File size: 21,296 Bytes
f7ac35e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
# Copyright (c) OpenMMLab. All rights reserved.
import annotator.uniformer.mmcv as mmcv
from .hook import HOOKS, Hook
from .lr_updater import annealing_cos, annealing_linear, format_param


class MomentumUpdaterHook(Hook):

    def __init__(self,
                 by_epoch=True,
                 warmup=None,
                 warmup_iters=0,
                 warmup_ratio=0.9):
        # validate the "warmup" argument
        if warmup is not None:
            if warmup not in ['constant', 'linear', 'exp']:
                raise ValueError(
                    f'"{warmup}" is not a supported type for warming up, valid'
                    ' types are "constant" and "linear"')
        if warmup is not None:
            assert warmup_iters > 0, \
                '"warmup_iters" must be a positive integer'
            assert 0 < warmup_ratio <= 1.0, \
                '"warmup_momentum" must be in range (0,1]'

        self.by_epoch = by_epoch
        self.warmup = warmup
        self.warmup_iters = warmup_iters
        self.warmup_ratio = warmup_ratio

        self.base_momentum = []  # initial momentum for all param groups
        self.regular_momentum = [
        ]  # expected momentum if no warming up is performed

    def _set_momentum(self, runner, momentum_groups):
        if isinstance(runner.optimizer, dict):
            for k, optim in runner.optimizer.items():
                for param_group, mom in zip(optim.param_groups,
                                            momentum_groups[k]):
                    if 'momentum' in param_group.keys():
                        param_group['momentum'] = mom
                    elif 'betas' in param_group.keys():
                        param_group['betas'] = (mom, param_group['betas'][1])
        else:
            for param_group, mom in zip(runner.optimizer.param_groups,
                                        momentum_groups):
                if 'momentum' in param_group.keys():
                    param_group['momentum'] = mom
                elif 'betas' in param_group.keys():
                    param_group['betas'] = (mom, param_group['betas'][1])

    def get_momentum(self, runner, base_momentum):
        raise NotImplementedError

    def get_regular_momentum(self, runner):
        if isinstance(runner.optimizer, dict):
            momentum_groups = {}
            for k in runner.optimizer.keys():
                _momentum_group = [
                    self.get_momentum(runner, _base_momentum)
                    for _base_momentum in self.base_momentum[k]
                ]
                momentum_groups.update({k: _momentum_group})
            return momentum_groups
        else:
            return [
                self.get_momentum(runner, _base_momentum)
                for _base_momentum in self.base_momentum
            ]

    def get_warmup_momentum(self, cur_iters):

        def _get_warmup_momentum(cur_iters, regular_momentum):
            if self.warmup == 'constant':
                warmup_momentum = [
                    _momentum / self.warmup_ratio
                    for _momentum in self.regular_momentum
                ]
            elif self.warmup == 'linear':
                k = (1 - cur_iters / self.warmup_iters) * (1 -
                                                           self.warmup_ratio)
                warmup_momentum = [
                    _momentum / (1 - k) for _momentum in self.regular_mom
                ]
            elif self.warmup == 'exp':
                k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters)
                warmup_momentum = [
                    _momentum / k for _momentum in self.regular_mom
                ]
            return warmup_momentum

        if isinstance(self.regular_momentum, dict):
            momentum_groups = {}
            for key, regular_momentum in self.regular_momentum.items():
                momentum_groups[key] = _get_warmup_momentum(
                    cur_iters, regular_momentum)
            return momentum_groups
        else:
            return _get_warmup_momentum(cur_iters, self.regular_momentum)

    def before_run(self, runner):
        # NOTE: when resuming from a checkpoint,
        # if 'initial_momentum' is not saved,
        # it will be set according to the optimizer params
        if isinstance(runner.optimizer, dict):
            self.base_momentum = {}
            for k, optim in runner.optimizer.items():
                for group in optim.param_groups:
                    if 'momentum' in group.keys():
                        group.setdefault('initial_momentum', group['momentum'])
                    else:
                        group.setdefault('initial_momentum', group['betas'][0])
                _base_momentum = [
                    group['initial_momentum'] for group in optim.param_groups
                ]
                self.base_momentum.update({k: _base_momentum})
        else:
            for group in runner.optimizer.param_groups:
                if 'momentum' in group.keys():
                    group.setdefault('initial_momentum', group['momentum'])
                else:
                    group.setdefault('initial_momentum', group['betas'][0])
            self.base_momentum = [
                group['initial_momentum']
                for group in runner.optimizer.param_groups
            ]

