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88 lines
3.0 KiB
88 lines
3.0 KiB
3 years ago
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# Copyright (c) 2020 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
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import glob
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from paddlers.models.ppseg.datasets import Dataset
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from paddlers.models.ppseg.cvlibs import manager
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from paddlers.models.ppseg.transforms import Compose
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@manager.DATASETS.add_component
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class Cityscapes(Dataset):
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"""
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Cityscapes dataset `https://www.cityscapes-dataset.com/`.
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The folder structure is as follow:
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cityscapes
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|--leftImg8bit
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| |--train
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| |--val
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| |--test
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|--gtFine
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| |--train
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| |--val
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| |--test
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Make sure there are **labelTrainIds.png in gtFine directory. If not, please run the conver_cityscapes.py in tools.
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Args:
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transforms (list): Transforms for image.
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dataset_root (str): Cityscapes dataset directory.
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mode (str, optional): Which part of dataset to use. it is one of ('train', 'val', 'test'). Default: 'train'.
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edge (bool, optional): Whether to compute edge while training. Default: False
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"""
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NUM_CLASSES = 19
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def __init__(self, transforms, dataset_root, mode='train', edge=False):
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self.dataset_root = dataset_root
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self.transforms = Compose(transforms)
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self.file_list = list()
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mode = mode.lower()
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self.mode = mode
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self.num_classes = self.NUM_CLASSES
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self.ignore_index = 255
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self.edge = edge
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if mode not in ['train', 'val', 'test']:
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raise ValueError(
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"mode should be 'train', 'val' or 'test', but got {}.".format(
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mode))
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if self.transforms is None:
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raise ValueError("`transforms` is necessary, but it is None.")
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img_dir = os.path.join(self.dataset_root, 'leftImg8bit')
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label_dir = os.path.join(self.dataset_root, 'gtFine')
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if self.dataset_root is None or not os.path.isdir(
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self.dataset_root) or not os.path.isdir(
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img_dir) or not os.path.isdir(label_dir):
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raise ValueError(
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"The dataset is not Found or the folder structure is nonconfoumance."
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)
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label_files = sorted(
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glob.glob(
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os.path.join(label_dir, mode, '*',
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'*_gtFine_labelTrainIds.png')))
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img_files = sorted(
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glob.glob(os.path.join(img_dir, mode, '*', '*_leftImg8bit.png')))
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self.file_list = [[
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img_path, label_path
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] for img_path, label_path in zip(img_files, label_files)]
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