Precision: [tensor(0.1144, device='cuda:0'), tensor(0.1309, device='cuda:0'), tensor(0.1317, device='cuda:0'), tensor(0.1166, device='cuda:0'), tensor(0.1137, device='cuda:0'), tensor(0.1201, device='cuda:0'), tensor(0.1222, device='cuda:0'), tensor(0.1213, device='cuda:0'), tensor(0.1217, device='cuda:0'), tensor(0.1266, device='cuda:0')]

Output distance: [tensor(21.3392, device='cuda:0'), tensor(21.2400, device='cuda:0'), tensor(21.2355, device='cuda:0'), tensor(21.3259, device='cuda:0'), tensor(21.3431, device='cuda:0'), tensor(21.3047, device='cuda:0'), tensor(21.2923, device='cuda:0'), tensor(21.2978, device='cuda:0'), tensor(21.2950, device='cuda:0'), tensor(21.2657, device='cuda:0')]

Prediction loss: [tensor(111.4105, device='cuda:0'), tensor(108.6291, device='cuda:0'), tensor(110.0041, device='cuda:0'), tensor(111.2549, device='cuda:0'), tensor(112.9961, device='cuda:0'), tensor(109.3651, device='cuda:0'), tensor(109.9371, device='cuda:0'), tensor(110.1650, device='cuda:0'), tensor(110.4946, device='cuda:0'), tensor(110.3724, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=3, microseconds=713418), datetime.timedelta(seconds=3, microseconds=733096), datetime.timedelta(seconds=3, microseconds=707697), datetime.timedelta(seconds=3, microseconds=724460), datetime.timedelta(seconds=3, microseconds=740221), datetime.timedelta(seconds=3, microseconds=727000), datetime.timedelta(seconds=3, microseconds=730079), datetime.timedelta(seconds=3, microseconds=723443), datetime.timedelta(seconds=3, microseconds=750855), datetime.timedelta(seconds=3, microseconds=729521)]

Phi time: [datetime.timedelta(seconds=4, microseconds=200166), datetime.timedelta(seconds=4, microseconds=251581), datetime.timedelta(seconds=4, microseconds=288462), datetime.timedelta(seconds=4, microseconds=299004), datetime.timedelta(seconds=4, microseconds=250115), datetime.timedelta(seconds=4, microseconds=305094), datetime.timedelta(seconds=4, microseconds=279882), datetime.timedelta(seconds=4, microseconds=289634), datetime.timedelta(seconds=4, microseconds=339475), datetime.timedelta(seconds=4, microseconds=300245)]

