Precision: [tensor(0.2192, device='cuda:0'), tensor(0.2195, device='cuda:0'), tensor(0.2242, device='cuda:0'), tensor(0.2217, device='cuda:0'), tensor(0.2210, device='cuda:0'), tensor(0.2200, device='cuda:0'), tensor(0.2185, device='cuda:0'), tensor(0.2218, device='cuda:0'), tensor(0.2193, device='cuda:0'), tensor(0.2193, device='cuda:0')]
Output distance: [tensor(19553050., device='cuda:0'), tensor(19550072., device='cuda:0'), tensor(19522834., device='cuda:0'), tensor(19543218., device='cuda:0'), tensor(19536644., device='cuda:0'), tensor(19545076., device='cuda:0'), tensor(19559130., device='cuda:0'), tensor(19524674., device='cuda:0'), tensor(19557214., device='cuda:0'), tensor(19561828., device='cuda:0')]
Prediction loss: [tensor(13599409., device='cuda:0'), tensor(13731025., device='cuda:0'), tensor(13629737., device='cuda:0'), tensor(13656901., device='cuda:0'), tensor(13617626., device='cuda:0'), tensor(13615542., device='cuda:0'), tensor(13596346., device='cuda:0'), tensor(13661040., device='cuda:0'), tensor(13641033., device='cuda:0'), tensor(13614920., device='cuda:0')]
Others: [{'iter_num': 7, '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': 7, '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': 7, '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': 7, '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': 7, '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')}]
Compressed training loss: [tensor(2.4696e+11, device='cuda:0'), tensor(2.4897e+11, device='cuda:0'), tensor(2.4723e+11, device='cuda:0'), tensor(2.4772e+11, device='cuda:0'), tensor(2.4709e+11, device='cuda:0'), tensor(2.4742e+11, device='cuda:0'), tensor(2.4694e+11, device='cuda:0'), tensor(2.4807e+11, device='cuda:0'), tensor(2.4747e+11, device='cuda:0'), tensor(2.4668e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=567519), datetime.timedelta(microseconds=561618), datetime.timedelta(microseconds=563606), datetime.timedelta(microseconds=550614), datetime.timedelta(microseconds=550665), datetime.timedelta(microseconds=549668), datetime.timedelta(microseconds=563611), datetime.timedelta(microseconds=555644), datetime.timedelta(microseconds=569584), datetime.timedelta(microseconds=570584)]
Phi time: [datetime.timedelta(microseconds=932739), datetime.timedelta(microseconds=856195), datetime.timedelta(microseconds=864549), datetime.timedelta(microseconds=871132), datetime.timedelta(microseconds=855953), datetime.timedelta(microseconds=861101), datetime.timedelta(microseconds=856557), datetime.timedelta(microseconds=860536), datetime.timedelta(microseconds=857971), datetime.timedelta(microseconds=861573)]
