Precision: [tensor(0.2122, device='cuda:0'), tensor(0.2120, device='cuda:0'), tensor(0.2154, device='cuda:0'), tensor(0.2034, device='cuda:0'), tensor(0.2174, device='cuda:0'), tensor(0.2218, device='cuda:0'), tensor(0.2234, device='cuda:0'), tensor(0.2217, device='cuda:0'), tensor(0.2219, device='cuda:0'), tensor(0.2204, device='cuda:0')]

Output distance: [tensor(20.7524, device='cuda:0'), tensor(20.7533, device='cuda:0'), tensor(20.7328, device='cuda:0'), tensor(20.8047, device='cuda:0'), tensor(20.7210, device='cuda:0'), tensor(20.6944, device='cuda:0'), tensor(20.6850, device='cuda:0'), tensor(20.6953, device='cuda:0'), tensor(20.6941, device='cuda:0'), tensor(20.7031, device='cuda:0')]

Prediction loss: [tensor(108.0337, device='cuda:0'), tensor(108.6427, device='cuda:0'), tensor(108.2783, device='cuda:0'), tensor(109.3728, device='cuda:0'), tensor(107.9533, device='cuda:0'), tensor(108.0501, device='cuda:0'), tensor(108.3716, device='cuda:0'), tensor(107.8487, device='cuda:0'), tensor(107.1126, device='cuda:0'), tensor(109.4558, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(19848, 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=5, microseconds=803535), datetime.timedelta(seconds=5, microseconds=648943), datetime.timedelta(seconds=5, microseconds=637713), datetime.timedelta(seconds=5, microseconds=643077), datetime.timedelta(seconds=5, microseconds=661153), datetime.timedelta(seconds=5, microseconds=628422), datetime.timedelta(seconds=5, microseconds=675731), datetime.timedelta(seconds=5, microseconds=687104), datetime.timedelta(seconds=5, microseconds=676169), datetime.timedelta(seconds=5, microseconds=643837)]

Phi time: [datetime.timedelta(seconds=4, microseconds=508843), datetime.timedelta(seconds=4, microseconds=499185), datetime.timedelta(seconds=4, microseconds=349850), datetime.timedelta(seconds=4, microseconds=329322), datetime.timedelta(seconds=4, microseconds=409000), datetime.timedelta(seconds=4, microseconds=374872), datetime.timedelta(seconds=4, microseconds=352249), datetime.timedelta(seconds=4, microseconds=366503), datetime.timedelta(seconds=4, microseconds=406436), datetime.timedelta(seconds=4, microseconds=415183)]

