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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().

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    Does this answer your question? What does model.train() do in pytorch? Commented Feb 1, 2020 at 18:16
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    is there a flag to detect if the model is in eval mode? e.g. mdl.is_eval()? Commented May 12, 2021 at 17:41
  • I recommend for any questions regarding any tool with nice documentation, look into documentation: pytorch.org/docs/stable/generated/torch.nn.Module.html . If documentation is not clear - just small comment some computation blocks working in different modes in training/optimization variables and use it for make prediction. One example is that model: arxiv.org/abs/1502.03167 Commented Oct 22, 2021 at 11:18
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    it simple changes the 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/… Commented Dec 19, 2021 at 19:07

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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 them off 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()
...
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11 Comments

torch.no_grad() is a context manager, so you should use it in a form of with torch.no_grad():, that guarantees when leaving with ... block model will turn on gradients computations automatically
so, model.train() and model.eval() have effect only on Layers, not on gradients, by default grad comp is switch on, but using context manager torch.no_grad() during evaluation allows you easily turn off and then autimatically turn on gradients comp at the end
Why do we need to turn off grad comp on Eval?
@shtse8 we don't compute or use gradients during evaluation, so turning off the autograd will speed up execution and will reduce memory usage
@NagabhushanSN yes! They work recursively, it looks like this for .eval(): for module in self.children(): module.train(False) and for module in self.children(): module.train(True) for .train()
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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).

  1. e.g. BatchNorm, InstanceNorm
  2. This includes sub-modules of RNN modules etc.
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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).

Ensures the model’s forward pass is deterministic and matches its trained behavior.
Note: Forgetting to call model.eval() can lead to incorrect results during inference, especially if the model uses Dropout or BatchNorm.

The opposite method is model.train .

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is there a flag to detect if the model is in eval mode? e.g. mdl.is_eval()?
Yes, @CharlieParker self.training flag as explained stackoverflow.com/a/56828547/5884955
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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.

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sorry I saw 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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In PyTorch, model.eval() switches the model to evaluation mode, which is crucial when performing inference or validating model performance. This is because certain layers, specifically Dropout and BatchNorm, behave differently during training versus evaluation. For instance, during training, Dropout randomly zeroes some of the elements of the input tensor to prevent overfitting, and BatchNorm computes running statistics from the current batch. However, during evaluation, these behaviors are not desirable, Dropout should be turned off to ensure deterministic results, and BatchNorm should use the running statistics accumulated during training for stable outputs.

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model.eval()

GFG has very clear explanation about it.

  • sets the PyTorch model to evaluation mode, disabling operations like dropout, useful for inference and testing.
  • This method plays a pivotal role in ensuring consistent and reliable model behaviour during inference and testing.
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