Precision: [tensor(0.3169, device='cuda:0'), tensor(0.3121, device='cuda:0'), tensor(0.3039, device='cuda:0'), tensor(0.3178, device='cuda:0'), tensor(0.3067, device='cuda:0'), tensor(0.3233, device='cuda:0'), tensor(0.3196, device='cuda:0'), tensor(0.3222, device='cuda:0'), tensor(0.3206, device='cuda:0'), tensor(0.3158, device='cuda:0')]

Output distance: [tensor(20.1239, device='cuda:0'), tensor(20.1527, device='cuda:0'), tensor(20.2022, device='cuda:0'), tensor(20.1185, device='cuda:0'), tensor(20.1853, device='cuda:0'), tensor(20.0855, device='cuda:0'), tensor(20.1079, device='cuda:0'), tensor(20.0919, device='cuda:0'), tensor(20.1019, device='cuda:0'), tensor(20.1306, device='cuda:0')]

Prediction loss: [tensor(104.6550, device='cuda:0'), tensor(104.2034, device='cuda:0'), tensor(103.5651, device='cuda:0'), tensor(103.2550, device='cuda:0'), tensor(103.5837, device='cuda:0'), tensor(104.5802, device='cuda:0'), tensor(103.8223, device='cuda:0'), tensor(102.8127, device='cuda:0'), tensor(103.7183, device='cuda:0'), tensor(104.2951, device='cuda:0')]

Others: [{'iter_num': 17, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(19848, 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=796568), datetime.timedelta(seconds=2, microseconds=703434), datetime.timedelta(seconds=2, microseconds=714020), datetime.timedelta(seconds=2, microseconds=690358), datetime.timedelta(seconds=2, microseconds=699893), datetime.timedelta(seconds=2, microseconds=594093), datetime.timedelta(seconds=2, microseconds=683204), datetime.timedelta(seconds=2, microseconds=715511), datetime.timedelta(seconds=2, microseconds=699986), datetime.timedelta(seconds=2, microseconds=631298)]

Phi time: [datetime.timedelta(seconds=4, microseconds=349258), datetime.timedelta(seconds=4, microseconds=398003), datetime.timedelta(seconds=4, microseconds=390944), datetime.timedelta(seconds=4, microseconds=381876), datetime.timedelta(seconds=4, microseconds=336923), datetime.timedelta(seconds=4, microseconds=330824), datetime.timedelta(seconds=4, microseconds=349908), datetime.timedelta(seconds=4, microseconds=373932), datetime.timedelta(seconds=4, microseconds=388433), datetime.timedelta(seconds=4, microseconds=317994)]

