from typing import Any, Optional, Union import inspect import torch.nn as nn import torch from ..configs.config.config import Config, ConfigDict from .registry import Registry from ..utils.manager import ManagerMixin TORCH_VERSION = torch.__version__ def build_from_cfg( cfg: Union[dict, ConfigDict, Config], registry: Registry, default_args: Optional[Union[dict, ConfigDict, Config]] = None) -> Any: """Build a module from config dict when it is a class configuration, or call a function from config dict when it is a function configuration. If the global variable default scope (:obj:`DefaultScope`) exists, :meth:`build` will firstly get the responding registry and then call its own :meth:`build`. At least one of the ``cfg`` and ``default_args`` contains the key "type", which should be either str or class. If they all contain it, the key in ``cfg`` will be used because ``cfg`` has a high priority than ``default_args`` that means if a key exists in both of them, the value of the key will be ``cfg[key]``. They will be merged first and the key "type" will be popped up and the remaining keys will be used as initialization arguments. Args: cfg (dict or ConfigDict or Config): Config dict. It should at least contain the key "type". registry (:obj:`Registry`): The registry to search the type from. default_args (dict or ConfigDict or Config, optional): Default initialization arguments. Defaults to None. Returns: object: The constructed object. """ if not isinstance(cfg, (dict, ConfigDict, Config)): raise TypeError( f'cfg should be a dict, ConfigDict or Config, but got {type(cfg)}') if 'type' not in cfg: if default_args is None or 'type' not in default_args: raise KeyError( '`cfg` or `default_args` must contain the key "type", ' f'but got {cfg}\n{default_args}') if not isinstance(registry, Registry): raise TypeError('registry must be a mmengine.Registry object, ' f'but got {type(registry)}') if not (isinstance(default_args, (dict, ConfigDict, Config)) or default_args is None): raise TypeError( 'default_args should be a dict, ConfigDict, Config or None, ' f'but got {type(default_args)}') args = cfg.copy() if default_args is not None: for name, value in default_args.items(): args.setdefault(name, value) scope = args.pop('_scope_', None) with registry.switch_scope_and_registry(scope) as registry: obj_type = args.pop('type') if isinstance(obj_type, str): obj_cls = registry.get(obj_type) if obj_cls is None: raise KeyError( f'{obj_type} is not in the {registry.scope}::{registry.name} registry. ' # noqa: E501 f'Please check whether the value of `{obj_type}` is ' ) # this will include classes, functions, partial functions and more elif callable(obj_type): obj_cls = obj_type else: raise TypeError( f'type must be a str or valid type, but got {type(obj_type)}') # If `obj_cls` inherits from `ManagerMixin`, it should be # instantiated by `ManagerMixin.get_instance` to ensure that it # can be accessed globally. if inspect.isclass(obj_cls) and \ issubclass(obj_cls, ManagerMixin): # type: ignore obj = obj_cls.get_instance(**args) # type: ignore else: obj = obj_cls(**args) # type: ignore return obj def build_model_from_cfg( cfg: Union[dict, ConfigDict, Config], registry: Registry, default_args: Optional[Union[dict, 'ConfigDict', 'Config']] = None ) -> 'nn.Module': """Build a PyTorch model from config dict(s). Different from ``build_from_cfg``, if cfg is a list, a ``nn.Sequential`` will be built. Args: cfg (dict, list[dict]): The config of modules, which is either a config dict or a list of config dicts. If cfg is a list, the built modules will be wrapped with ``nn.Sequential``. registry (:obj:`Registry`): A registry the module belongs to. default_args (dict, optional): Default arguments to build the module. Defaults to None. Returns: nn.Module: A built nn.Module. """ from ..model.base_module import Sequential if isinstance(cfg, list): modules = [ build_from_cfg(_cfg, registry, default_args) for _cfg in cfg ] return Sequential(*modules) else: return build_from_cfg(cfg, registry, default_args) class SyncBatchNorm(torch.nn.SyncBatchNorm): # type: ignore def _check_input_dim(self, input): if TORCH_VERSION == 'parrots': if input.dim() < 2: raise ValueError( f'expected at least 2D input (got {input.dim()}D input)') else: super()._check_input_dim(input)