Precision: [tensor(0.4599, device='cuda:0'), tensor(0.4616, device='cuda:0'), tensor(0.4530, device='cuda:0'), tensor(0.4547, device='cuda:0'), tensor(0.4560, device='cuda:0'), tensor(0.4596, device='cuda:0'), tensor(0.4587, device='cuda:0'), tensor(0.4610, device='cuda:0'), tensor(0.4581, device='cuda:0'), tensor(0.4537, device='cuda:0')]

Output distance: [tensor(5.5469, device='cuda:0'), tensor(5.5364, device='cuda:0'), tensor(5.5883, device='cuda:0'), tensor(5.5778, device='cuda:0'), tensor(5.5700, device='cuda:0'), tensor(5.5484, device='cuda:0'), tensor(5.5537, device='cuda:0'), tensor(5.5400, device='cuda:0'), tensor(5.5574, device='cuda:0'), tensor(5.5836, device='cuda:0')]

Prediction loss: [tensor(18916194., device='cuda:0'), tensor(16422617., device='cuda:0'), tensor(17720964., device='cuda:0'), tensor(21386512., device='cuda:0'), tensor(17428288., device='cuda:0'), tensor(16616289., device='cuda:0'), tensor(15194632., device='cuda:0'), tensor(18391162., device='cuda:0'), tensor(17415066., device='cuda:0'), tensor(16794918., device='cuda:0')]

Others: [{'iter_num': 13, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 15, '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': 13, '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': 13, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(41160.8828, device='cuda:0'), tensor(40931.0078, device='cuda:0'), tensor(40831.9062, device='cuda:0'), tensor(40677.6758, device='cuda:0'), tensor(41095.2500, device='cuda:0'), tensor(40673.9258, device='cuda:0'), tensor(40976.8438, device='cuda:0'), tensor(40845.8945, device='cuda:0'), tensor(40651.8398, device='cuda:0'), tensor(40728.2656, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=161076), datetime.timedelta(seconds=1, microseconds=112282), datetime.timedelta(seconds=1, microseconds=108151), datetime.timedelta(seconds=1, microseconds=52190), datetime.timedelta(seconds=1, microseconds=62526), datetime.timedelta(seconds=1, microseconds=76353), datetime.timedelta(seconds=1, microseconds=89474), datetime.timedelta(seconds=1, microseconds=56985), datetime.timedelta(seconds=1, microseconds=84536), datetime.timedelta(seconds=1, microseconds=81257)]

Phi time: [datetime.timedelta(microseconds=195172), datetime.timedelta(microseconds=201147), datetime.timedelta(microseconds=195172), datetime.timedelta(microseconds=195291), datetime.timedelta(microseconds=176488), datetime.timedelta(microseconds=200422), datetime.timedelta(microseconds=199714), datetime.timedelta(microseconds=199805), datetime.timedelta(microseconds=177090), datetime.timedelta(microseconds=199914)]

