Using a pytorch model for inference
I am using the fastai library (fast.ai) to train an image classifier. The model created by fastai is actually a pytorch model.

type(model)
&lt;class 'torch.nn.modules.container.Sequential'&gt;


Now, I want to use this model from pytorch for inference. Here is my code so far:

torch.save(model,"./torch_model_v1")
the_model = torch.load("./torch_model_v1")
the_model.eval() # shows the entire network architecture


Based on the example shown here: http://pytorch.org/tutorials/beginner/data_loading_tutorial.html#sphx-glr-beginner-data-loading-tutorial-py, I understand that I need to write my own data loading class which will override some of the functions in the Dataset class. But what is not clear to me is the transformations that I need to apply at test time? In particular, how do I normalize the images at test time?

Another question: is my approach of saving and loading the model in pytorch fine? I read in the tutorial here: http://pytorch.org/docs/master/notes/serialization.html that the approach that I have used is not recommended. The reason is not clear though.

Just to clarify: the_model.eval() not only prints the architecture, but sets the model to evaluation mode.




  In particular, how do I normalize the images at test time?


It depends on the model you have. For instance, for torchvision modules, you have to normalize the inputs this way.



Regarding on how to save / load models, torch.save/torch.load "saves/loads an object to a disk file."

So, if you save the_model, it will save the entire model object, including its architecture definition and some other internal aspects. If  you save the_model.state_dict(), it will save a dictionary containing the model state (i.e. parameters and buffers) only. Saving the model can break the code in various ways, so the preferred method is to save and load only the model state. However, I'm not sure if fast.ai "model file" is actually a full model or the state of a model. You have to check this so you can correctly load it. 
