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Running
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Zero
| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| # ModelEMA implementation is taken from | |
| # https://github.com/facebookresearch/demucs | |
| from collections import defaultdict | |
| import typing as tp | |
| import torch | |
| import torch.nn as nn | |
| def _get_all_non_persistent_buffers_set(module: nn.Module, root: str = "") -> set: | |
| names: set = set() | |
| for (name, sub_module) in module.named_modules(): | |
| if name == '': | |
| buffer_names = module._non_persistent_buffers_set | |
| buffer_names = {f"{root}.{buff_name}" if len(root) > 0 else buff_name | |
| for buff_name in buffer_names} | |
| names.update(buffer_names) | |
| else: | |
| sub_name = f"{root}.{name}" if len(root) > 0 else name | |
| sub_buffer_names = _get_all_non_persistent_buffers_set(sub_module, sub_name) | |
| names.update(sub_buffer_names) | |
| return names | |
| def _get_named_tensors(module: nn.Module): | |
| non_persistent_buffers_set = _get_all_non_persistent_buffers_set(module) | |
| named_buffers = [(name, buffer) for (name, buffer) in module.named_buffers() | |
| if name not in non_persistent_buffers_set] | |
| named_parameters = list(module.named_parameters()) | |
| return named_parameters + named_buffers | |
| class ModuleDictEMA: | |
| """Exponential Moving Average over a nn.ModuleDict. | |
| You can switch to the EMA weights temporarily. | |
| """ | |
| def __init__(self, module_dict: nn.ModuleDict, decay: float = 0.999, | |
| unbias: bool = True, device: tp.Union[torch.device, str] = 'cpu'): | |
| self.decay = decay | |
| self.module_dict = module_dict | |
| self.state: dict = defaultdict(dict) | |
| self.count = 0 | |
| self.device = device | |
| self.unbias = unbias | |
| self._init() | |
| def _init(self): | |
| for module_name, module in self.module_dict.items(): | |
| for key, val in _get_named_tensors(module): | |
| if not val.is_floating_point(): | |
| continue | |
| device = self.device or val.device | |
| if key not in self.state[module_name]: | |
| self.state[module_name][key] = val.detach().to(device, copy=True) | |
| def step(self): | |
| if self.unbias: | |
| self.count = self.count * self.decay + 1 | |
| w = 1 / self.count | |
| else: | |
| w = 1 - self.decay | |
| for module_name, module in self.module_dict.items(): | |
| for key, val in _get_named_tensors(module): | |
| if not val.is_floating_point(): | |
| continue | |
| device = self.device or val.device | |
| self.state[module_name][key].mul_(1 - w) | |
| self.state[module_name][key].add_(val.detach().to(device), alpha=w) | |
| def state_dict(self): | |
| return {'state': self.state, 'count': self.count} | |
| def load_state_dict(self, state): | |
| self.count = state['count'] | |
| for module_name, module in state['state'].items(): | |
| for key, val in module.items(): | |
| self.state[module_name][key].copy_(val) | |