Precision: [tensor(0.6009, device='cuda:0'), tensor(0.6030, device='cuda:0'), tensor(0.6030, device='cuda:0'), tensor(0.6036, device='cuda:0'), tensor(0.6070, device='cuda:0'), tensor(0.6044, device='cuda:0'), tensor(0.6051, device='cuda:0'), tensor(0.6062, device='cuda:0'), tensor(0.5918, device='cuda:0'), tensor(0.5999, device='cuda:0')]

Output distance: [tensor(5.1042, device='cuda:0'), tensor(5.1000, device='cuda:0'), tensor(5.1000, device='cuda:0'), tensor(5.0990, device='cuda:0'), tensor(5.0921, device='cuda:0'), tensor(5.0974, device='cuda:0'), tensor(5.0958, device='cuda:0'), tensor(5.0937, device='cuda:0'), tensor(5.1226, device='cuda:0'), tensor(5.1063, device='cuda:0')]

Prediction loss: [tensor(15354123., device='cuda:0'), tensor(16234970., device='cuda:0'), tensor(22740694., device='cuda:0'), tensor(19454342., device='cuda:0'), tensor(19043518., device='cuda:0'), tensor(14952927., device='cuda:0'), tensor(18131744., device='cuda:0'), tensor(19339054., device='cuda:0'), tensor(20923254., device='cuda:0'), tensor(17785182., device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(41027.6523, device='cuda:0'), tensor(40537.3633, device='cuda:0'), tensor(40696.3008, device='cuda:0'), tensor(41210.8594, device='cuda:0'), tensor(41093.7031, device='cuda:0'), tensor(40824.6367, device='cuda:0'), tensor(40749.6172, device='cuda:0'), tensor(41042.1484, device='cuda:0'), tensor(40907.3633, device='cuda:0'), tensor(41088.2305, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=124231), datetime.timedelta(seconds=1, microseconds=143151), datetime.timedelta(seconds=1, microseconds=122240), datetime.timedelta(seconds=1, microseconds=134190), datetime.timedelta(seconds=1, microseconds=119253), datetime.timedelta(seconds=1, microseconds=111287), datetime.timedelta(seconds=1, microseconds=131203), datetime.timedelta(seconds=1, microseconds=115270), datetime.timedelta(seconds=1, microseconds=146138), datetime.timedelta(seconds=1, microseconds=136181)]

Phi time: [datetime.timedelta(microseconds=216084), datetime.timedelta(microseconds=225046), datetime.timedelta(microseconds=213095), datetime.timedelta(microseconds=214092), datetime.timedelta(microseconds=226041), datetime.timedelta(microseconds=216083), datetime.timedelta(microseconds=215087), datetime.timedelta(microseconds=223054), datetime.timedelta(microseconds=225046), datetime.timedelta(microseconds=228033)]

