def test_concat_scheduler_state_dict():
tensor = torch.zeros([1], requires_grad=True)
optimizer = torch.optim.SGD([tensor], lr=0)
scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=1.0, end_value=0.0, cycle_size=10)
scheduler_2 = CosineAnnealingScheduler(optimizer, "lr", start_value=0.0, end_value=1.0, cycle_size=10)
durations = [10]
concat_scheduler = ConcatScheduler(schedulers=[scheduler_1, scheduler_2], durations=durations, save_history=False)
state_dict = concat_scheduler.state_dict()
assert state_dict["durations"] == durations
assert state_dict["_current_duration"] == durations[0]
assert state_dict["_scheduler_index"] == 0
for _ in range(20):
concat_scheduler(None, None)
concat_scheduler.load_state_dict(state_dict)
assert concat_scheduler.durations == durations
assert concat_scheduler._current_duration == durations[0]
assert id(concat_scheduler._current_scheduler) == id(scheduler_1)
with pytest.raises(ValueError, match=r"Required state attribute 'schedulers' is absent in provided state_dict"):
concat_scheduler.load_state_dict({"a": 1})
with pytest.raises(ValueError, match=r"Input state_dict contains 0 state_dicts of concatenated schedulers"):
concat_scheduler.load_state_dict({"schedulers": []})
def test_concat_scheduler_asserts():
tensor = torch.zeros([1], requires_grad=True)
optimizer = torch.optim.SGD([tensor], lr=0)
scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=1.0, end_value=0.0, cycle_size=10)
scheduler_2 = CosineAnnealingScheduler(optimizer, "lr", start_value=0.0, end_value=1.0, cycle_size=10)
with pytest.raises(ValueError):
ConcatScheduler(schedulers=[], durations=[])
with pytest.raises(ValueError):
ConcatScheduler(schedulers=[scheduler_1], durations=[10])
with pytest.raises(TypeError):
ConcatScheduler(schedulers=[scheduler_1, 12], durations=[10])
with pytest.raises(ValueError):
ConcatScheduler(schedulers=[scheduler_1, scheduler_2], durations=[10, 5])
with pytest.raises(ValueError):
ConcatScheduler(schedulers=[scheduler_1, scheduler_2, scheduler_2], durations=[15, 12.0])
with pytest.raises(ValueError):
ConcatScheduler(schedulers=[scheduler_1, scheduler_2], durations="abc")
with pytest.raises(ValueError):
ConcatScheduler.simulate_values(
num_events=123, schedulers=[scheduler_1, scheduler_2], durations=[15], param_names="abc"
)
def _test(save_history):
tensor = torch.ones([1], requires_grad=True)
optimizer = torch.optim.SGD([tensor], lr=0.001)
max_epochs = 25
lr_max_value = 0.4
num_iterations_per_epoch = 128
num_iterations = max_epochs * num_iterations_per_epoch
warmup_duration = 5 * num_iterations_per_epoch
cooldown_duration = 5 * num_iterations_per_epoch
scheduler_1 = LinearCyclicalScheduler(
optimizer,
"lr",
start_value=lr_max_value,
end_value=lr_max_value * 0.9,
cycle_size=(num_iterations - warmup_duration - cooldown_duration) * 2,
)
scheduler_2 = LinearCyclicalScheduler(
optimizer, "lr", start_value=lr_max_value, end_value=0.0, cycle_size=cooldown_duration * 2
)
lr_scheduler = ConcatScheduler(
schedulers=[scheduler_1, scheduler_2],
durations=[num_iterations - warmup_duration - cooldown_duration],
save_history=False,
)
lr_values = [None] * num_iterations
scheduler = create_lr_scheduler_with_warmup(
lr_scheduler,
warmup_start_value=0.0,
warmup_end_value=lr_max_value,
warmup_duration=warmup_duration,
save_history=save_history,
output_simulated_values=lr_values,
)
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"])
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
data = [0] * num_iterations_per_epoch
for _ in range(2):
lrs = []
trainer.run(data, max_epochs=max_epochs)
assert lrs == pytest.approx([v for i, v in lr_values])
if save_history:
param_history = trainer.state.param_history["lr"]
assert lrs == pytest.approx([v[0] for v in param_history])
scheduler.load_state_dict(state_dict)
