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https://huggingface.co/deepsafe/model-code/resolve/main/clean/video/fakestormer/models/builder.py
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1.44 kB
| # -*- coding: utf-8 -*- | |
| import os | |
| import sys | |
| from typing import Any, Dict, Optional | |
| if not os.getcwd() in sys.path: | |
| sys.path.append(os.getcwd()) | |
| from register.register import Registry, build_from_cfg | |
| from torch.nn import Sequential | |
| def build_model_from_cfg(cfg, registry, default_args=None): | |
| """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, is is either a config | |
| dict or a list of config dicts. If cfg is a list, a | |
| 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. | |
| """ | |
| 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) | |
| MODELS = Registry("model", build_func=build_model_from_cfg) | |
| HEADS = MODELS | |
| BACKBONES = MODELS | |
| DETECTORS = MODELS | |
| NECKS = MODELS | |
| def build_model( | |
| cfg: Dict, | |
| model: Registry, | |
| build_func=build_model_from_cfg, | |
| default_args: Optional[Dict] = None, | |
| ) -> Any: | |
| return build_func(cfg, model, default_args) | |