|
|
| import os, sys
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| import os.path as osp
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| import numpy as np
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| import torch
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| from torch import nn
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| from torch.optim import Optimizer
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| from functools import reduce
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| from torch.optim import AdamW
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|
|
|
|
| class MultiOptimizer:
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| def __init__(self, optimizers={}, schedulers={}):
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| self.optimizers = optimizers
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| self.schedulers = schedulers
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| self.keys = list(optimizers.keys())
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| self.param_groups = reduce(
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| lambda x, y: x + y, [v.param_groups for v in self.optimizers.values()]
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| )
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|
|
| def state_dict(self):
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| state_dicts = [(key, self.optimizers[key].state_dict()) for key in self.keys]
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| return state_dicts
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|
|
| def load_state_dict(self, state_dict):
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| for key, val in state_dict:
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| try:
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| self.optimizers[key].load_state_dict(val)
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| except:
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| print("Unloaded %s" % key)
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|
|
| def step(self, key=None, scaler=None):
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| keys = [key] if key is not None else self.keys
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| _ = [self._step(key, scaler) for key in keys]
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|
|
| def _step(self, key, scaler=None):
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| if scaler is not None:
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| scaler.step(self.optimizers[key])
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| scaler.update()
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| else:
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| self.optimizers[key].step()
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|
|
| def zero_grad(self, key=None):
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| if key is not None:
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| self.optimizers[key].zero_grad()
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| else:
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| _ = [self.optimizers[key].zero_grad() for key in self.keys]
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|
|
| def scheduler(self, *args, key=None):
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| if key is not None:
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| self.schedulers[key].step(*args)
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| else:
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| _ = [self.schedulers[key].step(*args) for key in self.keys]
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|
|
|
|
| def define_scheduler(optimizer, params):
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| scheduler = torch.optim.lr_scheduler.OneCycleLR(
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| optimizer,
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| max_lr=params.get("max_lr", 2e-4),
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| epochs=params.get("epochs", 200),
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| steps_per_epoch=params.get("steps_per_epoch", 1000),
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| pct_start=params.get("pct_start", 0.0),
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| div_factor=1,
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| final_div_factor=1,
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| )
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|
|
| return scheduler
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|
|
|
|
| def build_optimizer(parameters_dict, scheduler_params_dict, lr):
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| optim = dict(
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| [
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| (key, AdamW(params, lr=lr, weight_decay=1e-4, betas=(0.0, 0.99), eps=1e-9))
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| for key, params in parameters_dict.items()
|
| ]
|
| )
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|
|
| schedulers = dict(
|
| [
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| (key, define_scheduler(opt, scheduler_params_dict[key]))
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| for key, opt in optim.items()
|
| ]
|
| )
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|
|
| multi_optim = MultiOptimizer(optim, schedulers)
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| return multi_optim
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|
|
