Everything You Need To Know About Saving Weights In PyTorch

n0obcoder
Aug 13 · 7 min read
PyTorch is fun !
PyTorch is fun !

What do we Deep Learning practitioners do once we are done with training our models ?

We chill !!!

Hahhhaha Just Kidding…

We either save the learnt weights or the entire model so that we could further train the model or maybe use the trained model for inference !

Next thing that you guys might be interested in knowing is, when do we save just the learnt weights and when do we save the whole model.

In this blog, we will try to find the answers to these questions.

I will keep it very straight and simple while explaining you the ins and outs of the art of saving a model’s architecture and it’s weights in PyTorch.

We will also learn how to access the different modules, nn.Modules to be precise, in any given PyTorch model .

So feel free to fork this kaggle kernel and play with the code : )

Let’s get started !!!


We start off by importing the bare necessities of coding using PyTorch.

Next, we define a CNN based model.

Let’s initialize and print model and see what’s inside it.

Printing the model shows you it’s architecture. But we are going to dive deeper, for we are the Deep Learning practitioners !

We need to make sure that we understand what exactly lies inside our model.

There is a way to access each and every learnable Parameter of a model along with their names. By the way, a torch.nn.Parameter is a Tensor subclass , which when used with torch.nn.Module gets automatically added to the list of its parameters and appers eg. in parameters() or named_parameters() iterator. Adding a torch.nn.Tensor on the other hand doesn’t have such an effect. More on that later !

Back to printing all the parameters of the model.

Wooouhooouhooou ! So what did just happen here ?

Let’s get into the named_parameters() function.

model.named_parameters() itself is a generator. It returns the name and param, which are nothing but the name of the parameter and the parameter itself. Here, the returned param is torch.nn.Parameter class which is a kind of tensor. Since param is a type of tensor, it has shape and requires_grad attributes too. param.shape is simply the shape of the tensor and param.requires_grad is a boolean which tells if the parameter is learnable or not. Since all the params in the model have requires_grad = True, it means that all the parameters are learnable and will update on training the model. Had it been set to False for a any specific param, that parameter’s weight would not update on training the model.

So, requires_grad is the flag that you might want to change when you want to train/freeze a specific set of layers of your model.


Now we will try to freeze all but the last layer of the model. If we go through all the names of all the parameters of the model, we can see that the name of the last layer is ‘fc’ which stands for ‘fully connected’.

So let’s freeze all the parameters except the ones with their names ‘fc.weight’ or ‘fc.bias’

We can verify that the desired changes have been made successfully by printing out the requires_grad for all the parameters of the model

We can see that the desired changes have been made successfully !

So we have learnt how to change the requires_grad flag for any desired parameter of the model. And we have also learnt that doing so can come in very handy in situations where we want to learn/freeze the weights of some specific parameters/layers in a model.

We will now learn 2 of the widely known ways of saving a model’s weights/parameters.

  1. torch.save(model.state_dict(), ‘weights_path_name.pth’)
    It saves only the weights of the model
  2. torch.save(model, ‘model_path_name.pth’)
    It saves the entire model (the architecture as well as the weights)

What Is state_dict() And Where To Use It ?

We will first see how to write the syntax for state_dict. It’s pretty easy.

Its just a python’s ordered dictionary.

But, printing this, would result in chaos. So we wouldn’t print the state_dict for the entire model here, but I encourage you guys to go ahead and print it out on your screens !

I guess it’s a good time to divert a little from the topic.

See, printing help(model) tells us that model is an instance of nn.Module

It could also be verified by using python’s isinstance function

Is model.fc also an instance of nn.Module ?

Apparently yes !

But what exactly is fc, and where does it come form ?

We can see what all nn.Module objects lie under the model

The named_children() applied on any nn.Module object returns all it’s immediate children (also nn.Module objects). Looking at the results of the above written piece of code, we know that ‘sequential’, ‘layer1’, ‘layer2’, and ‘fc’ are all the children of model and all of these are nn.Module class objects. Now we all know where ‘fc’ is coming from.

And you know what ? state_dict() works on any nn.Module object and returns all it’s immediate children(of class nn.Module).

