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# 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 |