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parser.py
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parser.py
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import os
import argparse
def parse_arguments():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# image-to-image translation
parser.add_argument("--is_aug", action="store_true", help="if augmentation is applied using the translation models")
# CosPlace Groups parameters
parser.add_argument("--M", type=int, default=10, help="_")
parser.add_argument("--alpha", type=int, default=30, help="_")
parser.add_argument("--N", type=int, default=5, help="_")
parser.add_argument("--L", type=int, default=2, help="_")
parser.add_argument("--groups_num", type=int, default=8, help="_")
parser.add_argument("--min_images_per_class", type=int, default=10, help="_")
# Model parameters
parser.add_argument("--backbone", type=str, default="resnet18",
choices=["vgg16", "resnet18", "resnet50", "resnet101"], help="_")
parser.add_argument("--fc_output_dim", type=int, default=512,
help="Output dimension of final fully connected layer")
# Training parameters
parser.add_argument("--use_amp16", action="store_true",
help="use Automatic Mixed Precision")
parser.add_argument("--augmentation_device", type=str, default="cuda",
choices=["cuda", "cpu"],
help="on which device to run data augmentation")
parser.add_argument("--batch_size", type=int, default=32, help="_")
parser.add_argument("--epochs_num", type=int, default=50, help="_")
parser.add_argument("--iterations_per_epoch", type=int, default=10000, help="_")
parser.add_argument("--lr", type=float, default=0.00001, help="_")
parser.add_argument("--classifiers_lr", type=float, default=0.01, help="_")
# Data augmentation
parser.add_argument("--brightness", type=float, default=0.7, help="_")
parser.add_argument("--contrast", type=float, default=0.7, help="_")
parser.add_argument("--hue", type=float, default=0.5, help="_")
parser.add_argument("--saturation", type=float, default=0.7, help="_")
parser.add_argument("--random_resized_crop", type=float, default=0.5, help="_")
# Validation / test parameters
parser.add_argument("--infer_batch_size", type=int, default=16,
help="Batch size for inference (validating and testing)")
parser.add_argument("--positive_dist_threshold", type=int, default=25,
help="distance in meters for a prediction to be considered a positive")
# Resume parameters
parser.add_argument("--resume_train", type=str, default=None,
help="path to checkpoint to resume, e.g. logs/.../last_checkpoint.pth")
parser.add_argument("--resume_model", type=str, default=None,
help="path to model to resume, e.g. logs/.../best_model.pth")
# Other parameters
parser.add_argument("--device", type=str, default="cuda",
choices=["cuda", "cpu"], help="_")
parser.add_argument("--seed", type=int, default=0, help="_")
parser.add_argument("--num_workers", type=int, default=8, help="_")
# Paths parameters
parser.add_argument("--dataset_folder", type=str, default=None,
help="path of the folder with train/val/test sets")
parser.add_argument("--save_dir", type=str, default="default",
help="name of directory on which to save the logs, under logs/save_dir")
args = parser.parse_args()
args.dataset_folder = "/content/small/"
args.train_set_folder = "/content/small/train/"
args.val_set_folder = "/content/small/val/"
args.test_set_folder = "/content/small/test/"
args.groups_num = 1
args.num_workers = 2
args.epochs_num = 5
# if args.dataset_folder == None:
# try:
# args.dataset_folder = os.environ['SF_XL_PROCESSED_FOLDER']
# except KeyError:
# raise Exception("You should set parameter --dataset_folder or export " +
# "the SF_XL_PROCESSED_FOLDER environment variable as such \n" +
# "export SF_XL_PROCESSED_FOLDER=/path/to/sf_xl/processed")
#
# if not os.path.exists(args.dataset_folder):
# raise FileNotFoundError(f"Folder {args.dataset_folder} does not exist")
#
# args.train_set_folder = os.path.join(args.dataset_folder, "train")
# if not os.path.exists(args.train_set_folder):
# raise FileNotFoundError(f"Folder {args.train_set_folder} does not exist")
#
# args.val_set_folder = os.path.join(args.dataset_folder, "val")
# if not os.path.exists(args.val_set_folder):
# raise FileNotFoundError(f"Folder {args.val_set_folder} does not exist")
#
# args.test_set_folder = os.path.join(args.dataset_folder, "test")
# if not os.path.exists(args.test_set_folder):
# raise FileNotFoundError(f"Folder {args.test_set_folder} does not exist")
return args