    def before_train_epoch(self, runner):
        if not self.by_epoch:
            return
        self.regular_mom = self.get_regular_momentum(runner)
        self._set_momentum(runner, self.regular_mom)

    def before_train_iter(self, runner):
        cur_iter = runner.iter
        if not self.by_epoch:
            self.regular_mom = self.get_regular_momentum(runner)
            if self.warmup is None or cur_iter >= self.warmup_iters:
                self._set_momentum(runner, self.regular_mom)
            else:
                warmup_momentum = self.get_warmup_momentum(cur_iter)
                self._set_momentum(runner, warmup_momentum)
        elif self.by_epoch:
            if self.warmup is None or cur_iter > self.warmup_iters:
                return
            elif cur_iter == self.warmup_iters:
                self._set_momentum(runner, self.regular_mom)
            else:
                warmup_momentum = self.get_warmup_momentum(cur_iter)
                self._set_momentum(runner, warmup_momentum)


@HOOKS.register_module()
class StepMomentumUpdaterHook(MomentumUpdaterHook):
    """Step momentum scheduler with min value clipping.

    Args:
        step (int | list[int]): Step to decay the momentum. If an int value is
            given, regard it as the decay interval. If a list is given, decay
            momentum at these steps.
        gamma (float, optional): Decay momentum ratio. Default: 0.5.
        min_momentum (float, optional): Minimum momentum value to keep. If
            momentum after decay is lower than this value, it will be clipped
            accordingly. If None is given, we don't perform lr clipping.
            Default: None.
    """

    def __init__(self, step, gamma=0.5, min_momentum=None, **kwargs):
        if isinstance(step, list):
            assert mmcv.is_list_of(step, int)
            assert all([s > 0 for s in step])
        elif isinstance(step, int):
            assert step > 0
        else:
            raise TypeError('"step" must be a list or integer')
        self.step = step
        self.gamma = gamma
        self.min_momentum = min_momentum
        super(StepMomentumUpdaterHook, self).__init__(**kwargs)

    def get_momentum(self, runner, base_momentum):
        progress = runner.epoch if self.by_epoch else runner.iter

        # calculate exponential term
        if isinstance(self.step, int):
            exp = progress // self.step
        else:
            exp = len(self.step)
            for i, s in enumerate(self.step):
                if progress < s:
                    exp = i
                    break

        momentum = base_momentum * (self.gamma**exp)
        if self.min_momentum is not None:
            # clip to a minimum value
            momentum = max(momentum, self.min_momentum)
        return momentum


@HOOKS.register_module()
class CosineAnnealingMomentumUpdaterHook(MomentumUpdaterHook):

    def __init__(self, min_momentum=None, min_momentum_ratio=None, **kwargs):
        assert (min_momentum is None) ^ (min_momentum_ratio is None)
        self.min_momentum = min_momentum
        self.min_momentum_ratio = min_momentum_ratio
        super(CosineAnnealingMomentumUpdaterHook, self).__init__(**kwargs)

    def get_momentum(self, runner, base_momentum):
        if self.by_epoch:
            progress = runner.epoch
            max_progress = runner.max_epochs
        else:
            progress = runner.iter
            max_progress = runner.max_iters
        if self.min_momentum_ratio is not None:
            target_momentum = base_momentum * self.min_momentum_ratio
        else:
            target_momentum = self.min_momentum
        return annealing_cos(base_momentum, target_momentum,
                             progress / max_progress)


@HOOKS.register_module()
class CyclicMomentumUpdaterHook(MomentumUpdaterHook):
    """Cyclic momentum Scheduler.

    Implement the cyclical momentum scheduler policy described in
    https://arxiv.org/pdf/1708.07120.pdf

    This momentum scheduler usually used together with the CyclicLRUpdater
    to improve the performance in the 3D detection area.

    Attributes:
        target_ratio (tuple[float]): Relative ratio of the lowest momentum and
            the highest momentum to the initial momentum.
        cyclic_times (int): Number of cycles during training
        step_ratio_up (float): The ratio of the increasing process of momentum
            in  the total cycle.
        by_epoch (bool): Whether to update momentum by epoch.
    """

    def __init__(self,
                 by_epoch=False,
                 target_ratio=(0.85 / 0.95, 1),
                 cyclic_times=1,
                 step_ratio_up=0.4,
                 **kwargs):
        if isinstance(target_ratio, float):
            target_ratio = (target_ratio, target_ratio / 1e5)
        elif isinstance(target_ratio, tuple):
            target_ratio = (target_ratio[0], target_ratio[0] / 1e5) \
                if len(target_ratio) == 1 else target_ratio
        else:
            raise ValueError('target_ratio should be either float '
                             f'or tuple, got {type(target_ratio)}')

