Precision: [tensor(0.5537, device='cuda:0'), tensor(0.5443, device='cuda:0'), tensor(0.5521, device='cuda:0'), tensor(0.5608, device='cuda:0'), tensor(0.5605, device='cuda:0'), tensor(0.5679, device='cuda:0'), tensor(0.5561, device='cuda:0'), tensor(0.5648, device='cuda:0'), tensor(0.5598, device='cuda:0'), tensor(0.5654, device='cuda:0')]
Output distance: [tensor(18.9290, device='cuda:0'), tensor(18.9457, device='cuda:0'), tensor(18.9318, device='cuda:0'), tensor(18.9155, device='cuda:0'), tensor(18.9161, device='cuda:0'), tensor(18.9033, device='cuda:0'), tensor(18.9238, device='cuda:0'), tensor(18.9084, device='cuda:0'), tensor(18.9173, device='cuda:0'), tensor(18.9073, device='cuda:0')]
Prediction loss: [tensor(108.4518, device='cuda:0'), tensor(108.7368, device='cuda:0'), tensor(107.8535, device='cuda:0'), tensor(109.5320, device='cuda:0'), tensor(109.3814, device='cuda:0'), tensor(107.8537, device='cuda:0'), tensor(108.9053, device='cuda:0'), tensor(108.1185, device='cuda:0'), tensor(108.3023, device='cuda:0'), tensor(108.5811, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(5936, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5949, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5943, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5977, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5971, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5946, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5988, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5968, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5975, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5973, 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=733087), datetime.timedelta(seconds=2, microseconds=716525), datetime.timedelta(seconds=2, microseconds=716500), datetime.timedelta(seconds=2, microseconds=733046), datetime.timedelta(seconds=2, microseconds=733325), datetime.timedelta(seconds=2, microseconds=733198), datetime.timedelta(seconds=2, microseconds=749511), datetime.timedelta(seconds=2, microseconds=716913), datetime.timedelta(seconds=2, microseconds=750089), datetime.timedelta(seconds=2, microseconds=733253)]
Phi time: [datetime.timedelta(seconds=99, microseconds=332881), datetime.timedelta(seconds=99, microseconds=503580), datetime.timedelta(seconds=99, microseconds=536968), datetime.timedelta(seconds=99, microseconds=536674), datetime.timedelta(seconds=99, microseconds=316244), datetime.timedelta(seconds=99, microseconds=499275), datetime.timedelta(seconds=99, microseconds=481459), datetime.timedelta(seconds=99, microseconds=582838), datetime.timedelta(seconds=99, microseconds=482595), datetime.timedelta(seconds=99, microseconds=532718)]
