73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
import torch
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from torch.optim import Optimizer
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class SGDNoBiasMomentum(Optimizer):
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r"""
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Copy of pytorch implementation of SGD with a modification which turns off momentum for params marked
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with `is_norm` or `is_bias`.
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"""
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def __init__(self, params, lr, momentum=0, dampening=0,
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weight_decay=0, nesterov=False):
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if lr < 0.0:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if momentum < 0.0:
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raise ValueError("Invalid momentum value: {}".format(momentum))
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if weight_decay < 0.0:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
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weight_decay=weight_decay, nesterov=nesterov)
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if nesterov and (momentum <= 0 or dampening != 0):
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raise ValueError("Nesterov momentum requires a momentum and zero dampening")
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super().__init__(params, defaults)
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def __setstate__(self, state):
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super().__setstate__(state)
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for group in self.param_groups:
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group.setdefault('nesterov', False)
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@torch.no_grad()
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def step(self, closure=None):
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"""Performs a single optimization step.
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Arguments:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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with torch.enable_grad():
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loss = closure()
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for group in self.param_groups:
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weight_decay = group['weight_decay']
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momentum = group['momentum']
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dampening = group['dampening']
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nesterov = group['nesterov']
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for p in group['params']:
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if p.grad is None:
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continue
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d_p = p.grad
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if weight_decay != 0:
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d_p = d_p.add(p, alpha=weight_decay)
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# **this is the only modification over standard torch.optim.SGD:
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is_bn_or_bias = (hasattr(p, 'is_norm') and p.is_norm) or (hasattr(p, 'is_bias') and p.is_bias)
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if not is_bn_or_bias and momentum != 0:
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param_state = self.state[p]
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if 'momentum_buffer' not in param_state:
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buf = param_state['momentum_buffer'] = torch.clone(d_p).detach()
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else:
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buf = param_state['momentum_buffer']
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buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
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if nesterov:
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d_p = d_p.add(buf, alpha=momentum)
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else:
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d_p = buf
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p.add_(d_p, alpha=-group['lr'])
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return loss
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