# Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#           http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import transforms as T


class DetectionPresetTrain:
    def __init__(self, data_augmentation, hflip_prob=0.5, mean=(123., 117., 104.)):
        if data_augmentation == 'hflip':
            self.transforms = T.Compose([
                T.RandomHorizontalFlip(p=hflip_prob),
                T.ToTensor(),
            ])
        elif data_augmentation == 'ssd':
            self.transforms = T.Compose([
                T.RandomPhotometricDistort(),
                T.RandomZoomOut(fill=list(mean)),
                T.RandomIoUCrop(),
                T.RandomHorizontalFlip(p=hflip_prob),
                T.ToTensor(),
            ])
        elif data_augmentation == 'ssdlite':
            self.transforms = T.Compose([
                T.RandomIoUCrop(),
                T.RandomHorizontalFlip(p=hflip_prob),
                T.ToTensor(),
            ])
        else:
            raise ValueError(f'Unknown data augmentation policy "{data_augmentation}"')

    def __call__(self, img, target):
        return self.transforms(img, target)


class DetectionPresetEval:
    def __init__(self):
        self.transforms = T.ToTensor()

    def __call__(self, img, target):
        return self.transforms(img, target)