wehub-resource-sync
55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
# Copyright 2024 MIT Han Lab
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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import copy
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import math
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import torch
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import torch.nn as nn
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from ...models.utils import is_parallel
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__all__ = ["EMA"]
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def update_ema(ema: nn.Module, new_state_dict: dict[str, torch.Tensor], decay: float) -> None:
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for k, v in ema.state_dict().items():
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if v.dtype.is_floating_point:
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v -= (1.0 - decay) * (v - new_state_dict[k].detach())
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class EMA:
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def __init__(self, model: nn.Module, decay: float, warmup_steps=2000):
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self.shadows = copy.deepcopy(model.module if is_parallel(model) else model).eval()
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self.decay = decay
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self.warmup_steps = warmup_steps
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for p in self.shadows.parameters():
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p.requires_grad = False
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def step(self, model: nn.Module, global_step: int) -> None:
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with torch.no_grad():
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msd = (model.module if is_parallel(model) else model).state_dict()
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update_ema(self.shadows, msd, self.decay * (1 - math.exp(-global_step / self.warmup_steps)))
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def state_dict(self) -> dict[float, dict[str, torch.Tensor]]:
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return {self.decay: self.shadows.state_dict()}
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def load_state_dict(self, state_dict: dict[float, dict[str, torch.Tensor]]) -> None:
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for decay in state_dict:
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if decay == self.decay:
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self.shadows.load_state_dict(state_dict[decay])
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