opts.py

import argparse

def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--data_path', default='./data/',
                        help='path to datasets')
    parser.add_argument('--data_name', default='precomp',
                        help='{coco,f30k}_precomp')
    parser.add_argument('--vocab_path', default='./vocab/',
                        help='Path to saved vocabulary json files.')
    parser.add_argument('--margin', default=0.2, type=float,
                        help='Rank loss margin.')
    parser.add_argument('--num_epochs', default=30, type=int,
                        help='Number of training epochs.')
    parser.add_argument('--batch_size', default=128, type=int,
                        help='Size of a training mini-batch.')
    parser.add_argument('--word_dim', default=300, type=int,
                        help='Dimensionality of the word embedding.')
    parser.add_argument('--embed_size', default=1024, type=int,
                        help='Dimensionality of the joint embedding.')
    parser.add_argument('--grad_clip', default=2., type=float,
                        help='Gradient clipping threshold.')
    parser.add_argument('--num_layers', default=1, type=int,
                        help='Number of GRU layers.')
    parser.add_argument('--learning_rate', default=.0002, type=float,
                        help='Initial learning rate.')
    parser.add_argument('--lr_update', default=15, type=int,
                        help='Number of epochs to update the learning rate.')
    parser.add_argument('--workers', default=10, type=int,
                        help='Number of data loader workers.')
    parser.add_argument('--log_step', default=10, type=int,
                        help='Number of steps to print and record the log.')
    parser.add_argument('--val_step', default=500, type=int,
                        help='Number of steps to run validation.')
    parser.add_argument('--logger_name', default='./runs/runX/log',
                        help='Path to save Tensorboard log.')
    parser.add_argument('--model_name', default='./runs/runX/checkpoint',
                        help='Path to save the model.')
    parser.add_argument('--resume', default='', type=str, metavar='PATH',
                        help='path to latest checkpoint (default: none)')
    parser.add_argument('--max_violation', action='store_true',
                        help='Use max instead of sum in the rank loss.')
    parser.add_argument('--img_dim', default=2048, type=int,
                        help='Dimensionality of the image embedding.')
    parser.add_argument('--no_imgnorm', action='store_true',
                        help='Do not normalize the image embeddings.')
    parser.add_argument('--no_txtnorm', action='store_true',
                        help='Do not normalize the text embeddings.')
    parser.add_argument('--raw_feature_norm', default="clipped_l2norm",
                        help='clipped_l2norm|l2norm|clipped_l1norm|l1norm|no_norm|softmax')
    parser.add_argument('--agg_func', default="LogSumExp",
                        help='LogSumExp|Mean|Max|Sum')
    parser.add_argument('--cross_attn', default="t2i",
                        help='t2i|i2t')
    parser.add_argument('--precomp_enc_type', default="basic",
                        help='basic|weight_norm')
    parser.add_argument('--bi_gru', action='store_true',
                        help='Use bidirectional GRU.')
    parser.add_argument('--lambda_lse', default=6., type=float,
                        help='LogSumExp temp.')
    parser.add_argument('--lambda_softmax', default=9., type=float,
                        help='Attention softmax temperature.')
    parser.add_argument('--pos_emb', action='store_true',
                        help="useing pos embedding")
    parser.add_argument('--gpuid', default=0, type=str,
                        help='gpuid')
    parser.add_argument('--model_mode', default="gate", type=str,
                        help='gate[_*], test, vsesc')
    parser.add_argument('--mse_lambda', default=1, type=float,
                        help='mse_lambda')
    args = parser.parse_args()
    return args