Let DefaultPredictor support HSV or LAB color schemes

## Instructions To Reproduce the 🐛 Bug:
1. Full runnable code or full changes you made:
```
#We are importing our own Trainer Module here to use the COCO validation evaluation during training. Otherwise no validation eval occurs.
from detectron2.engine import DefaultTrainer
from detectron2.evaluation import COCOEvaluator

class CocoTrainer(DefaultTrainer):
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
cfg.INPUT.FORMAT = "HSV"
if output_folder is None:
os.makedirs("coco_eval", exist_ok=True)
output_folder = "coco_eval"

return COCOEvaluator(dataset_name, cfg, False, output_folder)

from detectron2.engine import DefaultTrainer
from detectron2.config import get_cfg
import os

cfg = get_cfg()
cfg.merge_from_file("./detectron2_repo/configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml")
cfg.DATASETS.TRAIN = ("rwythr",)
cfg.DATASETS.TEST = () # no metrics implemented for this dataset
cfg.TEST.EVAL_PERIOD = 100
cfg.DATALOADER.NUM_WORKERS = 2
cfg.MODEL.WEIGHTS = "detectron2://COCO-Detection/faster_rcnn_R_101_FPN_3x/137851257/model_final_f6e8b1.pkl"
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.SOLVER.BASE_LR = 0.0002
cfg.SOLVER.MAX_ITER = 10 # 300 iterations seems good enough, but you can certainly train longer
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128 # faster, and good enough for this toy dataset
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
cfg.MODEL.DEVICE = "cpu"
cfg.INPUT.RANDOM_FLIP = "horizontal"
# Cropping type. See documentation of `detectron2.data.transforms.RandomCrop` for explanation.
cfg.INPUT.CROP.TYPE = "relative_range"
# Size of crop in range (0, 1] if CROP.TYPE is "relative" or "relative_range" and in number of
# pixels if CROP.TYPE is "absolute"
cfg.INPUT.CROP.SIZE = [0.9, 0.9]
cfg.INPUT.FORMAT = "HSV"

os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
If making changes to the project itself, please use output of the following command:
git rev-parse HEAD; git diff

```
2. What exact command you run:
#test evaluation
from detectron2.data import DatasetCatalog, MetadataCatalog, build_detection_test_loader
from detectron2.evaluation import COCOEvaluator, inference_on_dataset

cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.50
predictor = DefaultPredictor(cfg)
evaluator = COCOEvaluator("rwythr", cfg, False, output_dir="./output/")
val_loader = build_detection_test_loader(cfg, "rwythr")
inference_on_dataset(trainer.model, val_loader, evaluator)
3. __Full logs__ or other relevant observations:
```
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
in ()
5 cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth")
6 cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.50
----> 7 predictor = DefaultPredictor(cfg)
8 evaluator = COCOEvaluator("rwythr", cfg, False, output_dir="./output/")
9 val_loader = build_detection_test_loader(cfg, "rwythr")

/content/detectron2_repo/detectron2/engine/defaults.py in __init__(self, cfg)
294
295 self.input_format = cfg.INPUT.FORMAT
--> 296 assert self.input_format in ["RGB", "BGR"], self.input_format
297
298 def __call__(self, original_image):

AssertionError: HSV
```
4. please simplify the steps as much as possible so they do not require additional resources to
run, such as a private dataset.

## Expected behavior:

I assumed HSV shall work directly but failing in evaluation. Detectron configuration [documentation ](url)mentions that we can use HSV like
`_C.INPUT.FORMAT = "HSV"`

## Environment:

Provide your environment information using the following command:
```
wget -nc -q https://github.com/facebookresearch/detectron2/raw/master/detectron2/utils/collect_env.py && python collect_env.py
```
No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
--------------------- ---------------------------------------------------------------
sys.platform linux
Python 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]
numpy 1.19.5
detectron2 0.4.1 @/content/detectron2_repo/detectron2
Compiler GCC 7.5
CUDA compiler not available
DETECTRON2_ENV_MODULE
PyTorch 1.8.1+cu101 @/usr/local/lib/python3.7/dist-packages/torch
PyTorch debug build False
GPU available False
Pillow 7.1.2
torchvision 0.9.1+cu101 @/usr/local/lib/python3.7/dist-packages/torchvision
fvcore 0.1.5
iopath 0.1.8
cv2 4.1.2
--------------------- ---------------------------------------------------------------
PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- NNPACK is enabled
- CPU capability usage: AVX2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.1, CUDNN_VERSION=7.6.3, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.8.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

If your issue looks like an installation issue / environment issue,
please first try to solve it yourself with the instructions in
https://detectron2.readthedocs.io/tutorials/install.html#common-installation-issues

Start in detectron2/engine/defaults.py at the DefaultPredictor assertion shown in the traceback, then trace how cfg.INPUT.FORMAT is handled for predictor input and compare it with the configuration documentation's HSV declaration. Reproduce the failure with the provided predictor setup and verify that HSV and LAB are accepted consistently when the work is complete.

Written by the indexing model from the issue text.