Update Adam.py

GC_code/CIFAR100/algorithm/Adam.py

@@ -110,7 +110,6 @@ def __setstate__(self, state):
 
     def step(self, closure=None):
         """Performs a single optimization step.
-
         Arguments:
             closure (callable, optional): A closure that reevaluates the model
                 and returns the loss.
@@ -152,12 +151,7 @@ def step(self, closure=None):
 
                 if group['weight_decay'] != 0:
                     grad.add_(group['weight_decay'], p.data)
-
-                #GC operation for Conv layers
-                if len(list(p.data.size()))==4:
-                    weight_mean=p.data.mean(dim = tuple(range(1,len(list(grad.size())))), keepdim = True)
-                    grad.add_(-grad.mean(dim = 1, keepdim = True).mean(dim = 2, keepdim = True).mean(dim = 3, keepdim = True))
-
+
                 # 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)
@@ -170,11 +164,13 @@ def step(self, closure=None):
                     denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
 
                 step_size = group['lr'] / bias_correction1
-
-                p.data.addcdiv_(-step_size, exp_avg, denom)
-                # keep mean unchanged
-                if len(list(grad.size()))==4:
-                    p.data.add_(-p.data.mean(dim = tuple(range(1,len(list(grad.size())))), keepdim = True)).add_(weight_mean)                
+                #GC operation for Conv layers                
+                if len(list(grad.size()))>3:
+                  delta=(step_size*exp_avg/denom).clone()
+                  delta.add_(-delta.mean(dim = tuple(range(1,len(list(grad.size())))), keepdim = True))
+                  p.data.add_(-delta)
+                else:
+                  p.data.addcdiv_(-step_size, exp_avg, denom)
         return loss    
 
 class Adam_GC(Optimizer):
@@ -287,6 +283,90 @@ def step(self, closure=None):
         return loss
 
 
+class Adam_GC2(Optimizer):
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
+                 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]))
+        defaults = dict(lr=lr, betas=betas, eps=eps,
+                        weight_decay=weight_decay, amsgrad=amsgrad)
+        super(Adam_GC2, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(Adam_GC2, self).__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault('amsgrad', 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 in self.param_groups:
+            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')
+                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.data)
+                    # Exponential moving average of squared gradient values
+                    state['exp_avg_sq'] = torch.zeros_like(p.data)
+                    if amsgrad:
+                        # 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 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.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 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
+                #GC operation for Conv layers and FC layers               
+                if len(list(grad.size()))>1:
+                  delta=(step_size*exp_avg/denom).clone()
+                  delta.add_(-delta.mean(dim = tuple(range(1,len(list(grad.size())))), keepdim = True))
+                  p.data.add_(-delta)
+                else:
+                  p.data.addcdiv_(-step_size, exp_avg, denom)
+        return loss
 
 class AdamW(Optimizer):
     """Implements Adam algorithm.
@@ -597,8 +677,13 @@ def step(self, closure=None):
                 bias_correction2 = 1 - beta2 ** state['step']
                 step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
 
-                # p.data.addcdiv_(-step_size, exp_avg, denom)
-                p.data.add_(-step_size,  torch.mul(p.data, group['weight_decay']).addcdiv_(1, exp_avg, denom) )
+                # GC operation for Conv layers
                 if len(list(grad.size()))>3:
-                    p.data.add_(-p.data.mean(dim = tuple(range(1,len(list(grad.size())))), keepdim = True)).add_(weight_mean)
+                  delta=(step_size*torch.mul(p.data, group['weight_decay']).addcdiv_(1, exp_avg, denom)).clone()
+                  delta.add_(-delta.mean(dim = tuple(range(1,len(list(grad.size())))), keepdim = True))
+                  p.data.add_(-delta)
+                else:
+                  p.data.add_(-step_size,  torch.mul(p.data, group['weight_decay']).addcdiv_(1, exp_avg, denom) )
+
+
         return loss