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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 src.efficientvit.models.utils import is_parallel |
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__all__ = ["EMA"] |
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def update_ema( |
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ema: nn.Module, new_state_dict: dict[str, torch.Tensor], decay: float |
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) -> 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( |
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model.module if is_parallel(model) else model |
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).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( |
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self.shadows, |
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msd, |
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self.decay * (1 - math.exp(-global_step / self.warmup_steps)), |
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) |
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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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