Precision: [tensor(0.2755, device='cuda:0'), tensor(0.2060, device='cuda:0'), tensor(0.2488, device='cuda:0'), tensor(0.2263, device='cuda:0'), tensor(0.2548, device='cuda:0'), tensor(0.2414, device='cuda:0'), tensor(0.2121, device='cuda:0'), tensor(0.2261, device='cuda:0'), tensor(0.2672, device='cuda:0'), tensor(0.2263, device='cuda:0')]

Output distance: [tensor(19.4743, device='cuda:0'), tensor(19.6134, device='cuda:0'), tensor(19.5278, device='cuda:0'), tensor(19.5729, device='cuda:0'), tensor(19.5157, device='cuda:0'), tensor(19.5426, device='cuda:0'), tensor(19.6013, device='cuda:0'), tensor(19.5732, device='cuda:0'), tensor(19.4909, device='cuda:0'), tensor(19.5729, device='cuda:0')]

Prediction loss: [tensor(109.0913, device='cuda:0'), tensor(107.1895, device='cuda:0'), tensor(109.0025, device='cuda:0'), tensor(109.5470, device='cuda:0'), tensor(109.0559, device='cuda:0'), tensor(108.6987, device='cuda:0'), tensor(108.3797, device='cuda:0'), tensor(109.0893, device='cuda:0'), tensor(108.9882, device='cuda:0'), tensor(108.1336, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, '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=4, microseconds=81686), datetime.timedelta(seconds=4, microseconds=12979), datetime.timedelta(seconds=4, microseconds=141435), datetime.timedelta(seconds=4, microseconds=131479), datetime.timedelta(seconds=4, microseconds=174296), datetime.timedelta(seconds=4, microseconds=242008), datetime.timedelta(seconds=4, microseconds=167325), datetime.timedelta(seconds=4, microseconds=133470), datetime.timedelta(seconds=4, microseconds=151391), datetime.timedelta(seconds=4, microseconds=148406)]

Phi time: [datetime.timedelta(seconds=4, microseconds=657534), datetime.timedelta(seconds=4, microseconds=671221), datetime.timedelta(seconds=4, microseconds=694127), datetime.timedelta(seconds=4, microseconds=759968), datetime.timedelta(seconds=4, microseconds=808148), datetime.timedelta(seconds=4, microseconds=825807), datetime.timedelta(seconds=4, microseconds=812591), datetime.timedelta(seconds=4, microseconds=817372), datetime.timedelta(seconds=4, microseconds=797228), datetime.timedelta(seconds=4, microseconds=814377)]

