0.5 * (open_seen_acc + open_unseen_acc), open_seen_acc, open_unseen_acc, objoracle_acc, bias, )) all_accuracies = [all_accuracies, area] return all_accuracies #----------------------------------------------------------------# #----------------------------------------------------------------# trainset = dset.CompositionDatasetActivations(root=args.data_dir, phase='train', split=args.splitname) trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers) valset = dset.CompositionDatasetActivations(root=args.data_dir, phase=args.test_set, split=args.splitname, subset=args.subset) valloader = torch.utils.data.DataLoader(valset, batch_size=args.test_batch_size, shuffle=False, num_workers=args.workers) if args.model == 'modularpretrained':
open_seen_acc, open_unseen_acc, objoracle_acc, bias, )) all_accuracies = [all_accuracies, area] return all_accuracies #----------------------------------------------------------------# #----------------------------------------------------------------# testset = dset.CompositionDatasetActivations(root=args.data_dir, phase='test', split='compositional-split') testloader = torch.utils.data.DataLoader(testset, batch_size=args.batch_size, shuffle=False, num_workers=2) if args.model == 'visprodNN': model = models.VisualProductNN(testset, args) elif args.model == 'redwine': model = models.RedWine(testset, args) elif args.model =='labelembed+': model = models.LabelEmbedPlus(testset, args) elif args.model =='attributeop': model = models.AttributeOperator(testset, args) model.cuda() evaluator = models.Evaluator(testset,model) checkpoint = torch.load(args.load)
(open_seen_acc * open_unseen_acc)**0.5, 0.5 * (open_seen_acc + open_unseen_acc), open_seen_acc, open_unseen_acc, objoracle_acc, meanAP, bias, )) return all_accuracies #----------------------------------------------------------------# trainset = dset.CompositionDatasetActivations( root=args.data_dir, phase='train', split=args.splitname, num_negs=args.num_negs, pair_dropout=args.pair_dropout, ) trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers) valset = dset.CompositionDatasetActivations( root=args.data_dir, phase=args.test_set, split=args.splitname, subset=args.subset, ) valloader = torch.utils.data.DataLoader(valset, batch_size=args.test_batch_size,