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import pdb
import torch
import numpy as np
import torch.optim as optim
class Optimizer(object):
def __init__(self, model, optim_dict):
self.optim_dict = optim_dict
if self.optim_dict["optimizer"] == 'SGD':
self.optimizer = optim.SGD(
model,
lr=self.optim_dict['base_lr'],
momentum=0.9,
nesterov=self.optim_dict['nesterov'],
weight_decay=self.optim_dict['weight_decay']
)
elif self.optim_dict["optimizer"] == 'Adam':
alpha = self.optim_dict['learning_ratio']
self.optimizer = optim.Adam(
# [
# {'params': model.conv2d.parameters(), 'lr': self.optim_dict['base_lr']*alpha},
# {'params': model.conv1d.parameters(), 'lr': self.optim_dict['base_lr']*alpha},
# {'params': model.rnn.parameters()},
# {'params': model.classifier.parameters()},
# ],
# model.conv1d.fc.parameters(),
model.parameters(),
lr=self.optim_dict['base_lr'],
weight_decay=self.optim_dict['weight_decay']
)
else:
raise ValueError()
self.scheduler = self.define_lr_scheduler(self.optimizer, self.optim_dict['step'])
def define_lr_scheduler(self, optimizer, milestones):
if self.optim_dict["optimizer"] in ['SGD', 'Adam']:
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=milestones, gamma=0.2)
return lr_scheduler
else:
raise ValueError()
def zero_grad(self):
self.optimizer.zero_grad()
def step(self):
self.optimizer.step()
def state_dict(self):
return self.optimizer.state_dict()
def load_state_dict(self, state_dict):
self.optimizer.load_state_dict(state_dict)
def to(self, device):
for state in self.optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.to(device)
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