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https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-17 01:11:44 +03:00
82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
import tqdm
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class LearnScheduleIterator:
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def __init__(self, learn_rate, max_steps, cur_step=0):
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"""
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specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000
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"""
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pairs = learn_rate.split(',')
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self.rates = []
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self.it = 0
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self.maxit = 0
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try:
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for pair in pairs:
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if not pair.strip():
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continue
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tmp = pair.split(':')
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if len(tmp) == 2:
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step = int(tmp[1])
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if step > cur_step:
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self.rates.append((float(tmp[0]), min(step, max_steps)))
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self.maxit += 1
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if step > max_steps:
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return
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elif step == -1:
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self.rates.append((float(tmp[0]), max_steps))
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self.maxit += 1
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return
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else:
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self.rates.append((float(tmp[0]), max_steps))
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self.maxit += 1
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return
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assert self.rates
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except (ValueError, AssertionError) as e:
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raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.') from e
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def __iter__(self):
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return self
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def __next__(self):
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if self.it < self.maxit:
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self.it += 1
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return self.rates[self.it - 1]
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else:
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raise StopIteration
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class LearnRateScheduler:
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def __init__(self, learn_rate, max_steps, cur_step=0, verbose=True):
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self.schedules = LearnScheduleIterator(learn_rate, max_steps, cur_step)
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(self.learn_rate, self.end_step) = next(self.schedules)
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self.verbose = verbose
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if self.verbose:
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print(f'Training at rate of {self.learn_rate} until step {self.end_step}')
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self.finished = False
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def step(self, step_number):
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if step_number < self.end_step:
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return False
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try:
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(self.learn_rate, self.end_step) = next(self.schedules)
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except StopIteration:
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self.finished = True
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return False
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return True
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def apply(self, optimizer, step_number):
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if not self.step(step_number):
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return
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if self.verbose:
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tqdm.tqdm.write(f'Training at rate of {self.learn_rate} until step {self.end_step}')
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for pg in optimizer.param_groups:
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pg['lr'] = self.learn_rate
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