Special test with strict convergence condition

Precision: [tensor(0.6308, device='cuda:0'), tensor(0.6298, device='cuda:0'), tensor(0.6248, device='cuda:0'), tensor(0.6266, device='cuda:0'), tensor(0.6294, device='cuda:0'), tensor(0.6324, device='cuda:0'), tensor(0.6313, device='cuda:0'), tensor(0.6305, device='cuda:0'), tensor(0.6272, device='cuda:0'), tensor(0.6275, device='cuda:0')]

Output distance: [tensor(4.9173, device='cuda:0'), tensor(4.9189, device='cuda:0'), tensor(4.9352, device='cuda:0'), tensor(4.9294, device='cuda:0'), tensor(4.9252, device='cuda:0'), tensor(4.9152, device='cuda:0'), tensor(4.9142, device='cuda:0'), tensor(4.9197, device='cuda:0'), tensor(4.9317, device='cuda:0'), tensor(4.9296, device='cuda:0')]

Prediction loss: [tensor(18143664., device='cuda:0'), tensor(19614980., device='cuda:0'), tensor(19406788., device='cuda:0'), tensor(17345744., device='cuda:0'), tensor(18921426., device='cuda:0'), tensor(17194270., device='cuda:0'), tensor(19222314., device='cuda:0'), tensor(18049052., device='cuda:0'), tensor(18225792., device='cuda:0'), tensor(19757364., device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(5663, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(5681, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(5663, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(5669, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(5607, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(5625, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(5687, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(5642, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(5606, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(5624, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40843.3516, device='cuda:0'), tensor(40866.1016, device='cuda:0'), tensor(40693.9336, device='cuda:0'), tensor(40862.5234, device='cuda:0'), tensor(40793.3047, device='cuda:0'), tensor(40888.3516, device='cuda:0'), tensor(40802.9766, device='cuda:0'), tensor(40938.9375, device='cuda:0'), tensor(40957.0781, device='cuda:0'), tensor(40710., device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=77431), datetime.timedelta(seconds=1, microseconds=86393), datetime.timedelta(seconds=1, microseconds=62494), datetime.timedelta(seconds=1, microseconds=80418), datetime.timedelta(seconds=1, microseconds=93363), datetime.timedelta(seconds=1, microseconds=86393), datetime.timedelta(seconds=1, microseconds=59507), datetime.timedelta(seconds=1, microseconds=99338), datetime.timedelta(seconds=1, microseconds=71457), datetime.timedelta(seconds=1, microseconds=51540)]

Phi time: [datetime.timedelta(microseconds=237990), datetime.timedelta(microseconds=238986), datetime.timedelta(microseconds=253923), datetime.timedelta(microseconds=237991), datetime.timedelta(microseconds=234007), datetime.timedelta(microseconds=242970), datetime.timedelta(microseconds=251932), datetime.timedelta(microseconds=238985), datetime.timedelta(microseconds=254918), datetime.timedelta(microseconds=258902)]

