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import argparse
import os
def get_parser():
parser = argparse.ArgumentParser(description='dmmi training and testing')
parser.add_argument('--amsgrad', action='store_true',
help='if true, set amsgrad to True in an Adam or AdamW optimizer.')
parser.add_argument('-b', '--batch-size', default=8, type=int)
parser.add_argument('--bert_tokenizer', default='bert-base-uncased', help='BERT tokenizer')
parser.add_argument('--ck_bert', default='bert-base-uncased', help='pre-trained BERT weights')
parser.add_argument('--dataset', default='refcoco', help='refcoco, refcoco+, or refcocog')
parser.add_argument('--ddp_trained_weights', action='store_true',
help='Only needs specified when testing,'
'whether the weights to be loaded are from a DDP-trained model')
parser.add_argument('--device', default='cuda:0', help='device') # only used when testing on a single machine
parser.add_argument('--epochs', default=40, type=int, metavar='N', help='number of total epochs to run')
parser.add_argument('--fusion_drop', default=0.0, type=float, help='dropout rate for PWAMs')
parser.add_argument('--img_size', default=480, type=int, help='input image size')
# parser.add_argument("--local_rank", default=int(os.getenv("LOCAL_RANK", 0)), type=int, help='local rank for DistributedDataParallel')
parser.add_argument('--lr', default=0.00005, type=float, help='the initial learning rate')
parser.add_argument('--mha', default='', help='If specified, should be in the format of a-b-c-d, e.g., 4-4-4-4,'
'where a, b, c, and d refer to the numbers of heads in stage-1,'
'stage-2, stage-3, and stage-4 PWAMs')
parser.add_argument('--model', default='dmmi', help='model: dmmi')
parser.add_argument('--model_id', default='dmmi', help='name to identify the model')
parser.add_argument('--output_dir', default='./', help='path to save the paramters')
parser.add_argument('--pin_mem', action='store_true',
help='If true, pin memory when using the data loader.')
parser.add_argument('--pretrained_backbone', default='',
help='path to pre-trained Swin backbone weights')
parser.add_argument('--print-freq', default=10, type=int, help='print frequency')
parser.add_argument('--refer_data_root', default='/data2/dataset/COCO2014/', help='REFER dataset root directory')
parser.add_argument('--resume', default='', help='resume from checkpoint')
parser.add_argument('--test_parameter', default='', help='test from this parameter')
parser.add_argument('--split', default='val', help='only used when testing')
parser.add_argument('--splitBy', default='unc', help='change to umd or google when the dataset is G-Ref (RefCOCOg)')
parser.add_argument('--swin_type', default='base',
help='tiny, small, base, or large variants of the Swin Transformer')
parser.add_argument('--wd', '--weight-decay', default=1e-2, type=float, metavar='W', help='weight decay',
dest='weight_decay')
parser.add_argument('--window12', action='store_true',
help='only needs specified when testing,'
'when training, window size is inferred from pre-trained weights file name'
'(containing \'window12\'). Initialize Swin with window size 12 instead of the default 7.')
parser.add_argument('-j', '--workers', default=8, type=int, metavar='N', help='number of data loading workers')
# metric loss related ones
parser.add_argument('--metric_learning', action='store_true',help='whether to use metric learning')
parser.add_argument('--metric_loss_weight', default=0.1, type=float, help='weight for metric loss')
parser.add_argument('--metric_mode', default='hardpos_rev3', help='test options..')
parser.add_argument('--exclude_multiobj', action='store_true', help='exclude multi-object images')
parser.add_argument('--hn_prob', default=0.0, type=float, help='hard negative probability')
parser.add_argument('--hp_selection', default='strict', help='test options..')
parser.add_argument('--margin_value', default=10, type=float, help='weight for metric loss')
parser.add_argument('--temperature', default=0.05, type=float, help='test options..')
# parser.add_argument('--addzero', action='store_true', help='test options..')
return parser
if __name__ == "__main__":
parser = get_parser()
args_dict = parser.parse_args()
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