def _test(lr_schedulers, optimizer):
num_iterations = 10
max_epochs = 20
scheduler = ParamGroupScheduler(
lr_schedulers,
names=["s_{}".format(i) for i in range(len(lr_schedulers))])
state_dict = scheduler.state_dict()
trainer = Engine(lambda engine, batch: None)
@trainer.on(Events.ITERATION_COMPLETED)
def save_lr(engine):
lrs.append((optimizer.param_groups[0]["lr"],
optimizer.param_groups[1]["lr"]))
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
data = [0] * num_iterations
for _ in range(2):
lrs = []
trainer.run(data, max_epochs=max_epochs)
assert [lr[0]
for lr in lrs] == pytest.approx([lr[1] for lr in lrs])
scheduler.load_state_dict(state_dict)
def test_param_group_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_scheduler1 = LinearCyclicalScheduler(
optimizer, "lr", param_group_index=0, start_value=1.0, end_value=0.0, cycle_size=10
)
lr_scheduler2 = LinearCyclicalScheduler(
optimizer, "lr", param_group_index=1, start_value=1.0, end_value=0.0, cycle_size=10
)
with pytest.raises(TypeError, match=r"Argument schedulers should be a list/tuple"):
ParamGroupScheduler(schedulers=None, names=["a", "b", "c"])
with pytest.raises(ValueError, match=r"Argument schedulers should be a list/tuple of parameter schedulers"):
ParamGroupScheduler(schedulers=[0, 1, 2], names=["a", "b", "c"])
with pytest.raises(ValueError, match=r"Argument schedulers should be a list/tuple of parameter schedulers"):
ParamGroupScheduler(schedulers=[lr_scheduler1, "2"], names=["a", "b"])
with pytest.raises(TypeError, match=r"Argument names should be a list/tuple"):
ParamGroupScheduler(schedulers=[lr_scheduler1, lr_scheduler2], names="ab")
with pytest.raises(ValueError, match=r"Argument names should be a list/tuple of parameter scheduler's names"):
ParamGroupScheduler(schedulers=[lr_scheduler1, lr_scheduler2], names=[1, 2])
with pytest.raises(ValueError, match=r"\d should be equal \d"):
ParamGroupScheduler(schedulers=[lr_scheduler1, lr_scheduler2], names=["a"])
scheduler = ParamGroupScheduler(schedulers=[lr_scheduler1, lr_scheduler2], names=["a", "b"])
with pytest.raises(ValueError, match=r"Required state attribute 'schedulers' is absent in provided state_dict"):
scheduler.load_state_dict({"a": 1})
with pytest.raises(ValueError, match=r"Input state_dict contains 0 state_dicts of param group schedulers"):
scheduler.load_state_dict({"schedulers": []})
with pytest.raises(ValueError, match=r"Required state attribute 'schedulers' is absent in provided state_dict"):
scheduler.load_state_dict({})
with pytest.raises(
ValueError, match=r"Name of scheduler from input state dict does not " r"correspond to required one"
):
scheduler.load_state_dict({"schedulers": [("a", lr_scheduler1.state_dict()), ("bad_name", {})]})
optimizer2 = torch.optim.SGD([{"params": t1, "lr": 0.1}, {"params": t2, "lr": 0.1}])
lr_scheduler3 = LinearCyclicalScheduler(
optimizer2, "lr", param_group_index=0, start_value=1.0, end_value=0.0, cycle_size=10
)
with pytest.raises(ValueError, match=r"schedulers should be related to same optimizer"):
ParamGroupScheduler(schedulers=[lr_scheduler1, lr_scheduler3])
def test_param_group_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_scheduler1 = LinearCyclicalScheduler(
optimizer,
"lr",
param_group_index=0,
start_value=1.0,
end_value=0.0,
cycle_size=10,
)
lr_scheduler2 = LinearCyclicalScheduler(
optimizer,
"lr",
param_group_index=1,
start_value=1.0,
end_value=0.0,
cycle_size=10,
)
with pytest.raises(ValueError):
ParamGroupScheduler(schedulers=[0, 1, 2], names=["a", "b", "c"])
with pytest.raises(ValueError):
ParamGroupScheduler(schedulers=[lr_scheduler1, "2"], names=["a", "b"])
with pytest.raises(ValueError):
ParamGroupScheduler(schedulers=[lr_scheduler1, lr_scheduler2],
names="ab")
with pytest.raises(ValueError):
ParamGroupScheduler(
schedulers=[lr_scheduler1, lr_scheduler2],
names=[
"a",
],
)
scheduler = ParamGroupScheduler(schedulers=[lr_scheduler1, lr_scheduler2],
names=["a", "b"])
with pytest.raises(
ValueError,
match=
r"Required state attribute 'schedulers' is absent in provided state_dict",
):
scheduler.load_state_dict({"a": 1})
with pytest.raises(
ValueError,
match=
r"Input state_dict contains 0 state_dicts of param group schedulers",
):
scheduler.load_state_dict({"schedulers": []})
with pytest.raises(
ValueError,
match=r"Name of scheduler from input state dict does not "
r"correspond to required one",
):
scheduler.load_state_dict({
"schedulers": [("a", lr_scheduler1.state_dict()), ("bad_name", {})]
})
def test_param_group_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_scheduler1 = LinearCyclicalScheduler(optimizer,
"lr",
param_group_index=0,
start_value=1.0,
end_value=0.0,
cycle_size=10)
lr_scheduler2 = LinearCyclicalScheduler(optimizer,
"lr",
param_group_index=1,
start_value=1.0,
end_value=0.0,
cycle_size=10)
with pytest.raises(ValueError):
ParamGroupScheduler(schedulers=[0, 1, 2], names=['a', 'b', 'c'])
with pytest.raises(ValueError):
ParamGroupScheduler(schedulers=[lr_scheduler1, '2'], names=['a', 'b'])
with pytest.raises(ValueError):
ParamGroupScheduler(schedulers=[lr_scheduler1, lr_scheduler2],
names='ab')
with pytest.raises(ValueError):
ParamGroupScheduler(schedulers=[lr_scheduler1, lr_scheduler2],
names=[
'a',
])
scheduler = ParamGroupScheduler(schedulers=[lr_scheduler1, lr_scheduler2],
names=['a', 'b'])
with pytest.raises(
ValueError,
match=
r"Required state attribute 'schedulers' is absent in provided state_dict"
):
scheduler.load_state_dict({'a': 1})
with pytest.raises(
ValueError,
match=
r"Input state_dict contains 0 state_dicts of param group schedulers"
):
scheduler.load_state_dict({'schedulers': []})
with pytest.raises(
ValueError,
match=r"Name of scheduler from input state dict does not "
r"correspond to required one"):
scheduler.load_state_dict({
'schedulers': [('a', lr_scheduler1.state_dict()), ('bad_name', {})]
})