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86 lines
3.8 KiB
86 lines
3.8 KiB
# Copyright (c) 2022 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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import os.path as osp |
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import copy |
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from .base import BaseDataset |
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from paddlers.utils import logging, get_encoding, norm_path, is_pic |
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class SegDataset(BaseDataset): |
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""" |
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Dataset for semantic segmentation tasks. |
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Args: |
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data_dir (str): Root directory of the dataset. |
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file_list (str): Path of the file that contains relative paths of images and annotation files. |
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transforms (paddlers.transforms.Compose): Data preprocessing and data augmentation operators to apply. |
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label_list (str|None, optional): Path of the file that contains the category names. Defaults to None. |
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num_workers (int|str, optional): Number of processes used for data loading. If `num_workers` is 'auto', |
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the number of workers will be automatically determined according to the number of CPU cores: If |
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there are more than 16 cores，8 workers will be used. Otherwise, the number of workers will be half |
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the number of CPU cores. Defaults: 'auto'. |
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shuffle (bool, optional): Whether to shuffle the samples. Defaults to False. |
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""" |
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def __init__(self, |
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data_dir, |
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file_list, |
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transforms, |
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label_list=None, |
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num_workers='auto', |
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shuffle=False): |
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super(SegDataset, self).__init__(data_dir, label_list, transforms, |
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num_workers, shuffle) |
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# TODO batch padding |
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self.batch_transforms = None |
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self.file_list = list() |
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self.labels = list() |
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if label_list is not None: |
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with open(label_list, encoding=get_encoding(label_list)) as f: |
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for line in f: |
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item = line.strip() |
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self.labels.append(item) |
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with open(file_list, encoding=get_encoding(file_list)) as f: |
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for line in f: |
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items = line.strip().split() |
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if len(items) > 2: |
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raise ValueError( |
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"A space is defined as the delimiter to separate the image and label path, " \ |
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"so the space cannot be in the image or label path, but the line[{}] of " \ |
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" file_list[{}] has a space in the image or label path.".format(line, file_list)) |
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items[0] = norm_path(items[0]) |
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items[1] = norm_path(items[1]) |
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full_path_im = osp.join(data_dir, items[0]) |
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full_path_label = osp.join(data_dir, items[1]) |
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if not is_pic(full_path_im) or not is_pic(full_path_label): |
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continue |
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if not osp.exists(full_path_im): |
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raise IOError('Image file {} does not exist!'.format( |
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full_path_im)) |
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if not osp.exists(full_path_label): |
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raise IOError('Label file {} does not exist!'.format( |
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full_path_label)) |
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self.file_list.append({ |
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'image': full_path_im, |
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'mask': full_path_label |
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}) |
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self.num_samples = len(self.file_list) |
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logging.info("{} samples in file {}".format( |
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len(self.file_list), file_list)) |
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def __len__(self): |
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return len(self.file_list)
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