Precision: [tensor(0.6897, device='cuda:0'), tensor(0.6939, device='cuda:0'), tensor(0.6810, device='cuda:0'), tensor(0.6865, device='cuda:0'), tensor(0.6852, device='cuda:0'), tensor(0.6889, device='cuda:0'), tensor(0.6852, device='cuda:0'), tensor(0.6847, device='cuda:0'), tensor(0.6889, device='cuda:0'), tensor(0.6907, device='cuda:0')]
Output distance: [tensor(4.9268, device='cuda:0'), tensor(4.9184, device='cuda:0'), tensor(4.9441, device='cuda:0'), tensor(4.9331, device='cuda:0'), tensor(4.9357, device='cuda:0'), tensor(4.9283, device='cuda:0'), tensor(4.9357, device='cuda:0'), tensor(4.9367, device='cuda:0'), tensor(4.9283, device='cuda:0'), tensor(4.9247, device='cuda:0')]
Prediction loss: [tensor(18089092., device='cuda:0'), tensor(17857248., device='cuda:0'), tensor(19508694., device='cuda:0'), tensor(17844782., device='cuda:0'), tensor(19658874., device='cuda:0'), tensor(20285996., device='cuda:0'), tensor(18240768., device='cuda:0'), tensor(18464420., device='cuda:0'), tensor(18393098., device='cuda:0'), tensor(20827586., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40937.5977, device='cuda:0'), tensor(40797.5039, device='cuda:0'), tensor(40866.6016, device='cuda:0'), tensor(40817.3828, device='cuda:0'), tensor(40947.7422, device='cuda:0'), tensor(40905.5391, device='cuda:0'), tensor(41000.5586, device='cuda:0'), tensor(40935.3008, device='cuda:0'), tensor(40740.8086, device='cuda:0'), tensor(40969.4062, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=103320), datetime.timedelta(seconds=1, microseconds=99338), datetime.timedelta(seconds=1, microseconds=78427), datetime.timedelta(seconds=1, microseconds=16688), datetime.timedelta(seconds=1, microseconds=53532), datetime.timedelta(seconds=1, microseconds=53532), datetime.timedelta(seconds=1, microseconds=27641), datetime.timedelta(seconds=1, microseconds=58510), datetime.timedelta(seconds=1, microseconds=59506), datetime.timedelta(seconds=1, microseconds=23658)]
Phi time: [datetime.timedelta(microseconds=468014), datetime.timedelta(microseconds=261889), datetime.timedelta(microseconds=266867), datetime.timedelta(microseconds=229028), datetime.timedelta(microseconds=238986), datetime.timedelta(microseconds=234008), datetime.timedelta(microseconds=236995), datetime.timedelta(microseconds=232017), datetime.timedelta(microseconds=251932), datetime.timedelta(microseconds=250937)]