def test_concat_scheduler_two_linear():
tensor = torch.zeros([1], requires_grad=True)
optimizer = torch.optim.SGD([tensor], lr=0)
scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.0, end_value=0.1, cycle_size=2)
scheduler_2 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.2, end_value=1.0, cycle_size=2)
durations = [5, ]
concat_scheduler = ConcatScheduler(schedulers=[scheduler_1, scheduler_2],
durations=durations, save_history=True)
assert concat_scheduler.get_param() == 0.0
data = [0] * 10
max_epochs = 2
simulated_values = ConcatScheduler.simulate_values(num_events=len(data) * max_epochs,
schedulers=[scheduler_1, scheduler_2],
durations=durations)
lrs = []
def save_lr(engine):
lrs.append(optimizer.param_groups[0]['lr'])
trainer = Engine(lambda engine, batch: None)
trainer.add_event_handler(Events.ITERATION_STARTED, concat_scheduler)
trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr)
trainer.run(data, max_epochs=max_epochs)
assert lrs == list(map(pytest.approx, [
# first LinearCyclicalScheduler
0.0, 0.1, 0.0, 0.1, 0.0,
# second LinearCyclicalScheduler
0.2, 1.0, 0.2, 1.0, 0.2, 1.0, 0.2, 1.0,
0.2, 1.0, 0.2, 1.0, 0.2, 1.0, 0.2,
]))
state_lrs = trainer.state.param_history['lr']
assert len(state_lrs) == len(lrs)
# Unpack singleton lists
assert [group[0] for group in state_lrs] == lrs
assert lrs == pytest.approx([v for i, v in simulated_values])
def test_concat_scheduler_3_schedulers():
tensor = torch.zeros([1], requires_grad=True)
optimizer = torch.optim.SGD([tensor], lr=0)
scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=1.0, end_value=0.5, cycle_size=20)
scheduler_2 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.5, end_value=0.45, cycle_size=10)
scheduler_3 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.5, end_value=0.0, cycle_size=20)
durations = [10, 5]
concat_scheduler = ConcatScheduler(schedulers=[scheduler_1, scheduler_2, scheduler_3],
durations=durations, save_history=True)
data = [0] * 10
max_epochs = 2
simulated_values = ConcatScheduler.simulate_values(num_events=len(data) * max_epochs,
schedulers=[scheduler_1, scheduler_2, scheduler_3],
durations=durations)
lrs = []
def save_lr(engine):
lrs.append(optimizer.param_groups[0]['lr'])
trainer = Engine(lambda engine, batch: None)
trainer.add_event_handler(Events.ITERATION_STARTED, concat_scheduler)
trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr)
trainer.run(data, max_epochs=max_epochs)
assert lrs == list(map(pytest.approx, [
# Cycle 1 of the first LinearCyclicalScheduler
1.0, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65, 0.6, 0.55,
# Cycle 1 of the second LinearCyclicalScheduler
0.5, 0.49, 0.48, 0.47, 0.46,
# Cycle 1 of the third LinearCyclicalScheduler
0.5, 0.45, 0.4, 0.35, 0.3,
]))
state_lrs = trainer.state.param_history['lr']
assert len(state_lrs) == len(lrs)
# Unpack singleton lists
assert [group[0] for group in state_lrs] == lrs
assert lrs == pytest.approx([v for i, v in simulated_values])
def _test(duration_vals_as_np_int):
scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=1.0, end_value=0.0, cycle_size=10)
scheduler_2 = CosineAnnealingScheduler(optimizer, "lr", start_value=0.0, end_value=1.0, cycle_size=10)
durations = [10, ]
if duration_vals_as_np_int:
durations = [np.int64(t) for t in durations]
concat_scheduler = ConcatScheduler(schedulers=[scheduler_1, scheduler_2],
durations=durations, save_history=True)
data = [0] * 10
max_epochs = 2
simulated_values = ConcatScheduler.simulate_values(num_events=len(data) * max_epochs,
schedulers=[scheduler_1, scheduler_2],
durations=durations)
lrs = []
def save_lr(engine):
lrs.append(optimizer.param_groups[0]['lr'])
trainer = Engine(lambda engine, batch: None)
trainer.add_event_handler(Events.ITERATION_STARTED, concat_scheduler)
trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr)
