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| import torch | |
| import torch.optim as optim | |
| import numpy as np | |
| import copy | |
| from ... import sync | |
| from ...cfg_holder import cfg_unique_holder as cfguh | |
| def singleton(class_): | |
| instances = {} | |
| def getinstance(*args, **kwargs): | |
| if class_ not in instances: | |
| instances[class_] = class_(*args, **kwargs) | |
| return instances[class_] | |
| return getinstance | |
| class get_scheduler(object): | |
| def __init__(self): | |
| self.lr_scheduler = {} | |
| def register(self, lrsf, name): | |
| self.lr_scheduler[name] = lrsf | |
| def __call__(self, cfg): | |
| if cfg is None: | |
| return None | |
| if isinstance(cfg, list): | |
| schedulers = [] | |
| for ci in cfg: | |
| t = ci.type | |
| schedulers.append( | |
| self.lr_scheduler[t](**ci.args)) | |
| if len(schedulers) == 0: | |
| raise ValueError | |
| else: | |
| return compose_scheduler(schedulers) | |
| t = cfg.type | |
| return self.lr_scheduler[t](**cfg.args) | |
| def register(name): | |
| def wrapper(class_): | |
| get_scheduler().register(class_, name) | |
| return class_ | |
| return wrapper | |
| class template_scheduler(object): | |
| def __init__(self, step): | |
| self.step = step | |
| def __getitem__(self, idx): | |
| raise ValueError | |
| def set_lr(self, optim, new_lr, pg_lrscale=None): | |
| """ | |
| Set Each parameter_groups in optim with new_lr | |
| New_lr can be find according to the idx. | |
| pg_lrscale tells how to scale each pg. | |
| """ | |
| # new_lr = self.__getitem__(idx) | |
| pg_lrscale = copy.deepcopy(pg_lrscale) | |
| for pg in optim.param_groups: | |
| if pg_lrscale is None: | |
| pg['lr'] = new_lr | |
| else: | |
| pg['lr'] = new_lr * pg_lrscale.pop(pg['name']) | |
| assert (pg_lrscale is None) or (len(pg_lrscale)==0), \ | |
| "pg_lrscale doesn't match pg" | |
| class constant_scheduler(template_scheduler): | |
| def __init__(self, lr, step): | |
| super().__init__(step) | |
| self.lr = lr | |
| def __getitem__(self, idx): | |
| if idx >= self.step: | |
| raise ValueError | |
| return self.lr | |
| class poly_scheduler(template_scheduler): | |
| def __init__(self, start_lr, end_lr, power, step): | |
| super().__init__(step) | |
| self.start_lr = start_lr | |
| self.end_lr = end_lr | |
| self.power = power | |
| def __getitem__(self, idx): | |
| if idx >= self.step: | |
| raise ValueError | |
| a, b = self.start_lr, self.end_lr | |
| p, n = self.power, self.step | |
| return b + (a-b)*((1-idx/n)**p) | |
| class linear_scheduler(template_scheduler): | |
| def __init__(self, start_lr, end_lr, step): | |
| super().__init__(step) | |
| self.start_lr = start_lr | |
| self.end_lr = end_lr | |
| def __getitem__(self, idx): | |
| if idx >= self.step: | |
| raise ValueError | |
| a, b, n = self.start_lr, self.end_lr, self.step | |
| return b + (a-b)*(1-idx/n) | |
| class constant_scheduler(template_scheduler): | |
| def __init__(self, start_lr, milestones, gamma, step): | |
| super().__init__(step) | |
| self.start_lr = start_lr | |
| m = [0] + milestones + [step] | |
| lr_iter = start_lr | |
| self.lr = [] | |
| for ms, me in zip(m[0:-1], m[1:]): | |
| for _ in range(ms, me): | |
| self.lr.append(lr_iter) | |
| lr_iter *= gamma | |
| def __getitem__(self, idx): | |
| if idx >= self.step: | |
| raise ValueError | |
| return self.lr[idx] | |
| class compose_scheduler(template_scheduler): | |
| def __init__(self, schedulers): | |
| self.schedulers = schedulers | |
| self.step = [si.step for si in schedulers] | |
| self.step_milestone = [] | |
| acc = 0 | |
| for i in self.step: | |
| acc += i | |
| self.step_milestone.append(acc) | |
| self.step = sum(self.step) | |
| def __getitem__(self, idx): | |
| if idx >= self.step: | |
| raise ValueError | |
| ms = self.step_milestone | |
| for idx, (mi, mj) in enumerate(zip(ms[:-1], ms[1:])): | |
| if mi <= idx < mj: | |
| return self.schedulers[idx-mi] | |
