_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'

preprocess_cfg = dict(
    mean=[103.530, 116.280, 123.675],
    std=[57.375, 57.120, 58.395],
    to_rgb=False,
    pad_size_divisor=32)

model = dict(
    # ResNeXt-101-32x8d model trained with Caffe2 at FB,
    # so the mean and std need to be changed.
    preprocess_cfg=preprocess_cfg,
    backbone=dict(
        type='ResNeXt',
        depth=101,
        groups=32,
        base_width=8,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=False),
        style='pytorch',
        init_cfg=dict(
            type='Pretrained',
            checkpoint='open-mmlab://detectron2/resnext101_32x8d')))

_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'

model = dict(
    backbone=dict(
        type='ResNeXt',
        depth=101,
        groups=32,
        base_width=8,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=False),
        style='pytorch',
        init_cfg=dict(
            type='Pretrained',
            checkpoint='open-mmlab://detectron2/resnext101_32x8d')))

# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
img_norm_cfg = dict(
    mean=[103.530, 116.280, 123.675],
    std=[57.375, 57.120, 58.395],
    to_rgb=False)

# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(
        type='Resize',
        img_scale=[(1333, 640), (1333, 800)],
        multiscale_mode='range',
        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(dataset=dict(pipeline=train_pipeline)),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline))
