parser.add_argument('--lr-steps', default=[16, 19], nargs='+', type=int, help='decrease lr every step-size epochs') parser.add_argument('--lr-gamma', default=0.1, type=float, help='decrease lr by a factor of lr-gamma') parser.add_argument('--print-freq', default=1000, type=int, help='print frequency') parser.add_argument('--output-dir', default=None, help='path where to save') parser.add_argument('--resume', default='', help='resume from checkpoint') parser.add_argument('-rp', '--results-path', default='results', help='path to save detection results (only for voc)') parser.add_argument('--start_epoch', default=0, type=int, help='start epoch') parser.add_argument('--aspect-ratio-group-factor', default=3, type=int) parser.add_argument('-i', "--init", dest="init", help="if use init sample", action="store_true") parser.add_argument("--test-only", dest="test_only", help="Only test the model", action="store_true") parser.add_argument('-s', "--skip", dest="skip", help="Skip first cycle and use pretrained model to save time", action="store_true") parser.add_argument('-m', "--mutual", dest="mutual", help="use mutual information", action="store_true") parser.add_argument('-mr', default=1.2, type=float, help='mutual range') parser.add_argument('-bp', default=1.15, type=float, help='base point') parser.add_argument("--pretrained", dest="pretrained", help="Use pre-trained models from the modelzoo", action="store_true") # distributed training parameters parser.add_argument('--world-size', default=1, type=int, help='number of distributed processes') parser.add_argument('--dist-url', default='env://', help='url used to set up distributed training') args = parser.parse_args() if args.output_dir: utils.mkdir(args.output_dir) main(args)
default=0.05, help='used during inference') parser.add_argument('--iou_threshold', type=float, default=0.5, help='used during inference') parser.add_argument('--max-detections', type=int, default=300, help='used during inference') parser.add_argument('--resume', default='', help='resume from checkpoint') args = parser.parse_args() exp_name = (args.resume).split("/")[-2] out_dir = os.path.join('../jsons', args.dataset, exp_name) utils.mkdir(out_dir) root = '../../../../datasets/coco/images' if args.dataset == 'lvis': annotations = "../../../../datasets/coco/annotations/lvis_v1_val.json" dset = LVISDetection(root, annotations, transforms=transforms.ToTensor()) num_classes = 1204 elif args.dataset == 'coco': annotations = "../../../../datasets/coco/annotations/instances_val2017.json" dset = CocoDetection(root, annotations, transforms=transforms.ToTensor()) num_classes = 91 else: