mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-28 09:21:03 +01:00
67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# Inspired from https://github.com/rwightman/pytorch-image-models
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from contextlib import contextmanager
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import torch
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from .states import swap_state
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class ModelEMA:
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"""
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Perform EMA on a model. You can switch to the EMA weights temporarily
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with the `swap` method.
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ema = ModelEMA(model)
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with ema.swap():
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# compute valid metrics with averaged model.
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"""
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def __init__(self, model, decay=0.9999, unbias=True, device='cpu'):
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self.decay = decay
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self.model = model
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self.state = {}
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self.count = 0
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self.device = device
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self.unbias = unbias
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self._init()
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def _init(self):
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for key, val in self.model.state_dict().items():
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if val.dtype != torch.float32:
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continue
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device = self.device or val.device
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if key not in self.state:
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self.state[key] = val.detach().to(device, copy=True)
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def update(self):
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if self.unbias:
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self.count = self.count * self.decay + 1
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w = 1 / self.count
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else:
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w = 1 - self.decay
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for key, val in self.model.state_dict().items():
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if val.dtype != torch.float32:
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continue
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device = self.device or val.device
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self.state[key].mul_(1 - w)
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self.state[key].add_(val.detach().to(device), alpha=w)
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@contextmanager
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def swap(self):
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with swap_state(self.model, self.state):
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yield
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def state_dict(self):
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return {'state': self.state, 'count': self.count}
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def load_state_dict(self, state):
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self.count = state['count']
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for k, v in state['state'].items():
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self.state[k].copy_(v)
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