Precision: [tensor(0.6647, device='cuda:0'), tensor(0.6613, device='cuda:0'), tensor(0.6584, device='cuda:0'), tensor(0.6521, device='cuda:0'), tensor(0.6548, device='cuda:0'), tensor(0.6524, device='cuda:0'), tensor(0.6582, device='cuda:0'), tensor(0.6579, device='cuda:0'), tensor(0.6629, device='cuda:0'), tensor(0.6605, device='cuda:0')]

Output distance: [tensor(4.9766, device='cuda:0'), tensor(4.9835, device='cuda:0'), tensor(4.9892, device='cuda:0'), tensor(5.0018, device='cuda:0'), tensor(4.9966, device='cuda:0'), tensor(5.0013, device='cuda:0'), tensor(4.9898, device='cuda:0'), tensor(4.9903, device='cuda:0'), tensor(4.9803, device='cuda:0'), tensor(4.9850, device='cuda:0')]

Prediction loss: [tensor(19046300., device='cuda:0'), tensor(19340216., device='cuda:0'), tensor(19875822., device='cuda:0'), tensor(18453204., device='cuda:0'), tensor(17499376., device='cuda:0'), tensor(18561256., device='cuda:0'), tensor(20672740., device='cuda:0'), tensor(19302644., device='cuda:0'), tensor(18144362., device='cuda:0'), tensor(17346688., device='cuda:0')]

Others: [{'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40519.9414, device='cuda:0'), tensor(41289.7383, device='cuda:0'), tensor(40905.2422, device='cuda:0'), tensor(40833.5273, device='cuda:0'), tensor(40922.9375, device='cuda:0'), tensor(40886.9492, device='cuda:0'), tensor(40704.1406, device='cuda:0'), tensor(41106.8555, device='cuda:0'), tensor(40506.0508, device='cuda:0'), tensor(40835.1016, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(microseconds=996627), datetime.timedelta(microseconds=958086), datetime.timedelta(microseconds=981456), datetime.timedelta(seconds=1, microseconds=86217), datetime.timedelta(microseconds=973043), datetime.timedelta(microseconds=965821), datetime.timedelta(microseconds=952809), datetime.timedelta(microseconds=992019), datetime.timedelta(seconds=1, microseconds=62962), datetime.timedelta(microseconds=981244)]

Phi time: [datetime.timedelta(microseconds=187938), datetime.timedelta(microseconds=189339), datetime.timedelta(microseconds=199908), datetime.timedelta(microseconds=200702), datetime.timedelta(microseconds=206191), datetime.timedelta(microseconds=183195), datetime.timedelta(microseconds=199895), datetime.timedelta(microseconds=174079), datetime.timedelta(microseconds=187985), datetime.timedelta(microseconds=183376)]

