What does model.eval() do in pytorch?
When should I use .eval()? I understand it is supposed to allow me to "evaluate my model". How do I turn it back off for training?
Example training code using .eval().
mdl.is_eval()?
self.training via self.training = training recursively for all modules by doing self.train(False). In fact that is what self.train does, changes the flag to true recursively for all modules. see code: github.com/pytorch/pytorch/blob/…
model.eval() is a kind of switch for some specific layers/parts of the model that behave differently during training and inference (evaluating) time. For example, Dropouts Layers, BatchNorm Layers etc. You need to turn off them during model evaluation, and .eval() will do it for you. In addition, the common practice for evaluating/validation is using torch.no_grad() in pair with model.eval() to turn off gradients computation:
# evaluate model:
model.eval()
with torch.no_grad():
...
out_data = model(data)
...
BUT, don't forget to turn back to training mode after eval step:
# training step
...
model.train()
...
model.train() model.eval() Sets model in training mode: • normalisation layers1 use per-batch statistics • activates Dropout layers2 Sets model in evaluation (inference) mode: • normalisation layers use running statistics • de-activates Dropout layers Equivalent to model.train(False).
You can turn off evaluation mode by running model.train(). You should use it when running your model as an inference engine - i.e. when testing, validating, and predicting (though practically it will make no difference if your model does not include any of the differently behaving layers).
e.g. BatchNorm, InstanceNorm This includes sub-modules of RNN modules etc.
model.eval is a method of torch.nn.Module:
eval() Sets the module in evaluation mode. This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc. This is equivalent with self.train(False).
The opposite method is model.train explained nicely by Umang Gupta.
mdl.is_eval()?
self.training flag as explained stackoverflow.com/a/56828547/5884955
An extra addition to the above answers:
I recently started working with Pytorch-lightning, which wraps much of the boilerplate in the training-validation-testing pipelines.
Among other things, it makes model.eval() and model.train() near redundant by allowing the train_step and validation_step callbacks which wrap the eval and train so you never forget to.
delete not elaborate. a bit dyslectic.to the question: Lightning handles the train/test loop for you, and you only have to define train_step and val_step and so on. the model.eval() and model.train() are done in he background, and you don't have to worry about them. I recommend you watch some of their videos, it is a well worth 30 minute investment.
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torch.no_grad()is a context manager, so you should use it in a form ofwith torch.no_grad():, that guarantees when leavingwith ...block model will turn on gradients computations automaticallymodel.train()andmodel.eval()have effect only on Layers, not on gradients, by default grad comp is switch on, but using context managertorch.no_grad()during evaluation allows you easily turn off and then autimatically turn on gradients comp at the end.eval():for module in self.children(): module.train(False)andfor module in self.children(): module.train(True)for.train()