I was reading Save and Load model but it wasn’t clear why I’d use
What worries me is that my Neural Net modules/object have many more things inside of them besides only parameters. Since pytorch saves things using the
state_dict that worried me that something might be missing (https://pytorch.org/tutorials/beginner/saving_loading_models.html).
Isn’t for my use case easier to simply use the
pickle.dump function? Why would I use
torch.save in general?
Note I am using gpu’s. Not sure if that matters.
The easiest thing for me is if I could just save the whole python program state (similar to how matlab used to allow you to do it https://www.mathworks.com/help/matlab/ref/save.html).
state_dict will store all registered parameters and buffers.
If you need to serialize some tensors, you should thus create an
nn.Parameter, if it’s trainable, or a buffer via
self.register_buffer(name, tensor), if it’s not trainable.
Answered in the other thread.
That’s generally not recommended.
Instead you should store the
state_dicts and the source files separately.
Storing e.g. the complete model could force you to recreate exactly the same file and folder structure.
Hi @ptrblck thanks for your responses! Greatly appreciated
I am curious, why is it not recommended to store the whole python program state? (or as close to that as possible).
I am currently using
dill to simulate this as much as possible because my neural network module classes contain pointers to many things, including lambda functions I’d like to restore properly.
As mentioned in my last post, you could be forced to recreate the exact find and folder structure, if you save the complete model via
I don’t know how
dill is working in this case.
yes, the same file structure needs to maintain.
moreover, the tensor function will still be the one used when saving.
i loaded a yolov5 model which is version 4.0 and uses silu as act. but i accidentally used local version of yolov5 version1.0 which uses leaky relu. however, the model still uses silu. i guess the function is stored in tensor.