Precision: [tensor(0.6913, device='cuda:0'), tensor(0.6873, device='cuda:0'), tensor(0.6815, device='cuda:0'), tensor(0.6894, device='cuda:0'), tensor(0.6931, device='cuda:0'), tensor(0.6881, device='cuda:0'), tensor(0.6884, device='cuda:0'), tensor(0.6865, device='cuda:0'), tensor(0.6957, device='cuda:0'), tensor(0.6892, device='cuda:0')]
Output distance: [tensor(4.9236, device='cuda:0'), tensor(4.9315, device='cuda:0'), tensor(4.9430, device='cuda:0'), tensor(4.9273, device='cuda:0'), tensor(4.9199, device='cuda:0'), tensor(4.9299, device='cuda:0'), tensor(4.9294, device='cuda:0'), tensor(4.9331, device='cuda:0'), tensor(4.9147, device='cuda:0'), tensor(4.9278, device='cuda:0')]
Prediction loss: [tensor(19674272., device='cuda:0'), tensor(19273608., device='cuda:0'), tensor(16429371., device='cuda:0'), tensor(18842360., device='cuda:0'), tensor(18906514., device='cuda:0'), tensor(19988572., device='cuda:0'), tensor(19234884., device='cuda:0'), tensor(18416828., device='cuda:0'), tensor(17043104., device='cuda:0'), tensor(18720914., device='cuda:0')]
Others: [{'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': 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': 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')}]
Compressed training loss: [tensor(40946.5234, device='cuda:0'), tensor(40787.7773, device='cuda:0'), tensor(40814.1836, device='cuda:0'), tensor(40893.2383, device='cuda:0'), tensor(40909.1875, device='cuda:0'), tensor(40803.2422, device='cuda:0'), tensor(40874.8164, device='cuda:0'), tensor(40814.4375, device='cuda:0'), tensor(40869.7695, device='cuda:0'), tensor(40802.8086, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=6, microseconds=355100), datetime.timedelta(seconds=6, microseconds=228586), datetime.timedelta(seconds=6, microseconds=22463), datetime.timedelta(seconds=6, microseconds=260434), datetime.timedelta(seconds=6, microseconds=49342), datetime.timedelta(seconds=6, microseconds=249477), datetime.timedelta(seconds=6, microseconds=82204), datetime.timedelta(seconds=6, microseconds=234560), datetime.timedelta(seconds=6, microseconds=46358), datetime.timedelta(seconds=6, microseconds=227538)]
Phi time: [datetime.timedelta(microseconds=382363), datetime.timedelta(microseconds=338563), datetime.timedelta(microseconds=419219), datetime.timedelta(microseconds=378401), datetime.timedelta(microseconds=402290), datetime.timedelta(microseconds=354517), datetime.timedelta(microseconds=435155), datetime.timedelta(microseconds=317656), datetime.timedelta(microseconds=440147), datetime.timedelta(microseconds=341550)]
