You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 

83 lines
2.8 KiB

# Copyright (c) 2020 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 glob
from paddlers.models.ppseg.datasets import Dataset
from paddlers.models.ppseg.cvlibs import manager
from paddlers.models.ppseg.transforms import Compose
@manager.DATASETS.add_component
class CocoStuff(Dataset):
"""
COCO-Stuff dataset `https://github.com/nightrome/cocostuff`.
The folder structure is as follow:
cocostuff
|
|--images
| |--train2017
| |--val2017
|
|--annotations
| |--train2017
| |--val2017
Args:
transforms (list): Transforms for image.
dataset_root (str): Cityscapes dataset directory.
mode (str): Which part of dataset to use. it is one of ('train', 'val'). Default: 'train'.
edge (bool, optional): Whether to compute edge while training. Default: False
"""
NUM_CLASSES = 171
def __init__(self, transforms, dataset_root, mode='train', edge=False):
self.dataset_root = dataset_root
self.transforms = Compose(transforms)
self.file_list = list()
mode = mode.lower()
self.mode = mode
self.num_classes = self.NUM_CLASSES
self.ignore_index = 255
self.edge = edge
if mode not in ['train', 'val']:
raise ValueError(
"mode should be 'train', 'val', but got {}.".format(mode))
if self.transforms is None:
raise ValueError("`transforms` is necessary, but it is None.")
img_dir = os.path.join(self.dataset_root, 'images')
label_dir = os.path.join(self.dataset_root, 'annotations')
if self.dataset_root is None or not os.path.isdir(
self.dataset_root) or not os.path.isdir(
img_dir) or not os.path.isdir(label_dir):
raise ValueError(
"The dataset is not Found or the folder structure is nonconfoumance."
)
label_files = sorted(
glob.glob(os.path.join(label_dir, mode + '2017', '*.png')))
img_files = sorted(
glob.glob(os.path.join(img_dir, mode + '2017', '*.jpg')))
self.file_list = [
[img_path, label_path]
for img_path, label_path in zip(img_files, label_files)
]