So let’s try the state_dict() function on the ‘fc’ layer of the model.

Remember that model.fc.state_dict() or any nnModule.state_dict() is an ordered dictionary. So iterating over it gives us the keys of the dictionary which can be used to access the parameter tensor which, by the way, is not a nn.Module object, but a simple torch.Tensor with a shape and requires_grad attribute.

So it must be noted that when we save the state_dict() of a nn.Module object e.g. the model, the torch.Tensor objects are saved !

This is how we save the state_dict of the entire model.

This makes a ‘weights_only.pth’ file in the working directory and it holds, in an ordered dictionary, the torch.Tensor objects of all the layers of the model.

We will try to load the saved weights now. But before we do that, we need to define the model architecture first. It makes sense to define the model first and then to load the weights in it because the saved information is just the weights and not the model architecture.

Once the weights are loaded in the defined model, let’s check the requires_grad attribute of all the layers of model_new.

Wait ! What ?

What happened to all the requires_grad flags that we had set for all the different layers ? It seems like all the requires_grad flags have been reset to True.

Actually, we never saved the required_grad flag of the parameters in the first place. Remember, a state_dict is simply a python dictionary object that maps each layer to its parameter tensor. It does not save the requires_grad attribute of the parameters.

So we would need to again make the necessary changes to the requires_grad attribute of all the parameters before resume training of the model for more epochs


How To Save The Entire Model And When To Do It ?

Yes we have this second way of saving things, in which we can save the entire model too. By entire model, I mean the architecture of the model as well as it’s weights.

So we will resume from the point where we had frozen all but the last layer (the ‘fc’ layer) of the model and save the entire model.

This makes a ‘entire_model.pth’ file in the working directory and it contains the model architecture as well as the saved weights.

We will try to load the saved model now. And this time, we do not need to define the model architecture as the information about the model architecture is already stored in the saved file.

Once the model is loaded, let’s check the requires_grad attribute of all the layers of model_new.

That is exactly what we wanted to see, isn’t it ? :D

So when we saved the entire model, we saved the nn.Module object and doing so saves the requires_grad flags of all it’s parameters too.


I would strongly suggest you guys to fork this public kaggle kernel and play with the code, to get the feel of it !


Summary

We learnt a lot of things in this blog.

  1. Applying named_parameters() on an nn.Module object e.g. model or
    model.layer2 or model.fc returns all the names and the respective parameters. These parameters are nn.Parameter (subclass of torch.Tensor) objects and therefore they have shape and requires_grad attributes.

2. The requires_grad attribute of a nn.Parameter object (learnable parameter object) decides whether to train or freeze a particular parameter. For example, if we want to freeze the layer1 of the model, we would use the following code.

3. Applying named_children() on any nn.Module object returns all it’s immediate children (also nn.Module objects).

4. A state_dict() of any nn.Module object e.g. model or model.layer2 or model.fc is simply a python ordered dictionary object that maps each parameter to its parameter tensor (torch.Tensor object). The keys of this ordered dictionary are the names of the parameters, which can be used to access the respective parameter tensors.

5. Saving a nn.Module object’s state_dict only saves the weights of the various parameters of that object and not the model architecture. Neither does it involve the requires_grad attribute of the weights. So before loading the state_dict, one must define the model first.

6. Entire model (nn.Module object) can also be saved which would include the model architecture as well as its weights. Since we are saving the nn.Module object, the requires_grad attribute is also saved this way. Also we don’t need to define the model architecture before loading the saved file since the saved file already has the model architecture saved in it.

7. Saving the state_dict can be used to only save the weights of the model. It doesn’t save the required_grad flag, whereas saving the entire model does save the model architecture, it’s weights and the requires_grad attributes of all its parameters.

8. Both state_dict as well as the entire model can be saved to make inferences.


I am writing this blog because I have learnt a lot by reading other’s blogs and I feel that I should also write and share my learnings and knowledge, as much as I can. So please leave your feedback in the comments section down below. Also I am new to writing blogs, so any suggestions on how to improve my writing would be appreciated ! :D

n0obcoder

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n0obcoder

DL Engineering in the making and a Struggling Musician

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