Precision: [tensor(0.9590, device='cuda:0'), tensor(0.9576, device='cuda:0'), tensor(0.9587, device='cuda:0'), tensor(0.9577, device='cuda:0'), tensor(0.9582, device='cuda:0'), tensor(0.9602, device='cuda:0'), tensor(0.9600, device='cuda:0'), tensor(0.9568, device='cuda:0'), tensor(0.9582, device='cuda:0'), tensor(0.9596, device='cuda:0')]

Output distance: [tensor(114.8254, device='cuda:0'), tensor(130.7041, device='cuda:0'), tensor(122.2578, device='cuda:0'), tensor(120.7990, device='cuda:0'), tensor(130.8824, device='cuda:0'), tensor(139.3779, device='cuda:0'), tensor(109.6470, device='cuda:0'), tensor(118.8766, device='cuda:0'), tensor(119.3501, device='cuda:0'), tensor(118.2070, device='cuda:0')]

Prediction loss: [tensor(375.2122, device='cuda:0'), tensor(379.6519, device='cuda:0'), tensor(393.7681, device='cuda:0'), tensor(388.0620, device='cuda:0'), tensor(404.3490, device='cuda:0'), tensor(412.7652, device='cuda:0'), tensor(375.0323, device='cuda:0'), tensor(383.4731, device='cuda:0'), tensor(396.8653, device='cuda:0'), tensor(383.8720, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3456408., device='cuda:0'), tensor(3458762.7500, device='cuda:0'), tensor(3558184.5000, device='cuda:0'), tensor(3538370.7500, device='cuda:0'), tensor(3652136., device='cuda:0'), tensor(3619970.2500, device='cuda:0'), tensor(3433879.5000, device='cuda:0'), tensor(3520376., device='cuda:0'), tensor(3588807.5000, device='cuda:0'), tensor(3481865.7500, device='cuda:0')]

Training loss: 3553245.75

Prediction time: [datetime.timedelta(seconds=1, microseconds=818289), datetime.timedelta(seconds=1, microseconds=850154), datetime.timedelta(seconds=1, microseconds=817293), datetime.timedelta(seconds=1, microseconds=831234), datetime.timedelta(seconds=1, microseconds=812314), datetime.timedelta(seconds=1, microseconds=820278), datetime.timedelta(microseconds=869316), datetime.timedelta(seconds=1, microseconds=815301), datetime.timedelta(seconds=1, microseconds=819284), datetime.timedelta(seconds=1, microseconds=832229)]

Phi time: [datetime.timedelta(seconds=1, microseconds=450655), datetime.timedelta(microseconds=936029), datetime.timedelta(microseconds=884252), datetime.timedelta(microseconds=870279), datetime.timedelta(microseconds=889278), datetime.timedelta(microseconds=866139), datetime.timedelta(microseconds=868336), datetime.timedelta(microseconds=898287), datetime.timedelta(microseconds=865828), datetime.timedelta(microseconds=868895)]

