Precision: [tensor(0.5551, device='cuda:0'), tensor(0.5533, device='cuda:0'), tensor(0.5563, device='cuda:0'), tensor(0.5551, device='cuda:0'), tensor(0.5535, device='cuda:0'), tensor(0.5546, device='cuda:0'), tensor(0.5596, device='cuda:0'), tensor(0.5569, device='cuda:0'), tensor(0.5531, device='cuda:0'), tensor(0.5534, device='cuda:0')]

Output distance: [tensor(4.9756, device='cuda:0'), tensor(4.9866, device='cuda:0'), tensor(4.9682, device='cuda:0'), tensor(4.9756, device='cuda:0'), tensor(4.9850, device='cuda:0'), tensor(4.9787, device='cuda:0'), tensor(4.9483, device='cuda:0'), tensor(4.9646, device='cuda:0'), tensor(4.9877, device='cuda:0'), tensor(4.9856, device='cuda:0')]

Prediction loss: [tensor(18172822., device='cuda:0'), tensor(19308600., device='cuda:0'), tensor(18278454., device='cuda:0'), tensor(19440698., device='cuda:0'), tensor(18743726., device='cuda:0'), tensor(17338774., device='cuda:0'), tensor(18749734., device='cuda:0'), tensor(18951018., device='cuda:0'), tensor(18736566., device='cuda:0'), tensor(18509020., device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40778.1250, device='cuda:0'), tensor(40787.5234, device='cuda:0'), tensor(40851.8516, device='cuda:0'), tensor(40880.2617, device='cuda:0'), tensor(40729.7500, device='cuda:0'), tensor(40784.5352, device='cuda:0'), tensor(40618.8438, device='cuda:0'), tensor(40898.2812, device='cuda:0'), tensor(40873.7812, device='cuda:0'), tensor(40907.1602, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=29344), datetime.timedelta(seconds=1, microseconds=27028), datetime.timedelta(seconds=1, microseconds=58559), datetime.timedelta(seconds=1, microseconds=37403), datetime.timedelta(seconds=1, microseconds=34339), datetime.timedelta(seconds=1, microseconds=29683), datetime.timedelta(seconds=1, microseconds=57660), datetime.timedelta(seconds=1, microseconds=42744), datetime.timedelta(seconds=1, microseconds=32200), datetime.timedelta(seconds=1, microseconds=36068)]

Phi time: [datetime.timedelta(microseconds=236265), datetime.timedelta(microseconds=228439), datetime.timedelta(microseconds=241720), datetime.timedelta(microseconds=258055), datetime.timedelta(microseconds=251028), datetime.timedelta(microseconds=223905), datetime.timedelta(microseconds=224544), datetime.timedelta(microseconds=255215), datetime.timedelta(microseconds=224881), datetime.timedelta(microseconds=223541)]

