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106 lines
4.8 KiB
106 lines
4.8 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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from paddlers.transforms import decode_seg_mask |
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class SegDataset(BaseDataset): |
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"""读取语义分割任务数据集，并对样本进行相应的处理。 |
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Args: |
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data_dir (str): 数据集所在的目录路径。 |
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file_list (str): 描述数据集图片文件和对应标注文件的文件路径（文本内每行路径为相对data_dir的相对路）。 |
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label_list (str): 描述数据集包含的类别信息文件路径。默认值为None。 |
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transforms (paddlers.transforms.Compose): 数据集中每个样本的预处理/增强算子。 |
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num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。当设为'auto'时，根据 |
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系统的实际CPU核数设置`num_workers`: 如果CPU核数的一半大于8，则`num_workers`为8，否则为CPU核数的 |
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一半。 |
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shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。 |
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apply_im_only (bool, optional): 是否绕过对标签的数据增强和预处理。在模型验证和推理阶段一般指定此选项为True。默认为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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label_list=None, |
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transforms=None, |
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num_workers='auto', |
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shuffle=False, |
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apply_im_only=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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self.apply_im_only = apply_im_only |
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# TODO：非None时，让用户跳转数据集分析生成label_list |
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# 不要在此处分析label file |
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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 Exception( |
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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 __getitem__(self, idx): |
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sample = copy.deepcopy(self.file_list[idx]) |
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if self.apply_im_only: |
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has_mask = False |
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if 'mask' in sample: |
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has_mask = True |
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mask = decode_seg_mask(sample['mask']) |
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del sample['mask'] |
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sample = self.transforms.apply_transforms(sample) |
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if has_mask: |
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sample['mask'] = mask |
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outputs = self.transforms.arrange_outputs(sample) |
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else: |
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outputs = super().__getitem__(idx) |
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return outputs |
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def __len__(self): |
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return len(self.file_list)
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