Precision: [tensor(0.6282, device='cuda:0'), tensor(0.6266, device='cuda:0'), tensor(0.6356, device='cuda:0'), tensor(0.6286, device='cuda:0'), tensor(0.6247, device='cuda:0'), tensor(0.6244, device='cuda:0'), tensor(0.6307, device='cuda:0'), tensor(0.6286, device='cuda:0'), tensor(0.6300, device='cuda:0'), tensor(0.6283, device='cuda:0')]
Output distance: [tensor(4.9275, device='cuda:0'), tensor(4.9312, device='cuda:0'), tensor(4.9073, device='cuda:0'), tensor(4.9210, device='cuda:0'), tensor(4.9383, device='cuda:0'), tensor(4.9362, device='cuda:0'), tensor(4.9184, device='cuda:0'), tensor(4.9241, device='cuda:0'), tensor(4.9220, device='cuda:0'), tensor(4.9262, device='cuda:0')]
Prediction loss: [tensor(19072082., device='cuda:0'), tensor(18313272., device='cuda:0'), tensor(18181342., device='cuda:0'), tensor(19078546., device='cuda:0'), tensor(18257614., device='cuda:0'), tensor(17325956., device='cuda:0'), tensor(19754330., device='cuda:0'), tensor(18811118., device='cuda:0'), tensor(18230046., device='cuda:0'), tensor(17947490., device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(5626, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5640, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5599, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5705, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5619, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5661, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5649, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5659, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5625, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5641, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40775.1172, device='cuda:0'), tensor(40800.0781, device='cuda:0'), tensor(40697.9453, device='cuda:0'), tensor(40756.7070, device='cuda:0'), tensor(40856.6484, device='cuda:0'), tensor(41038.0625, device='cuda:0'), tensor(40861.8242, device='cuda:0'), tensor(40821.4922, device='cuda:0'), tensor(40826.6875, device='cuda:0'), tensor(40801.5508, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=75440), datetime.timedelta(seconds=1, microseconds=81413), datetime.timedelta(seconds=1, microseconds=50545), datetime.timedelta(seconds=1, microseconds=74444), datetime.timedelta(seconds=1, microseconds=45566), datetime.timedelta(seconds=1, microseconds=44568), datetime.timedelta(seconds=1, microseconds=77430), datetime.timedelta(seconds=1, microseconds=66476), datetime.timedelta(seconds=1, microseconds=44570), datetime.timedelta(seconds=1, microseconds=69464)]
Phi time: [datetime.timedelta(microseconds=238987), datetime.timedelta(microseconds=257906), datetime.timedelta(microseconds=252927), datetime.timedelta(microseconds=235002), datetime.timedelta(microseconds=235003), datetime.timedelta(microseconds=258904), datetime.timedelta(microseconds=235003), datetime.timedelta(microseconds=257856), datetime.timedelta(microseconds=238986), datetime.timedelta(microseconds=254919)]
