parser.add_argument('--lr', '--learning-rate', default=0.0003, type=float,
help="initial learning rate")
parser.add_argument('--stepsize', default=[20, 40], nargs='+', type=int,
help="stepsize to decay learning rate")
parser.add_argument('--gamma', default=0.1, type=float,
help="learning rate decay")
parser.add_argument('--weight-decay', default=5e-04, type=float,
help="weight decay (default: 5e-04)")
parser.add_argument('--fixbase-epoch', default=0, type=int,
help="epochs to fix base network (only train classifier, default: 0)")
parser.add_argument('--fixbase-lr', default=0.0003, type=float,
help="learning rate (when base network is frozen)")
parser.add_argument('--freeze-bn', action='store_true',
help="freeze running statistics in BatchNorm layers during training (default: False)")
# Architecture
parser.add_argument('-a', '--arch', type=str, default='resnet50', choices=models.get_names())
# Miscs
parser.add_argument('--print-freq', type=int, default=10,
help="print frequency")
parser.add_argument('--seed', type=int, default=1,
help="manual seed")
parser.add_argument('--resume', type=str, default='', metavar='PATH')
parser.add_argument('--load-weights', type=str, default='',
help="load pretrained weights but ignores layers that don't match in size")
parser.add_argument('--evaluate', action='store_true',
help="evaluation only")
parser.add_argument('--eval-step', type=int, default=-1,
help="run evaluation for every N epochs (set to -1 to test after training)")
parser.add_argument('--start-eval', type=int, default=0,
help="start to evaluate after specific epoch")
parser.add_argument('--save-dir', type=str, default='log')
default=0.3,
help="margin for triplet loss")
parser.add_argument('--num-instances',
type=int,
default=4,
help="number of instances per identity")
parser.add_argument('--htri-only',
action='store_true',
default=False,
help="if this is True, only htri loss is used in training")
# Architecture
parser.add_argument('-a',
'--arch',
type=str,
default='resnet50',
choices=models.get_names())
# Miscs
parser.add_argument('--print-freq',
type=int,
default=10,
help="print frequency")
parser.add_argument('--seed', type=int, default=1, help="manual seed")
parser.add_argument('--resume', type=str, default='', metavar='PATH')
parser.add_argument('--evaluate', action='store_true', help="evaluation only")
parser.add_argument(
'--eval-step',
type=int,
default=-1,
help="run evaluation for every N epochs (set to -1 to test after training)"
)
parser.add_argument('--start-eval',
help="maximum epochs to run")
parser.add_argument('--start-epoch', default=0, type=int,
help="manual epoch number (useful on restarts)")
parser.add_argument('--train-batch', default=128, type=int,
help="train batch size")
parser.add_argument('--test-batch', default=128, type=int, help="test batch size")
parser.add_argument('--lr', '--learning-rate', default=0.0003, type=float,
help="initial learning rate")
parser.add_argument('--stepsize', default=20, type=int,
help="stepsize to decay learning rate (>0 means this is enabled)")
parser.add_argument('--gamma', default=0.1, type=float,
help="learning rate decay")
parser.add_argument('--weight-decay', default=5e-04, type=float,
help="weight decay (default: 5e-04)")
# Architecture
parser.add_argument('-a', '--arch', type=str, default='resnet50', choices=models.get_names())
# Miscs
parser.add_argument('--print-freq', type=int, default=10, help="print frequency")
parser.add_argument('--seed', type=int, default=1, help="manual seed")
parser.add_argument('--resume', type=str, default='', metavar='PATH')
parser.add_argument('--evaluate', action='store_true', help="evaluation only")
parser.add_argument('--eval-step', type=int, default=-1,
help="run evaluation for every N epochs (set to -1 to test after training)")
parser.add_argument('--start-eval', type=int, default=0, help="start to evaluate after specific epoch")
parser.add_argument('--save-dir', type=str, default='log')
parser.add_argument('--use-cpu', action='store_true', help="use cpu")
parser.add_argument('--gpu-devices', default='0', type=str, help='gpu device ids for CUDA_VISIBLE_DEVICES')
parser.add_argument('--reranking',action= 'store_true', help= 'result re_ranking')
args = parser.parse_args()
import models
from models.SparseConvNet import *
import datasets
from datasets.depth_loader import DepthDataset, depth_transform
from util.utils import AverageMeter, Logger, save_checkpoint, Evaluate
from util.criterion import init_criterion, get_criterions
parser = argparse.ArgumentParser(description='PyTorch Depth Completion Testing')
parser.add_argument('resume', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--dataset', default='kitti', choices=datasets.get_names(),
help='name of dataset')
parser.add_argument('--data-root', default='./data', help='root path to datasets')
parser.add_argument('--arch', '-a', metavar='ARCH', default='sparseconv',
choices=models.get_names(),
help='model architecture: ' +
' | '.join(models.get_names()) +
' (default: sparseconv)')
parser.add_argument('--tag', default='test', help='tag in save path')
parser.add_argument('--gpu-ids', default='0', type=str, help='gpu device ids for CUDA_VISIBLE_DEVICES')
def main():
global args
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_ids
cudnn.benchmark = True
args.resume = osp.join(args.resume, 'best_model.pth.tar')
