Remove adabound optimizer, never got it working well on larger datasets

optim/__init__.py

@@ -1,4 +1,3 @@
-from optim.adabound import AdaBound
 from optim.nadam import Nadam
 from optim.rmsprop_tf import RMSpropTF
 from optim.optim_factory import create_optimizer

optim/adabound.py

@@ -1,121 +0,0 @@
-import math
-import torch
-from torch.optim import Optimizer
-
-
-class AdaBound(Optimizer):
-    """Implements AdaBound algorithm.
-    It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of Learning Rate`_.
-    Arguments:
-        params (iterable): iterable of parameters to optimize or dicts defining
-            parameter groups
-        lr (float, optional): Adam 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))
-        final_lr (float, optional): final (SGD) learning rate (default: 0.1)
-        gamma (float, optional): convergence speed of the bound functions (default: 1e-3)
-        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)
-        amsbound (boolean, optional): whether to use the AMSBound variant of this algorithm
-    .. Adaptive Gradient Methods with Dynamic Bound of Learning Rate:
-        https://openreview.net/forum?id=Bkg3g2R9FX
-
-    Originally taken from https://github.com/Luolc/AdaBound
-    NOTE: Has not provided good (or even decent) results on large datasets like ImageNet
-    """
-
-    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), final_lr=0.1, gamma=1e-3,
-                 eps=1e-8, weight_decay=0, amsbound=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 <= final_lr:
-            raise ValueError("Invalid final learning rate: {}".format(final_lr))
-        if not 0.0 <= gamma < 1.0:
-            raise ValueError("Invalid gamma parameter: {}".format(gamma))
-        defaults = dict(lr=lr, betas=betas, final_lr=final_lr, gamma=gamma, eps=eps,
-                        weight_decay=weight_decay, amsbound=amsbound)
-        super(AdaBound, self).__init__(params, defaults)
-
-        self.base_lrs = list(map(lambda group: group['lr'], self.param_groups))
-
-    def __setstate__(self, state):
-        super(AdaBound, self).__setstate__(state)
-        for group in self.param_groups:
-            group.setdefault('amsbound', False)
-
-    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:
-            loss = closure()
-
-        for group, base_lr in zip(self.param_groups, self.base_lrs):
-            for p in group['params']:
-                if p.grad is None:
-                    continue
-                grad = p.grad.data
-                if grad.is_sparse:
-                    raise RuntimeError(
-                        'Adam does not support sparse gradients, please consider SparseAdam instead')
-                amsbound = group['amsbound']
-
-                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.data)
-                    # Exponential moving average of squared gradient values
-                    state['exp_avg_sq'] = torch.zeros_like(p.data)
-                    if amsbound:
-                        # Maintains max of all exp. moving avg. of sq. grad. values
-                        state['max_exp_avg_sq'] = torch.zeros_like(p.data)
-
-                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
-                if amsbound:
-                    max_exp_avg_sq = state['max_exp_avg_sq']
-                beta1, beta2 = group['betas']
-
-                state['step'] += 1
-
-                if group['weight_decay'] != 0:
-                    grad = grad.add(group['weight_decay'], p.data)
-
-                # Decay the first and second moment running average coefficient
-                exp_avg.mul_(beta1).add_(1 - beta1, grad)
-                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
-                if amsbound:
-                    # 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().add_(group['eps'])
-                else:
-                    denom = exp_avg_sq.sqrt().add_(group['eps'])
-
-                bias_correction1 = 1 - beta1 ** state['step']
-                bias_correction2 = 1 - beta2 ** state['step']
-                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
-
-                # Applies bounds on actual learning rate
-                # lr_scheduler cannot affect final_lr, this is a workaround to apply lr decay
-                final_lr = group['final_lr'] * group['lr'] / base_lr
-                lower_bound = final_lr * (1 - 1 / (group['gamma'] * state['step'] + 1))
-                upper_bound = final_lr * (1 + 1 / (group['gamma'] * state['step']))
-                step_size = torch.full_like(denom, step_size)
-                step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_(exp_avg)
-
-                p.data.add_(-step_size)
-
-        return loss

optim/optim_factory.py

@@ -1,5 +1,5 @@
 from torch import optim as optim
-from optim import Nadam, AdaBound, RMSpropTF
+from optim import Nadam, RMSpropTF
 
 
 def add_weight_decay(model, weight_decay=1e-5, skip_list=()):
@@ -35,10 +35,6 @@ def create_optimizer(args, model, filter_bias_and_bn=True):
     elif args.opt.lower() == 'nadam':
         optimizer = Nadam(
             parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)
-    elif args.opt.lower() == 'adabound':
-        optimizer = AdaBound(
-            parameters, lr=args.lr / 100, weight_decay=weight_decay, eps=args.opt_eps,
-            final_lr=args.lr)
     elif args.opt.lower() == 'adadelta':
         optimizer = optim.Adadelta(
             parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)