Precision: [tensor(0.7323, device='cuda:0'), tensor(0.7345, device='cuda:0'), tensor(0.7310, device='cuda:0'), tensor(0.7351, device='cuda:0'), tensor(0.7345, device='cuda:0'), tensor(0.7410, device='cuda:0'), tensor(0.7330, device='cuda:0'), tensor(0.7260, device='cuda:0'), tensor(0.7317, device='cuda:0'), tensor(0.7342, device='cuda:0')]
Output distance: [tensor(5.0459, device='cuda:0'), tensor(5.0362, device='cuda:0'), tensor(5.0352, device='cuda:0'), tensor(5.0326, device='cuda:0'), tensor(5.0417, device='cuda:0'), tensor(5.0276, device='cuda:0'), tensor(5.0375, device='cuda:0'), tensor(5.0509, device='cuda:0'), tensor(5.0412, device='cuda:0'), tensor(5.0383, device='cuda:0')]
Prediction loss: [tensor(17404436., device='cuda:0'), tensor(18815704., device='cuda:0'), tensor(16709726., device='cuda:0'), tensor(18735240., device='cuda:0'), tensor(18181620., device='cuda:0'), tensor(18651910., device='cuda:0'), tensor(18643014., device='cuda:0'), tensor(20116940., device='cuda:0'), tensor(18107628., device='cuda:0'), tensor(16467538., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(2133, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2192, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2234, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 3, 'num_positive': tensor(2216, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2147, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2201, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2195, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2150, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2177, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2178, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40844.5234, device='cuda:0'), tensor(40817.6914, device='cuda:0'), tensor(40850.4922, device='cuda:0'), tensor(40917.6250, device='cuda:0'), tensor(40841.2227, device='cuda:0'), tensor(40912.3516, device='cuda:0'), tensor(40785.2539, device='cuda:0'), tensor(40782.3906, device='cuda:0'), tensor(40852.5664, device='cuda:0'), tensor(40749.4570, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=6, microseconds=228584), datetime.timedelta(seconds=6, microseconds=268415), datetime.timedelta(seconds=6, microseconds=61295), datetime.timedelta(seconds=3, microseconds=991073), datetime.timedelta(seconds=6, microseconds=292315), datetime.timedelta(seconds=6, microseconds=321192), datetime.timedelta(seconds=6, microseconds=283351), datetime.timedelta(seconds=5, microseconds=990593), datetime.timedelta(seconds=6, microseconds=287334), datetime.timedelta(seconds=6, microseconds=50339)]
Phi time: [datetime.timedelta(microseconds=375408), datetime.timedelta(microseconds=321637), datetime.timedelta(microseconds=415238), datetime.timedelta(microseconds=448100), datetime.timedelta(microseconds=388353), datetime.timedelta(microseconds=398310), datetime.timedelta(microseconds=339561), datetime.timedelta(microseconds=409265), datetime.timedelta(microseconds=379392), datetime.timedelta(microseconds=411257)]
