Precision: [tensor(0.4429, device='cuda:0'), tensor(0.4423, device='cuda:0'), tensor(0.4217, device='cuda:0'), tensor(0.4285, device='cuda:0'), tensor(0.4273, device='cuda:0'), tensor(0.4438, device='cuda:0'), tensor(0.4743, device='cuda:0'), tensor(0.4435, device='cuda:0'), tensor(0.4513, device='cuda:0'), tensor(0.4415, device='cuda:0')]

Output distance: [tensor(19.1397, device='cuda:0'), tensor(19.1409, device='cuda:0'), tensor(19.1820, device='cuda:0'), tensor(19.1684, device='cuda:0'), tensor(19.1708, device='cuda:0'), tensor(19.1378, device='cuda:0'), tensor(19.0768, device='cuda:0'), tensor(19.1385, device='cuda:0'), tensor(19.1227, device='cuda:0'), tensor(19.1424, device='cuda:0')]

Prediction loss: [tensor(109.0724, device='cuda:0'), tensor(108.3883, device='cuda:0'), tensor(108.2781, device='cuda:0'), tensor(108.2345, device='cuda:0'), tensor(107.3433, device='cuda:0'), tensor(108.6244, device='cuda:0'), tensor(108.7529, device='cuda:0'), tensor(108.0952, device='cuda:0'), tensor(108.0224, device='cuda:0'), tensor(108.0487, 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=471105), datetime.timedelta(seconds=2, microseconds=514288), datetime.timedelta(seconds=2, microseconds=493326), datetime.timedelta(seconds=2, microseconds=453104), datetime.timedelta(seconds=2, microseconds=475109), datetime.timedelta(seconds=2, microseconds=469638), datetime.timedelta(seconds=2, microseconds=484609), datetime.timedelta(seconds=2, microseconds=502999), datetime.timedelta(seconds=2, microseconds=497059), datetime.timedelta(seconds=2, microseconds=460663)]

Phi time: [datetime.timedelta(seconds=4, microseconds=424169), datetime.timedelta(seconds=4, microseconds=403721), datetime.timedelta(seconds=4, microseconds=397672), datetime.timedelta(seconds=4, microseconds=418032), datetime.timedelta(seconds=4, microseconds=448545), datetime.timedelta(seconds=4, microseconds=442558), datetime.timedelta(seconds=4, microseconds=429066), datetime.timedelta(seconds=4, microseconds=428213), datetime.timedelta(seconds=4, microseconds=407607), datetime.timedelta(seconds=4, microseconds=425955)]

