_base_ = './queryinst_r50_fpn_1x_coco.py'

train_pipeline = [
    dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(
        type='RandomChoiceResize',
        scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
                (608, 1333), (640, 1333), (672, 1333), (704, 1333),
                (736, 1333), (768, 1333), (800, 1333)],
        keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]

train_dataloader = dict(dataset=dict(pipeline=train_pipeline))

# learning policy
max_epochs = 36
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=max_epochs)

param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
    dict(
        type='MultiStepLR',
        begin=0,
        end=max_epochs,
        by_epoch=True,
        milestones=[27, 33],
        gamma=0.1)
]

_base_ = './queryinst_r50_fpn_1x_coco.py'

train_pipeline = [
    dict(
        type='LoadImageFromFile',
        file_client_args={{_base_.file_client_args}}),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(
        type='RandomChoiceResize',
        scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
                (608, 1333), (640, 1333), (672, 1333), (704, 1333),
                (736, 1333), (768, 1333), (800, 1333)],
        keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]

train_dataloader = dict(dataset=dict(pipeline=train_pipeline))

# learning policy
max_epochs = 36
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=max_epochs)

param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
    dict(
        type='MultiStepLR',
        begin=0,
        end=max_epochs,
        by_epoch=True,
        milestones=[27, 33],
        gamma=0.1)
]
