99 lines
2.7 KiB
Python
99 lines
2.7 KiB
Python
|
# --------------------------------------------------------
|
||
|
|
# mcan-vqa (Deep Modular Co-Attention Networks)
|
||
|
|
# Licensed under The MIT License [see LICENSE for details]
|
||
|
|
# Written by Yuhao Cui https://github.com/cuiyuhao1996
|
||
|
|
# --------------------------------------------------------
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch.optim as Optim
|
||
|
|
|
||
|
|
|
||
|
|
class WarmupOptimizer(object):
|
||
|
|
def __init__(self, lr_base, optimizer, data_size, batch_size):
|
||
|
|
self.optimizer = optimizer
|
||
|
|
self._step = 0
|
||
|
|
self.lr_base = lr_base
|
||
|
|
self._rate = 0
|
||
|
|
self.data_size = data_size
|
||
|
|
self.batch_size = batch_size
|
||
|
|
|
||
|
|
def step(self):
|
||
|
|
self._step += 1
|
||
|
|
|
||
|
|
rate = self.rate()
|
||
|
|
for p in self.optimizer.param_groups:
|
||
|
|
p['lr'] = rate
|
||
|
|
self._rate = rate
|
||
|
|
|
||
|
|
self.optimizer.step()
|
||
|
|
|
||
|
|
|
||
|
|
def zero_grad(self):
|
||
|
|
self.optimizer.zero_grad()
|
||
|
|
|
||
|
|
|
||
|
|
def rate(self, step=None):
|
||
|
|
if step is None:
|
||
|
|
step = self._step
|
||
|
|
|
||
|
|
if step <= int(self.data_size / self.batch_size * 1):
|
||
|
|
r = self.lr_base * 1/4.
|
||
|
|
elif step <= int(self.data_size / self.batch_size * 2):
|
||
|
|
r = self.lr_base * 2/4.
|
||
|
|
elif step <= int(self.data_size / self.batch_size * 3):
|
||
|
|
r = self.lr_base * 3/4.
|
||
|
|
else:
|
||
|
|
r = self.lr_base
|
||
|
|
|
||
|
|
return r
|
||
|
|
|
||
|
|
|
||
|
|
def get_optim(__C, model, data_size, optimizer, lr_base=None):
|
||
|
|
if lr_base is None:
|
||
|
|
lr_base = __C.LR_BASE
|
||
|
|
|
||
|
|
# modules = model._modules
|
||
|
|
# params_list = []
|
||
|
|
# for m in modules:
|
||
|
|
# if 'dnc' in m:
|
||
|
|
# params_list.append({
|
||
|
|
# 'params': filter(lambda p: p.requires_grad, modules[m].parameters()),
|
||
|
|
# 'lr': __C.LR_DNC_BASE,
|
||
|
|
# 'flag': True
|
||
|
|
# })
|
||
|
|
# else:
|
||
|
|
# params_list.append({
|
||
|
|
# 'params': filter(lambda p: p.requires_grad, modules[m].parameters()),
|
||
|
|
|
||
|
|
# })
|
||
|
|
if optimizer == 'adam':
|
||
|
|
optim = Optim.Adam(
|
||
|
|
filter(lambda p: p.requires_grad, model.parameters()),
|
||
|
|
lr=0,
|
||
|
|
betas=__C.OPT_BETAS,
|
||
|
|
eps=__C.OPT_EPS,
|
||
|
|
|
||
|
|
)
|
||
|
|
elif optimizer == 'rmsprop':
|
||
|
|
optim = Optim.RMSprop(
|
||
|
|
filter(lambda p: p.requires_grad, model.parameters()),
|
||
|
|
lr=0,
|
||
|
|
eps=__C.OPT_EPS,
|
||
|
|
weight_decay=__C.OPT_WEIGHT_DECAY
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
raise ValueError('{} optimizer is not supported'.fromat(optimizer))
|
||
|
|
return WarmupOptimizer(
|
||
|
|
lr_base,
|
||
|
|
optim,
|
||
|
|
data_size,
|
||
|
|
__C.BATCH_SIZE
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def adjust_lr(optim, decay_r):
|
||
|
|
optim.lr_base *= decay_r
|
||
|
|
|
||
|
|
def adjust_lr_dnc(optim, decay_r):
|
||
|
|
optim.lr_dnc_base *= decay_r
|