Precision: [tensor(0.9950, device='cuda:0'), tensor(0.9282, device='cuda:0'), tensor(0.9852, device='cuda:0'), tensor(0.9975, device='cuda:0'), tensor(0.9972, device='cuda:0'), tensor(0.9898, device='cuda:0'), tensor(0.9772, device='cuda:0'), tensor(0.9905, device='cuda:0'), tensor(0.9858, device='cuda:0'), tensor(0.9777, device='cuda:0')]

Output distance: [tensor(160845.6250, device='cuda:0'), tensor(5320422.5000, device='cuda:0'), tensor(196409.7969, device='cuda:0'), tensor(183586.5938, device='cuda:0'), tensor(158649.5625, device='cuda:0'), tensor(225848.8125, device='cuda:0'), tensor(1831172.1250, device='cuda:0'), tensor(371922.3438, device='cuda:0'), tensor(322476.5938, device='cuda:0'), tensor(821318.5625, device='cuda:0')]

Prediction loss: [tensor(149079.7344, device='cuda:0'), tensor(6345956.5000, device='cuda:0'), tensor(194668.4062, device='cuda:0'), tensor(180242.9375, device='cuda:0'), tensor(139048.1094, device='cuda:0'), tensor(243418.1562, device='cuda:0'), tensor(2387325.7500, device='cuda:0'), tensor(438587.8438, device='cuda:0'), tensor(357011.5312, device='cuda:0'), tensor(1077231.8750, 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(1.8762e+08, device='cuda:0'), tensor(1.9537e+08, device='cuda:0'), tensor(1.9084e+08, device='cuda:0'), tensor(1.8414e+08, device='cuda:0'), tensor(1.8488e+08, device='cuda:0'), tensor(1.8899e+08, device='cuda:0'), tensor(1.8800e+08, device='cuda:0'), tensor(1.9038e+08, device='cuda:0'), tensor(1.9139e+08, device='cuda:0'), tensor(1.9649e+08, device='cuda:0')]

Training loss: 192702352.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=53248), datetime.timedelta(seconds=1, microseconds=91977), datetime.timedelta(seconds=1, microseconds=87064), datetime.timedelta(seconds=1, microseconds=77949), datetime.timedelta(seconds=1, microseconds=86223), datetime.timedelta(seconds=1, microseconds=93786), datetime.timedelta(seconds=1, microseconds=72121), datetime.timedelta(seconds=1, microseconds=78973), datetime.timedelta(seconds=1, microseconds=100123), datetime.timedelta(seconds=1, microseconds=84834)]

Phi time: [datetime.timedelta(seconds=1, microseconds=61741), datetime.timedelta(microseconds=633501), datetime.timedelta(microseconds=549955), datetime.timedelta(microseconds=552030), datetime.timedelta(microseconds=557510), datetime.timedelta(microseconds=549784), datetime.timedelta(microseconds=562204), datetime.timedelta(microseconds=554133), datetime.timedelta(microseconds=549905), datetime.timedelta(microseconds=554838)]

