args.py

import argparse
import os

PRESAVE_DIR = ""
MODEL_DIR = ""
DATA_DIR = ""
SSD_DIR = ""
name2folder = {
    "msrvtt": "./meta_data/msrvtt",
    "msvd": "./meta_data/msvd",
    "activitynet": "./meta_data/activitynet",
    "tgif": "./meta_data/tgif",
}


def get_args_parser():
    parser = argparse.ArgumentParser("Set OVQA", add_help=False)

    # Dataset specific
    parser.add_argument("--dataset", help="dataset", type=str)

    # Training hyper-parameters
    parser.add_argument("--mlm_prob", type=float, default=0.15, help="masking probability for the MLM objective")
    parser.add_argument("--lr", default=3e-4, type=float, help="learning rate")
    parser.add_argument("--beta1", default=0.9, type=float, help="Adam optimizer parameter")
    parser.add_argument("--beta2", default=0.95, type=float, help="Adam optimizer parameter")
    parser.add_argument("--batch_size", default=32, type=int, help="batch size used for training")
    parser.add_argument("--batch_size_test", default=32, type=int, help="batch size used for test")
    parser.add_argument("--weight_decay", default=0, type=float)
    parser.add_argument("--epochs", default=10, type=int, help="number of training epochs")
    parser.add_argument("--lr_drop", default=10, type=int, help="number of epochs after which the learning rate is reduced when not using linear decay")
    parser.add_argument("--optimizer", default="adam", type=str)
    parser.add_argument("--clip_max_norm", default=0.1, type=float, help="gradient clipping max norm")
    parser.add_argument("--schedule", default="linear_with_warmup", choices=["", "linear_with_warmup"], help="learning rate decay schedule, default is constant")
    parser.add_argument("--fraction_warmup_steps", default=0.1, type=float, help="fraction of number of steps used for warmup when using linear schedule")
    parser.add_argument("--eval_skip", default=1, type=int, help='do evaluation every "eval_skip" epochs')
    parser.add_argument("--print_freq", type=int, default=100, help="print log every print_freq iterations")

    # Model parameters
    parser.add_argument("--ft_lm", dest="freeze_lm", action="store_false", help="whether to finetune the weights of the language model")
    parser.add_argument("--model_name", default="deberta-v2-xlarge", choices=("deberta-v2-xlarge"))
    parser.add_argument("--ds_factor_attn", type=int, default=8, help="downsampling factor for adapter attn")
    parser.add_argument("--ds_factor_ff", type=int, default=8, help="downsampling factor for adapter ff")
    parser.add_argument("--freeze_ln", dest="ft_ln", action="store_false", help="whether or not to freeze layer norm parameters")
    parser.add_argument("--ft_mlm", dest="freeze_mlm", action="store_false", help="whether or not to finetune the mlm head parameters")
    parser.add_argument("--dropout", default=0.1, type=float, help="dropout to use in the adapter")
    parser.add_argument("--scratch", action="store_true", help="whether to train the LM with or without language init")
    parser.add_argument("--n_ans", type=int, default=0, help="number of answers in the answer embedding module, it is automatically set")
    parser.add_argument("--ft_last", dest="freeze_last", action="store_false", help="whether to finetune answer embedding module or not")
    parser.add_argument("--eps", default=1.0, type=float, help="strength")

    # Run specific
    parser.add_argument("--test", action="store_true", help="whether to run evaluation on val or test set")
    parser.add_argument("--save_dir", default="", help="path where to save, empty for no saving")
    parser.add_argument("--presave_dir", default=PRESAVE_DIR, help="the actual save_dir is an union of presave_dir and save_dir")
    parser.add_argument("--device", default="cuda", help="device to use")
    parser.add_argument("--seed", default=42, type=int, help="random seed")
    parser.add_argument("--load", default="./pretrained/pretrained.pth", help="path to load checkpoint")
    parser.add_argument("--resume", action="store_true", help="continue training if loading checkpoint")
    parser.add_argument("--start-epoch", default=0, type=int, metavar="N", help="start epoch")
    parser.add_argument("--eval", action="store_true", help="only run evaluation")
    parser.add_argument("--num_workers", default=3, type=int, help="number of workers for dataloader")

    # Distributed training parameters
    parser.add_argument("--world-size", default=1, type=int, help="number of distributed processes")
    parser.add_argument("--dist-url", default="env://", help="url used to set up distributed training")

    # Video and Text parameters
    parser.add_argument("--max_feats", type=int, default=10, help="maximum number of video features considered, one per frame")
    parser.add_argument("--features_dim", type=int, default=768, help="dimension of the visual embedding space")
    parser.add_argument("--no_video", dest="use_video", action="store_false", help="disables usage of video")
    parser.add_argument("--no_context", dest="use_context", action="store_false", help="disables usage of speech")
    parser.add_argument("--max_tokens", type=int, default=256, help="maximum number of tokens in the input text prompt")
    parser.add_argument("--max_atokens", type=int, default=5, help="maximum number of tokens in the answer")
    parser.add_argument("--prefix", default="", type=str, help="task induction before question for videoqa")
    parser.add_argument("--suffix", default=".", type=str, help="suffix after the answer mask for videoqa")

    return parser