Precision: [tensor(0.5729, device='cuda:0'), tensor(0.5800, device='cuda:0'), tensor(0.5648, device='cuda:0'), tensor(0.5726, device='cuda:0'), tensor(0.5744, device='cuda:0'), tensor(0.5726, device='cuda:0'), tensor(0.5750, device='cuda:0'), tensor(0.5726, device='cuda:0'), tensor(0.5668, device='cuda:0'), tensor(0.5680, device='cuda:0')]

Output distance: [tensor(18.8797, device='cuda:0'), tensor(18.8655, device='cuda:0'), tensor(18.8957, device='cuda:0'), tensor(18.8803, device='cuda:0'), tensor(18.8767, device='cuda:0'), tensor(18.8803, device='cuda:0'), tensor(18.8755, device='cuda:0'), tensor(18.8803, device='cuda:0'), tensor(18.8918, device='cuda:0'), tensor(18.8894, device='cuda:0')]

Prediction loss: [tensor(108.5224, device='cuda:0'), tensor(108.4793, device='cuda:0'), tensor(108.9207, device='cuda:0'), tensor(109.0058, device='cuda:0'), tensor(108.8808, device='cuda:0'), tensor(109.1957, device='cuda:0'), tensor(109.5865, device='cuda:0'), tensor(108.7523, device='cuda:0'), tensor(109.0602, device='cuda:0'), tensor(108.6810, device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6616, 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=186616), datetime.timedelta(seconds=3, microseconds=199562), datetime.timedelta(seconds=3, microseconds=205560), datetime.timedelta(seconds=3, microseconds=191596), datetime.timedelta(seconds=3, microseconds=189601), datetime.timedelta(seconds=3, microseconds=335044), datetime.timedelta(seconds=3, microseconds=86038), datetime.timedelta(seconds=3, microseconds=84047), datetime.timedelta(seconds=3, microseconds=116909), datetime.timedelta(seconds=3, microseconds=132896)]

Phi time: [datetime.timedelta(seconds=6, microseconds=327794), datetime.timedelta(seconds=6, microseconds=201487), datetime.timedelta(seconds=6, microseconds=228143), datetime.timedelta(seconds=6, microseconds=305003), datetime.timedelta(seconds=6, microseconds=285899), datetime.timedelta(seconds=6, microseconds=560764), datetime.timedelta(seconds=6, microseconds=150993), datetime.timedelta(seconds=6, microseconds=200739), datetime.timedelta(seconds=6, microseconds=221657), datetime.timedelta(seconds=6, microseconds=166259)]

