Precision: [tensor(0.9993, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9998, device='cuda:0')]

Output distance: [tensor(25207.7617, device='cuda:0'), tensor(25742.6230, device='cuda:0'), tensor(24016.1152, device='cuda:0'), tensor(24550.6738, device='cuda:0'), tensor(24204.3008, device='cuda:0'), tensor(26546.2109, device='cuda:0'), tensor(23698.1055, device='cuda:0'), tensor(27160.2793, device='cuda:0'), tensor(28735.8906, device='cuda:0'), tensor(27823.2871, device='cuda:0')]

Prediction loss: [tensor(25018.8184, device='cuda:0'), tensor(25199.3047, device='cuda:0'), tensor(22029.2324, device='cuda:0'), tensor(24443.9863, device='cuda:0'), tensor(24011., device='cuda:0'), tensor(26670.8281, device='cuda:0'), tensor(22318.5898, device='cuda:0'), tensor(27973.6074, device='cuda:0'), tensor(30881.4707, device='cuda:0'), tensor(29887.5508, device='cuda:0')]

Others: [{'iter_num': 29, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 29, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(9096079., device='cuda:0'), tensor(8765005., device='cuda:0'), tensor(8563528., device='cuda:0'), tensor(8771098., device='cuda:0'), tensor(8769602., device='cuda:0'), tensor(8654452., device='cuda:0'), tensor(8600803., device='cuda:0'), tensor(8942982., device='cuda:0'), tensor(8968563., device='cuda:0'), tensor(9112060., device='cuda:0')]

Training loss: 8856575.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=310442), datetime.timedelta(seconds=1, microseconds=391100), datetime.timedelta(microseconds=902099), datetime.timedelta(seconds=1, microseconds=331354), datetime.timedelta(seconds=1, microseconds=385126), datetime.timedelta(seconds=1, microseconds=366206), datetime.timedelta(microseconds=558630), datetime.timedelta(seconds=1, microseconds=384128), datetime.timedelta(seconds=1, microseconds=365212), datetime.timedelta(seconds=1, microseconds=386121)]

Phi time: [datetime.timedelta(seconds=1, microseconds=271990), datetime.timedelta(microseconds=756290), datetime.timedelta(microseconds=701886), datetime.timedelta(microseconds=723669), datetime.timedelta(microseconds=716123), datetime.timedelta(microseconds=703251), datetime.timedelta(microseconds=691242), datetime.timedelta(microseconds=696267), datetime.timedelta(microseconds=701664), datetime.timedelta(microseconds=717318)]

