_base_ = './mask_rcnn_r50_fpn_1x_coco.py'

model = dict(
    # use caffe img_norm
    data_preprocessor=dict(
        mean=[103.530, 116.280, 123.675],
        std=[1.0, 1.0, 1.0],
        bgr_to_rgb=False),
    backbone=dict(
        norm_cfg=dict(requires_grad=False),
        style='caffe',
        init_cfg=dict(
            type='Pretrained',
            checkpoint='open-mmlab://detectron2/resnet50_caffe')))

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=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
                (1333, 768), (1333, 800)],
        keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs'),
]

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

_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
data_preprocessor = dict(
    mean=[103.530, 116.280, 123.675],
    std=[1.0, 1.0, 1.0],
    bgr_to_rgb=False,
    pad_size_divisor=32)
model = dict(
    # use caffe img_norm
    data_preprocessor=data_preprocessor,
    backbone=dict(
        norm_cfg=dict(requires_grad=False),
        style='caffe',
        init_cfg=dict(
            type='Pretrained',
            checkpoint='open-mmlab://detectron2/resnet50_caffe')))
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(
        type='RandomChoiceResize',
        scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
                (1333, 768), (1333, 800)],
        keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs'),
]

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