# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch.nn as nn import importlib def zero_module(module): if isinstance(module, nn.Linear): module.weight.data.zero_() if module.bias is not None: module.bias.data.zero_() return module def get_obj_from_str(string, reload=False): module, cls = string.rsplit(".", 1) if reload: module_imp = importlib.import_module(module) importlib.reload(module_imp) return getattr(importlib.import_module(module, package=None), cls) def instantiate_from_config(config): if not "target" in config: if config == '__is_first_stage__': return None elif config == "__is_unconditional__": return None raise KeyError("Expected key `target` to instantiate.") return get_obj_from_str(config["target"])(**config.get("params", dict())) def update_dict(old_dict, new_dict): old_keys = old_dict.keys() for new_key in new_dict.keys(): if new_key in old_keys: if type(old_dict[new_key]) == list: if type(new_dict[new_key]) == list: old_dict[new_key].extend(new_dict[new_key]) else: old_dict[new_key].append(new_dict[new_key]) else: old_dict[new_key] = [old_dict[new_key]] old_dict[new_key].append(new_dict[new_key]) else: old_dict[new_key] = new_dict[new_key] return old_dict