def test_lr_scheduling_on_non_torch_optimizers():
# tests https://github.com/pytorch/ignite/issues/1162
optimizer = MagicMock()
optimizer.param_groups = [{"params": 0}]
FakeParamScheduler(optimizer, "lr")
tensor = torch.zeros([1], requires_grad=True)
base_optimizer = torch.optim.SGD([tensor], lr=0)
optimizer = MockFP16DeepSpeedZeroOptimizer(base_optimizer)
milestones_values = [(5, 0.5), (15, 1.0)]
scheduler = PiecewiseLinear(optimizer, "lr", milestones_values=milestones_values)
def save_lr(engine):
lrs.append(optimizer.param_groups[0]["lr"])
trainer = Engine(lambda engine, batch: None)
trainer.add_event_handler(Events.ITERATION_COMPLETED, scheduler)
trainer.add_event_handler(Events.ITERATION_COMPLETED, save_lr)
lrs = []
trainer.run([0] * 15, max_epochs=1)
assert lrs == list(
map(pytest.approx, [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95])
)
def test_opt_params_handler_on_non_torch_optimizers():
tensor = torch.zeros([1], requires_grad=True)
base_optimizer = torch.optim.SGD([tensor], lr=0.1234)
optimizer = MockFP16DeepSpeedZeroOptimizer(base_optimizer)
handler = DummyOptParamsHandler(optimizer=optimizer, param_name="lr")
res = handler(engine=None, logger=None, event_name=None)
assert isinstance(res, dict)
assert "lr/group_0" in res and res["lr/group_0"] == 0.1234