_base_ = './decoupled_solo_r50_fpn_3x_coco.py'

# model settings
model = dict(
    mask_head=dict(
        type='DecoupledSOLOLightHead',
        num_classes=80,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        strides=[8, 8, 16, 32, 32],
        scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)),
        pos_scale=0.2,
        num_grids=[40, 36, 24, 16, 12],
        cls_down_index=0,
        loss_mask=dict(
            type='DiceLoss', use_sigmoid=True, activate=False,
            loss_weight=3.0),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))

train_pipeline = [
    dict(
        type='LoadImageFromFile',
        file_client_args={{_base_.file_client_args}}),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(
        # TODO: Update after mmcv.RandomChoiceResize finish refactor
        type='RandomChoiceResize',
        scales=[(852, 512), (852, 480), (852, 448), (852, 416), (852, 384),
                (852, 352)],
        resize_cfg=dict(type='Resize', keep_ratio=True)),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(
        type='LoadImageFromFile',
        file_client_args={{_base_.file_client_args}}),
    dict(type='Resize', scale=(852, 512), keep_ratio=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]

train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader

_base_ = './decoupled_solo_r50_fpn_3x_coco.py'

# model settings
model = dict(
    mask_head=dict(
        type='DecoupledSOLOLightHead',
        num_classes=80,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        strides=[8, 8, 16, 32, 32],
        scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)),
        pos_scale=0.2,
        num_grids=[40, 36, 24, 16, 12],
        cls_down_index=0,
        loss_mask=dict(
            type='DiceLoss', use_sigmoid=True, activate=False,
            loss_weight=3.0),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(
        type='Resize',
        img_scale=[(852, 512), (852, 480), (852, 448), (852, 416), (852, 384),
                   (852, 352)],
        multiscale_mode='value',
        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=(852, 512),
        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))
