Precision: [tensor(0.3708, device='cuda:0'), tensor(0.3637, device='cuda:0'), tensor(0.3771, device='cuda:0'), tensor(0.3514, device='cuda:0'), tensor(0.3906, device='cuda:0'), tensor(0.3771, device='cuda:0'), tensor(0.3885, device='cuda:0'), tensor(0.3815, device='cuda:0'), tensor(0.3628, device='cuda:0'), tensor(0.4034, device='cuda:0')]

Output distance: [tensor(19.2839, device='cuda:0'), tensor(19.2981, device='cuda:0'), tensor(19.2712, device='cuda:0'), tensor(19.3226, device='cuda:0'), tensor(19.2443, device='cuda:0'), tensor(19.2712, device='cuda:0'), tensor(19.2485, device='cuda:0'), tensor(19.2624, device='cuda:0'), tensor(19.2999, device='cuda:0'), tensor(19.2186, device='cuda:0')]

Prediction loss: [tensor(108.4663, device='cuda:0'), tensor(107.0499, device='cuda:0'), tensor(107.0687, device='cuda:0'), tensor(107.6802, device='cuda:0'), tensor(107.5451, device='cuda:0'), tensor(108.0778, device='cuda:0'), tensor(108.5497, device='cuda:0'), tensor(107.3871, device='cuda:0'), tensor(107.5513, device='cuda:0'), tensor(109.3166, device='cuda:0')]

Others: [{'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, '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=554167), datetime.timedelta(seconds=2, microseconds=538234), datetime.timedelta(seconds=2, microseconds=270373), datetime.timedelta(seconds=2, microseconds=264396), datetime.timedelta(seconds=2, microseconds=268377), datetime.timedelta(seconds=2, microseconds=259420), datetime.timedelta(seconds=2, microseconds=265389), datetime.timedelta(seconds=2, microseconds=293273), datetime.timedelta(seconds=2, microseconds=425709), datetime.timedelta(seconds=2, microseconds=276345)]

Phi time: [datetime.timedelta(seconds=5, microseconds=315612), datetime.timedelta(seconds=4, microseconds=794908), datetime.timedelta(seconds=4, microseconds=394903), datetime.timedelta(seconds=4, microseconds=442963), datetime.timedelta(seconds=4, microseconds=357670), datetime.timedelta(seconds=4, microseconds=399263), datetime.timedelta(seconds=4, microseconds=496449), datetime.timedelta(seconds=4, microseconds=431928), datetime.timedelta(seconds=4, microseconds=402255), datetime.timedelta(seconds=4, microseconds=535901)]

