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| import numpy as np | |
| class LambdaWarmUpCosineScheduler: | |
| """ | |
| note: use with a base_lr of 1.0 | |
| """ | |
| def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): | |
| 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, **kwargs): | |
| 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 __call__(self, n, **kwargs): | |
| return self.schedule(n,**kwargs) | |
| class LambdaWarmUpCosineScheduler2: | |
| """ | |
| supports repeated iterations, configurable via lists | |
| note: use with a base_lr of 1.0. | |
| """ | |
| def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0, gamma=0.99, step_size=1000): | |
| 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.gamma = gamma | |
| self.step_size = step_size | |
| 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, **kwargs): | |
| 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 __call__(self, n, **kwargs): | |
| return self.schedule(n, **kwargs) | |
| class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2): | |
| def schedule(self, n, **kwargs): | |
| 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 | |
| class LambdaLinearScheduler_step(LambdaWarmUpCosineScheduler2): | |
| def schedule(self, n, **kwargs): | |
| 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.gamma ** ((n-self.lr_warm_up_steps[cycle]) // self.step_size) | |
| # 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 | |
| # class LambdaCustomScheduler: | |