Precision: [tensor(0.5431, device='cuda:0'), tensor(0.5571, device='cuda:0'), tensor(0.5556, device='cuda:0'), tensor(0.5417, device='cuda:0'), tensor(0.5531, device='cuda:0'), tensor(0.5384, device='cuda:0'), tensor(0.5342, device='cuda:0'), tensor(0.5528, device='cuda:0'), tensor(0.5464, device='cuda:0'), tensor(0.5453, device='cuda:0')]
Output distance: [tensor(18.9392, device='cuda:0'), tensor(18.9111, device='cuda:0'), tensor(18.9141, device='cuda:0'), tensor(18.9420, device='cuda:0'), tensor(18.9193, device='cuda:0'), tensor(18.9486, device='cuda:0'), tensor(18.9571, device='cuda:0'), tensor(18.9199, device='cuda:0'), tensor(18.9326, device='cuda:0'), tensor(18.9347, device='cuda:0')]
Prediction loss: [tensor(109.3648, device='cuda:0'), tensor(108.5098, device='cuda:0'), tensor(109.3435, device='cuda:0'), tensor(108.1171, device='cuda:0'), tensor(108.6187, device='cuda:0'), tensor(108.3792, device='cuda:0'), tensor(108.4279, device='cuda:0'), tensor(108.3572, device='cuda:0'), tensor(108.1403, device='cuda:0'), tensor(108.2782, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]
Compressed training loss: [tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=2, microseconds=591628), datetime.timedelta(seconds=2, microseconds=626463), datetime.timedelta(seconds=2, microseconds=640550), datetime.timedelta(seconds=2, microseconds=595652), datetime.timedelta(seconds=2, microseconds=618194), datetime.timedelta(seconds=2, microseconds=609316), datetime.timedelta(seconds=2, microseconds=650298), datetime.timedelta(seconds=2, microseconds=624208), datetime.timedelta(seconds=2, microseconds=630812), datetime.timedelta(seconds=2, microseconds=620188)]
Phi time: [datetime.timedelta(seconds=97, microseconds=624707), datetime.timedelta(seconds=97, microseconds=438654), datetime.timedelta(seconds=97, microseconds=233752), datetime.timedelta(seconds=97, microseconds=524878), datetime.timedelta(seconds=97, microseconds=624964), datetime.timedelta(seconds=97, microseconds=605124), datetime.timedelta(seconds=97, microseconds=553698), datetime.timedelta(seconds=97, microseconds=393653), datetime.timedelta(seconds=97, microseconds=326544), datetime.timedelta(seconds=97, microseconds=584477)]
