Precision: [tensor(0.9747, device='cuda:0'), tensor(0.9182, device='cuda:0'), tensor(0.9977, device='cuda:0'), tensor(0.9760, device='cuda:0'), tensor(0.9972, device='cuda:0'), tensor(0.9772, device='cuda:0'), tensor(0.9743, device='cuda:0'), tensor(0.9977, device='cuda:0'), tensor(0.9947, device='cuda:0'), tensor(0.9985, device='cuda:0')]

Output distance: [tensor(2600429., device='cuda:0'), tensor(5809499.5000, device='cuda:0'), tensor(26303.0059, device='cuda:0'), tensor(1727324.8750, device='cuda:0'), tensor(25111.2207, device='cuda:0'), tensor(98205.3828, device='cuda:0'), tensor(97254.1953, device='cuda:0'), tensor(159220.9844, device='cuda:0'), tensor(41546.5391, device='cuda:0'), tensor(24337.5703, device='cuda:0')]

Prediction loss: [tensor(3029484.2500, device='cuda:0'), tensor(8062528., device='cuda:0'), tensor(25240.6719, device='cuda:0'), tensor(2525777.5000, device='cuda:0'), tensor(22567.6699, device='cuda:0'), tensor(118936.6406, device='cuda:0'), tensor(115174.0469, device='cuda:0'), tensor(214978.3906, device='cuda:0'), tensor(47117.7109, device='cuda:0'), tensor(22987.6641, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(9050134., device='cuda:0'), tensor(9578195., device='cuda:0'), tensor(8598358., device='cuda:0'), tensor(8985036., device='cuda:0'), tensor(8420869., device='cuda:0'), tensor(9404602., device='cuda:0'), tensor(9253061., device='cuda:0'), tensor(8744744., device='cuda:0'), tensor(9102288., device='cuda:0'), tensor(8461744., device='cuda:0')]

Training loss: 8893901.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=213880), datetime.timedelta(seconds=1, microseconds=228822), datetime.timedelta(seconds=1, microseconds=232804), datetime.timedelta(seconds=1, microseconds=237783), datetime.timedelta(seconds=1, microseconds=229766), datetime.timedelta(seconds=1, microseconds=233801), datetime.timedelta(seconds=1, microseconds=239774), datetime.timedelta(seconds=1, microseconds=249734), datetime.timedelta(seconds=1, microseconds=282593), datetime.timedelta(seconds=1, microseconds=256703)]

Phi time: [datetime.timedelta(seconds=1, microseconds=201791), datetime.timedelta(microseconds=697611), datetime.timedelta(microseconds=620818), datetime.timedelta(microseconds=626558), datetime.timedelta(microseconds=626091), datetime.timedelta(microseconds=625550), datetime.timedelta(microseconds=629775), datetime.timedelta(microseconds=638274), datetime.timedelta(microseconds=656187), datetime.timedelta(microseconds=659444)]

