o zi`+@sVddlZddlZddlZddlZddlmZddlZddlmZGdddejZ dS)N)Path)nnc @seZdZdZgdZ gZ ddgggfdededede d e d e f d d Z e d dZ e ddddededefddZgggfddZe d ddZ  d!dejeefdedefddZe  d"dejeefdedefddZdS)# BaseModelaThis is a class that adds useful save/load functionality to a ``torch.nn.Module`` object. ``BaseModel`` objects can be saved as ``torch.package`` easily, making them super easy to port between machines without requiring a ton of dependencies. Files can also be saved as just weights, in the standard way. >>> class Model(ml.BaseModel): >>> def __init__(self, arg1: float = 1.0): >>> super().__init__() >>> self.arg1 = arg1 >>> self.linear = nn.Linear(1, 1) >>> >>> def forward(self, x): >>> return self.linear(x) >>> >>> model1 = Model() >>> >>> with tempfile.NamedTemporaryFile(suffix=".pth") as f: >>> model1.save( >>> f.name, >>> ) >>> model2 = Model.load(f.name) >>> out2 = seed_and_run(model2, x) >>> assert torch.allclose(out1, out2) >>> >>> model1.save(f.name, package=True) >>> model2 = Model.load(f.name) >>> model2.save(f.name, package=False) >>> model3 = Model.load(f.name) >>> out3 = seed_and_run(model3, x) >>> >>> with tempfile.TemporaryDirectory() as d: >>> model1.save_to_folder(d, {"data": 1.0}) >>> Model.load_from_folder(d) )z audiotools.**tqdm__main__znumpy.**z julius.**z torchaudio.**zscipy.**einopsNTpathmetadatapackageinternexternmockcCst|j}i}|jD]\} } | j} | tjjur| || <q t|D] } | |vr0t || || <q#|dur7in|}||d<t |dsEi|_ |j ||s\| |d} t| ||S|j||||d|S)aSaves the model, either as a torch package, or just as weights, alongside some specified metadata. Parameters ---------- path : str Path to save model to. metadata : dict, optional Any metadata to save alongside the model, by default None package : bool, optional Whether to use ``torch.package`` to save the model in a format that is portable, by default True intern : list, optional List of additional libraries that are internal to the model, used with torch.package, by default [] extern : list, optional List of additional libraries that are external to the model, used with torch.package, by default [] mock : list, optional List of libraries to mock, used with torch.package, by default [] Returns ------- str Path to saved model. Nkwargsr ) state_dictr )r r r )inspect signature __class__ parametersitemsdefault Parameteremptydirgetattrhasattrr updatertorchsave _save_package)selfrr r r r r sigargskeyvalarg_val attributerr&M/home/ubuntu/.local/lib/python3.10/site-packages/audiotools/ml/layers/base.pyrBs, %     zBaseModel.savecCst|djS)zGets the device the model is on by looking at the device of the first parameter. May not be valid if model is split across multiple devices. r)listrdevice)rr&r&r'r)szBaseModel.deviceF) package_namestrictlocationr*r+c Osz |j||d}W|St|d}|d}|d|t|} t| j} t|dD] } | | vr@|d | q3||i|d}|j |d|d||_ Y|S)aLoad model from a path. Tries first to load as a package, and if that fails, tries to load as weights. The arguments to the class are specified inside the model weights file. Parameters ---------- location : str Path to file. package_name : str, optional Name of package, by default ``cls.__name__``. strict : bool, optional Ignore unmatched keys, by default False kwargs : dict Additional keyword arguments to the model instantiation, if not loading from package. Returns ------- BaseModel A model that inherits from BaseModel. )r*cpur rrr+) _load_packagerloadrrrr(rkeyspopload_state_dictr ) clsr,r*r+r!rmodel model_dictr r class_keyskr&r&r'r0s"  zBaseModel.loadc Kst|j}t|jd}t|dr|jtjjf|d<|`tjddV}tjj |j fi|4} | |j || || |j|| |||t|dr\| ||d|jWdn1sfwYt|j |Wdn1s|wYd|vr|dd|_|S)N.pthimporter)suffixr .metadatar)type__name__rr:rr sys_importertempfileNamedTemporaryFilePackageExporternamer INTERNr r EXTERN save_pickler shutilcopyfile) rrr r r rr* resource_namefexpr&r&r'rs,     zBaseModel._save_packagecCsb|dur|jn|}|d}tj|}|||d}z |||d|_WnY||_|S)Nr9r-r<)r>rr PackageImporter load_pickler r:)r4rr*rIimpr5r&r&r'r/s  zBaseModel._load_packagefolder extra_datac Cs|durin|}t|j}t|d|d}|jddd|r+|d}|||d}|j|dd|D] \}} t| ||q:|S) aDumps a model into a folder, as both a package and as weights, as well as anything specified in ``extra_data``. ``extra_data`` is a dictionary of other pickleable files, with the keys being the paths to save them in. The model is saved under a subfolder specified by the name of the class (e.g. ``folder/generator/[package, weights].pth`` if the model name was ``Generator``). >>> with tempfile.TemporaryDirectory() as d: >>> extra_data = { >>> "optimizer.pth": optimizer.state_dict() >>> } >>> model.save_to_folder(d, extra_data) >>> Model.load_from_folder(d) Parameters ---------- folder : typing.Union[str, Path] _description_ extra_data : dict, optional _description_, by default None Returns ------- str Path to folder N/T)exist_okparents package.pth weights.pthF)r )r=r>lowerrmkdirrrr) rrOrPr model_name target_base package_path weights_pathrobjr&r&r'save_to_folders! zBaseModel.save_to_folderc  st||j}|r dnd}||}|j||d}i}ddgfdd|dD}|D]} tj| fi||| j<q.||fS)a?Loads the model from a folder generated by :py:func:`audiotools.ml.layers.base.BaseModel.save_to_folder`. Like that function, this one looks for a subfolder that has the name of the class (e.g. ``folder/generator/[package, weights].pth`` if the model name was ``Generator``). Parameters ---------- folder : typing.Union[str, Path] _description_ package : bool, optional Whether to use ``torch.package`` to load the model, loading the model from ``package.pth``. strict : bool, optional Ignore unmatched keys, by default False Returns ------- tuple tuple of model and extra data as saved by :py:func:`audiotools.ml.layers.base.BaseModel.save_to_folder`. rTrUr.cs"g|] }|r|jvr|qSr&)is_filerC).0xexcludedr&r' Ds"z.BaseModel.load_from_folder..*)rr>rVr0globrrC) r4rOr r+r model_pthr5rPfilesrJr&rar'load_from_folders zBaseModel.load_from_folder)N)NT)TF)r> __module__ __qualname____doc__rErDstrdictboolr(rpropertyr) classmethodr0rr/typingUnionrr]rhr&r&r&r'r st%  A  0"   2 r) rrGr@rqpathlibrrrModulerr&r&r&r's