[Feature] Add AutoAugment and clean dataset mapper

[rentainhe]

TODO

• Add AutoAugment as mmdet.pipeline.auto_augment
• Clean detr_dataset_mapper for better usage and code quality
• Verify DINO-R50 AP: 48.94AP (exp-1)

Example

• The cleaned config: only with one augmentation args (without passing augmentation and augmentation_with_crop)

from omegaconf import OmegaConf

import detectron2.data.transforms as T
from detectron2.config import LazyCall as L
from detectron2.data import (
    build_detection_test_loader,
    build_detection_train_loader,
    get_detection_dataset_dicts,
)
from detectron2.evaluation import COCOEvaluator

from detrex.data.dataset_mappers.detr_dataset_mapper import DetrDatasetMapper
from detrex.data.transforms import AutoAugment

dataloader = OmegaConf.create()

dataloader.train = L(build_detection_train_loader)(
    dataset=L(get_detection_dataset_dicts)(names="coco_2017_train"),
    mapper=L(DetrDatasetMapper)(
        augmentation=L(AutoAugment)(
            policies=[
                [
                    L(T.RandomFlip)(),
                    L(T.ResizeShortestEdge)(
                        short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800),
                        max_size=1333,
                        sample_style="choice",
                    ),
                ],
                [
                    L(T.RandomFlip)(),
                    L(T.ResizeShortestEdge)(
                        short_edge_length=(400, 500, 600),
                        sample_style="choice",
                    ),
                    L(T.RandomCrop)(
                        crop_type="absolute_range",
                        crop_size=(384, 600),
                    ),
                    L(T.ResizeShortestEdge)(
                        short_edge_length=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800),
                        max_size=1333,
                        sample_style="choice",
                    ),
                ],
            ],
        ),
        is_train=True,
        mask_on=False,
        img_format="RGB",
    ),
    total_batch_size=16,
    num_workers=4,
)

dataloader.test = L(build_detection_test_loader)(
    dataset=L(get_detection_dataset_dicts)(names="coco_2017_val", filter_empty=False),
    mapper=L(DetrDatasetMapper)(
        augmentation=L(AutoAugment)(
            policies=[
                L(T.ResizeShortestEdge)(
                    short_edge_length=800,
                    max_size=1333,
                ),
            ],
        ),
        is_train=False,
        mask_on=False,
        img_format="RGB",
    ),
    num_workers=4,
)

dataloader.evaluator = L(COCOEvaluator)(
    dataset_name="${..test.dataset.names}",
)