batch_size=opt.cacheBatchSize, shuffle=False, pin_memory=cuda) train_set = dataset.get_training_query_set(opt.margin) print('====> Training query set:', len(train_set)) whole_test_set = dataset.get_whole_val_set() print('===> Evaluating on val set, query count:', whole_test_set.dbStruct.numQ) elif opt.mode.lower() == 'test': if opt.split.lower() == 'test': whole_test_set = dataset.get_whole_test_set() print('===> Evaluating on test set') elif opt.split.lower() == 'test250k': whole_test_set = dataset.get_250k_test_set() print('===> Evaluating on test250k set') elif opt.split.lower() == 'train': whole_test_set = dataset.get_whole_training_set() print('===> Evaluating on train set') elif opt.split.lower() == 'val': whole_test_set = dataset.get_whole_val_set() print('===> Evaluating on val set') else: raise ValueError('Unknown dataset split: ' + opt.split) print('====> Query count:', whole_test_set.dbStruct.numQ) elif opt.mode.lower() == 'cluster': whole_train_set = dataset.get_whole_training_set(onlyDB=True) print('===> Building model')
opt.arch.lower(), opt.margin) print('====> Training query set:', len(train_set_list[0])) whole_test_set = dataset.get_whole_val_set(opt.arch.lower()) print('===> Evaluating on val set, query count:', whole_test_set.dbStruct.numQ) elif opt.mode.lower() == 'test': if opt.split.lower() == 'test': whole_test_set = dataset.get_whole_test_set(opt.arch.lower()) print('===> Evaluating on test set') elif opt.split.lower() == 'test_hard': whole_test_set = dataset.get_test_hard_set(opt.arch.lower()) print('===> Evaluating on test hard') elif opt.split.lower() == 'test250k': if opt.dataset.lower() == 'pittsburgh': whole_test_set = dataset.get_250k_test_set(opt.arch.lower()) print('===> Evaluating on test250k set') else: raise Exception(str(opt.dataset) + ' has no test250k set') elif opt.split.lower() == 'train': whole_test_set = dataset.get_whole_training_set(opt.arch.lower()) print('===> Evaluating on train set') elif opt.split.lower() == 'val': whole_test_set = dataset.get_whole_val_set(opt.arch.lower()) print('===> Evaluating on val set') else: raise ValueError('Unknown dataset split: ' + opt.split) print('====> Query count:', whole_test_set.dbStruct.numQ) elif opt.mode.lower() == 'cluster': whole_train_set = dataset.get_whole_training_set(opt.arch.lower(), onlyDB=False)[0]