# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
from mmengine.optim.scheduler.lr_scheduler import LinearLR, MultiStepLR
from mmengine.runner.loops import EpochBasedTrainLoop, TestLoop, ValLoop
from torch.optim.sgd import SGD

# training schedule for 1x
train_cfg = dict(type=EpochBasedTrainLoop, max_epochs=24, val_interval=1)
val_cfg = dict(type=ValLoop)
test_cfg = dict(type=TestLoop)

# learning rate
param_scheduler = [
    dict(type=LinearLR, start_factor=0.001, by_epoch=False, begin=0, end=500),
    dict(
        type=MultiStepLR,
        begin=0,
        end=24,
        by_epoch=True,
        milestones=[16, 22],
        gamma=0.1)
]

# optimizer
optim_wrapper = dict(
    type=OptimWrapper,
    optimizer=dict(type=SGD, lr=0.02, momentum=0.9, weight_decay=0.0001))

# Default setting for scaling LR automatically
#   - `enable` means enable scaling LR automatically
#       or not by default.
#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)