Precision: [tensor(0.4249, device='cuda:0'), tensor(0.4541, device='cuda:0'), tensor(0.4125, device='cuda:0'), tensor(0.4250, device='cuda:0'), tensor(0.4453, device='cuda:0'), tensor(0.4454, device='cuda:0'), tensor(0.3986, device='cuda:0'), tensor(0.4456, device='cuda:0'), tensor(0.4465, device='cuda:0'), tensor(0.4633, device='cuda:0')]

Output distance: [tensor(19.1756, device='cuda:0'), tensor(19.1173, device='cuda:0'), tensor(19.2004, device='cuda:0'), tensor(19.1753, device='cuda:0'), tensor(19.1348, device='cuda:0'), tensor(19.1345, device='cuda:0'), tensor(19.2282, device='cuda:0'), tensor(19.1342, device='cuda:0'), tensor(19.1324, device='cuda:0'), tensor(19.0989, device='cuda:0')]

Prediction loss: [tensor(108.2116, device='cuda:0'), tensor(108.7215, device='cuda:0'), tensor(108.7445, device='cuda:0'), tensor(108.6948, device='cuda:0'), tensor(108.0214, device='cuda:0'), tensor(108.1975, device='cuda:0'), tensor(107.1966, device='cuda:0'), tensor(107.6762, device='cuda:0'), tensor(108.0043, device='cuda:0'), tensor(108.9769, device='cuda:0')]

Others: [{'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=2, microseconds=200164), datetime.timedelta(seconds=2, microseconds=221073), datetime.timedelta(seconds=2, microseconds=210748), datetime.timedelta(seconds=2, microseconds=204285), datetime.timedelta(seconds=2, microseconds=200107), datetime.timedelta(seconds=2, microseconds=214641), datetime.timedelta(seconds=2, microseconds=227662), datetime.timedelta(seconds=2, microseconds=229146), datetime.timedelta(seconds=2, microseconds=226253), datetime.timedelta(seconds=2, microseconds=200116)]

Phi time: [datetime.timedelta(seconds=4, microseconds=550164), datetime.timedelta(seconds=4, microseconds=430369), datetime.timedelta(seconds=4, microseconds=377420), datetime.timedelta(seconds=4, microseconds=421954), datetime.timedelta(seconds=4, microseconds=422072), datetime.timedelta(seconds=4, microseconds=418986), datetime.timedelta(seconds=4, microseconds=383983), datetime.timedelta(seconds=4, microseconds=425030), datetime.timedelta(seconds=4, microseconds=451567), datetime.timedelta(seconds=4, microseconds=366674)]

