fastreid.solver.optim.adam.Adam Class Reference

Inheritance diagram for fastreid.solver.optim.adam.Adam:

Public Member Functions
	__init__ (self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, amsgrad=False)
	__setstate__ (self, state)
	step (self, closure=None)

Detailed Description

Implements Adam algorithm.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
The implementation of the L2 penalty follows changes proposed in
`Decoupled Weight Decay Regularization`_.
Arguments:
    params (iterable): iterable of parameters to optimize or dicts defining
        parameter groups
    lr (float, optional): learning rate (default: 1e-3)
    betas (Tuple[float, float], optional): coefficients used for computing
        running averages of gradient and its square (default: (0.9, 0.999))
    eps (float, optional): term added to the denominator to improve
        numerical stability (default: 1e-8)
    weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
    amsgrad (boolean, optional): whether to use the AMSGrad variant of this
        algorithm from the paper `On the Convergence of Adam and Beyond`_
        (default: False)
.. _Adam\: A Method for Stochastic Optimization:
    https://arxiv.org/abs/1412.6980
.. _Decoupled Weight Decay Regularization:
    https://arxiv.org/abs/1711.05101
.. _On the Convergence of Adam and Beyond:
    https://openreview.net/forum?id=ryQu7f-RZ

Definition at line 7 of file adam.py.

Constructor & Destructor Documentation

__init__()

fastreid.solver.optim.adam.Adam.__init__	(		self,
			params,
			lr = 1e-3,
			betas = (0.9, 0.999),
			eps = 1e-8,
			weight_decay = 0,
			amsgrad = False )

Definition at line 32 of file adam.py.

                 weight_decay=0, amsgrad=False):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
        if not 0.0 <= weight_decay:
            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
        defaults = dict(lr=lr, betas=betas, eps=eps,
                        weight_decay=weight_decay, amsgrad=amsgrad)
        super(Adam, self).__init__(params, defaults)
 

Member Function Documentation

__setstate__()

fastreid.solver.optim.adam.Adam.__setstate__	(		self,
			state )

Definition at line 48 of file adam.py.

    def __setstate__(self, state):
        super(Adam, self).__setstate__(state)
        for group in self.param_groups:
            group.setdefault('amsgrad', False)
 

step()

fastreid.solver.optim.adam.Adam.step	(		self,
			closure = None )

Performs a single optimization step.
Arguments:
    closure (callable, optional): A closure that reevaluates the model
        and returns the loss.

Definition at line 54 of file adam.py.

    def step(self, closure=None):
        """Performs a single optimization step.
        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            with torch.enable_grad():
                loss = closure()
 
        for group in self.param_groups:
            if group['freeze']: continue
 
            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad
                if grad.is_sparse:
                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
                amsgrad = group['amsgrad']
 
                state = self.state[p]
 
                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    # Exponential moving average of gradient values
                    state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                    if amsgrad:
                        # Maintains max of all exp. moving avg. of sq. grad. values
                        state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
 
                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                if amsgrad:
                    max_exp_avg_sq = state['max_exp_avg_sq']
                beta1, beta2 = group['betas']
 
                state['step'] += 1
                bias_correction1 = 1 - beta1 ** state['step']
                bias_correction2 = 1 - beta2 ** state['step']
 
                if group['weight_decay'] != 0:
                    grad = grad.add(p, alpha=group['weight_decay'])
 
                # Decay the first and second moment running average coefficient
                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
                if amsgrad:
                    # Maintains the maximum of all 2nd moment running avg. till now
                    torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
                    # Use the max. for normalizing running avg. of gradient
                    denom = (max_exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
                else:
                    denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
 
                step_size = group['lr'] / bias_correction1
 
                p.addcdiv_(exp_avg, denom, value=-step_size)
 
        return loss

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The documentation for this class was generated from the following file:

• smreid/fastreid/solver/optim/adam.py