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() ...