Precision: [tensor(0.7057, device='cuda:0'), tensor(0.7057, device='cuda:0'), tensor(0.7078, device='cuda:0'), tensor(0.7070, device='cuda:0'), tensor(0.7036, device='cuda:0'), tensor(0.6989, device='cuda:0'), tensor(0.7012, device='cuda:0'), tensor(0.7054, device='cuda:0'), tensor(0.7028, device='cuda:0'), tensor(0.6989, device='cuda:0')]
Output distance: [tensor(4.8947, device='cuda:0'), tensor(4.8947, device='cuda:0'), tensor(4.8905, device='cuda:0'), tensor(4.8921, device='cuda:0'), tensor(4.8989, device='cuda:0'), tensor(4.9084, device='cuda:0'), tensor(4.9036, device='cuda:0'), tensor(4.8952, device='cuda:0'), tensor(4.9005, device='cuda:0'), tensor(4.9084, device='cuda:0')]
Prediction loss: [tensor(37.5349, device='cuda:0'), tensor(36.3584, device='cuda:0'), tensor(37.4454, device='cuda:0'), tensor(36.7569, device='cuda:0'), tensor(36.1725, device='cuda:0'), tensor(36.7425, device='cuda:0'), tensor(36.7376, device='cuda:0'), tensor(35.1435, device='cuda:0'), tensor(35.9000, device='cuda:0'), tensor(36.5006, device='cuda:0')]
Others: [{'iter_num': 7, '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': 7, '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': 7, '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': 7, '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': 7, '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')}]
Compressed training loss: [tensor(48958.0859, device='cuda:0'), tensor(48646.3281, device='cuda:0'), tensor(48765.8945, device='cuda:0'), tensor(48939.9961, device='cuda:0'), tensor(48722.5273, device='cuda:0'), tensor(48813.4141, device='cuda:0'), tensor(48847.4688, device='cuda:0'), tensor(48808.8125, device='cuda:0'), tensor(48832.1172, device='cuda:0'), tensor(48867.3281, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=55473), datetime.timedelta(seconds=1, microseconds=60500), datetime.timedelta(seconds=1, microseconds=55522), datetime.timedelta(seconds=1, microseconds=66452), datetime.timedelta(seconds=1, microseconds=57462), datetime.timedelta(seconds=1, microseconds=49601), datetime.timedelta(seconds=1, microseconds=69466), datetime.timedelta(seconds=1, microseconds=74387), datetime.timedelta(seconds=1, microseconds=58562), datetime.timedelta(seconds=1, microseconds=42584)]
Phi time: [datetime.timedelta(seconds=5, microseconds=674875), datetime.timedelta(seconds=5, microseconds=653016), datetime.timedelta(seconds=5, microseconds=670987), datetime.timedelta(seconds=5, microseconds=649060), datetime.timedelta(seconds=5, microseconds=662975), datetime.timedelta(seconds=5, microseconds=652018), datetime.timedelta(seconds=5, microseconds=648035), datetime.timedelta(seconds=5, microseconds=648031), datetime.timedelta(seconds=5, microseconds=659985), datetime.timedelta(seconds=5, microseconds=652016)]
