def main(): maybe_install_wordnet() parser = get_parser() args = parser.parse_args() generate_hierarchy(**vars(args)) test_hierarchy(args) generate_hierarchy_vis(args)
def NBDTLoss(cfg, criterion): maybe_install_wordnet() class_criterion = getattr(nbdtloss, cfg.MODEL.ROI_RELATION_HEAD.LOSS.NBDT.TYPE) NBDTloss = class_criterion( dataset='VG150', criterion=criterion, sample_nums=cfg.MODEL.ROI_RELATION_HEAD.REL_SAMPLES[1:], path_graph=cfg.MODEL.ROI_RELATION_HEAD.LOSS.NBDT.PATH_GRAPH, path_wnids=cfg.MODEL.ROI_RELATION_HEAD.LOSS.NBDT.PATH_WNIDS, tree_supervision_weight=cfg.MODEL.ROI_RELATION_HEAD.LOSS.NBDT.FACTOR) return NBDTloss
import torch.optim as optim import torch.nn.functional as F import torch.backends.cudnn as cudnn from nbdt import data, analysis, loss, models import torchvision import torchvision.transforms as transforms import os import argparse import numpy as np from nbdt.utils import (progress_bar, generate_fname, generate_kwargs, Colors, maybe_install_wordnet) maybe_install_wordnet() datasets = ('CIFAR10', 'CIFAR100') + data.imagenet.names + data.custom.names parser = argparse.ArgumentParser(description='PyTorch CIFAR Training') parser.add_argument('--batch-size', default=512, type=int, help='Batch size used for training') parser.add_argument('--epochs', '-e', default=200, type=int, help='By default, lr schedule is scaled accordingly') parser.add_argument('--dataset', default='CIFAR10', choices=datasets) parser.add_argument('--arch',