dest='timelapse') parser.add_argument( '--rangewidth', action='store', default=-1, help="uses range_width to limit number of images to match with.", type=int, dest='rangewidth') __doc__ += '\n' + parser.format_help() from options import Options args = Options() args.add_argument("work_megapix", 0.6) args.add_argument('features', list(FEATURES_FIND_CHOICES.keys())[0]) args.add_argument('matcher', 'homography') args.add_argument('estimator', list(ESTIMATOR_CHOICES.keys())[0]) args.add_argument('match_conf', 0.3) args.add_argument('conf_thresh', 0.3) args.add_argument('ba', list(BA_COST_CHOICES.keys())[0]) args.add_argument('ba_refine_mask', 'xxxxx') args.add_argument('wave_correct', WAVE_CORRECT_CHOICES[0]) args.add_argument('save_graph', None) args.add_argument('warp', WARP_CHOICES[3]) args.add_argument('seam_megapix', 0.1) args.add_argument('seam', list(SEAM_FIND_CHOICES.keys())[0]) args.add_argument('compose_megapix', -1) args.add_argument('expos_comp', list(EXPOS_COMP_CHOICES.keys())[0]) args.add_argument('expos_comp_nr_feeds', 1)
from utils import AverageMeter, Logger from options import Options from models.rram import get_rram_param_groups from sparse_sgd import * model_names = sorted(name for name in models.__dict__ if name.islower() and not name.startswith("__") and callable(models.__dict__[name])) options = Options(description='PyTorch ImageNet Sparse Training') options.add_argument('--arch', '-a', metavar='ARCH', default='resnet50', choices=model_names, help='model architecture: ' + ' | '.join(model_names) + ' (default: resnet50)') options.set_defaults(data='gen/imagenet', epochs=90, batch_size=256, lr=0.1, momentum=0.9, nesterov=False, weight_decay=1e-4, lr_decay=0.1, lr_decay_step='30') best_prec1 = 0