# ****************************************************************************
# BSD 3-Clause License
# # This model is originally from the PyTorch torchvision repo
# (https://github.com/pytorch/vision/blob/main/references/detection/presets.py)
# ****************************************************************************

# ****************************************************************************
# Copyright (c) Soumith Chintala 2016, 
# All rights reserved.

# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:

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#   list of conditions and the following disclaimer.

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# ****************************************************************************

import torch
import transforms as T


class DetectionPresetTrain:
    def __init__(self, data_augmentation, hflip_prob=0.5, mean=(123.0, 117.0, 104.0)):
        if data_augmentation == "hflip":
            self.transforms = T.Compose(
                [
                    T.RandomHorizontalFlip(p=hflip_prob),
                    T.PILToTensor(),
                    T.ConvertImageDtype(torch.float),
                ]
            )
        elif data_augmentation == "ssd":
            self.transforms = T.Compose(
                [
                    T.RandomPhotometricDistort(),
                    T.RandomZoomOut(fill=list(mean)),
                    T.RandomIoUCrop(),
                    T.RandomHorizontalFlip(p=hflip_prob),
                    T.PILToTensor(),
                    T.ConvertImageDtype(torch.float),
                ]
            )
        elif data_augmentation == "ssdlite":
            self.transforms = T.Compose(
                [
                    T.RandomIoUCrop(),
                    T.RandomHorizontalFlip(p=hflip_prob),
                    T.PILToTensor(),
                    T.ConvertImageDtype(torch.float),
                ]
            )
        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)
