Precision: [tensor(0.4579, device='cuda:0'), tensor(0.4644, device='cuda:0'), tensor(0.4636, device='cuda:0'), tensor(0.4627, device='cuda:0'), tensor(0.4605, device='cuda:0'), tensor(0.4615, device='cuda:0'), tensor(0.4640, device='cuda:0'), tensor(0.4636, device='cuda:0'), tensor(0.4633, device='cuda:0'), tensor(0.4621, device='cuda:0')]

Output distance: [tensor(19.2781, device='cuda:0'), tensor(19.2388, device='cuda:0'), tensor(19.2437, device='cuda:0'), tensor(19.2491, device='cuda:0'), tensor(19.2624, device='cuda:0'), tensor(19.2563, device='cuda:0'), tensor(19.2412, device='cuda:0'), tensor(19.2437, device='cuda:0'), tensor(19.2455, device='cuda:0'), tensor(19.2527, device='cuda:0')]

Prediction loss: [tensor(105.3542, device='cuda:0'), tensor(105.2322, device='cuda:0'), tensor(105.0047, device='cuda:0'), tensor(104.6357, device='cuda:0'), tensor(104.8679, device='cuda:0'), tensor(105.1765, device='cuda:0'), tensor(105.2527, device='cuda:0'), tensor(104.7738, device='cuda:0'), tensor(105.0747, device='cuda:0'), tensor(105.1126, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=9, microseconds=304548), datetime.timedelta(seconds=9, microseconds=250767), datetime.timedelta(seconds=9, microseconds=270680), datetime.timedelta(seconds=9, microseconds=254746), datetime.timedelta(seconds=9, microseconds=268696), datetime.timedelta(seconds=9, microseconds=280643), datetime.timedelta(seconds=9, microseconds=264708), datetime.timedelta(seconds=9, microseconds=225872), datetime.timedelta(seconds=9, microseconds=259728), datetime.timedelta(seconds=9, microseconds=257880)]

Phi time: [datetime.timedelta(seconds=6, microseconds=30020), datetime.timedelta(seconds=6, microseconds=222098), datetime.timedelta(seconds=6, microseconds=157484), datetime.timedelta(seconds=6, microseconds=251835), datetime.timedelta(seconds=6, microseconds=131641), datetime.timedelta(seconds=6, microseconds=108774), datetime.timedelta(seconds=6, microseconds=95576), datetime.timedelta(seconds=6, microseconds=210637), datetime.timedelta(seconds=6, microseconds=233005), datetime.timedelta(seconds=6, microseconds=106082)]

