Precision: [tensor(0.9219, device='cuda:0'), tensor(0.9233, device='cuda:0'), tensor(0.9229, device='cuda:0'), tensor(0.9208, device='cuda:0'), tensor(0.9218, device='cuda:0'), tensor(0.9200, device='cuda:0'), tensor(0.9255, device='cuda:0'), tensor(0.9198, device='cuda:0'), tensor(0.9247, device='cuda:0'), tensor(0.9268, device='cuda:0')]
Output distance: [tensor(10334.4004, device='cuda:0'), tensor(9927.7422, device='cuda:0'), tensor(10142.6709, device='cuda:0'), tensor(10408.4521, device='cuda:0'), tensor(10302.4668, device='cuda:0'), tensor(10534.6699, device='cuda:0'), tensor(9751.8926, device='cuda:0'), tensor(10716.5596, device='cuda:0'), tensor(10006.4336, device='cuda:0'), tensor(9498.3057, device='cuda:0')]
Prediction loss: [tensor(21817.8418, device='cuda:0'), tensor(21506.9570, device='cuda:0'), tensor(21259.4258, device='cuda:0'), tensor(21741.0410, device='cuda:0'), tensor(21563.6484, device='cuda:0'), tensor(20996.2305, device='cuda:0'), tensor(20830.8105, device='cuda:0'), tensor(20923.2148, device='cuda:0'), tensor(21530.8145, device='cuda:0'), tensor(21107.9570, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(2.1971e+08, device='cuda:0'), tensor(2.1670e+08, device='cuda:0'), tensor(2.1383e+08, device='cuda:0'), tensor(2.1869e+08, device='cuda:0'), tensor(2.1662e+08, device='cuda:0'), tensor(2.1022e+08, device='cuda:0'), tensor(2.0856e+08, device='cuda:0'), tensor(2.0975e+08, device='cuda:0'), tensor(2.1547e+08, device='cuda:0'), tensor(2.1132e+08, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=588505), datetime.timedelta(microseconds=607377), datetime.timedelta(microseconds=606377), datetime.timedelta(microseconds=603440), datetime.timedelta(microseconds=609365), datetime.timedelta(microseconds=599457), datetime.timedelta(microseconds=601450), datetime.timedelta(microseconds=679120), datetime.timedelta(microseconds=684098), datetime.timedelta(microseconds=678124)]
Phi time: [datetime.timedelta(microseconds=910579), datetime.timedelta(microseconds=862077), datetime.timedelta(microseconds=859403), datetime.timedelta(microseconds=861982), datetime.timedelta(microseconds=866124), datetime.timedelta(microseconds=873154), datetime.timedelta(microseconds=859621), datetime.timedelta(microseconds=853591), datetime.timedelta(microseconds=868782), datetime.timedelta(microseconds=859070)]
