CODEWITHSUNDEEP
pythonpytorchcomputer-visiondetectiondetectron
muhammad ali
muhammad ali

Reputation: 41

How to change the default optimizer in detectron2

So from what I can tell by reading the docs, the default optimizer used in detectron2 is SGD with momentum. But I was wondering if there is a way to change the default optimizer to something like Adam for example.

The closest I was able to get to was by looking at cfg.SOLVER attributes; specifically cfg.SOLVER.OPTIMIZER. I even took a look at their source code to see if different optimizer options were available.

However, I was unable to change the optimizer type.

Any help will be greatly appreciated!

Upvotes: 3

Views: 2076

Answers (2)

upper
upper

Reputation: 432

While it's best not to modify the source code, you may want to change torch.optim.SGD in the following lines:

(line 129, detectron2/detectron2/solver/build.py, inside build_optimizer function)

def build_optimizer(cfg: CfgNode, model: torch.nn.Module) -> torch.optim.Optimizer:
    """
    Build an optimizer from config.
    """
    params = get_default_optimizer_params(
        model,
        base_lr=cfg.SOLVER.BASE_LR,
        weight_decay_norm=cfg.SOLVER.WEIGHT_DECAY_NORM,
        bias_lr_factor=cfg.SOLVER.BIAS_LR_FACTOR,
        weight_decay_bias=cfg.SOLVER.WEIGHT_DECAY_BIAS,
    )
    return maybe_add_gradient_clipping(cfg, torch.optim.SGD)(
        params,
        lr=cfg.SOLVER.BASE_LR,
        momentum=cfg.SOLVER.MOMENTUM,
        nesterov=cfg.SOLVER.NESTEROV,
        weight_decay=cfg.SOLVER.WEIGHT_DECAY,
    )

Upvotes: 0

Vishal T
Vishal T

Reputation: 79

You can create a subclass from DefaultTrainer and add the build_optimizer method. Check out the below code which uses Adam -

from detectron2.config import CfgNode
from detectron2.solver.build import get_default_optimizer_params, maybe_add_gradient_clipping

class MyTrainer(DefaultTrainer):
    @classmethod
    def build_optimizer(cls, cfg: CfgNode, model: torch.nn.Module) -> torch.optim.Optimizer:
        """
        Build an optimizer from config.
        """
        params = get_default_optimizer_params(
            model,
            base_lr=cfg.SOLVER.BASE_LR,
            weight_decay_norm=cfg.SOLVER.WEIGHT_DECAY_NORM,
            bias_lr_factor=cfg.SOLVER.BIAS_LR_FACTOR,
            weight_decay_bias=cfg.SOLVER.WEIGHT_DECAY_BIAS,
        )
        return maybe_add_gradient_clipping(cfg, torch.optim.Adam)(
            params,
            lr=cfg.SOLVER.BASE_LR,
            weight_decay=cfg.SOLVER.WEIGHT_DECAY,
        )

Upvotes: 4

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