Comments (10)
yup!
in the lightningModule you can call self.experiment. that’s a summarywriter for tensorboard. for the api of that refer to pytorch summarywriter.
https://pytorch.org/docs/stable/tensorboard
it’s all explained here: https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#tensorboard-support
realized self.experiment from docs. adding now!
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example:
https://pytorch-lightning-bolts.readthedocs.io/en/latest/vision_callbacks.html#confused-logit
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@qmpzzpmq
There should be
tensorboard = self.logger.experiment
grid = torchvision.utils.make_grid(...)
tensorboard.add_image(f'{stage}_samples', grid.clamp(0., 1.), self.current_epoch*self.batch_size + batch_idx)
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@tmquan can you update to the latest test-tube version and try again?
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Is there an approach to only log a subset of images instead of all over the validation_step
or test_step
call?
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@williamFalcon I don't think it's a feasible solution.
It means returning the outputs of the forward pass from training_step
.
However, as soon as you override training_epoch_end
, it will stack things up in the GPU, until it fill its/their memory.
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@ceceu have you tried
torchvision.utils.make_grid()
and then log images?
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@epignatelli you can create a variable such as
self.imgs = # something
and then plot these. This will work with DDP and the regular single GPU case but will not work with DP the default version does not support state maintenance.
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Thanks @ananyahjha93 and @williamFalcon, I guess the callback approach is the best practice.
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Hi, right now is there any method to call tensorboard's add_image
add_audio
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Related Issues (20)
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- IndexError: Pytorch-lightning CompositionalMetric require tensor.item() if dim=0 whether I did so
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