# Copyright (c) 2020 PaddlePaddle Authors. 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 os
import math
import random
import numpy as np
from PIL import Image

from paddle.vision.datasets import DatasetFolder
from paddle.vision.transforms import transforms


class ImageNetDataset(DatasetFolder):
    def __init__(self,
                 path,
                 mode='train',
                 image_size=224,
                 resize_short_size=256):
        super(ImageNetDataset, self).__init__(path)
        self.mode = mode

        self.samples = []
        list_file = "train_list.txt" if self.mode == "train" else "val_list.txt"
        with open(os.path.join(path, list_file), 'r') as f:
            for line in f:
                _image, _label = line.strip().split(" ")
                _image = os.path.join(path, _image)
                self.samples.append((_image, int(_label)))
        normalize = transforms.Normalize(
            mean=[123.675, 116.28, 103.53], std=[58.395, 57.120, 57.375])
        if self.mode == 'train':
            self.transform = transforms.Compose([
                transforms.RandomResizedCrop(image_size),
                transforms.RandomHorizontalFlip(), transforms.Transpose(),
                normalize
            ])
        else:
            self.transform = transforms.Compose([
                transforms.Resize(resize_short_size),
                transforms.CenterCrop(image_size), transforms.Transpose(),
                normalize
            ])

    def __getitem__(self, idx):
        img_path, label = self.samples[idx]
        img = Image.open(img_path).convert('RGB')
        label = np.array([label]).astype(np.int64)
        return self.transform(img), label

    def __len__(self):
        return len(self.samples)
