torch(四)、Serialization

torch.save(obj, f, pickle_module=<module 'pickle' from '/opt/conda/lib/python3.6/pickle.py'>, pickle_protocol=2)[source]

Saves an object to a disk file.

See also: Recommended approach for saving a model

Parameters

• obj – saved object
• f – a file-like object (has to implement write and flush) or a string containing a file name
• pickle_module – module used for pickling metadata and objects
• pickle_protocol – can be specified to override the default protocol

Warning

If you are using Python 2, torch.save() does NOT support StringIO.StringIO as a valid file-like object. This is because the write method should return the number of bytes written; StringIO.write() does not do this.

Please use something like io.BytesIO instead.

Example

>>> # Save to file
>>> x = torch.tensor([0, 1, 2, 3, 4])
>>> torch.save(x, 'tensor.pt')
>>> # Save to io.BytesIO buffer
>>> buffer = io.BytesIO()
>>> torch.save(x, buffer)

torch.load(f, map_location=None, pickle_module=<module 'pickle' from '/opt/conda/lib/python3.6/pickle.py'>, **pickle_load_args)[source]

Loads an object saved with torch.save() from a file.

torch.load() uses Python’s unpickling facilities but treats storages, which underlie tensors, specially. They are first deserialized on the CPU and are then moved to the device they were saved from. If this fails (e.g. because the run time system doesn’t have certain devices), an exception is raised. However, storages can be dynamically remapped to an alternative set of devices using the map_location argument.

If map_location is a callable, it will be called once for each serialized storage with two arguments: storage and location. The storage argument will be the initial deserialization of the storage, residing on the CPU. Each serialized storage has a location tag associated with it which identifies the device it was saved from, and this tag is the second argument passed to map_location. The builtin location tags are 'cpu' for CPU tensors and 'cuda:device_id' (e.g. 'cuda:2') for CUDA tensors. map_location should return either None or a storage. If map_location returns a storage, it will be used as the final deserialized object, already moved to the right device. Otherwise, torch.load() will fall back to the default behavior, as if map_location wasn’t specified.

If map_location is a torch.device object or a string contraining a device tag, it indicates the location where all tensors should be loaded.

Otherwise, if map_location is a dict, it will be used to remap location tags appearing in the file (keys), to ones that specify where to put the storages (values).

User extensions can register their own location tags and tagging and deserialization methods using torch.serialization.register_package().

Parameters

• f – a file-like object (has to implement read(), :meth`readline`, :meth`tell`, and :meth`seek`), or a string containing a file name
• map_location – a function, torch.device, string or a dict specifying how to remap storage locations
• pickle_module – module used for unpickling metadata and objects (has to match the pickle_module used to serialize file)
• pickle_load_args – optional keyword arguments passed over to pickle_module.load() and pickle_module.Unpickler(), e.g., encoding=....

Note

When you call torch.load() on a file which contains GPU tensors, those tensors will be loaded to GPU by default. You can call torch.load(.., map_location='cpu') and then load_state_dict() to avoid GPU RAM surge when loading a model checkpoint.

Note

In Python 3, when loading files saved by Python 2, you may encounter UnicodeDecodeError: 'ascii' codec can't decode byte 0x.... This is caused by the difference of handling in byte strings in Python2 and Python 3. You may use extra encoding keyword argument to specify how these objects should be loaded, e.g., encoding='latin1' decodes them to strings using latin1 encoding, and encoding='bytes' keeps them as byte arrays which can be decoded later with byte_array.decode(...).

Example

>>> torch.load('tensors.pt')
# Load all tensors onto the CPU
>>> torch.load('tensors.pt', map_location=torch.device('cpu'))
# Load all tensors onto the CPU, using a function
>>> torch.load('tensors.pt', map_location=lambda storage, loc: storage)
# Load all tensors onto GPU 1
>>> torch.load('tensors.pt', map_location=lambda storage, loc: storage.cuda(1))
# Map tensors from GPU 1 to GPU 0
>>> torch.load('tensors.pt', map_location={'cuda:1':'cuda:0'})
# Load tensor from io.BytesIO object
>>> with open('tensor.pt', 'rb') as f:
        buffer = io.BytesIO(f.read())
>>> torch.load(buffer)