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2023-06-14 09:00:01
Training:  54% 3594318/6600000 [111:03:11<77:04:33, 10.83it/s]
2023-06-14 09:00:03
Training:  54% 3594342/6600000 [111:03:13<72:54:20, 11.45it/s]
2023-06-14 09:00:05
Training:  54% 3594364/6600000 [111:03:15<77:31:34, 10.77it/s]
2023-06-14 09:00:07
Training:  54% 3594385/6600000 [111:03:17<73:11:37, 11.41it/s]
2023-06-14 09:00:09
Training:  54% 3594406/6600000 [111:03:19<91:48:50,  9.09it/s]
2023-06-14 09:00:11
Training:  54% 3594427/6600000 [111:03:21<71:26:25, 11.69it/s]
2023-06-14 09:00:13
Training:  54% 3594447/6600000 [111:03:23<81:33:26, 10.24it/s]
2023-06-14 09:00:15
Training:  54% 3594465/6600000 [111:03:25<86:53:09,  9.61it/s]
2023-06-14 09:00:17
Training:  54% 3594486/6600000 [111:03:27<83:20:10, 10.02it/s]
2023-06-14 09:00:19
Training:  54% 3594507/6600000 [111:03:29<77:34:49, 10.76it/s]
2023-06-14 09:00:21
Training:  54% 3594527/6600000 [111:03:31<79:36:10, 10.49it/s]
2023-06-14 09:00:23
Training:  54% 3594546/6600000 [111:03:33<85:20:16,  9.78it/s]
2023-06-14 09:00:25
         [nan, nan, nan,  ..., nan, nan, nan]]], device='cuda:0')21)) of distribution Categorical(logits: torch.Size([64, 16, 121])) to satisfy the constraint IndependentConstraint(Real(), 1), but found invalid values:
2023-06-14 09:00:25
         [nan, nan, nan,  ..., nan, nan, nan]]], device='cuda:0')21)) of distribution Categorical(logits: torch.Size([64, 16, 121])) to satisfy the constraint IndependentConstraint(Real(), 1), but found invalid values:
2023-06-14 09:00:26
         [nan, nan, nan,  ..., nan, nan, nan]]], device='cuda:0')21)) of distribution Categorical(logits: torch.Size([64, 16, 121])) to satisfy the constraint IndependentConstraint(Real(), 1), but found invalid values: