Precision: [tensor(0.4268, device='cuda:0'), tensor(0.4281, device='cuda:0'), tensor(0.4325, device='cuda:0'), tensor(0.4336, device='cuda:0'), tensor(0.4261, device='cuda:0'), tensor(0.4325, device='cuda:0'), tensor(0.4360, device='cuda:0'), tensor(0.4234, device='cuda:0'), tensor(0.4279, device='cuda:0'), tensor(0.4350, device='cuda:0')]

Output distance: [tensor(19.4643, device='cuda:0'), tensor(19.4571, device='cuda:0'), tensor(19.4302, device='cuda:0'), tensor(19.4238, device='cuda:0'), tensor(19.4686, device='cuda:0'), tensor(19.4302, device='cuda:0'), tensor(19.4096, device='cuda:0'), tensor(19.4849, device='cuda:0'), tensor(19.4583, device='cuda:0'), tensor(19.4154, device='cuda:0')]

Prediction loss: [tensor(104.5982, device='cuda:0'), tensor(105.0249, device='cuda:0'), tensor(105.8049, device='cuda:0'), tensor(104.4834, device='cuda:0'), tensor(104.8951, device='cuda:0'), tensor(105.0577, device='cuda:0'), tensor(104.1027, device='cuda:0'), tensor(103.8249, device='cuda:0'), tensor(104.0198, device='cuda:0'), tensor(104.7781, 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=7, microseconds=247264), datetime.timedelta(seconds=7, microseconds=239297), datetime.timedelta(seconds=7, microseconds=261204), datetime.timedelta(seconds=7, microseconds=267177), datetime.timedelta(seconds=7, microseconds=224360), datetime.timedelta(seconds=7, microseconds=241290), datetime.timedelta(seconds=7, microseconds=283115), datetime.timedelta(seconds=7, microseconds=272156), datetime.timedelta(seconds=7, microseconds=204445), datetime.timedelta(seconds=7, microseconds=250251)]

Phi time: [datetime.timedelta(seconds=5, microseconds=103418), datetime.timedelta(seconds=5, microseconds=110607), datetime.timedelta(seconds=5, microseconds=174283), datetime.timedelta(seconds=5, microseconds=143213), datetime.timedelta(seconds=5, microseconds=184813), datetime.timedelta(seconds=5, microseconds=158353), datetime.timedelta(seconds=5, microseconds=126674), datetime.timedelta(seconds=5, microseconds=161570), datetime.timedelta(seconds=5, microseconds=120034), datetime.timedelta(seconds=5, microseconds=183400)]