trainer.run(data, max_epochs=max_epochs)
assert lrs == list(map(pytest.approx, [
# Cycle 1 of the LinearCyclicalScheduler
1.0, 0.8, 0.6, 0.4, 0.2,
0.0, 0.2, 0.4, 0.6, 0.8,
# Cycle 1 of the CosineAnnealingScheduler
0.0, 0.02447174185242318, 0.09549150281252627, 0.20610737385376332, 0.3454915028125263,
0.5, 0.6545084971874737, 0.7938926261462365, 0.9045084971874737, 0.9755282581475768,
]))
state_lrs = trainer.state.param_history['lr']
assert len(state_lrs) == len(lrs)
# Unpack singleton lists
assert [group[0] for group in state_lrs] == lrs
assert lrs == pytest.approx([v for i, v in simulated_values])
def test_concat_scheduler():
tensor = torch.zeros([1], requires_grad=True)
optimizer = torch.optim.SGD([tensor], lr=0)
concat_scheduler = ConcatScheduler(
optimizer=optimizer,
param_name='lr',
schedulers_list=[
(
LinearCyclicalScheduler,
dict(
start_value=1,
end_value=0,
cycle_size=10
),
10
),
(
CosineAnnealingScheduler,
dict(
start_value=0,
end_value=1,
cycle_size=10
),
None
)
],
save_history=True
)
lrs = []
def save_lr(engine):
lrs.append(optimizer.param_groups[0]['lr'])
trainer = Engine(lambda engine, batch: None)
trainer.add_event_handler(Events.ITERATION_COMPLETED, concat_scheduler)
trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr)
trainer.run([0] * 10, max_epochs=2)
assert lrs == list(map(pytest.approx, [
# Cycle 1 of the LinearCyclicalScheduler
1.0, 0.8, 0.6, 0.4, 0.2,
0.0, 0.2, 0.4, 0.6, 0.8,
# Cycle 1 of the CosineAnnealingScheduler
0.0, 0.02447174185242318, 0.09549150281252627, 0.20610737385376332, 0.3454915028125263,
0.5, 0.6545084971874737, 0.7938926261462365, 0.9045084971874737, 0.9755282581475768,
]))
def test_concat_scheduler_asserts():
tensor = torch.zeros([1], requires_grad=True)
optimizer = torch.optim.SGD([tensor], lr=0)
scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=1.0, end_value=0.0, cycle_size=10)
scheduler_2 = CosineAnnealingScheduler(optimizer, "lr", start_value=0.0, end_value=1.0, cycle_size=10)
with pytest.raises(TypeError, match=r"Argument schedulers should be a sequence"):
ConcatScheduler(schedulers=None, durations=[])
with pytest.raises(ValueError, match=r"Argument schedulers should be of more than one parameter schedulers"):
ConcatScheduler(schedulers=[], durations=[])
with pytest.raises(ValueError, match=r"Argument schedulers should be of more than one parameter schedulers"):
ConcatScheduler(schedulers=[scheduler_1], durations=[10])
with pytest.raises(TypeError, match=r"Value at index 1 of schedulers should be a parameter scheduler"):
ConcatScheduler(schedulers=[scheduler_1, 12], durations=[10])
with pytest.raises(ValueError, match=r"Incorrect number schedulers or duration values"):
ConcatScheduler(schedulers=[scheduler_1, scheduler_2], durations=[10, 5])
with pytest.raises(ValueError, match=r"Argument durations should be list/tuple of integers"):
ConcatScheduler(schedulers=[scheduler_1, scheduler_2, scheduler_2], durations=[15, 12.0])
with pytest.raises(TypeError, match=r"Argument durations should be list/tuple"):
ConcatScheduler(schedulers=[scheduler_1, scheduler_2], durations="abc")
with pytest.raises(TypeError, match=r"Argument param_names should be list or tuple"):
ConcatScheduler.simulate_values(
num_events=123, schedulers=[scheduler_1, scheduler_2], durations=[15], param_names="abc"
)
with pytest.raises(ValueError, match=r"Argument param_names should be list or tuple of strings"):
ConcatScheduler.simulate_values(
num_events=123, schedulers=[scheduler_1, scheduler_2], durations=[15], param_names=[1]
)
optimizer_2 = torch.optim.SGD([tensor], lr=0)
scheduler_3 = CosineAnnealingScheduler(optimizer_2, "lr", start_value=0.0, end_value=1.0, cycle_size=10)
with pytest.raises(ValueError, match=r"schedulers should be related to same optimizer"):
ConcatScheduler([scheduler_1, scheduler_3], durations=[30,])
