Precision: [tensor(0.5641, device='cuda:0'), tensor(0.5617, device='cuda:0'), tensor(0.5649, device='cuda:0'), tensor(0.5651, device='cuda:0'), tensor(0.5631, device='cuda:0'), tensor(0.5624, device='cuda:0'), tensor(0.5638, device='cuda:0'), tensor(0.5633, device='cuda:0'), tensor(0.5621, device='cuda:0'), tensor(0.5626, device='cuda:0')]

Output distance: [tensor(4.9215, device='cuda:0'), tensor(4.9362, device='cuda:0'), tensor(4.9168, device='cuda:0'), tensor(4.9157, device='cuda:0'), tensor(4.9278, device='cuda:0'), tensor(4.9315, device='cuda:0'), tensor(4.9236, device='cuda:0'), tensor(4.9262, device='cuda:0'), tensor(4.9336, device='cuda:0'), tensor(4.9304, device='cuda:0')]

Prediction loss: [tensor(19143162., device='cuda:0'), tensor(17787626., device='cuda:0'), tensor(18906176., device='cuda:0'), tensor(18251270., device='cuda:0'), tensor(18323688., device='cuda:0'), tensor(18341730., device='cuda:0'), tensor(19472408., device='cuda:0'), tensor(18843422., device='cuda:0'), tensor(18243322., device='cuda:0'), tensor(18293108., device='cuda:0')]

Others: [{'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, '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': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, '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': 5, '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(40821.3867, device='cuda:0'), tensor(40848.3086, device='cuda:0'), tensor(40895.2188, device='cuda:0'), tensor(40838.1914, device='cuda:0'), tensor(40918.2461, device='cuda:0'), tensor(40835.5820, device='cuda:0'), tensor(40868.5117, device='cuda:0'), tensor(40839.2891, device='cuda:0'), tensor(40926.3828, device='cuda:0'), tensor(40741.0703, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=12616), datetime.timedelta(seconds=1, microseconds=39556), datetime.timedelta(seconds=1, microseconds=39697), datetime.timedelta(seconds=1, microseconds=56036), datetime.timedelta(seconds=1, microseconds=27703), datetime.timedelta(seconds=1, microseconds=34380), datetime.timedelta(seconds=1, microseconds=50934), datetime.timedelta(seconds=1, microseconds=13488), datetime.timedelta(seconds=1, microseconds=49985), datetime.timedelta(seconds=1, microseconds=43227)]

Phi time: [datetime.timedelta(microseconds=284189), datetime.timedelta(microseconds=303128), datetime.timedelta(microseconds=288109), datetime.timedelta(microseconds=281047), datetime.timedelta(microseconds=298607), datetime.timedelta(microseconds=299989), datetime.timedelta(microseconds=291117), datetime.timedelta(microseconds=288770), datetime.timedelta(microseconds=279521), datetime.timedelta(microseconds=302422)]

