Precision: [tensor(0.4164, device='cuda:0'), tensor(0.4373, device='cuda:0'), tensor(0.4389, device='cuda:0'), tensor(0.4536, device='cuda:0'), tensor(0.4209, device='cuda:0'), tensor(0.4324, device='cuda:0'), tensor(0.4279, device='cuda:0'), tensor(0.4519, device='cuda:0'), tensor(0.4690, device='cuda:0'), tensor(0.4095, device='cuda:0')]

Output distance: [tensor(19.1926, device='cuda:0'), tensor(19.1508, device='cuda:0'), tensor(19.1475, device='cuda:0'), tensor(19.1182, device='cuda:0'), tensor(19.1835, device='cuda:0'), tensor(19.1605, device='cuda:0'), tensor(19.1696, device='cuda:0'), tensor(19.1215, device='cuda:0'), tensor(19.0874, device='cuda:0'), tensor(19.2065, device='cuda:0')]

Prediction loss: [tensor(107.8740, device='cuda:0'), tensor(108.7482, device='cuda:0'), tensor(108.1934, device='cuda:0'), tensor(109.1073, device='cuda:0'), tensor(107.4916, device='cuda:0'), tensor(108.1576, device='cuda:0'), tensor(108.4293, device='cuda:0'), tensor(108.4988, device='cuda:0'), tensor(108.9037, device='cuda:0'), tensor(107.5245, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, 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=2, microseconds=614978), datetime.timedelta(seconds=2, microseconds=590084), datetime.timedelta(seconds=2, microseconds=621950), datetime.timedelta(seconds=2, microseconds=605968), datetime.timedelta(seconds=2, microseconds=592125), datetime.timedelta(seconds=2, microseconds=590084), datetime.timedelta(seconds=2, microseconds=609005), datetime.timedelta(seconds=2, microseconds=584063), datetime.timedelta(seconds=2, microseconds=600043), datetime.timedelta(seconds=2, microseconds=609005)]

Phi time: [datetime.timedelta(seconds=4, microseconds=592266), datetime.timedelta(seconds=4, microseconds=576137), datetime.timedelta(seconds=4, microseconds=565925), datetime.timedelta(seconds=4, microseconds=541193), datetime.timedelta(seconds=4, microseconds=595663), datetime.timedelta(seconds=4, microseconds=570239), datetime.timedelta(seconds=4, microseconds=581140), datetime.timedelta(seconds=4, microseconds=563283), datetime.timedelta(seconds=4, microseconds=517176), datetime.timedelta(seconds=4, microseconds=531064)]

