Precision: [tensor(0.7302, device='cuda:0'), tensor(0.7287, device='cuda:0'), tensor(0.7356, device='cuda:0'), tensor(0.7351, device='cuda:0'), tensor(0.7403, device='cuda:0'), tensor(0.7336, device='cuda:0'), tensor(0.7290, device='cuda:0'), tensor(0.7319, device='cuda:0'), tensor(0.7347, device='cuda:0'), tensor(0.7376, device='cuda:0')]
Output distance: [tensor(5.0176, device='cuda:0'), tensor(5.0179, device='cuda:0'), tensor(5.0105, device='cuda:0'), tensor(5.0116, device='cuda:0'), tensor(5.0039, device='cuda:0'), tensor(5.0123, device='cuda:0'), tensor(5.0168, device='cuda:0'), tensor(5.0150, device='cuda:0'), tensor(5.0089, device='cuda:0'), tensor(5.0084, device='cuda:0')]
Prediction loss: [tensor(19059812., device='cuda:0'), tensor(20088258., device='cuda:0'), tensor(18049452., device='cuda:0'), tensor(17370682., device='cuda:0'), tensor(18445216., device='cuda:0'), tensor(18281262., device='cuda:0'), tensor(19598120., device='cuda:0'), tensor(17972832., device='cuda:0'), tensor(19454596., device='cuda:0'), tensor(18462810., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(2387, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2400, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2390, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2386, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2395, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2395, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2406, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2391, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2412, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2386, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40833.7266, device='cuda:0'), tensor(40937.1094, device='cuda:0'), tensor(40772.7578, device='cuda:0'), tensor(40922.6719, device='cuda:0'), tensor(40722.1211, device='cuda:0'), tensor(40898.0312, device='cuda:0'), tensor(40769.9805, device='cuda:0'), tensor(40802.5039, device='cuda:0'), tensor(40587.0977, device='cuda:0'), tensor(40875.6094, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(microseconds=987755), datetime.timedelta(microseconds=986819), datetime.timedelta(microseconds=981834), datetime.timedelta(microseconds=994783), datetime.timedelta(seconds=1, microseconds=4791), datetime.timedelta(microseconds=981838), datetime.timedelta(microseconds=977855), datetime.timedelta(microseconds=996771), datetime.timedelta(microseconds=989796), datetime.timedelta(microseconds=976860)]
Phi time: [datetime.timedelta(microseconds=233016), datetime.timedelta(microseconds=252929), datetime.timedelta(microseconds=241974), datetime.timedelta(microseconds=251934), datetime.timedelta(microseconds=248890), datetime.timedelta(microseconds=236997), datetime.timedelta(microseconds=230021), datetime.timedelta(microseconds=259857), datetime.timedelta(microseconds=221058), datetime.timedelta(microseconds=251934)]
