报错FloatingPointError: Loss became infinite or NaN at iteration=88!,程序员大本营,技术文章内容聚合第一站。
loss_dict = {'loss_fcos_cls': tensor(nan, device='cuda:0', grad_fn=<DivBackward0>), 'loss_fcos_loc': tensor(0.5552, device='cuda:0', grad_fn=<DivBackward0>), 'loss_fcos_ctr': tensor(0.7676, device='cuda:0', grad_fn=<DivBackward0>), 'loss_mask': tensor(0.8649, device='...
is that the logits the model seems to return during training aretorch.tensor([[np.nan, np.nan, np.nan]])and when you apply.argmax(-1)to this, you get torch.tensor(0). The big mystery for me is why the logits would become "nan", because the model does not do that wh...
Search before asking I have searched the YOLOv5 issues and discussions and found no similar questions. Question When I use VisDrone dataset to train YOLOv5, after a few epoch, the loss becomes nan, and there is no prediction. Someone sai...
However, I notice that if I try to calculate the gradient of BCELoss when the loss is infinite, I get NaN, which makes some sense--I'm guessing that's why BCEWithLogitsLoss clamps the loss to a finite value. My question is: should I mimick BCEWithLogitsLoss? In my opinion, it sho...
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In the network of [52], the data of an infinite number of nodes can be merged into one data packet when they meet, so the amount of data that the network needs to transmit is hardly increased. Thus, in the method of [52], each node routes during broadcast, and the receiver node ...
If you divide0/0,7/0or overflow the maximum representable number, you won’t get an exception. You will get a special marker result. You can test for it withMath.isInfiniteorisNaN.NaN(NotANumber)s are strange beasts; they were included in theIEEE754standard so that the arithmetic would...
Second, there is a certain amount of surface charge density (ρ) in the electric system (first electrode) while the electric system operates. Third, the medium is an infinite plane, hence the volume charge density of the system (ρ) can be replaced by a surface charge density (σ). ...
@mcarilliWhen autocast disabled and the scaler is used, the model runs fine, not seeing NaNs so far. 👍8 Contributor pomelyucommentedAug 5, 2020 In my case, thescale_in scaler finally goes down to about 1e-39, which causeinv_scalegoes to infinite while callingscaler.unscale_(), ...