Precision: [tensor(0.5083, device='cuda:0'), tensor(0.5079, device='cuda:0'), tensor(0.5073, device='cuda:0'), tensor(0.5066, device='cuda:0'), tensor(0.5092, device='cuda:0'), tensor(0.5028, device='cuda:0'), tensor(0.5078, device='cuda:0'), tensor(0.5105, device='cuda:0'), tensor(0.5070, device='cuda:0'), tensor(0.5065, device='cuda:0')]

Output distance: [tensor(5.2565, device='cuda:0'), tensor(5.2586, device='cuda:0'), tensor(5.2623, device='cuda:0'), tensor(5.2665, device='cuda:0'), tensor(5.2507, device='cuda:0'), tensor(5.2891, device='cuda:0'), tensor(5.2591, device='cuda:0'), tensor(5.2428, device='cuda:0'), tensor(5.2638, device='cuda:0'), tensor(5.2670, device='cuda:0')]

Prediction loss: [tensor(18432608., device='cuda:0'), tensor(19239218., device='cuda:0'), tensor(19110738., device='cuda:0'), tensor(18872116., device='cuda:0'), tensor(19100278., device='cuda:0'), tensor(18705696., device='cuda:0'), tensor(18649500., device='cuda:0'), tensor(17262058., device='cuda:0'), tensor(18752042., device='cuda:0'), tensor(17782140., device='cuda:0')]

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

Compressed training loss: [tensor(40790.6562, device='cuda:0'), tensor(40801.9844, device='cuda:0'), tensor(40820.7773, device='cuda:0'), tensor(40852.0547, device='cuda:0'), tensor(40853.8867, device='cuda:0'), tensor(40698.5625, device='cuda:0'), tensor(40975.2031, device='cuda:0'), tensor(40526.3398, device='cuda:0'), tensor(40690.9883, device='cuda:0'), tensor(41120.0742, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=29120), datetime.timedelta(seconds=1, microseconds=26787), datetime.timedelta(seconds=1, microseconds=16731), datetime.timedelta(seconds=1, microseconds=4274), datetime.timedelta(seconds=1, microseconds=25126), datetime.timedelta(seconds=1, microseconds=23281), datetime.timedelta(seconds=1, microseconds=72471), datetime.timedelta(seconds=1, microseconds=23693), datetime.timedelta(seconds=1, microseconds=40023), datetime.timedelta(seconds=1, microseconds=63197)]

Phi time: [datetime.timedelta(microseconds=202197), datetime.timedelta(microseconds=211681), datetime.timedelta(microseconds=191118), datetime.timedelta(microseconds=202583), datetime.timedelta(microseconds=188451), datetime.timedelta(microseconds=203466), datetime.timedelta(microseconds=177452), datetime.timedelta(microseconds=174455), datetime.timedelta(microseconds=203813), datetime.timedelta(microseconds=190763)]

