save() and load() in PyTorch

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*Memos:

• My post explains how to save a model with save() in PyTorch.
• My post explains how to load a saved model with load() in PyTorch.

save() can save an object as shown below:

*Memos:

• save() can be used with torch but not with a tensor.
• The 1st argument with torch is obj(Required-Type:object).
• The 2nd argument with torch is f(Required-Type:Union[str, PathLike, BinaryIO, IO[bytes]]).
• The 3rd argument with torch is pickle_module(Optional-Default:pickle-Type:Any).
• The 4th argument with torch is pickle_protocol(Optional-Default:2-Type:int).
• The 5th argument with torch is _use_new_zipfile_serialization(Optional-Default:True-Type:bool).
• The 6th argument with torch is _disable_byteorder_record(Optional-Default:False-Type:bool).
• Basically, it's used to save a model.

1. Create my_project folder manually:
*Memos:

• You can create my_project folder with Path.mkdir() in Python.
• My post explains Path.mkdir().

my_project 

2. Save file1(str) in my_project:

import torch import pickle torch.save(obj="Hello World", f="my_project/file1") # Or # torch.save(obj="Hello World", f="my_project/file1", # pickle_module=pickle, # pickle_protocol=2, # _use_new_zipfile_serialization=True, # _disable_byteorder_record=False)  # my_project # └-file1 <- Here 

3. Save file2(int), file3(float), file4(complex) and file5(bool) in my_project:

import torch torch.save(obj=3, f="my_project/file2") torch.save(obj=3.14, f="my_project/file3") torch.save(obj=3.14+7.j, f="my_project/file4") torch.save(obj=True, f="my_project/file5") # my_project # |-file1 # |-file2 <- Here # |-file3 <- Here # |-file4 <- Here # └-file5 <- Here 

load() can load an object as shown below:

*Memos:

• load() can be used with torch but not with a tensor.
• The 1st argument with torch is f(Required-Type:Union[str, PathLike, BinaryIO, IO[bytes]]).
• The 2nd argument with torch is map_location(Optional-Default:None-Type:Optional[Union[Callable[Storage, str], Storage], device, str, Dict[str, str]]]).
• The 3rd argument with torch is pickle_module(Optional-Default:pickle-Type:Any). *It must be None if weights_only is True but pickle is set to it implicitly.
• There is weights_only argument with torch(Optional-Default:False-Type:Optional(bool)): *Memos:
  • weights_only= must be used.
  • Basically, True should be set to it. *A warning occurs if weights_only=False isn't explicitly set.
  • It will be True by default in the later version of PyTorch.
• There is mmap argument with torch(Optional-Default:None-Type:Optional(bool)). *mmap= must be used.
• There is pickle_load_args argument with torch(Optional-Type:Any): *Memos:
  • A keyword argument must be used: *Memos:
  • pickle_load_args= cannot be used.
  • errors= can be used.
  • It cannot be used if weights_only is True.
  • It's only for Python 3.
• It can load a lot more types than I show below. *complex type can be loaded if weights_only is False.
• Basically, it's used to save a model.

1. There are file1(str), file2(int), file3(float), file4(complex) and file5(bool) in my_project as saved above with save():

my_project |-file1 |-file2 |-file3 |-file4 └-file5 

2. Load file1(str) from my_project:

import torch import pickle torch.load(f="my_project/file1", weights_only=True) # 'Hello World'  torch.load(f="my_project/file1", map_location=None, pickle_module=None, weights_only=True, mmap=None) # 'Hello World'  torch.load(f="my_project/file1", map_location=None, pickle_module=pickle, weights_only=False, mmap=None, errors="There are errors") # 'Hello World' 

3. Load file2(int), file3(float), file4(complex) and file5(bool) in my_project. *complex type can be loaded if weights_only is False:

import torch torch.load(f="my_project/file2", weights_only=True) # 3  torch.load(f="my_project/file3", weights_only=True) # 3.14  torch.load(f="my_project/file4", weights_only=False) # (3.14+7j)  torch.load(f="my_project/file5", weights_only=True) # True