replace CosineAnnealingMomentum (#1706)

configs/_base_/schedules/cyclic_20e.py

@@ -29,7 +29,7 @@ param_scheduler = [
         by_epoch=True,
         convert_to_iter_based=True),
     dict(
-        type='CosineAnnealingBetas',
+        type='CosineAnnealingMomentum',
         T_max=8,
         eta_min=0.85 / 0.95,
         begin=0,
@@ -37,7 +37,7 @@ param_scheduler = [
         by_epoch=True,
         convert_to_iter_based=True),
     dict(
-        type='CosineAnnealingBetas',
+        type='CosineAnnealingMomentum',
         T_max=12,
         eta_min=1,
         begin=8,

configs/_base_/schedules/cyclic_40e.py

@@ -28,7 +28,7 @@ param_scheduler = [
         by_epoch=True,
         convert_to_iter_based=True),
     dict(
-        type='CosineAnnealingBetas',
+        type='CosineAnnealingMomentum',
         T_max=16,
         eta_min=0.85 / 0.95,
         begin=0,
@@ -36,7 +36,7 @@ param_scheduler = [
         by_epoch=True,
         convert_to_iter_based=True),
     dict(
-        type='CosineAnnealingBetas',
+        type='CosineAnnealingMomentum',
         T_max=24,
         eta_min=1,
         begin=16,

configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py

@@ -88,7 +88,7 @@ param_scheduler = [
         by_epoch=True,
         convert_to_iter_based=True),
     dict(
-        type='CosineAnnealingBetas',
+        type='CosineAnnealingMomentum',
         T_max=epoch_num * 0.4,
         eta_min=0.85 / 0.95,
         begin=0,
@@ -96,7 +96,7 @@ param_scheduler = [
         by_epoch=True,
         convert_to_iter_based=True),
     dict(
-        type='CosineAnnealingBetas',
+        type='CosineAnnealingMomentum',
         T_max=epoch_num * 0.6,
         eta_min=1,
         begin=epoch_num * 0.4,

mmdet3d/engine/__init__.py

-# Copyright (c) OpenMMLab. All rights reserved.
-from .scheduler import BetasSchedulerMixin, CosineAnnealingBetas
-
-__all__ = ['BetasSchedulerMixin', 'CosineAnnealingBetas']

mmdet3d/engine/scheduler/__init__.py

-# Copyright (c) OpenMMLab. All rights reserved.
-from .betas_scheduler import BetasSchedulerMixin, CosineAnnealingBetas
-
-__all__ = ['BetasSchedulerMixin', 'CosineAnnealingBetas']

