Precision: [tensor(0.4355, device='cuda:0'), tensor(0.4320, device='cuda:0'), tensor(0.4248, device='cuda:0'), tensor(0.4363, device='cuda:0'), tensor(0.4325, device='cuda:0'), tensor(0.4309, device='cuda:0'), tensor(0.4258, device='cuda:0'), tensor(0.4307, device='cuda:0'), tensor(0.4388, device='cuda:0'), tensor(0.4248, device='cuda:0')]
Output distance: [tensor(19.4123, device='cuda:0'), tensor(19.4332, device='cuda:0'), tensor(19.4764, device='cuda:0'), tensor(19.4075, device='cuda:0'), tensor(19.4302, device='cuda:0'), tensor(19.4398, device='cuda:0'), tensor(19.4707, device='cuda:0'), tensor(19.4411, device='cuda:0'), tensor(19.3924, device='cuda:0'), tensor(19.4764, device='cuda:0')]
Prediction loss: [tensor(105.5148, device='cuda:0'), tensor(103.9952, device='cuda:0'), tensor(104.8200, device='cuda:0'), tensor(104.6821, device='cuda:0'), tensor(105.3577, device='cuda:0'), tensor(104.2255, device='cuda:0'), tensor(104.8049, device='cuda:0'), tensor(104.8430, device='cuda:0'), tensor(104.9959, device='cuda:0'), tensor(104.0563, device='cuda:0')]
Others: [{'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': 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': 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')}]
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=749828), datetime.timedelta(seconds=2, microseconds=883155), datetime.timedelta(seconds=2, microseconds=866553), datetime.timedelta(seconds=2, microseconds=733339), datetime.timedelta(seconds=2, microseconds=866703), datetime.timedelta(seconds=2, microseconds=833185), datetime.timedelta(seconds=2, microseconds=866566), datetime.timedelta(seconds=2, microseconds=900279), datetime.timedelta(seconds=2, microseconds=733616), datetime.timedelta(seconds=2, microseconds=849640)]
Phi time: [datetime.timedelta(seconds=98, microseconds=966112), datetime.timedelta(seconds=98, microseconds=882891), datetime.timedelta(seconds=98, microseconds=801785), datetime.timedelta(seconds=98, microseconds=799413), datetime.timedelta(seconds=99, microseconds=82721), datetime.timedelta(seconds=99, microseconds=202055), datetime.timedelta(seconds=98, microseconds=920740), datetime.timedelta(seconds=98, microseconds=736988), datetime.timedelta(seconds=98, microseconds=867098), datetime.timedelta(seconds=98, microseconds=749734)]
