Precision: [tensor(0.3058, device='cuda:0'), tensor(0.2628, device='cuda:0'), tensor(0.2787, device='cuda:0'), tensor(0.2666, device='cuda:0'), tensor(0.2279, device='cuda:0'), tensor(0.2467, device='cuda:0'), tensor(0.2384, device='cuda:0'), tensor(0.2515, device='cuda:0'), tensor(0.2746, device='cuda:0'), tensor(0.2127, device='cuda:0')]

Output distance: [tensor(19.4138, device='cuda:0'), tensor(19.4997, device='cuda:0'), tensor(19.4680, device='cuda:0'), tensor(19.4921, device='cuda:0'), tensor(19.5695, device='cuda:0'), tensor(19.5320, device='cuda:0'), tensor(19.5487, device='cuda:0'), tensor(19.5224, device='cuda:0'), tensor(19.4761, device='cuda:0'), tensor(19.6001, device='cuda:0')]

Prediction loss: [tensor(107.7217, device='cuda:0'), tensor(106.8234, device='cuda:0'), tensor(108.0657, device='cuda:0'), tensor(108.7458, device='cuda:0'), tensor(106.3538, device='cuda:0'), tensor(107.5927, device='cuda:0'), tensor(108.5518, device='cuda:0'), tensor(108.6859, device='cuda:0'), tensor(107.5683, device='cuda:0'), tensor(106.1887, device='cuda:0')]

Others: [{'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, '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': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, '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': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, '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=2, microseconds=345056), datetime.timedelta(seconds=2, microseconds=267436), datetime.timedelta(seconds=2, microseconds=231534), datetime.timedelta(seconds=2, microseconds=344055), datetime.timedelta(seconds=2, microseconds=244469), datetime.timedelta(seconds=2, microseconds=260415), datetime.timedelta(seconds=2, microseconds=335096), datetime.timedelta(seconds=2, microseconds=330119), datetime.timedelta(seconds=2, microseconds=263400), datetime.timedelta(seconds=2, microseconds=247468)]

Phi time: [datetime.timedelta(seconds=4, microseconds=490495), datetime.timedelta(seconds=4, microseconds=346519), datetime.timedelta(seconds=4, microseconds=361737), datetime.timedelta(seconds=4, microseconds=376647), datetime.timedelta(seconds=4, microseconds=318890), datetime.timedelta(seconds=4, microseconds=352681), datetime.timedelta(seconds=4, microseconds=333069), datetime.timedelta(seconds=4, microseconds=343219), datetime.timedelta(seconds=4, microseconds=326835), datetime.timedelta(seconds=4, microseconds=345370)]