mmdet3d/engine/scheduler/betas_scheduler.py

-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-import warnings
-from typing import Union
-
-from mmengine.optim import OptimWrapper
-from mmengine.optim.scheduler.param_scheduler import \
-    CosineAnnealingParamScheduler
-from mmengine.registry import PARAM_SCHEDULERS
-from torch.optim import Optimizer
-
-OptimizerType = Union[OptimWrapper, Optimizer]
-
-
-class BetasSchedulerMixin:
-    """A mixin class for betas schedulers."""
-
-    def __init__(self, optimizer, *args, **kwargs):
-        super().__init__(optimizer, 'betas', *args, **kwargs)
-
-
-@PARAM_SCHEDULERS.register_module()
-class CosineAnnealingBetas(BetasSchedulerMixin, CosineAnnealingParamScheduler):
-    r"""Set the betas of each parameter group using a cosine annealing
-    schedule, where :math:`\eta_{max}` is set to the initial value and
-    :math:`T_{cur}` is the number of epochs since the last restart in SGDR:
-
-    .. math::
-        \begin{aligned}
-            \eta_t & = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1
-            + \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right),
-            & T_{cur} \neq (2k+1)T_{max}; \\
-            \eta_{t+1} & = \eta_{t} + \frac{1}{2}(\eta_{max} - \eta_{min})
-            \left(1 - \cos\left(\frac{1}{T_{max}}\pi\right)\right),
-            & T_{cur} = (2k+1)T_{max}.
-        \end{aligned}
-
-    Notice that because the schedule
-    is defined recursively, the betas can be simultaneously modified
-    outside this scheduler by other operators. If the betas is set
-    solely by this scheduler, the betas at each step becomes:
-
-    .. math::
-        \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 +
-        \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right)
-
-    It has been proposed in
-    `SGDR: Stochastic Gradient Descent with Warm Restarts`_. Note that this
-    only implements the cosine annealing part of SGDR, and not the restarts.
-
-    Args:
-        optimizer (Optimizer or OptimWrapper): optimizer or Wrapped
-            optimizer.
-        T_max (int): Maximum number of iterations.
-        eta_min (float): Minimum betas value. Defaults to 0.
-        begin (int): Step at which to start updating the betas.
-            Defaults to 0.
-        end (int): Step at which to stop updating the betas.
-            Defaults to INF.
-        last_step (int): The index of last step. Used for resume without
-            state dict. Defaults to -1.
-        by_epoch (bool): Whether the scheduled betas is updated by
-            epochs. Defaults to True.
-        verbose (bool): Whether to print the betas for each update.
-            Defaults to False.
-
-    .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
-        https://arxiv.org/abs/1608.03983
-    """
-
-    def _get_value(self):
-        """Compute value using chainable form of the scheduler."""
-        if self.last_step == 0:
-            return [
-                group[self.param_name][0]
-                for group in self.optimizer.param_groups
-            ]
-        elif (self.last_step - 1 - self.T_max) % (2 * self.T_max) == 0:
-            return [
-                group[self.param_name][0] + (base_value - self.eta_min) *
-                (1 - math.cos(math.pi / self.T_max)) / 2
-                for base_value, group in zip(self.base_values,
-                                             self.optimizer.param_groups)
-            ]
-        return [(1 + math.cos(math.pi * self.last_step / self.T_max)) /
-                (1 + math.cos(math.pi * (self.last_step - 1) / self.T_max)) *
-                (group[self.param_name][0] - self.eta_min) + self.eta_min
-                for group in self.optimizer.param_groups]
-
-    def step(self):
-        """Adjusts the parameter value of each parameter group based on the
-        specified schedule."""
-        # Raise a warning if old pattern is detected
-        # https://github.com/pytorch/pytorch/issues/20124
-        if self._global_step == 0:
-            if not hasattr(self.optimizer.step, '_with_counter'):
-                warnings.warn(
-                    'Seems like `optimizer.step()` has been overridden after'
-                    'parameter value scheduler initialization. Please, make'
-                    'sure to call `optimizer.step()` before'
-                    '`scheduler.step()`. See more details at'
-                    'https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate',  # noqa: E501
-                    UserWarning)
-
-            # Just check if there were two first scheduler.step() calls
-            # before optimizer.step()
-            elif self.optimizer._global_step < 0:
-                warnings.warn(
-                    'Detected call of `scheduler.step()` before'
-                    '`optimizer.step()`. In PyTorch 1.1.0 and later, you'
-                    'should call them in the opposite order: '
-                    '`optimizer.step()` before `scheduler.step()`. '
-                    'Failure to do this will result in PyTorch skipping '
-                    'the first value of the parameter value schedule. '
-                    'See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate',  # noqa: E501
-                    UserWarning)
-        self._global_step += 1
-
-        # Compute parameter value per param group in the effective range
-        if self.begin <= self._global_step < self.end:
-            self.last_step += 1
-            values = self._get_value()
-
-            for i, data in enumerate(zip(self.optimizer.param_groups, values)):
-                param_group, value = data
-                param_group[self.param_name] = (
-                    value, param_group[self.param_name][1])
-                self.print_value(self.verbose, i, value)
-
-        self._last_value = [
-            group[self.param_name] for group in self.optimizer.param_groups
-        ]