Precision: [tensor(0.5505, device='cuda:0'), tensor(0.5480, device='cuda:0'), tensor(0.5522, device='cuda:0'), tensor(0.5515, device='cuda:0'), tensor(0.5518, device='cuda:0'), tensor(0.5508, device='cuda:0'), tensor(0.5573, device='cuda:0'), tensor(0.5548, device='cuda:0'), tensor(0.5496, device='cuda:0'), tensor(0.5508, device='cuda:0')]
Output distance: [tensor(5.0029, device='cuda:0'), tensor(5.0181, device='cuda:0'), tensor(4.9929, device='cuda:0'), tensor(4.9971, device='cuda:0'), tensor(4.9955, device='cuda:0'), tensor(5.0013, device='cuda:0'), tensor(4.9625, device='cuda:0'), tensor(4.9772, device='cuda:0'), tensor(5.0087, device='cuda:0'), tensor(5.0013, device='cuda:0')]
Prediction loss: [tensor(19159814., device='cuda:0'), tensor(19363454., device='cuda:0'), tensor(18861074., device='cuda:0'), tensor(19207178., device='cuda:0'), tensor(18776324., device='cuda:0'), tensor(18859370., device='cuda:0'), tensor(18591228., device='cuda:0'), tensor(18344828., device='cuda:0'), tensor(18396210., device='cuda:0'), tensor(18299276., device='cuda:0')]
Others: [{'iter_num': 7, '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': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40923.7656, device='cuda:0'), tensor(40863.4531, device='cuda:0'), tensor(40789.5781, device='cuda:0'), tensor(40829.3359, device='cuda:0'), tensor(40779.8945, device='cuda:0'), tensor(41042.2500, device='cuda:0'), tensor(40821.9453, device='cuda:0'), tensor(40819.8438, device='cuda:0'), tensor(40976.0352, device='cuda:0'), tensor(40791.0508, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=80418), datetime.timedelta(seconds=1, microseconds=113279), datetime.timedelta(seconds=1, microseconds=87387), datetime.timedelta(seconds=1, microseconds=82409), datetime.timedelta(seconds=1, microseconds=107303), datetime.timedelta(seconds=1, microseconds=96351), datetime.timedelta(seconds=1, microseconds=77431), datetime.timedelta(seconds=1, microseconds=86392), datetime.timedelta(seconds=1, microseconds=76434), datetime.timedelta(seconds=1, microseconds=87387)]
Phi time: [datetime.timedelta(microseconds=364453), datetime.timedelta(microseconds=235999), datetime.timedelta(microseconds=250937), datetime.timedelta(microseconds=249940), datetime.timedelta(microseconds=236000), datetime.timedelta(microseconds=234008), datetime.timedelta(microseconds=251932), datetime.timedelta(microseconds=236001), datetime.timedelta(microseconds=250936), datetime.timedelta(microseconds=233013)]
