_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
    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=norm_cfg,
        init_cfg=dict(
            type='Pretrained',
            checkpoint='open-mmlab://detectron/resnet50_gn')),
    neck=dict(norm_cfg=norm_cfg),
    roi_head=dict(
        bbox_head=dict(
            type='Shared4Conv1FCBBoxHead',
            conv_out_channels=256,
            norm_cfg=norm_cfg),
        mask_head=dict(norm_cfg=norm_cfg)))

# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)

# learning rate
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_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
    backbone=dict(
        norm_cfg=norm_cfg,
        init_cfg=dict(
            type='Pretrained',
            checkpoint='open-mmlab://detectron/resnet50_gn')),
    neck=dict(norm_cfg=norm_cfg),
    roi_head=dict(
        bbox_head=dict(
            type='Shared4Conv1FCBBoxHead',
            conv_out_channels=256,
            norm_cfg=norm_cfg),
        mask_head=dict(norm_cfg=norm_cfg)))
img_norm_cfg = dict(
    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    train=dict(pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline))
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
