# Copyright (c) 2019 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 numpy as np from collections import OrderedDict try: from collections.abc import Sequence except Exception: from collections import Sequence from paddle.io import Dataset from ppdet.core.workspace import register, serializable from ppdet.utils.download import get_dataset_path import copy @serializable class DetDataset(Dataset): """ Load detection dataset. Args: dataset_dir (str): root directory for dataset. image_dir (str): directory for images. anno_path (str): annotation file path. data_fields (list): key name of data dictionary, at least have 'image'. sample_num (int): number of samples to load, -1 means all. use_default_label (bool): whether to load default label list. """ def __init__(self, dataset_dir=None, image_dir=None, anno_path=None, data_fields=['image'], sample_num=-1, use_default_label=None, **kwargs): super(DetDataset, self).__init__() self.dataset_dir = dataset_dir if dataset_dir is not None else '' self.anno_path = anno_path self.image_dir = image_dir if image_dir is not None else '' self.data_fields = data_fields self.sample_num = sample_num self.use_default_label = use_default_label self._epoch = 0 self._curr_iter = 0 def __len__(self, ): return len(self.roidbs) def __getitem__(self, idx): # data batch roidb = copy.deepcopy(self.roidbs[idx]) if self.mixup_epoch == 0 or self._epoch < self.mixup_epoch: n = len(self.roidbs) idx = np.random.randint(n) roidb = [roidb, copy.deepcopy(self.roidbs[idx])] elif self.cutmix_epoch == 0 or self._epoch < self.cutmix_epoch: n = len(self.roidbs) idx = np.random.randint(n) roidb = [roidb, copy.deepcopy(self.roidbs[idx])] elif self.mosaic_epoch == 0 or self._epoch < self.mosaic_epoch: n = len(self.roidbs) roidb = [roidb, ] + [ copy.deepcopy(self.roidbs[np.random.randint(n)]) for _ in range(3) ] roidb['curr_iter'] = self._curr_iter self._curr_iter += 1 return self.transform(roidb) def check_or_download_dataset(self): self.dataset_dir = get_dataset_path(self.dataset_dir, self.anno_path, self.image_dir) def set_kwargs(self, **kwargs): self.mixup_epoch = kwargs.get('mixup_epoch', -1) self.cutmix_epoch = kwargs.get('cutmix_epoch', -1) self.mosaic_epoch = kwargs.get('mosaic_epoch', -1) def set_transform(self, transform): self.transform = transform def set_epoch(self, epoch_id): self._epoch = epoch_id def parse_dataset(self, ): raise NotImplementedError( "Need to implement parse_dataset method of Dataset") def get_anno(self): if self.anno_path is None: return return os.path.join(self.dataset_dir, self.anno_path) def _is_valid_file(f, extensions=('.jpg', '.jpeg', '.png', '.bmp')): return f.lower().endswith(extensions) def _make_dataset(dir): dir = os.path.expanduser(dir) if not os.path.isdir(dir): raise ('{} should be a dir'.format(dir)) images = [] for root, _, fnames in sorted(os.walk(dir, followlinks=True)): for fname in sorted(fnames): path = os.path.join(root, fname) if _is_valid_file(path): images.append(path) return images @register @serializable class ImageFolder(DetDataset): def __init__(self, dataset_dir=None, image_dir=None, anno_path=None, sample_num=-1, use_default_label=None, **kwargs): super(ImageFolder, self).__init__( dataset_dir, image_dir, anno_path, sample_num=sample_num, use_default_label=use_default_label) self._imid2path = {} self.roidbs = None self.sample_num = sample_num def check_or_download_dataset(self): return def parse_dataset(self, ): if not self.roidbs: self.roidbs = self._load_images() def _parse(self): image_dir = self.image_dir if not isinstance(image_dir, Sequence): image_dir = [image_dir] images = [] for im_dir in image_dir: if os.path.isdir(im_dir): im_dir = os.path.join(self.dataset_dir, im_dir) images.extend(_make_dataset(im_dir)) elif os.path.isfile(im_dir) and _is_valid_file(im_dir): images.append(im_dir) return images def _load_images(self): images = self._parse() ct = 0 records = [] for image in images: assert image != '' and os.path.isfile(image), \ "Image {} not found".format(image) if self.sample_num > 0 and ct >= self.sample_num: break rec = {'im_id': np.array([ct]), 'im_file': image} self._imid2path[ct] = image ct += 1 records.append(rec) assert len(records) > 0, "No image file found" return records def get_imid2path(self): return self._imid2path def set_images(self, images): self.image_dir = images self.roidbs = self._load_images()