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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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]
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)
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schedulers = dict(
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[
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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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]
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)
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multi_optim = MultiOptimizer(optim, schedulers)
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return multi_optim
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