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"""
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D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement
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Copyright (c) 2024 The D-FINE Authors. All Rights Reserved.
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---------------------------------------------------------------------------------
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Modified from RT-DETR (https://github.com/lyuwenyu/RT-DETR)
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Copyright (c) 2023 lyuwenyu. All Rights Reserved.
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"""
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import math
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from copy import deepcopy
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import torch
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import torch.nn as nn
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from ..core import register
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from ..misc import dist_utils
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__all__ = ["ModelEMA"]
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@register()
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class ModelEMA(object):
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"""
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Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
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Keep a moving average of everything in the model state_dict (parameters and buffers).
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This is intended to allow functionality like
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https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
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A smoothed version of the weights is necessary for some training schemes to perform well.
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This class is sensitive where it is initialized in the sequence of model init,
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GPU assignment and distributed training wrappers.
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"""
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def __init__(
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self, model: nn.Module, decay: float = 0.9999, warmups: int = 1000, start: int = 0
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):
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super().__init__()
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self.module = deepcopy(dist_utils.de_parallel(model)).eval()
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self.decay = decay
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self.warmups = warmups
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self.before_start = 0
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self.start = start
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self.updates = 0
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if warmups == 0:
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self.decay_fn = lambda x: decay
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else:
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self.decay_fn = lambda x: decay * (
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1 - math.exp(-x / warmups)
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)
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for p in self.module.parameters():
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p.requires_grad_(False)
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def update(self, model: nn.Module):
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if self.before_start < self.start:
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self.before_start += 1
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return
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with torch.no_grad():
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self.updates += 1
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d = self.decay_fn(self.updates)
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msd = dist_utils.de_parallel(model).state_dict()
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for k, v in self.module.state_dict().items():
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if v.dtype.is_floating_point:
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v *= d
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v += (1 - d) * msd[k].detach()
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def to(self, *args, **kwargs):
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self.module = self.module.to(*args, **kwargs)
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return self
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def state_dict(
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self,
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):
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return dict(module=self.module.state_dict(), updates=self.updates)
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def load_state_dict(self, state, strict=True):
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self.module.load_state_dict(state["module"], strict=strict)
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if "updates" in state:
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self.updates = state["updates"]
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def forwad(
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self,
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):
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raise RuntimeError("ema...")
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def extra_repr(self) -> str:
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return f"decay={self.decay}, warmups={self.warmups}"
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class ExponentialMovingAverage(torch.optim.swa_utils.AveragedModel):
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"""Maintains moving averages of model parameters using an exponential decay.
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``ema_avg = decay * avg_model_param + (1 - decay) * model_param``
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`torch.optim.swa_utils.AveragedModel <https://pytorch.org/docs/stable/optim.html#custom-averaging-strategies>`_
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is used to compute the EMA.
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"""
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def __init__(self, model, decay, device="cpu", use_buffers=True):
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self.decay_fn = lambda x: decay * (1 - math.exp(-x / 2000))
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def ema_avg(avg_model_param, model_param, num_averaged):
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decay = self.decay_fn(num_averaged)
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return decay * avg_model_param + (1 - decay) * model_param
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super().__init__(model, device, ema_avg, use_buffers=use_buffers)
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