_base_ = './fcos_hrnetv2p-w32-gn-head_4xb4-1x_coco.py'

model = dict(
    data_preprocessor=dict(
        mean=[103.53, 116.28, 123.675],
        std=[57.375, 57.12, 58.395],
        bgr_to_rgb=False))

train_pipeline = [
    dict(
        type='LoadImageFromFile',
        file_client_args={{_base_.file_client_args}}),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='RandomChoiceResize',
        scales=[(1333, 640), (1333, 800)],
        keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]

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

# learning policy
max_epochs = 24
train_cfg = dict(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=[16, 22],
        gamma=0.1)
]

_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'

model = dict(
    data_preprocessor=dict(
        mean=[103.53, 116.28, 123.675],
        std=[57.375, 57.12, 58.395],
        bgr_to_rgb=False))

train_pipeline = [
    dict(
        type='LoadImageFromFile',
        file_client_args={{_base_.file_client_args}}),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='RandomChoiceResize',
        scales=[(1333, 640), (1333, 800)],
        keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]

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

# learning policy
max_epochs = 24
train_cfg = dict(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=[16, 22],
        gamma=0.1)
]
