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How to use add_argparser method in avocado

Best Python code snippet using avocado_python

argparser.py

Source:argparser.py

...15    parser.add_argument('--model', help="Path to model to evaluate")16    parser.add_argument('--splits', type=str, default="random_test", choices=["random_test", "temporal_test", "train", "val"],17                        help='The split to use for validation')18    if add_argparser:19        parser = add_argparser(parser)20    args, unk = parser.parse_known_args()21    args = vars(args)22    with open(os.path.join(args["model"], "model.json"), "r") as f:23        model_args = json.load(f)24    25    with open(os.path.join(args["model"], "dataset.json"), "r") as f:26        ds_args = json.load(f)27    with open(os.path.join(args["model"], "train_config.json"), "r") as f:28        train_args = json.load(f)29    train_args = merge_configs(train_args, args)30    train_args["splits"] = [args["splits"]]31    train_args["load_path"] = args["model"]32    33    return train_args, model_args, ds_args34def parse_ecg_args(m=None, d=None, t=None, o=None, add_argparser = None) -> dict:35    parser = argparse.ArgumentParser(add_help=True,36        description='Train model to predict electrolyte concentrations from ecg tracing.')37    general_parser = parser.add_argument_group("General")38    general_parser.add_argument('--folder', type=str, default="./model/", help="Path to store the output at (eg. model, evaluations)")39    general_parser.add_argument('--gpu_id', type=positive_int, default=0, help="Id of the gpu to use.")40    general_parser.add_argument('--m', help="Path to the json config file of the model")41    general_parser.add_argument('--d', help="Path to the json config file of the dataset")42    general_parser.add_argument("--seed", type=int, default=np.random.randint(0, 10000), help="Seed to use for pytorch.")43    optimizer_parser = parser.add_argument_group("Optimizer")44    optimizer_parser.add_argument("--lr", type=float, default=0.001, help="Learning rate to use for the optimizer")45    optimizer_parser.add_argument("--patience", type=positive_int, default=7, help="Patience for lr scheduler")46    optimizer_parser.add_argument("--lr_factor", type=float, default=0.1, help="Factor to decrease lr by.")47    optimizer_parser.add_argument("--min_lr", type=float, default=1e-7, help="Minimum lr to stop.")48    dset_parser = parser.add_argument_group("Dataset")49    dset_parser.add_argument("--train_split", type=float, default=0.9, help="Amount of data to use for train split.")50    dset_parser.add_argument("--valid_split", type=float, default=0.1, help="Amount of data to use for validation split.")51    dset_parser.add_argument("--normalize", action="store_true", help="Normalizes the targets to mean 0 and variance 1")52    dset_parser.add_argument("--log_transform", action="store_true", help="Log transforms the target.")53    dset_parser.add_argument("--batch_size", type=positive_int, default=32, help="Batch size for training.")54    train_parser = parser.add_argument_group("Training")55    train_parser.add_argument("--task", default="regression", choices=["regression", "classification", "ordinal", "gaussian"], help="Method to use for training")56    train_parser.add_argument("--epochs", type=positive_int, default=30, help="Number of epochs to run training for.")57    train_parser.add_argument("--buckets", type=float, nargs="+", default=None, help="If given, uses these buckets/intervals to discretize targets for example for classification or weighting")58    train_parser.add_argument("--buckets_linspace", type=float, nargs=3, default=None, help="Constructs the buckets in linspace(lower, upper, steps) by passing the arguments in that order")59    train_parser.add_argument("--weighted", action="store_true", help="Use weighted training, requires specification of buckets")60    train_parser.add_argument("--use_metadata", action="store_true", help="Additionally uses age and sex metadata as input to the model.")61    train_parser.add_argument("--even_split", action="store_true", help="Use even split of training data, requires specification of buckets")62    train_parser.add_argument("--burnin", type=int, default=0, help="Set the burnin for Gaussian models.")63    if add_argparser:64        parser = add_argparser(parser)65    args, unk = parser.parse_known_args()66    args = vars(args)67    # Check for unknown options68    if unk:69        warn("Unknown arguments:" + str(unk) + ".")70    # load the model dict71    model_json = m if m else args["m"]72    with open(model_json, "r") as f:73        model_args = json.load(f)74    75    # load the dataset dict76    dset_json = d if d else args["d"]77    with open(dset_json, "r") as f:78        ds_args = json.load(f)...

check_answer.py

Source:check_answer.py

1import argparse2import cv23import numpy as np4def add_argparser():5    parser = argparse.ArgumentParser()6    parser.add_argument('arg1', 'test pic file')7    parser.add_argument('arg2', 'answer pic file')8    return parser9def is_correct(src, dst):10    im = cv2.imread(src)11    dst_im = cv2.imread(dst)12    return np.array_equal(im, dst_im)13def main():14    parser = add_argparser()15    args = parser.parse_args()16    src = args.arg117    dst = args.arg218    ans = 'Correct!' if is_correct(src, dst) else 'NOT Correct!'19    print(ans)20    return21if __name__ == '__main__':...

read_pic.py

Source:read_pic.py

1import argparse2import cv23import numpy as np4def add_argparser():5    parser = argparse.ArgumentParser()6    parser.add_argument('arg1', 'pic reading file')7    return parser8def is_correct(src, dst):9    im = cv2.imread(src)10    dst_im = cv2.imread(dst)11    return np.array_equal(im, dst_im)12def read():13    parser = add_argparser()14    args = parser.parse_args()15    src = args.arg116    return17def main():18    read()19if __name__ == '__main__':...

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