Precision: [tensor(0.7356, device='cuda:0'), tensor(0.7354, device='cuda:0'), tensor(0.7374, device='cuda:0'), tensor(0.7328, device='cuda:0'), tensor(0.7314, device='cuda:0'), tensor(0.7374, device='cuda:0'), tensor(0.7428, device='cuda:0'), tensor(0.7272, device='cuda:0'), tensor(0.7375, device='cuda:0'), tensor(0.7272, device='cuda:0')]
Output distance: [tensor(5.0100, device='cuda:0'), tensor(5.0131, device='cuda:0'), tensor(5.0100, device='cuda:0'), tensor(5.0116, device='cuda:0'), tensor(5.0165, device='cuda:0'), tensor(5.0066, device='cuda:0'), tensor(5.0013, device='cuda:0'), tensor(5.0210, device='cuda:0'), tensor(5.0081, device='cuda:0'), tensor(5.0223, device='cuda:0')]
Prediction loss: [tensor(19252706., device='cuda:0'), tensor(18911274., device='cuda:0'), tensor(19789724., device='cuda:0'), tensor(19881476., device='cuda:0'), tensor(18586498., device='cuda:0'), tensor(17633640., device='cuda:0'), tensor(18018300., device='cuda:0'), tensor(19310500., device='cuda:0'), tensor(18996948., device='cuda:0'), tensor(19025000., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(2394, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2370, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2376, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2410, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2383, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2403, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2391, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2390, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2389, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2379, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40965.2500, device='cuda:0'), tensor(40704.6406, device='cuda:0'), tensor(40883.3008, device='cuda:0'), tensor(40914.5859, device='cuda:0'), tensor(40888.6406, device='cuda:0'), tensor(40851.7266, device='cuda:0'), tensor(40828.3398, device='cuda:0'), tensor(40910.3281, device='cuda:0'), tensor(40791.0078, device='cuda:0'), tensor(40956.0469, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=34612), datetime.timedelta(seconds=1, microseconds=69465), datetime.timedelta(seconds=1, microseconds=10714), datetime.timedelta(seconds=1, microseconds=15693), datetime.timedelta(seconds=1, microseconds=34611), datetime.timedelta(seconds=1, microseconds=21668), datetime.timedelta(seconds=1, microseconds=18680), datetime.timedelta(seconds=1, microseconds=19677), datetime.timedelta(seconds=1, microseconds=38595), datetime.timedelta(seconds=1, microseconds=8723)]
Phi time: [datetime.timedelta(microseconds=235999), datetime.timedelta(microseconds=254919), datetime.timedelta(microseconds=235003), datetime.timedelta(microseconds=234008), datetime.timedelta(microseconds=236000), datetime.timedelta(microseconds=254919), datetime.timedelta(microseconds=235998), datetime.timedelta(microseconds=235002), datetime.timedelta(microseconds=238986), datetime.timedelta(microseconds=238986)]
