Precision: [tensor(0.9485, device='cuda:0'), tensor(0.9348, device='cuda:0'), tensor(0.9453, device='cuda:0'), tensor(0.9418, device='cuda:0'), tensor(0.9427, device='cuda:0'), tensor(0.9438, device='cuda:0'), tensor(0.9481, device='cuda:0'), tensor(0.9248, device='cuda:0'), tensor(0.9453, device='cuda:0'), tensor(0.8750, device='cuda:0')]

Output distance: [tensor(164.7075, device='cuda:0'), tensor(214.5538, device='cuda:0'), tensor(144.6493, device='cuda:0'), tensor(800.2361, device='cuda:0'), tensor(147.1565, device='cuda:0'), tensor(137.8176, device='cuda:0'), tensor(387.7366, device='cuda:0'), tensor(1779.1235, device='cuda:0'), tensor(206.1797, device='cuda:0'), tensor(88069.3906, device='cuda:0')]

Prediction loss: [tensor(422.2803, device='cuda:0'), tensor(350.2575, device='cuda:0'), tensor(372.5014, device='cuda:0'), tensor(1134.3422, device='cuda:0'), tensor(383.4610, device='cuda:0'), tensor(363.4468, device='cuda:0'), tensor(657.3264, device='cuda:0'), tensor(1786.5681, device='cuda:0'), tensor(457.3115, device='cuda:0'), tensor(154608.7344, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3878747.5000, device='cuda:0'), tensor(3440602., device='cuda:0'), tensor(3487221.5000, device='cuda:0'), tensor(3377335.5000, device='cuda:0'), tensor(3740841.5000, device='cuda:0'), tensor(3551009.5000, device='cuda:0'), tensor(3629557.5000, device='cuda:0'), tensor(3335386.2500, device='cuda:0'), tensor(3804412.2500, device='cuda:0'), tensor(4208060.5000, device='cuda:0')]

Training loss: 3599853.5

Prediction time: [datetime.timedelta(seconds=1, microseconds=153109), datetime.timedelta(seconds=1, microseconds=188957), datetime.timedelta(seconds=1, microseconds=179995), datetime.timedelta(seconds=1, microseconds=195930), datetime.timedelta(seconds=1, microseconds=190950), datetime.timedelta(seconds=1, microseconds=175016), datetime.timedelta(seconds=1, microseconds=183363), datetime.timedelta(seconds=1, microseconds=186967), datetime.timedelta(seconds=1, microseconds=182986), datetime.timedelta(seconds=1, microseconds=195928)]

Phi time: [datetime.timedelta(seconds=1, microseconds=258727), datetime.timedelta(microseconds=756104), datetime.timedelta(microseconds=673622), datetime.timedelta(microseconds=667180), datetime.timedelta(microseconds=670031), datetime.timedelta(microseconds=670960), datetime.timedelta(microseconds=675393), datetime.timedelta(microseconds=671205), datetime.timedelta(microseconds=669621), datetime.timedelta(microseconds=673763)]

