_utils/track_args.py

import configargparse


def parse_train_args():
    parser = configargparse.ArgumentParser(
        formatter_class=configargparse.ArgumentDefaultsHelpFormatter,
        config_file_parser_class=configargparse.YAMLConfigFileParser,
        allow_abbrev=False,
    )
    parser.add_argument(
        "-c",
        "--config",
        default="_utils/example_config.yaml",
        is_config_file=True,
        help="config file path",
    )
    parser.add_argument("--device", type=str, choices=["cuda", "cpu"], default="cuda")
    parser.add_argument("-o", "--outdir", type=str, default="runs")
    parser.add_argument("--name", type=str, help="Name to append to timestamp")
    parser.add_argument("--timestamp", type=bool, default=True)
    parser.add_argument(
        "-m",
        "--model",
        type=str,
        default="",
        help="load this model at start (e.g. to continue training)",
    )
    parser.add_argument(
        "--ndim", type=int, default=2, help="number of spatial dimensions"
    )
    parser.add_argument("-d", "--d_model", type=int, default=256)
    parser.add_argument("-w", "--window", type=int, default=10)
    parser.add_argument("--epochs", type=int, default=100)
    parser.add_argument("--warmup_epochs", type=int, default=10)
    parser.add_argument(
        "--detection_folders",
        type=str,
        nargs="+",
        default=["TRA"],
        help=(
            "Subfolders to search for detections. Defaults to `TRA`, which corresponds"
            " to using only the GT."
        ),
    )
    parser.add_argument("--downscale_temporal", type=int, default=1)
    parser.add_argument("--downscale_spatial", type=int, default=1)
    parser.add_argument("--spatial_pos_cutoff", type=int, default=256)
    parser.add_argument("--from_subfolder", action="store_true")
    # parser.add_argument("--train_samples", type=int, default=50000)
    parser.add_argument("--num_encoder_layers", type=int, default=6)
    parser.add_argument("--num_decoder_layers", type=int, default=6)
    parser.add_argument("--pos_embed_per_dim", type=int, default=32)
    parser.add_argument("--feat_embed_per_dim", type=int, default=8)
    parser.add_argument("--dropout", type=float, default=0.00)
    parser.add_argument("--num_workers", type=int, default=4)
    parser.add_argument("--batch_size", type=int, default=1)
    parser.add_argument("--max_tokens", type=int, default=None)
    parser.add_argument("--delta_cutoff", type=int, default=2)
    parser.add_argument("--lr", type=float, default=1e-4)
    parser.add_argument(
        "--attn_positional_bias",
        type=str,
        choices=["rope", "bias", "none"],
        default="rope",
    )
    parser.add_argument("--attn_positional_bias_n_spatial", type=int, default=16)
    parser.add_argument("--attn_dist_mode", default="v0")
    parser.add_argument("--mixedp", type=bool, default=True)
    parser.add_argument("--dry", action="store_true")
    parser.add_argument("--profile", action="store_true")
    parser.add_argument(
        "--features",
        type=str,
        choices=[
            "none",
            "regionprops",
            "regionprops2",
            "patch",
            "patch_regionprops",
            "wrfeat",
        ],
        default="wrfeat",
    )
    parser.add_argument(
        "--causal_norm",
        type=str,
        choices=["none", "linear", "softmax", "quiet_softmax"],
        default="quiet_softmax",
    )
    parser.add_argument("--div_upweight", type=float, default=2)

    parser.add_argument("--augment", type=int, default=3)
    parser.add_argument("--tracking_frequency", type=int, default=-1)

    parser.add_argument("--sanity_dist", action="store_true")
    parser.add_argument("--preallocate", type=bool, default=False)
    parser.add_argument("--only_prechecks", action="store_true")
    parser.add_argument(
        "--compress", type=bool, default=True, help="compress dataset"
    )


    parser.add_argument("--seed", type=int, default=None)
    parser.add_argument(
        "--logger",
        type=str,
        default="tensorboard",
        choices=["tensorboard", "wandb", "none"],
    )
    parser.add_argument("--wandb_project", type=str, default="trackastra")
    parser.add_argument(
        "--crop_size",
        type=int,
        # required=True,
        nargs="+",
        default=None,
        help="random crop size for augmentation",
    )
    parser.add_argument(
        "--weight_by_ndivs",
        type=bool,
        default=True,
        help="Oversample windows that contain divisions",
    )
    parser.add_argument(
        "--weight_by_dataset",
        type=bool,
        default=False,
        help=(
            "Inversely weight datasets by number of samples (to counter dataset size"
            " imbalance)"
        ),
    )

    args, unknown_args = parser.parse_known_args()

    # # Hack to allow for --input_test
    # allowed_unknown = ["input_test"]
    # if not set(a.split("=")[0].strip("-") for a in unknown_args).issubset(
    #     set(allowed_unknown)
    # ):
    #     raise ValueError(f"Unknown args: {unknown_args}")

    # pprint(vars(args))

    # for backward compatibility
    # if args.attn_positional_bias == "True":
    #     args.attn_positional_bias = "bias"
    # elif args.attn_positional_bias == "False":
    #     args.attn_positional_bias = False

    # if args.train_samples == 0:
    #     raise NotImplementedError(
    #         "--train_samples must be > 0, full dataset pass not supported."
    #     )

    return args