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]