataset', help="The dataset you want to evaluation, which is one of {}".format( str(list(DATASETS.keys()))), type=str, default='Map') parser.add_argument('--dataset_root', dest='dataset_root', help="dataset root directory", type=str, default=None) # params of evaluate parser.add_argument("--input_size", dest="input_size", help="The image size for net inputs.", nargs=2, default=[256, 256], type=int) parser.add_argument('--model_dir', dest='model_dir', help='The path of model for evaluation', type=str, default=None) return parser.parse_args()
lsh = LSHBuilder.build(len(data[0]), exp_file['dist_threshold'], k, L, exp_file['lsh'], validate) res_fn = get_result_fn(exp_file['dataset'], exp_file['lsh']['type'], method, repr(lsh)) if os.path.exists(res_fn) and not args.force: print(f"{res_fn} exists, skipping.") else: params.setdefault((k, L), []).append(method) return params if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( '--dataset', choices=DATASETS.keys(), default="mnist-784-euclidean", ) parser.add_argument( '--seed', default=3, type=int, ) parser.add_argument( '--exp-file', required=True, ) parser.add_argument( '--force', action='store_true', )
def parse_args(): parser = argparse.ArgumentParser(description='Model training') # params of model parser.add_argument( '--model_name', dest='model_name', help='Model type for training, which is one of {}'.format( str(list(MODELS.keys()))), type=str, default='FarNet') # params of dataset parser.add_argument( '--dataset', dest='dataset', help="The dataset you want to train, which is one of {}".format( str(list(DATASETS.keys()))), type=str, default='ImagePairs') parser.add_argument('--train_root', dest='train_root', help="train dataset root directory", type=str, required=True) parser.add_argument('--val_root', dest='val_root', help="val dataset root directory", type=str, default=None) parser.add_argument('--num_workers', dest='num_workers', help="number works of data loader", type=int, default=0) parser.add_argument('--device', dest='device', help='device for training', type=str, default="cuda") # params of training parser.add_argument('--epochs', dest='epochs', help='epochs for training', type=int, default=20) parser.add_argument('--batch_size', dest='batch_size', help='Mini batch size of one gpu or cpu', type=int, default=32) parser.add_argument('--lr', dest='lr', help='Learning rate', type=float, default=0.0005) parser.add_argument('--resume', dest='resume', help='The path of resume model', type=str, default=None) parser.add_argument('--save_dir', dest='save_dir', help='The directory for saving the model snapshot', type=str, default='./outputs') parser.add_argument('--logs_dir', dest='logs_dir', help='The directory for saving the log message', type=str, default='./logs') return parser.parse_args()
################ # Top Level ################ parser.add_argument('--mode', type=str, default='train', choices=['train']) parser.add_argument('--template', type=str, default=None) ################ # Test ################ parser.add_argument('--test_model_path', type=str, default=None) ################ # Dataset ################ parser.add_argument('--dataset_code', type=str, default='ml-20m', choices=DATASETS.keys()) parser.add_argument('--min_rating', type=int, default=4, help='Only keep ratings greater than equal to this value') parser.add_argument('--min_uc', type=int, default=5, help='Only keep users with more than min_uc ratings') parser.add_argument('--min_sc', type=int, default=0, help='Only keep items with more than min_sc ratings') parser.add_argument('--split', type=str, default='leave_one_out', help='How to split the datasets') parser.add_argument('--dataset_split_seed', type=int, default=98765) parser.add_argument('--eval_set_size', type=int, default=500, help='Size of val and test set. 500 for ML-1m and 10000 for ML-20m recommended') ################ # Dataloader ################ parser.add_argument('--dataloader_code', type=str, default='bert', choices=DATALOADERS.keys()) parser.add_argument('--dataloader_random_seed', type=int, default=0.0) parser.add_argument('--train_batch_size', type=int, default=64) parser.add_argument('--val_batch_size', type=int, default=64)
f.write(report) acc = np.sum(_confusion_matrix.diagonal()) / np.sum(_confusion_matrix) print(f"Overall {data_set} accuracy: {acc*100}%") # top 5 accuracy if data_set != "PDX": real_int = [from_label_to_int[k] for k in y] pred_probs = keras_model.predict(x) top5_acc = top_n_accuracy(real_int, pred_probs, n=5) print(f"Overall top 5 accuracy {data_set}': {top5_acc*100}%") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("datasets", nargs="+", choices=DATASETS.keys()) parser.add_argument("--data-dir", default="data") parser.add_argument( "--model", required=True, default="inception", choices=["inception", "cnn", "resnet"] ) parser.add_argument("--models-dir", default="models") parser.add_argument("--output-dir", default="output") args = parser.parse_args() start_time = tt.time() model_files = { "inception": f"{args.models_dir}/inception_net_1d.h5", "cnn": f"{args.models_dir}/cnn.h5", "resnet": f"{args.models_dir}/resnet.h5"