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Make optimizer static method inside trainer (#103)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Ayush Chaurasia 2022-12-27 21:32:03 +05:30 committed by GitHub
parent 5c6d11bdb2
commit 8028e2b1b8
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1 changed files with 31 additions and 33 deletions
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ultralytics/yolo/engine/trainer.py
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@ -142,7 +142,7 @@ class BaseTrainer:
# Optimizer
self.accumulate = max(round(self.args.nbs / self.batch_size), 1) # accumulate loss before optimizing
self.args.weight_decay *= self.batch_size * self.accumulate / self.args.nbs # scale weight_decay
self.optimizer = build_optimizer(model=self.model,
self.optimizer = self.build_optimizer(model=self.model,
name=self.args.optimizer,
lr=self.args.lr0,
momentum=self.args.momentum,
@ -459,10 +459,8 @@ class BaseTrainer:
self.best_fitness = best_fitness
self.start_epoch = start_epoch
@staticmethod
def build_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
# TODO: 1. docstring with example? 2. Move this inside Trainer? or utils?
# YOLOv5 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) biases no decay
g = [], [], [] # optimizer parameter groups
bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d()
for v in model.modules():