Add `adamp` and 'sgdp' optimizers.

Update requirements.txt

Update optim_factory.py

Add `adamp` optimizer

Update __init__.py

copy files of adamp & sgdp

Create adamp.py

Update __init__.py

Create sgdp.py

Update optim_factory.py

Update optim_factory.py

Update requirements.txt

Update adamp.py

Update sgdp.py

Update sgdp.py

Update adamp.py

timm/optim/__init__.py

@@ -5,4 +5,6 @@ from .radam import RAdam
 from .novograd import NovoGrad
 from .nvnovograd import NvNovoGrad
 from .lookahead import Lookahead
+from .adamp import AdamP
+from .sgdp import SGDP
 from .optim_factory import create_optimizer

timm/optim/adamp.py

@@ -0,0 +1,107 @@
+"""
+AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.py
+
+Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
+Code: https://github.com/clovaai/AdamP
+
+Copyright (c) 2020-present NAVER Corp.
+MIT license
+"""
+
+import torch
+import torch.nn as nn
+from torch.optim.optimizer import Optimizer, required
+import math
+
+class AdamP(Optimizer):
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
+                 weight_decay=0, delta=0.1, wd_ratio=0.1, nesterov=False):
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
+                        delta=delta, wd_ratio=wd_ratio, nesterov=nesterov)
+        super(AdamP, self).__init__(params, defaults)
+
+    def _channel_view(self, x):
+        return x.view(x.size(0), -1)
+
+    def _layer_view(self, x):
+        return x.view(1, -1)
+
+    def _cosine_similarity(self, x, y, eps, view_func):
+        x = view_func(x)
+        y = view_func(y)
+
+        x_norm = x.norm(dim=1).add_(eps)
+        y_norm = y.norm(dim=1).add_(eps)
+        dot = (x * y).sum(dim=1)
+
+        return dot.abs() / x_norm / y_norm
+
+    def _projection(self, p, grad, perturb, delta, wd_ratio, eps):
+        wd = 1
+        expand_size = [-1] + [1] * (len(p.shape) - 1)
+        for view_func in [self._channel_view, self._layer_view]:
+
+            cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func)
+
+            if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)):
+                p_n = p.data / view_func(p.data).norm(dim=1).view(expand_size).add_(eps)
+                perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view(expand_size)
+                wd = wd_ratio
+
+                return perturb, wd
+
+        return perturb, wd
+
+    def step(self, closure=None):
+        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
+                beta1, beta2 = group['betas']
+                nesterov = group['nesterov']
+
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state['step'] = 0
+                    state['exp_avg'] = torch.zeros_like(p.data)
+                    state['exp_avg_sq'] = torch.zeros_like(p.data)
+
+                # Adam
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+
+                state['step'] += 1
+                bias_correction1 = 1 - beta1 ** state['step']
+                bias_correction2 = 1 - beta2 ** state['step']
+
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+
+                denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
+                step_size = group['lr'] / bias_correction1
+
+                if nesterov:
+                    perturb = (beta1 * exp_avg + (1 - beta1) * grad) / denom
+                else:
+                    perturb = exp_avg / denom
+
+                # Projection
+                wd_ratio = 1
+                if len(p.shape) > 1:
+                    perturb, wd_ratio = self._projection(p, grad, perturb, group['delta'], group['wd_ratio'], group['eps'])
+
+                # Weight decay
+                if group['weight_decay'] > 0:
+                    p.data.mul_(1 - group['lr'] * group['weight_decay'] * wd_ratio)
+
+                # Step
+                p.data.add_(-step_size, perturb)
+
+        return loss

timm/optim/optim_factory.py

@@ -1,6 +1,6 @@
 import torch
 from torch import optim as optim
-from timm.optim import Nadam, RMSpropTF, AdamW, RAdam, NovoGrad, NvNovoGrad, Lookahead
+from timm.optim import Nadam, RMSpropTF, AdamW, RAdam, NovoGrad, NvNovoGrad, Lookahead, AdamP, SGDP
 try:
     from apex.optimizers import FusedNovoGrad, FusedAdam, FusedLAMB, FusedSGD
     has_apex = True
@@ -60,6 +60,14 @@ def create_optimizer(args, model, filter_bias_and_bn=True):
     elif opt_lower == 'radam':
         optimizer = RAdam(
             parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)
+    elif opt_lower == 'adamp':        
+        optimizer = AdamP(
+            parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps,
+            delta=0.1, wd_ratio=0.01, nesterov=True)
+    elif opt_lower == 'sgdp':        
+        optimizer = SGDP(
+            parameters, lr=args.lr, momentum=args.momentum, weight_decay=weight_decay, 
+            eps=args.opt_eps, nesterov=True)        
     elif opt_lower == 'adadelta':
         optimizer = optim.Adadelta(
             parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)

timm/optim/sgdp.py

@@ -0,0 +1,96 @@
+"""
+SGDP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/sgdp.py
+
+Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
+Code: https://github.com/clovaai/AdamP
+
+Copyright (c) 2020-present NAVER Corp.
+MIT license
+"""
+
+import torch
+import torch.nn as nn
+from torch.optim.optimizer import Optimizer, required
+import math
+
+class SGDP(Optimizer):
+    def __init__(self, params, lr=required, momentum=0, dampening=0,
+                 weight_decay=0, nesterov=False, eps=1e-8, delta=0.1, wd_ratio=0.1):
+        defaults = dict(lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay,
+                        nesterov=nesterov, eps=eps, delta=delta, wd_ratio=wd_ratio)
+        super(SGDP, self).__init__(params, defaults)
+
+    def _channel_view(self, x):
+        return x.view(x.size(0), -1)
+
+    def _layer_view(self, x):
+        return x.view(1, -1)
+
+    def _cosine_similarity(self, x, y, eps, view_func):
+        x = view_func(x)
+        y = view_func(y)
+
+        x_norm = x.norm(dim=1).add_(eps)
+        y_norm = y.norm(dim=1).add_(eps)
+        dot = (x * y).sum(dim=1)
+
+        return dot.abs() / x_norm / y_norm
+
+    def _projection(self, p, grad, perturb, delta, wd_ratio, eps):
+        wd = 1
+        expand_size = [-1] + [1] * (len(p.shape) - 1)
+        for view_func in [self._channel_view, self._layer_view]:
+
+            cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func)
+
+            if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)):
+                p_n = p.data / view_func(p.data).norm(dim=1).view(expand_size).add_(eps)
+                perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view(expand_size)
+                wd = wd_ratio
+
+                return perturb, wd
+
+        return perturb, wd
+
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+            weight_decay = group['weight_decay']
+            momentum = group['momentum']
+            dampening = group['dampening']
+            nesterov = group['nesterov']
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state['momentum'] = torch.zeros_like(p.data)
+
+                # SGD
+                buf = state['momentum']
+                buf.mul_(momentum).add_(1 - dampening, grad)
+                if nesterov:
+                    d_p = grad + momentum * buf
+                else:
+                    d_p = buf
+
+                # Projection
+                wd_ratio = 1
+                if len(p.shape) > 1:
+                    d_p, wd_ratio = self._projection(p, grad, d_p, group['delta'], group['wd_ratio'], group['eps'])
+
+                # Weight decay
+                if weight_decay != 0:
+                    p.data.mul_(1 - group['lr'] * group['weight_decay'] * wd_ratio / (1-momentum))
+
+                # Step
+                p.data.add_(-group['lr'], d_p)
+
+        return loss