def test():
from torch.optim import SGD
from argument import get_args
from model import Efficientnet_Bifpn_ATSS
args = get_args()
model = Efficientnet_Bifpn_ATSS(args, load_backboe_weight=False)
optimizer = SGD(
model.backbone.backbone_net.parameters(),
lr=0,
momentum=0.9,
weight_decay=0.0001,
nesterov=True,
)
optimizer.add_param_group({
'params': list(model.backbone.bifpn.parameters()),
'lr': 0,
'momentum': 0.9,
'weight_decay': 0.0001,
'nesterov': True
})
niters = int(1200)
warmup_scheduler = GluonLRScheduler(optimizer,
mode='linear',
nepochs=1,
iters_per_epoch=50,
target_lr=[1e-4, 1e-3])
scheduler = GluonLRScheduler(optimizer,
mode='cosine',
nepochs=24,
iters_per_epoch=50)
scheduler.set_baselrs(warmup_scheduler.target_lr)
initial_schedluer = False
#scheduler = torch.optim.lr_scheduler.StepLR(optimizer,step_size=10)
lrs_1 = []
lrs_2 = []
lrs_1.append(optimizer.param_groups[0]['lr'])
lrs_2.append(optimizer.param_groups[1]['lr'])
for i in range(niters):
optimizer.step()
if i < warmup_scheduler.niters:
warmup_scheduler.step()
else:
scheduler.step()
lrs_1.append(optimizer.param_groups[0]['lr'])
lrs_2.append(optimizer.param_groups[1]['lr'])
from matplotlib import pyplot as plt
steps = [i for i in range(niters + 1)]
fig = plt.figure()
fig.tight_layout()
ax_1 = fig.add_axes([0.1, 0.2, 0.35, 0.35])
ax_2 = fig.add_axes([0.6, 0.2, 0.35, 0.35])
line_1, = ax_1.plot(steps, lrs_1)
line_2, = ax_2.plot(steps, lrs_2)
line_1.set_label('learning rate group_1')
line_2.set_label('learning rate group_2')
ax_1.legend()
ax_2.legend()
plt.show()
def get_optimizer(
model: nn.Module,
optimizer_name: str,
learning_rate: float,
weight_decay: float = 1e-5,
no_weight_decay_on_bias: bool = False,
eps: float = 1e-5,
**kwargs,
) -> Optimizer:
"""
Construct an Optimizer for given model
Args:
model: Model to optimize. Only parameters that require_grad will be used
optimizer_name: Name of the optimizer. Case-insensitive
learning_rate: Target learning rate (regardless of the scheduler)
weight_decay: Target weight decay
no_weight_decay_on_bias: Whether to disable weight decay on bias parameters
eps: Default epsilon for Adam-like optimizers.
**kwargs: Additional parameters for optimizer
Returns:
"""
from torch.optim import ASGD, SGD, Adam, RMSprop, AdamW
from torch_optimizer import RAdam, Lamb, DiffGrad, NovoGrad, Ranger
# Optimizer parameter groups
default_pg, biases_pg = [], []
for k, v in model.named_parameters():
if v.requires_grad:
if str.endswith(k, ".bias"):
biases_pg.append(v) # biases
else:
default_pg.append(v) # all else
if no_weight_decay_on_bias:
parameters = default_pg
else:
parameters = default_pg + biases_pg
optimizer: Optimizer = None
if optimizer_name.lower() == "sgd":
optimizer = SGD(
parameters,
lr=learning_rate,
momentum=0.9,
nesterov=True,
weight_decay=weight_decay,
**kwargs,
)
elif optimizer_name.lower() == "asgd":
optimizer = ASGD(
parameters,
lr=learning_rate,
weight_decay=weight_decay,
**kwargs,
)
elif optimizer_name.lower() == "adam":
optimizer = Adam(
parameters,
lr=learning_rate,
weight_decay=weight_decay,
eps=eps,
**kwargs,
)
elif optimizer_name.lower() == "rms":
optimizer = RMSprop(parameters,
learning_rate,
weight_decay=weight_decay,
**kwargs)
elif optimizer_name.lower() == "adamw":
optimizer = AdamW(
parameters,
lr=learning_rate,
weight_decay=weight_decay,
eps=eps,
**kwargs,
)
elif optimizer_name.lower() == "radam":
optimizer = RAdam(
parameters,
lr=learning_rate,
weight_decay=weight_decay,
eps=eps,
**kwargs,
)
elif optimizer_name.lower() == "ranger":
optimizer = Ranger(
parameters,
lr=learning_rate,
eps=eps,
weight_decay=weight_decay,
**kwargs,
)
elif optimizer_name.lower() == "lamb":
optimizer = Lamb(
parameters,
lr=learning_rate,
eps=eps,
weight_decay=weight_decay,
**kwargs,
)
elif optimizer_name.lower() == "diffgrad":
optimizer = DiffGrad(
parameters,
lr=learning_rate,
eps=eps,
weight_decay=weight_decay,
**kwargs,
)
elif optimizer_name.lower() == "novograd":
optimizer = NovoGrad(
parameters,
lr=learning_rate,
eps=eps,
weight_decay=weight_decay,
**kwargs,
)
elif optimizer_name.lower() == "fused_lamb":
from apex.optimizers import FusedLAMB
optimizer = FusedLAMB(parameters,
learning_rate,
eps=eps,
weight_decay=weight_decay,
**kwargs)
elif optimizer_name.lower() == "fused_sgd":
from apex.optimizers import FusedSGD
optimizer = FusedSGD(parameters,
learning_rate,
momentum=0.9,
nesterov=True,
weight_decay=weight_decay,
**kwargs)
elif optimizer_name.lower() == "fused_adam":
from apex.optimizers import FusedAdam
optimizer = FusedAdam(parameters,
learning_rate,
eps=eps,
weight_decay=weight_decay,
adam_w_mode=True,
**kwargs)
else:
raise KeyError(f"Cannot get optimizer by name {optimizer_name}")
# Currently either no_wd or per-group lr
if no_weight_decay_on_bias:
optimizer.add_param_group({"params": biases_pg, "weight_decay": 0})
return optimizer