"""Data pipeline for DINO."""
# dataset settings
dataset_type = "CocoDataset"
data_root = "data/coco/"
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
__img_scale = [
    (480, 1333),
    (512, 1333),
    (544, 1333),
    (576, 1333),
    (608, 1333),
    (640, 1333),
    (672, 1333),
    (704, 1333),
    (736, 1333),
    (768, 1333),
    (800, 1333),
]

# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
# from the default setting in mmdet.
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(
        type="AutoAugment",
        policies=[
            [
                dict(
                    type="Resize",
                    img_scale=__img_scale,
                    multiscale_mode="value",
                    keep_ratio=True,
                )
            ],
            [
                dict(
                    type="Resize",
                    # The radio of all image in train dataset < 7
                    # follow the original impl
                    img_scale=[(400, 4200), (500, 4200), (600, 4200)],
                    multiscale_mode="value",
                    keep_ratio=True,
                ),
                dict(type="RandomCrop", crop_type="absolute_range", crop_size=(384, 600), allow_negative_crop=True),
                dict(
                    type="Resize",
                    img_scale=__img_scale,
                    multiscale_mode="value",
                    override=True,
                    keep_ratio=True,
                ),
            ],
        ],
    ),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=1),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
# test_pipeline, NOTE the Pad's size_divisor is different from the default
# setting (size_divisor=32). While there is little effect on the performance
# whether we use the default setting or use size_divisor=1.
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=1),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        filter_empty_gt=False,
        ann_file=data_root + "annotations/instances_train2017.json",
        img_prefix=data_root + "train2017/",
        pipeline=train_pipeline,
    ),
    val=dict(
        type=dataset_type,
        ann_file=data_root + "annotations/instances_val2017.json",
        img_prefix=data_root + "val2017/",
        pipeline=test_pipeline,
    ),
    test=dict(
        type=dataset_type,
        ann_file=data_root + "annotations/instances_val2017.json",
        img_prefix=data_root + "val2017/",
        pipeline=test_pipeline,
    ),
)
