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arguments.py
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import argparse
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', default='voc', help='coco, voc')
parser.add_argument('--dataroot', default='../data', help='path to dataset')
parser.add_argument('--class_embedding', default='VOC/fasttext_synonym.npy')
parser.add_argument('--syn_num', type=int, default=100, help='number features to generate per class')
parser.add_argument('--workers', type=int, help='number of data loading workers', default=1)
parser.add_argument('--batch_size', type=int, default=512, help='input batch size')
parser.add_argument('--resSize', type=int, default=1024, help='size of visual features')
parser.add_argument('--attSize', type=int, default=300, help='size of semantic features')
parser.add_argument('--nz', type=int, default=300, help='size of the latent z vector')
parser.add_argument('--ngh', type=int, default=4096, help='size of the hidden units in generator')
parser.add_argument('--ndh', type=int, default=4096, help='size of the hidden units in discriminator')
parser.add_argument('--nepoch', type=int, default=2000, help='number of epochs to train GAN')
parser.add_argument('--nepoch_cls', type=int, default=2000, help='number of epochs to train CLS')
parser.add_argument('--critic_iter', type=int, default=5, help='critic iteration, following WGAN-GP')
parser.add_argument('--lambda1', type=float, default=10, help='gradient penalty regularizer, following WGAN-GP')
parser.add_argument('--cls_weight', type=float, default=1, help='weight of the classification loss')
parser.add_argument('--cls_weight_unseen', type=float, default=1, help='weight of the classification loss')
parser.add_argument('--lr', type=float, default=0.0001, help='learning rate to train GANs ')
parser.add_argument('--lr_cls', type=float, default=0.0001, help='learning rate to train CLS ')
parser.add_argument('--testsplit', default='test', help='unseen classes feats and labels paths')
parser.add_argument('--trainsplit', default='train', help='seen classes feats and labels paths')
parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
parser.add_argument('--lz_ratio', type=float, default=1.0, help='mode seeking loss weight')
parser.add_argument('--cuda', action='store_true', default=False, help='enables cuda')
parser.add_argument('--ngpu', type=int, default=1, help='number of GPUs to use')
parser.add_argument('--pretrain_classifier', default='', help="path to pretrain classifier (for seen classes loss on fake features)")
parser.add_argument('--pretrain_classifier_unseen', default='', help="path to pretrain classifier (for unseen classes loss on fake features)")
# parser.add_argument('--netG', default='', help="path to netG (to continue training)")
# parser.add_argument('--netG', default='/disk4/hpl/zreo_shot_object_detection/SUZ_zero_shot/code/code_suz_contra/pascal/zero_shot_detection1/checkpoints/VOC_11_6_1/gen_best.pth', help="path to netG (to continue training)")
parser.add_argument('--netD', default='', help="path to netD (to continue training)")
parser.add_argument('--netG_name', default='')
parser.add_argument('--netD_name', default='')
parser.add_argument('--classes_split', default='16_4')
parser.add_argument('--outname', default='./checkpoints/', help='folder to output data and model checkpoints')
parser.add_argument('--val_every', type=int, default=10)
parser.add_argument('--start_epoch', type=int, default=0)
parser.add_argument('--manualSeed', type=int, help='manual seed')
parser.add_argument('--nclass_all', type=int, default=81, help='number of all classes')
parser.add_argument('--lr_step', type=int, default=30, help='number of all classes')
parser.add_argument('--gan_epoch_budget', type=int, default=10000, help='random pick subset of features to train GAN')
##intra contra
parser.add_argument('--lambda_contra', type=float, default=0.001, help='weight for contrastive loss')
parser.add_argument('--num_negative', type=int, default=10, help='number of latent negative samples')
parser.add_argument('--radius', type=float, default=0.0000001, help='positive sample - distance threshold')
parser.add_argument('--tau', type=float, default=0.1, help='temprature')
# parser.add_argument('--featnorm', action='store_true', help='whether featnorm')
parser.add_argument('--batch_size_cls', type=int, default=32, help='input batch size')
##inter conra
parser.add_argument('--inter_temp', type=float, default=0.1, help='inter temperature')
parser.add_argument('--inter_weight', type=float, default=0.001,
help='weight of the classification loss when learning G')
##cross contra
parser.add_argument('--cross_temp', type=float, default=0.1, help='cross temperature')
parser.add_argument('--corss_weight', type=float, default=0.001,
help='weight for cross contrastive loss')
parser.add_argument('--embedSize', type=int, default=1024, help='size of embedding h')
parser.add_argument('--outzSize', type=int, default=1024, help='size of non-liner projection z')
parser.add_argument('--resSize_vis', type=int, default=1024, help='size of visual features')
parser.add_argument('--resSize_sem', type=int, default=300, help='size of semantic features')
parser.add_argument('--sy_cross_contra_loss_dely', type=int, default=20, help='epoch of sy cross_contra loss dely')
##memory bank
parser.add_argument('--pretrain_net_G', default='/disk4/hpl/zreo_shot_object_detection/SUZ_zero_shot/code/code_suz_contra/pascal/zero_shot_detection/checkpoints/VOC_2022.08.15.66.2/gen_best.pth', help="path to pretrain net_G")
parser.add_argument('--cm_temp', type=float, default=0.1, help=' temperature')
# parser.add_argument('--cm_use_hard', action="store_true")
parser.add_argument('--cm_use_hard', type=str, default=False)
parser.add_argument('--cm_momentum', type=float, default=0.01,
help="update momentum for the hybrid memory")
parser.add_argument('--lambda_clus_contra', type=float, default=0.001, help='weight for cluster contrastive loss')
parser.add_argument('--first_stage_epoch', type=int, default=150000)
opt = parser.parse_args()
return opt