updates

train.py

@@ -10,6 +10,7 @@ import test  # import test.py to get mAP after each epoch
 from models import *
 from utils.datasets import *
 from utils.utils import *
+from utils.adabound import *
 
 # 320 --epochs 1
 #      0.109      0.297       0.15      0.126       7.04      1.666      4.062     0.1845       42.6       3.34      12.61      8.338     0.2705      0.001         -4        0.9     0.0005 a  320 giou + best_anchor False
@@ -89,7 +90,8 @@ def train(cfg,
     model = Darknet(cfg).to(device)
 
     # Optimizer
-    optimizer = optim.SGD(model.parameters(), lr=hyp['lr0'], momentum=hyp['momentum'], weight_decay=hyp['weight_decay'])
+    optimizer = optim.SGD(model.parameters(), lr=hyp['lr0'], momentum=hyp['momentum'], weight_decay=hyp['weight_decay'], nesterov=True)
+    # optimizer = AdaBound(model.parameters(), lr=hyp['lr0'], final_lr=0.1)
 
     cutoff = -1  # backbone reaches to cutoff layer
     start_epoch = 0
@@ -192,7 +194,7 @@ def train(cfg,
     nb = len(dataloader)
     maps = np.zeros(nc)  # mAP per class
     results = (0, 0, 0, 0, 0)  # P, R, mAP, F1, test_loss
-    n_burnin = min(round(nb / 5 + 1), 1000)  # burn-in batches
+    # n_burnin = min(round(nb / 5 + 1), 1000)  # burn-in batches
     t0 = time.time()
     for epoch in range(start_epoch, epochs):
         model.train()
@@ -234,11 +236,11 @@ def train(cfg,
                 plot_images(imgs=imgs, targets=targets, paths=paths, fname='train_batch%g.jpg' % i)
 
             # SGD burn-in
-            if epoch == 0 and i <= n_burnin:
+            # if epoch == 0 and i <= n_burnin:
-                g = (i / n_burnin) ** 4  # gain
+            #     g = (i / n_burnin) ** 4  # gain
-                for x in optimizer.param_groups:
+            #     for x in optimizer.param_groups:
-                    x['lr'] = hyp['lr0'] * g
+            #         x['lr'] = hyp['lr0'] * g
-                    x['weight_decay'] = hyp['weight_decay'] * g
+            #         x['weight_decay'] = hyp['weight_decay'] * g
 
             # Run model
             pred = model(imgs)

utils/adabound.py

@@ -0,0 +1,235 @@
+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
+    """
+
+    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
+
+
+class AdaBoundW(Optimizer):
+    """Implements AdaBound algorithm with Decoupled Weight Decay (arxiv.org/abs/1711.05101)
+    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
+    """
+
+    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(AdaBoundW, self).__init__(params, defaults)
+
+        self.base_lrs = list(map(lambda group: group['lr'], self.param_groups))
+
+    def __setstate__(self, state):
+        super(AdaBoundW, 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
+
+                # 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)
+
+                if group['weight_decay'] != 0:
+                    decayed_weights = torch.mul(p.data, group['weight_decay'])
+                    p.data.add_(-step_size)
+                    p.data.sub_(decayed_weights)
+                else:
+                    p.data.add_(-step_size)
+
+        return loss