| raise ValueError | |
| #################### | |
| # lambda schedular # | |
| #################### | |
| class LambdaWarmUpCosineScheduler(template_scheduler): | |
| """ | |
| note: use with a base_lr of 1.0 | |
| """ | |
| def __init__(self, | |
| base_lr, | |
| warm_up_steps, | |
| lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): | |
| cfgt = cfguh().cfg.train | |
| bs = cfgt.batch_size | |
| if 'gradacc_every' not in cfgt: | |
| print('Warning, gradacc_every is not found in xml, use 1 as default.') | |
| acc = cfgt.get('gradacc_every', 1) | |
| self.lr_multi = base_lr * bs * acc | |
| self.lr_warm_up_steps = warm_up_steps | |
| self.lr_start = lr_start | |
| self.lr_min = lr_min | |
| self.lr_max = lr_max | |
| self.lr_max_decay_steps = max_decay_steps | |
| self.last_lr = 0. | |
| self.verbosity_interval = verbosity_interval | |
| def schedule(self, n): | |
| if self.verbosity_interval > 0: | |
| if n % self.verbosity_interval == 0: | |
| print(f"current step: {n}, recent lr-multiplier: {self.last_lr}") | |
| if n < self.lr_warm_up_steps: | |
| lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start | |
| self.last_lr = lr | |
| return lr | |
| else: | |
| t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps) | |
| t = min(t, 1.0) | |
| lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * ( | |
| 1 + np.cos(t * np.pi)) | |
| self.last_lr = lr | |
| return lr | |
| def __getitem__(self, idx): | |
| return self.schedule(idx) * self.lr_multi | |
| class LambdaWarmUpCosineScheduler2(template_scheduler): | |
| """ | |
| supports repeated iterations, configurable via lists | |
| note: use with a base_lr of 1.0. | |
| """ | |
| def __init__(self, | |
| base_lr, | |
| warm_up_steps, | |
| f_min, f_max, f_start, cycle_lengths, verbosity_interval=0): | |
| cfgt = cfguh().cfg.train | |
| # bs = cfgt.batch_size | |
| # if 'gradacc_every' not in cfgt: | |
| # print('Warning, gradacc_every is not found in xml, use 1 as default.') | |
| # acc = cfgt.get('gradacc_every', 1) | |
| # self.lr_multi = base_lr * bs * acc | |
| self.lr_multi = base_lr | |
| assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths) | |
| self.lr_warm_up_steps = warm_up_steps | |
| self.f_start = f_start | |
| self.f_min = f_min | |
| self.f_max = f_max | |
| self.cycle_lengths = cycle_lengths | |
| self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths)) | |
| self.last_f = 0. | |
| self.verbosity_interval = verbosity_interval | |
| def find_in_interval(self, n): | |
| interval = 0 | |
| for cl in self.cum_cycles[1:]: | |
| if n <= cl: | |
| return interval | |
| interval += 1 | |
| def schedule(self, n): | |
| cycle = self.find_in_interval(n) | |
| n = n - self.cum_cycles[cycle] | |
| if self.verbosity_interval > 0: | |
| if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " | |
| f"current cycle {cycle}") | |
| if n < self.lr_warm_up_steps[cycle]: | |
| f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] | |
| self.last_f = f | |
| return f | |
| else: | |
| t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]) | |
| t = min(t, 1.0) | |
| f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * ( | |
| 1 + np.cos(t * np.pi)) | |
| self.last_f = f | |
| return f | |
| def __getitem__(self, idx): | |
| return self.schedule(idx) * self.lr_multi | |
| class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2): | |
| def schedule(self, n): | |
| cycle = self.find_in_interval(n) | |
| n = n - self.cum_cycles[cycle] | |
| if self.verbosity_interval > 0: | |
| if n % self.verbosity_interval == 0: | |
| print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " | |
| f"current cycle {cycle}") | |
| if n < self.lr_warm_up_steps[cycle]: | |
| f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] | |
| self.last_f = f | |
| return f | |
| else: | |
| f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle]) | |
| self.last_f = f | |
| return f |