Precision: [tensor(0.7374, device='cuda:0'), tensor(0.7345, device='cuda:0'), tensor(0.7289, device='cuda:0'), tensor(0.7316, device='cuda:0'), tensor(0.7297, device='cuda:0'), tensor(0.7280, device='cuda:0'), tensor(0.7366, device='cuda:0'), tensor(0.7316, device='cuda:0'), tensor(0.7328, device='cuda:0'), tensor(0.7371, device='cuda:0')]
Output distance: [tensor(5.0100, device='cuda:0'), tensor(5.0116, device='cuda:0'), tensor(5.0189, device='cuda:0'), tensor(5.0165, device='cuda:0'), tensor(5.0173, device='cuda:0'), tensor(5.0205, device='cuda:0'), tensor(5.0081, device='cuda:0'), tensor(5.0152, device='cuda:0'), tensor(5.0142, device='cuda:0'), tensor(5.0092, device='cuda:0')]
Prediction loss: [tensor(18053532., device='cuda:0'), tensor(19377886., device='cuda:0'), tensor(18395162., device='cuda:0'), tensor(19592104., device='cuda:0'), tensor(19089184., device='cuda:0'), tensor(17481374., device='cuda:0'), tensor(18329714., device='cuda:0'), tensor(19114608., device='cuda:0'), tensor(19732390., device='cuda:0'), tensor(19542758., device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(2376, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2392, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2390, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2381, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2394, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2386, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2399, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2392, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2388, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(2385, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40801.3203, device='cuda:0'), tensor(40952.1523, device='cuda:0'), tensor(40837.5469, device='cuda:0'), tensor(40798.5000, device='cuda:0'), tensor(40955.3398, device='cuda:0'), tensor(40872.8086, device='cuda:0'), tensor(40750.9766, device='cuda:0'), tensor(40931.4609, device='cuda:0'), tensor(40951.9844, device='cuda:0'), tensor(40734.1680, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=35608), datetime.timedelta(seconds=1, microseconds=36603), datetime.timedelta(seconds=1, microseconds=54527), datetime.timedelta(seconds=1, microseconds=23658), datetime.timedelta(seconds=1, microseconds=30628), datetime.timedelta(seconds=1, microseconds=34611), datetime.timedelta(seconds=1, microseconds=31625), datetime.timedelta(seconds=1, microseconds=33617), datetime.timedelta(seconds=1, microseconds=57515), datetime.timedelta(seconds=1, microseconds=40587)]
Phi time: [datetime.timedelta(microseconds=241974), datetime.timedelta(microseconds=242970), datetime.timedelta(microseconds=231021), datetime.timedelta(microseconds=273839), datetime.timedelta(microseconds=233013), datetime.timedelta(microseconds=232017), datetime.timedelta(microseconds=251932), datetime.timedelta(microseconds=232016), datetime.timedelta(microseconds=230025), datetime.timedelta(microseconds=235999)]
