108 lines
3.8 KiB
Python
108 lines
3.8 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import numpy as np
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import os
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from paddle.io import Dataset
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from paddle.vision import transforms
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import cv2
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import warnings
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from ppcls.data import preprocess
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from ppcls.data.preprocess import transform
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from ppcls.data.preprocess.ops.operators import DecodeImage
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from ppcls.utils import logger
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from ppcls.data.dataloader.common_dataset import create_operators
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class MultiScaleDataset(Dataset):
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def __init__(
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self,
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image_root,
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cls_label_path,
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transform_ops=None, ):
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self._img_root = image_root
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self._cls_path = cls_label_path
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self.transform_ops = transform_ops
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self.images = []
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self.labels = []
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self._load_anno()
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self.has_crop_flag = 1
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def _load_anno(self, seed=None):
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assert os.path.exists(self._cls_path)
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assert os.path.exists(self._img_root)
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self.images = []
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self.labels = []
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with open(self._cls_path) as fd:
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lines = fd.readlines()
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if seed is not None:
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np.random.RandomState(seed).shuffle(lines)
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for l in lines:
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l = l.strip().split(" ")
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self.images.append(os.path.join(self._img_root, l[0]))
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self.labels.append(np.int64(l[1]))
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assert os.path.exists(self.images[-1])
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def __getitem__(self, properties):
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# properites is a tuple, contains (width, height, index)
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img_width = properties[0]
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img_height = properties[1]
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index = properties[2]
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has_crop = False
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if self.transform_ops:
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for i in range(len(self.transform_ops)):
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op = self.transform_ops[i]
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resize_op = ['RandCropImage', 'ResizeImage', 'CropImage']
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for resize in resize_op:
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if resize in op:
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if self.has_crop_flag:
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logger.warning(
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"Multi scale dataset will crop image according to the multi scale resolution"
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)
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self.transform_ops[i][resize] = {
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'size': (img_width, img_height)
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}
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has_crop = True
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self.has_crop_flag = 0
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if has_crop == False:
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logger.error("Multi scale dateset requests RandCropImage")
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raise RuntimeError("Multi scale dateset requests RandCropImage")
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self._transform_ops = create_operators(self.transform_ops)
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try:
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with open(self.images[index], 'rb') as f:
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img = f.read()
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if self._transform_ops:
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img = transform(img, self._transform_ops)
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img = img.transpose((2, 0, 1))
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return (img, self.labels[index])
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except Exception as ex:
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logger.error("Exception occured when parse line: {} with msg: {}".
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format(self.images[index], ex))
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rnd_idx = np.random.randint(self.__len__())
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return self.__getitem__(rnd_idx)
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def __len__(self):
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return len(self.images)
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@property
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def class_num(self):
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return len(set(self.labels))
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