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119 lines
4.7 KiB
119 lines
4.7 KiB
# 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 numpy as np |
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from PIL import Image |
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from paddlers.models.ppseg.datasets import Dataset |
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from paddlers.models.ppseg.utils.download import download_file_and_uncompress |
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from paddlers.models.ppseg.utils import seg_env |
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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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import paddlers.models.ppseg.transforms.functional as F |
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URL = "http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip" |
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@manager.DATASETS.add_component |
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class ADE20K(Dataset): |
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""" |
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ADE20K dataset `http://sceneparsing.csail.mit.edu/`. |
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Args: |
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transforms (list): A list of image transformations. |
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dataset_root (str, optional): The ADK20K dataset directory. Default: None. |
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mode (str, optional): A subset of the entire dataset. It should be one of ('train', 'val'). 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 = 150 |
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def __init__(self, transforms, dataset_root=None, 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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mode = mode.lower() |
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self.mode = mode |
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self.file_list = list() |
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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']: |
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raise ValueError( |
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"`mode` should be one of ('train', 'val') in ADE20K dataset, but got {}." |
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.format(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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if self.dataset_root is None: |
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self.dataset_root = download_file_and_uncompress( |
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url=URL, |
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savepath=seg_env.DATA_HOME, |
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extrapath=seg_env.DATA_HOME, |
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extraname='ADEChallengeData2016') |
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elif not os.path.exists(self.dataset_root): |
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self.dataset_root = os.path.normpath(self.dataset_root) |
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savepath, extraname = self.dataset_root.rsplit( |
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sep=os.path.sep, maxsplit=1) |
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self.dataset_root = download_file_and_uncompress( |
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url=URL, |
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savepath=savepath, |
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extrapath=savepath, |
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extraname=extraname) |
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if mode == 'train': |
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img_dir = os.path.join(self.dataset_root, 'images/training') |
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label_dir = os.path.join(self.dataset_root, 'annotations/training') |
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elif mode == 'val': |
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img_dir = os.path.join(self.dataset_root, 'images/validation') |
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label_dir = os.path.join(self.dataset_root, |
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'annotations/validation') |
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img_files = os.listdir(img_dir) |
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label_files = [i.replace('.jpg', '.png') for i in img_files] |
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for i in range(len(img_files)): |
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img_path = os.path.join(img_dir, img_files[i]) |
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label_path = os.path.join(label_dir, label_files[i]) |
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self.file_list.append([img_path, label_path]) |
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def __getitem__(self, idx): |
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data = {} |
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data['trans_info'] = [] |
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image_path, label_path = self.file_list[idx] |
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data['img'] = image_path |
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data['gt_fields'] = [ |
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] # If key in gt_fields, the data[key] have transforms synchronous. |
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if self.mode == 'val': |
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data = self.transforms(data) |
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label = np.asarray(Image.open(label_path)) |
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# The class 0 is ignored. And it will equal to 255 after |
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# subtracted 1, because the dtype of label is uint8. |
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label = label - 1 |
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label = label[np.newaxis, :, :] |
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data['label'] = label |
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return data |
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else: |
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data['label'] = label_path |
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data['gt_fields'].append('label') |
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data = self.transforms(data) |
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data['label'] = data['label'] - 1 |
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# Recover the ignore pixels adding by transform |
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data['label'][data['label'] == 254] = 255 |
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if self.edge: |
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edge_mask = F.mask_to_binary_edge( |
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label, radius=2, num_classes=self.num_classes) |
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data['edge'] = edge_mask |
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return data
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