Precision: [tensor(0.5459, device='cuda:0'), tensor(0.5465, device='cuda:0'), tensor(0.5469, device='cuda:0'), tensor(0.5442, device='cuda:0'), tensor(0.5470, device='cuda:0'), tensor(0.5441, device='cuda:0'), tensor(0.5469, device='cuda:0'), tensor(0.5478, device='cuda:0'), tensor(0.5474, device='cuda:0'), tensor(0.5452, device='cuda:0')]

Output distance: [tensor(5.0307, device='cuda:0'), tensor(5.0270, device='cuda:0'), tensor(5.0249, device='cuda:0'), tensor(5.0407, device='cuda:0'), tensor(5.0244, device='cuda:0'), tensor(5.0417, device='cuda:0'), tensor(5.0249, device='cuda:0'), tensor(5.0192, device='cuda:0'), tensor(5.0218, device='cuda:0'), tensor(5.0349, device='cuda:0')]

Prediction loss: [tensor(17725040., device='cuda:0'), tensor(19712544., device='cuda:0'), tensor(18575178., device='cuda:0'), tensor(18284484., device='cuda:0'), tensor(18178932., device='cuda:0'), tensor(18524320., device='cuda:0'), tensor(18308622., device='cuda:0'), tensor(17638416., device='cuda:0'), tensor(19787760., device='cuda:0'), tensor(18773198., device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(41113.6562, device='cuda:0'), tensor(40733.4453, device='cuda:0'), tensor(40917.3633, device='cuda:0'), tensor(40862.6445, device='cuda:0'), tensor(40723., device='cuda:0'), tensor(40865.4414, device='cuda:0'), tensor(40690.4062, device='cuda:0'), tensor(40858.5625, device='cuda:0'), tensor(40817.2812, device='cuda:0'), tensor(40904.0547, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=39814), datetime.timedelta(seconds=1, microseconds=45934), datetime.timedelta(seconds=1, microseconds=49948), datetime.timedelta(seconds=1, microseconds=19346), datetime.timedelta(seconds=1, microseconds=18776), datetime.timedelta(seconds=1, microseconds=24969), datetime.timedelta(seconds=1, microseconds=20376), datetime.timedelta(seconds=1, microseconds=17170), datetime.timedelta(seconds=1, microseconds=34515), datetime.timedelta(seconds=1, microseconds=25110)]

Phi time: [datetime.timedelta(microseconds=204792), datetime.timedelta(microseconds=206143), datetime.timedelta(microseconds=200097), datetime.timedelta(microseconds=200042), datetime.timedelta(microseconds=203871), datetime.timedelta(microseconds=203714), datetime.timedelta(microseconds=204663), datetime.timedelta(microseconds=231181), datetime.timedelta(microseconds=199881), datetime.timedelta(microseconds=205794)]

