def test_lr_scheduler_asserts():
t1 = torch.zeros([1], requires_grad=True)
t2 = torch.zeros([1], requires_grad=True)
optimizer = torch.optim.SGD([
{
"params": t1,
'lr': 0.1
},
{
"params": t2,
'lr': 0.1
},
])
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer,
gamma=0.98)
with pytest.raises(ValueError):
LRScheduler(lr_scheduler)
with pytest.raises(ValueError):
LRScheduler.simulate_values(num_events=100, lr_scheduler=lr_scheduler)
with pytest.raises(TypeError):
LRScheduler(123)
def test_lr_scheduler_asserts():
with pytest.raises(TypeError):
LRScheduler(123)
with pytest.raises(TypeError):
LRScheduler.simulate_values(1, None)
def test_lr_scheduler_asserts():
with pytest.raises(
TypeError, match=r"Argument lr_scheduler should be a subclass of torch.optim.lr_scheduler._LRScheduler"
):
LRScheduler(123)
with pytest.raises(
TypeError, match=r"Argument lr_scheduler should be a subclass of torch.optim.lr_scheduler._LRScheduler"
):
LRScheduler.simulate_values(1, None)
def _test(torch_lr_scheduler_cls, **kwargs):
tensor = torch.zeros([1], requires_grad=True)
optimizer1 = torch.optim.SGD([tensor], lr=0.01)
optimizer2 = torch.optim.SGD([tensor], lr=0.01)
opt_state_dict1 = optimizer1.state_dict()
opt_state_dict2 = optimizer2.state_dict()
torch_lr_scheduler1 = torch_lr_scheduler_cls(optimizer=optimizer1,
**kwargs)
scheduler = LRScheduler(torch_lr_scheduler1)
state_dict1 = scheduler.state_dict()
torch_lr_scheduler2 = torch_lr_scheduler_cls(optimizer=optimizer2,
**kwargs)
state_dict2 = torch_lr_scheduler2.state_dict()
def dummy_update(engine, batch):
optimizer1.step()
optimizer2.step()
trainer = Engine(dummy_update)
@trainer.on(Events.ITERATION_STARTED)
def save_lr(engine):
lrs.append(optimizer1.param_groups[0]["lr"])
@trainer.on(Events.ITERATION_STARTED)
def save_true_lr(engine):
lrs_true.append(optimizer2.param_groups[0]["lr"])
@trainer.on(Events.ITERATION_COMPLETED)
def torch_lr_scheduler_step(engine):
torch_lr_scheduler2.step()
trainer.add_event_handler(Events.ITERATION_COMPLETED, scheduler)
for _ in range(2):
lrs = []
lrs_true = []
data = [0] * 10
max_epochs = 2
trainer.run(data, max_epochs=max_epochs)
assert lrs_true == pytest.approx(
lrs), "{}: {} ({}) vs {} ({})".format(_, lrs_true,
len(lrs_true), lrs,
len(lrs))
optimizer1.load_state_dict(opt_state_dict1)
scheduler.load_state_dict(state_dict1)
optimizer2.load_state_dict(opt_state_dict2)
torch_lr_scheduler2.load_state_dict(state_dict2)
optimizer3 = torch.optim.SGD([tensor], lr=0.01)
torch_lr_scheduler3 = torch_lr_scheduler_cls(optimizer=optimizer3,
**kwargs)
simulated_values = LRScheduler.simulate_values(
num_events=len(data) * max_epochs,
lr_scheduler=torch_lr_scheduler3)
assert lrs == pytest.approx([v for i, v in simulated_values])
def _test(torch_lr_scheduler_cls, **kwargs):
tensor = torch.zeros([1], requires_grad=True)
optimizer1 = torch.optim.SGD([tensor], lr=0.01)
optimizer2 = torch.optim.SGD([tensor], lr=0.01)
torch_lr_scheduler1 = torch_lr_scheduler_cls(optimizer=optimizer1,
**kwargs)
torch_lr_scheduler2 = torch_lr_scheduler_cls(optimizer=optimizer2,
**kwargs)
scheduler = LRScheduler(torch_lr_scheduler1)
lrs = []
lrs_true = []
def dummy_update(engine, batch):
optimizer1.step()
optimizer2.step()
trainer = Engine(dummy_update)
@trainer.on(Events.ITERATION_COMPLETED)
def torch_lr_scheduler_step(engine):
torch_lr_scheduler2.step()
@trainer.on(Events.ITERATION_COMPLETED)
def save_lr(engine):
lrs.append(optimizer1.param_groups[0]['lr'])
@trainer.on(Events.ITERATION_COMPLETED)
def save_true_lr(engine):
lrs_true.append(optimizer2.param_groups[0]['lr'])
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
data = [0] * 10
max_epochs = 2
trainer.run(data, max_epochs=max_epochs)
assert lrs_true == pytest.approx(lrs)
optimizer3 = torch.optim.SGD([tensor], lr=0.01)
torch_lr_scheduler3 = torch_lr_scheduler_cls(optimizer=optimizer3,
**kwargs)
simulated_values = LRScheduler.simulate_values(
num_events=len(data) * max_epochs,
lr_scheduler=torch_lr_scheduler3)
assert lrs == pytest.approx([v for i, v in simulated_values])