Precision: [tensor(0.6739, device='cuda:0'), tensor(0.6760, device='cuda:0'), tensor(0.6679, device='cuda:0'), tensor(0.6724, device='cuda:0'), tensor(0.6689, device='cuda:0'), tensor(0.6687, device='cuda:0'), tensor(0.6653, device='cuda:0'), tensor(0.6697, device='cuda:0'), tensor(0.6755, device='cuda:0'), tensor(0.6763, device='cuda:0')]

Output distance: [tensor(4.9583, device='cuda:0'), tensor(4.9541, device='cuda:0'), tensor(4.9703, device='cuda:0'), tensor(4.9614, device='cuda:0'), tensor(4.9682, device='cuda:0'), tensor(4.9688, device='cuda:0'), tensor(4.9756, device='cuda:0'), tensor(4.9667, device='cuda:0'), tensor(4.9551, device='cuda:0'), tensor(4.9535, device='cuda:0')]

Prediction loss: [tensor(19392086., device='cuda:0'), tensor(18241328., device='cuda:0'), tensor(18330128., device='cuda:0'), tensor(18665614., device='cuda:0'), tensor(18040218., device='cuda:0'), tensor(17076878., device='cuda:0'), tensor(19203610., device='cuda:0'), tensor(19314350., device='cuda:0'), tensor(18919810., device='cuda:0'), tensor(16838356., device='cuda:0')]

Others: [{'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': 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': 9, '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': 11, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40986.2734, device='cuda:0'), tensor(40982.3516, device='cuda:0'), tensor(40952.6094, device='cuda:0'), tensor(40770.1953, device='cuda:0'), tensor(40838.4258, device='cuda:0'), tensor(40797.3672, device='cuda:0'), tensor(40755.5000, device='cuda:0'), tensor(40937.4375, device='cuda:0'), tensor(40707.7656, device='cuda:0'), tensor(40728.4805, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=9588), datetime.timedelta(microseconds=965911), datetime.timedelta(microseconds=973917), datetime.timedelta(microseconds=948626), datetime.timedelta(microseconds=968786), datetime.timedelta(microseconds=968440), datetime.timedelta(microseconds=997200), datetime.timedelta(microseconds=957543), datetime.timedelta(microseconds=994604), datetime.timedelta(seconds=1, microseconds=92395)]

Phi time: [datetime.timedelta(microseconds=207794), datetime.timedelta(microseconds=206789), datetime.timedelta(microseconds=182021), datetime.timedelta(microseconds=191959), datetime.timedelta(microseconds=192277), datetime.timedelta(microseconds=190675), datetime.timedelta(microseconds=184382), datetime.timedelta(microseconds=207501), datetime.timedelta(microseconds=192770), datetime.timedelta(microseconds=190006)]