        assert len(target_ratio) == 2, \
            '"target_ratio" must be list or tuple of two floats'
        assert 0 <= step_ratio_up < 1.0, \
            '"step_ratio_up" must be in range [0,1)'

        self.target_ratio = target_ratio
        self.cyclic_times = cyclic_times
        self.step_ratio_up = step_ratio_up
        self.momentum_phases = []  # init momentum_phases
        # currently only support by_epoch=False
        assert not by_epoch, \
            'currently only support "by_epoch" = False'
        super(CyclicMomentumUpdaterHook, self).__init__(by_epoch, **kwargs)

    def before_run(self, runner):
        super(CyclicMomentumUpdaterHook, self).before_run(runner)
        # initiate momentum_phases
        # total momentum_phases are separated as up and down
        max_iter_per_phase = runner.max_iters // self.cyclic_times
        iter_up_phase = int(self.step_ratio_up * max_iter_per_phase)
        self.momentum_phases.append(
            [0, iter_up_phase, max_iter_per_phase, 1, self.target_ratio[0]])
        self.momentum_phases.append([
            iter_up_phase, max_iter_per_phase, max_iter_per_phase,
            self.target_ratio[0], self.target_ratio[1]
        ])

    def get_momentum(self, runner, base_momentum):
        curr_iter = runner.iter
        for (start_iter, end_iter, max_iter_per_phase, start_ratio,
             end_ratio) in self.momentum_phases:
            curr_iter %= max_iter_per_phase
            if start_iter <= curr_iter < end_iter:
                progress = curr_iter - start_iter
                return annealing_cos(base_momentum * start_ratio,
                                     base_momentum * end_ratio,
                                     progress / (end_iter - start_iter))


@HOOKS.register_module()
class OneCycleMomentumUpdaterHook(MomentumUpdaterHook):
    """OneCycle momentum Scheduler.

    This momentum scheduler usually used together with the OneCycleLrUpdater
    to improve the performance.

    Args:
        base_momentum (float or list): Lower momentum boundaries in the cycle
            for each parameter group. Note that momentum is cycled inversely
            to learning rate; at the peak of a cycle, momentum is
            'base_momentum' and learning rate is 'max_lr'.
            Default: 0.85
        max_momentum (float or list): Upper momentum boundaries in the cycle
            for each parameter group. Functionally,
            it defines the cycle amplitude (max_momentum - base_momentum).
            Note that momentum is cycled inversely
            to learning rate; at the start of a cycle, momentum is
            'max_momentum' and learning rate is 'base_lr'
            Default: 0.95
        pct_start (float): The percentage of the cycle (in number of steps)
            spent increasing the learning rate.
            Default: 0.3
        anneal_strategy (str): {'cos', 'linear'}
            Specifies the annealing strategy: 'cos' for cosine annealing,
            'linear' for linear annealing.
            Default: 'cos'
        three_phase (bool): If three_phase is True, use a third phase of the
            schedule to annihilate the learning rate according to
            final_div_factor instead of modifying the second phase (the first
            two phases will be symmetrical about the step indicated by
            pct_start).
            Default: False
    """

    def __init__(self,
                 base_momentum=0.85,
                 max_momentum=0.95,
                 pct_start=0.3,
                 anneal_strategy='cos',
                 three_phase=False,
                 **kwargs):
        # validate by_epoch, currently only support by_epoch=False
        if 'by_epoch' not in kwargs:
            kwargs['by_epoch'] = False
        else:
            assert not kwargs['by_epoch'], \
                'currently only support "by_epoch" = False'
        if not isinstance(base_momentum, (float, list, dict)):
            raise ValueError('base_momentum must be the type among of float,'
                             'list or dict.')
        self._base_momentum = base_momentum
        if not isinstance(max_momentum, (float, list, dict)):
            raise ValueError('max_momentum must be the type among of float,'
                             'list or dict.')
        self._max_momentum = max_momentum
        # validate pct_start
        if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
            raise ValueError('Expected float between 0 and 1 pct_start, but '
                             f'got {pct_start}')
        self.pct_start = pct_start
        # validate anneal_strategy
        if anneal_strategy not in ['cos', 'linear']:
            raise ValueError('anneal_strategy must by one of "cos" or '
                             f'"linear", instead got {anneal_strategy}')
        elif anneal_strategy == 'cos':
            self.anneal_func = annealing_cos
        elif anneal_strategy == 'linear':
            self.anneal_func = annealing_linear
        self.three_phase = three_phase
        self.momentum_phases = []  # init momentum_phases
        super(OneCycleMomentumUpdaterHook, self).__init__(**kwargs)

