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from fvcore.common.param_scheduler import MultiStepParamScheduler | |
from detectron2.config import LazyCall as L | |
from detectron2.solver import WarmupParamScheduler | |
def default_X_scheduler(num_X): | |
""" | |
Returns the config for a default multi-step LR scheduler such as "1x", "3x", | |
commonly referred to in papers, where every 1x has the total length of 1440k | |
training images (~12 COCO epochs). LR is decayed twice at the end of training | |
following the strategy defined in "Rethinking ImageNet Pretraining", Sec 4. | |
Args: | |
num_X: a positive real number | |
Returns: | |
DictConfig: configs that define the multiplier for LR during training | |
""" | |
# total number of iterations assuming 16 batch size, using 1440000/16=90000 | |
total_steps_16bs = num_X * 90000 | |
if num_X <= 2: | |
scheduler = L(MultiStepParamScheduler)( | |
values=[1.0, 0.1, 0.01], | |
# note that scheduler is scale-invariant. This is equivalent to | |
# milestones=[6, 8, 9] | |
milestones=[60000, 80000, 90000], | |
) | |
else: | |
scheduler = L(MultiStepParamScheduler)( | |
values=[1.0, 0.1, 0.01], | |
milestones=[total_steps_16bs - 60000, total_steps_16bs - 20000, total_steps_16bs], | |
) | |
return L(WarmupParamScheduler)( | |
scheduler=scheduler, | |
warmup_length=1000 / total_steps_16bs, | |
warmup_method="linear", | |
warmup_factor=0.001, | |
) | |
lr_multiplier_1x = default_X_scheduler(1) | |
lr_multiplier_2x = default_X_scheduler(2) | |
lr_multiplier_3x = default_X_scheduler(3) | |
lr_multiplier_6x = default_X_scheduler(6) | |
lr_multiplier_9x = default_X_scheduler(9) | |