Precision: [tensor(0.4290, device='cuda:0'), tensor(0.4258, device='cuda:0'), tensor(0.4289, device='cuda:0'), tensor(0.4310, device='cuda:0'), tensor(0.4295, device='cuda:0'), tensor(0.4355, device='cuda:0'), tensor(0.4323, device='cuda:0'), tensor(0.4321, device='cuda:0'), tensor(0.4218, device='cuda:0'), tensor(0.4261, device='cuda:0')]
Output distance: [tensor(19.4513, device='cuda:0'), tensor(19.4704, device='cuda:0'), tensor(19.4519, device='cuda:0'), tensor(19.4395, device='cuda:0'), tensor(19.4486, device='cuda:0'), tensor(19.4126, device='cuda:0'), tensor(19.4317, device='cuda:0'), tensor(19.4326, device='cuda:0'), tensor(19.4946, device='cuda:0'), tensor(19.4686, device='cuda:0')]
Prediction loss: [tensor(104.3438, device='cuda:0'), tensor(104.4337, device='cuda:0'), tensor(104.6167, device='cuda:0'), tensor(104.0265, device='cuda:0'), tensor(105.0489, device='cuda:0'), tensor(104.7819, device='cuda:0'), tensor(104.7977, device='cuda:0'), tensor(104.7825, device='cuda:0'), tensor(104.7414, device='cuda:0'), tensor(104.8255, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]
Compressed training loss: [tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=2, microseconds=688504), datetime.timedelta(seconds=2, microseconds=750064), datetime.timedelta(seconds=2, microseconds=883109), datetime.timedelta(seconds=2, microseconds=866979), datetime.timedelta(seconds=2, microseconds=733379), datetime.timedelta(seconds=2, microseconds=867955), datetime.timedelta(seconds=2, microseconds=733304), datetime.timedelta(seconds=2, microseconds=733145), datetime.timedelta(seconds=2, microseconds=735778), datetime.timedelta(seconds=2, microseconds=742913)]
Phi time: [datetime.timedelta(seconds=97, microseconds=638538), datetime.timedelta(seconds=100, microseconds=284090), datetime.timedelta(seconds=99, microseconds=67186), datetime.timedelta(seconds=98, microseconds=781547), datetime.timedelta(seconds=99, microseconds=34044), datetime.timedelta(seconds=99, microseconds=86828), datetime.timedelta(seconds=98, microseconds=969870), datetime.timedelta(seconds=99, microseconds=21049), datetime.timedelta(seconds=99, microseconds=132108), datetime.timedelta(seconds=98, microseconds=899505)]