    def before_run(self, runner):
        if isinstance(runner.optimizer, dict):
            for k, optim in runner.optimizer.items():
                if ('momentum' not in optim.defaults
                        and 'betas' not in optim.defaults):
                    raise ValueError('optimizer must support momentum with'
                                     'option enabled')
                self.use_beta1 = 'betas' in optim.defaults
                _base_momentum = format_param(k, optim, self._base_momentum)
                _max_momentum = format_param(k, optim, self._max_momentum)
                for group, b_momentum, m_momentum in zip(
                        optim.param_groups, _base_momentum, _max_momentum):
                    if self.use_beta1:
                        _, beta2 = group['betas']
                        group['betas'] = (m_momentum, beta2)
                    else:
                        group['momentum'] = m_momentum
                    group['base_momentum'] = b_momentum
                    group['max_momentum'] = m_momentum
        else:
            optim = runner.optimizer
            if ('momentum' not in optim.defaults
                    and 'betas' not in optim.defaults):
                raise ValueError('optimizer must support momentum with'
                                 'option enabled')
            self.use_beta1 = 'betas' in optim.defaults
            k = type(optim).__name__
            _base_momentum = format_param(k, optim, self._base_momentum)
            _max_momentum = format_param(k, optim, self._max_momentum)
            for group, b_momentum, m_momentum in zip(optim.param_groups,
                                                     _base_momentum,
                                                     _max_momentum):
                if self.use_beta1:
                    _, beta2 = group['betas']
                    group['betas'] = (m_momentum, beta2)
                else:
                    group['momentum'] = m_momentum
                group['base_momentum'] = b_momentum
                group['max_momentum'] = m_momentum

        if self.three_phase:
            self.momentum_phases.append({
                'end_iter':
                float(self.pct_start * runner.max_iters) - 1,
                'start_momentum':
                'max_momentum',
                'end_momentum':
                'base_momentum'
            })
            self.momentum_phases.append({
                'end_iter':
                float(2 * self.pct_start * runner.max_iters) - 2,
                'start_momentum':
                'base_momentum',
                'end_momentum':
                'max_momentum'
            })
            self.momentum_phases.append({
                'end_iter': runner.max_iters - 1,
                'start_momentum': 'max_momentum',
                'end_momentum': 'max_momentum'
            })
        else:
            self.momentum_phases.append({
                'end_iter':
                float(self.pct_start * runner.max_iters) - 1,
                'start_momentum':
                'max_momentum',
                'end_momentum':
                'base_momentum'
            })
            self.momentum_phases.append({
                'end_iter': runner.max_iters - 1,
                'start_momentum': 'base_momentum',
                'end_momentum': 'max_momentum'
            })

    def _set_momentum(self, runner, momentum_groups):
        if isinstance(runner.optimizer, dict):
            for k, optim in runner.optimizer.items():
                for param_group, mom in zip(optim.param_groups,
                                            momentum_groups[k]):
                    if 'momentum' in param_group.keys():
                        param_group['momentum'] = mom
                    elif 'betas' in param_group.keys():
                        param_group['betas'] = (mom, param_group['betas'][1])
        else:
            for param_group, mom in zip(runner.optimizer.param_groups,
                                        momentum_groups):
                if 'momentum' in param_group.keys():
                    param_group['momentum'] = mom
                elif 'betas' in param_group.keys():
                    param_group['betas'] = (mom, param_group['betas'][1])

    def get_momentum(self, runner, param_group):
        curr_iter = runner.iter
        start_iter = 0
        for i, phase in enumerate(self.momentum_phases):
            end_iter = phase['end_iter']
            if curr_iter <= end_iter or i == len(self.momentum_phases) - 1:
                pct = (curr_iter - start_iter) / (end_iter - start_iter)
                momentum = self.anneal_func(
                    param_group[phase['start_momentum']],
                    param_group[phase['end_momentum']], pct)
                break
            start_iter = end_iter
        return momentum

    def get_regular_momentum(self, runner):
        if isinstance(runner.optimizer, dict):
            momentum_groups = {}
            for k, optim in runner.optimizer.items():
                _momentum_group = [
                    self.get_momentum(runner, param_group)
                    for param_group in optim.param_groups
                ]
                momentum_groups.update({k: _momentum_group})
            return momentum_groups
        else:
            momentum_groups = []
            for param_group in runner.optimizer.param_groups:
                momentum_groups.append(self.get_momentum(runner, param_group))
            return momentum_groups