116 lines
4.6 KiB
Python
116 lines
4.6 KiB
Python
#-*- encoding:utf-8 -*-
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import torch
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from pytorch_lightning.callbacks import Callback
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from torch import nn
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class LitEma(nn.Module):
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def __init__(self, model, decay=0.9999, use_num_upates=True):
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super().__init__()
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if decay < 0.0 or decay > 1.0:
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raise ValueError('Decay must be between 0 and 1')
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self.m_name2s_name = {}
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self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
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self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
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else torch.tensor(-1,dtype=torch.int))
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for name, p in model.named_parameters():
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if p.requires_grad:
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#remove as '.'-character is not allowed in buffers
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s_name = name.replace('.','')
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self.m_name2s_name.update({name:s_name})
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self.register_buffer(s_name,p.clone().detach().data)
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self.collected_params = []
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def forward(self,model):
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decay = self.decay
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if self.num_updates >= 0:
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self.num_updates += 1
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decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
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one_minus_decay = 1.0 - decay
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with torch.no_grad():
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m_param = dict(model.named_parameters())
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shadow_params = dict(self.named_buffers())
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for key in m_param:
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if m_param[key].requires_grad:
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sname = self.m_name2s_name[key]
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shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
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shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
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else:
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assert not key in self.m_name2s_name
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def copy_to(self, model):
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m_param = dict(model.named_parameters())
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shadow_params = dict(self.named_buffers())
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for key in m_param:
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if m_param[key].requires_grad:
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m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
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else:
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assert not key in self.m_name2s_name
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def store(self, parameters):
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"""
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Save the current parameters for restoring later.
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Args:
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parameters: Iterable of `torch.nn.Parameter`; the parameters to be
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temporarily stored.
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"""
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self.collected_params = [param.clone() for param in parameters]
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def restore(self, parameters):
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"""
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Restore the parameters stored with the `store` method.
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Useful to validate the model with EMA parameters without affecting the
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original optimization process. Store the parameters before the
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`copy_to` method. After validation (or model saving), use this to
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restore the former parameters.
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Args:
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parameters: Iterable of `torch.nn.Parameter`; the parameters to be
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updated with the stored parameters.
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"""
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for c_param, param in zip(self.collected_params, parameters):
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param.data.copy_(c_param.data)
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class EMACallback(Callback):
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def __init__(self, decay=0.9999):
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self.decay = decay
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self.shadow_params = {}
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def on_train_start(self, trainer, pl_module):
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# initialize shadow parameters for original models
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total_ema_cnt = 0
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for name, param in pl_module.named_parameters():
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if name not in self.shadow_params:
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self.shadow_params[name] = param.data.clone()
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else: # already in dict, maybe load from checkpoint
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pass
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print('will calc ema for param: %s' % name)
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total_ema_cnt += 1
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print('total_ema_cnt=%d' % total_ema_cnt)
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
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# Update the shadow params at the end of each epoch
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for name, param in pl_module.named_parameters():
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assert name in self.shadow_params
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new_average = (1.0 - self.decay) * param.data + self.decay * self.shadow_params[name]
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self.shadow_params[name] = new_average.clone()
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def on_save_checkpoint(self, trainer, pl_module, checkpoint):
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# Save EMA parameters in the checkpoint
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checkpoint['ema_params'] = self.shadow_params
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def on_load_checkpoint(self, trainer, pl_module, checkpoint):
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# Restore EMA parameters from the checkpoint
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if 'ema_params' in checkpoint:
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self.shadow_params = checkpoint.get('ema_params', {})
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for k in self.shadow_params:
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self.shadow_params[k] = self.shadow_params[k].cuda()
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print('load shadow params from checkpoint, cnt=%d' % len(self.shadow_params))
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else:
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print('ema_params is not in checkpoint') |