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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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PyTorch model eval train is defined as a process to evaluate the train data. The eval() function is used to evaluate the train model. The eval() is type of switch for a particular parts of model which act differently during training and evaluating time.
train() tells your model that you are training the model. So effectively layers like dropout, batchnorm etc. which behave different on the train and test procedures know what is going on and hence can behave accordingly. More details: It sets the mode to train (see source code).
A state_dict is an integral entity if you are interested in saving or loading models from PyTorch. Because state_dict objects are Python dictionaries, they can be easily saved, updated, altered, and restored, adding a great deal of modularity to PyTorch models and optimizers.
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/… model.train () tells your model that you are training the model.
You can call either model.eval () or model.train (mode=False) to tell that you are testing. It is somewhat intuitive to expect train function to train model but it does not do that. It just sets the mode. is there a flag to detect if the model is in eval mode? e.g. mdl.is_eval ()? Use model.training flag. It is False, when in eval mode.
These days there is exist a documentation inside PyTorch: pytorch.org/docs/stable/generated/torch.nn.Module.html you can check documentation, it describes pretty clear I think. Another libraries/frameworks can have lack of documentation, but in PyTorch I think official documentation is pretty nice.
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.train () tells your model that you are training the model.
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).
BatchNorm, InstanceNorm
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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).
The opposite method is model.train explained nicely by Umang Gupta.
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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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