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')