Precision: [tensor(0.4662, device='cuda:0'), tensor(0.4637, device='cuda:0'), tensor(0.4648, device='cuda:0'), tensor(0.4627, device='cuda:0'), tensor(0.4613, device='cuda:0'), tensor(0.4574, device='cuda:0'), tensor(0.4678, device='cuda:0'), tensor(0.4615, device='cuda:0'), tensor(0.4591, device='cuda:0'), tensor(0.4647, device='cuda:0')]

Output distance: [tensor(19.2282, device='cuda:0'), tensor(19.2430, device='cuda:0'), tensor(19.2367, device='cuda:0'), tensor(19.2491, device='cuda:0'), tensor(19.2576, device='cuda:0'), tensor(19.2808, device='cuda:0'), tensor(19.2186, device='cuda:0'), tensor(19.2567, device='cuda:0'), tensor(19.2709, device='cuda:0'), tensor(19.2373, device='cuda:0')]

Prediction loss: [tensor(105.2366, device='cuda:0'), tensor(105.5463, device='cuda:0'), tensor(105.4123, device='cuda:0'), tensor(105.4193, device='cuda:0'), tensor(104.6833, device='cuda:0'), tensor(104.9542, device='cuda:0'), tensor(105.4751, device='cuda:0'), tensor(105.4323, device='cuda:0'), tensor(105.3979, device='cuda:0'), tensor(105.1364, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, '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=3, microseconds=346752), datetime.timedelta(seconds=3, microseconds=549947), datetime.timedelta(seconds=3, microseconds=361690), datetime.timedelta(seconds=3, microseconds=353778), datetime.timedelta(seconds=3, microseconds=355766), datetime.timedelta(seconds=3, microseconds=363735), datetime.timedelta(seconds=3, microseconds=371700), datetime.timedelta(seconds=3, microseconds=347799), datetime.timedelta(seconds=3, microseconds=359803), datetime.timedelta(seconds=3, microseconds=364729)]

Phi time: [datetime.timedelta(seconds=6, microseconds=84343), datetime.timedelta(seconds=6, microseconds=77775), datetime.timedelta(seconds=6, microseconds=203803), datetime.timedelta(seconds=6, microseconds=58709), datetime.timedelta(seconds=6, microseconds=160107), datetime.timedelta(seconds=6, microseconds=213860), datetime.timedelta(seconds=6, microseconds=112185), datetime.timedelta(seconds=6, microseconds=192236), datetime.timedelta(seconds=6, microseconds=65717), datetime.timedelta(seconds=6, microseconds=78405)]

