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Source code for torchvision.datasets.cityscapes

import json
import os
from collections import namedtuple

import torch.utils.data as data
from PIL import Image


[docs]class Cityscapes(data.Dataset): """`Cityscapes <http://www.cityscapes-dataset.com/>`_ Dataset. Args: root (string): Root directory of dataset where directory ``leftImg8bit`` and ``gtFine`` or ``gtCoarse`` are located. split (string, optional): The image split to use, ``train``, ``test`` or ``val`` if mode="gtFine" otherwise ``train``, ``train_extra`` or ``val`` mode (string, optional): The quality mode to use, ``gtFine`` or ``gtCoarse`` target_type (string or list, optional): Type of target to use, ``instance``, ``semantic``, ``polygon`` or ``color``. Can also be a list to output a tuple with all specified target types. transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed version. E.g, ``transforms.RandomCrop`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. Examples: Get semantic segmentation target .. code-block:: python dataset = Cityscapes('./data/cityscapes', split='train', mode='fine', target_type='semantic') img, smnt = dataset[0] Get multiple targets .. code-block:: python dataset = Cityscapes('./data/cityscapes', split='train', mode='fine', target_type=['instance', 'color', 'polygon']) img, (inst, col, poly) = dataset[0] Validate on the "coarse" set .. code-block:: python dataset = Cityscapes('./data/cityscapes', split='val', mode='coarse', target_type='semantic') img, smnt = dataset[0] """ # Based on https://github.com/mcordts/cityscapesScripts CityscapesClass = namedtuple('CityscapesClass', ['name', 'id', 'train_id', 'category', 'category_id', 'has_instances', 'ignore_in_eval', 'color']) classes = [ CityscapesClass('unlabeled', 0, 255, 'void', 0, False, True, (0, 0, 0)), CityscapesClass('ego vehicle', 1, 255, 'void', 0, False, True, (0, 0, 0)), CityscapesClass('rectification border', 2, 255, 'void', 0, False, True, (0, 0, 0)), CityscapesClass('out of roi', 3, 255, 'void', 0, False, True, (0, 0, 0)), CityscapesClass('static', 4, 255, 'void', 0, False, True, (0, 0, 0)), CityscapesClass('dynamic', 5, 255, 'void', 0, False, True, (111, 74, 0)), CityscapesClass('ground', 6, 255, 'void', 0, False, True, (81, 0, 81)), CityscapesClass('road', 7, 0, 'flat', 1, False, False, (128, 64, 128)), CityscapesClass('sidewalk', 8, 1, 'flat', 1, False, False, (244, 35, 232)), CityscapesClass('parking', 9, 255, 'flat', 1, False, True, (250, 170, 160)), CityscapesClass('rail track', 10, 255, 'flat', 1, False, True, (230, 150, 140)), CityscapesClass('building', 11, 2, 'construction', 2, False, False, (70, 70, 70)), CityscapesClass('wall', 12, 3, 'construction', 2, False, False, (102, 102, 156)), CityscapesClass('fence', 13, 4, 'construction', 2, False, False, (190, 153, 153)), CityscapesClass('guard rail', 14, 255, 'construction', 2, False, True, (180, 165, 180)), CityscapesClass('bridge', 15, 255, 'construction', 2, False, True, (150, 100, 100)), CityscapesClass('tunnel', 16, 255, 'construction', 2, False, True, (150, 120, 90)), CityscapesClass('pole', 17, 5, 'object', 3, False, False, (153, 153, 153)), CityscapesClass('polegroup', 18, 255, 'object', 3, False, True, (153, 153, 153)), CityscapesClass('traffic light', 19, 6, 'object', 3, False, False, (250, 170, 30)), CityscapesClass('traffic sign', 20, 7, 'object', 3, False, False, (220, 220, 0)), CityscapesClass('vegetation', 21, 8, 'nature', 4, False, False, (107, 142, 35)), CityscapesClass('terrain', 22, 9, 'nature', 4, False, False, (152, 251, 152)), CityscapesClass('sky', 23, 10, 'sky', 5, False, False, (70, 130, 180)), CityscapesClass('person', 24, 11, 'human', 6, True, False, (220, 20, 60)), CityscapesClass('rider', 25, 12, 'human', 6, True, False, (255, 0, 0)), CityscapesClass('car', 26, 13, 'vehicle', 7, True, False, (0, 0, 142)), CityscapesClass('truck', 27, 14, 'vehicle', 7, True, False, (0, 0, 70)), CityscapesClass('bus', 28, 15, 'vehicle', 7, True, False, (0, 60, 100)), CityscapesClass('caravan', 29, 255, 'vehicle', 7, True, True, (0, 0, 90)), CityscapesClass('trailer', 30, 255, 'vehicle', 7, True, True, (0, 0, 110)), CityscapesClass('train', 31, 16, 'vehicle', 7, True, False, (0, 80, 100)), CityscapesClass('motorcycle', 32, 17, 'vehicle', 7, True, False, (0, 0, 230)), CityscapesClass('bicycle', 33, 18, 'vehicle', 7, True, False, (119, 11, 32)), CityscapesClass('license plate', -1, -1, 'vehicle', 7, False, True, (0, 0, 142)), ] def __init__(self, root, split='train', mode='fine', target_type='instance', transform=None, target_transform=None): self.root = os.path.expanduser(root) self.mode = 'gtFine' if mode == 'fine' else 'gtCoarse' self.images_dir = os.path.join(self.root, 'leftImg8bit', split) self.targets_dir = os.path.join(self.root, self.mode, split) self.transform = transform self.target_transform = target_transform self.target_type = target_type self.split = split self.images = [] self.targets = [] if mode not in ['fine', 'coarse']: raise ValueError('Invalid mode! Please use mode="fine" or mode="coarse"') if mode == 'fine' and split not in ['train', 'test', 'val']: raise ValueError('Invalid split for mode "fine"! Please use split="train", split="test"' ' or split="val"') elif mode == 'coarse' and split not in ['train', 'train_extra', 'val']: raise ValueError('Invalid split for mode "coarse"! Please use split="train", split="train_extra"' ' or split="val"') if not isinstance(target_type, list): self.target_type = [target_type] if not all(t in ['instance', 'semantic', 'polygon', 'color'] for t in self.target_type): raise ValueError('Invalid value for "target_type"! Valid values are: "instance", "semantic", "polygon"' ' or "color"') if not os.path.isdir(self.images_dir) or not os.path.isdir(self.targets_dir): raise RuntimeError('Dataset not found or incomplete. Please make sure all required folders for the' ' specified "split" and "mode" are inside the "root" directory') for city in os.listdir(self.images_dir): img_dir = os.path.join(self.images_dir, city) target_dir = os.path.join(self.targets_dir, city) for file_name in os.listdir(img_dir): target_types = [] for t in self.target_type: target_name = '{}_{}'.format(file_name.split('_leftImg8bit')[0], self._get_target_suffix(self.mode, t)) target_types.append(os.path.join(target_dir, target_name)) self.images.append(os.path.join(img_dir, file_name)) self.targets.append(target_types)
[docs] def __getitem__(self, index): """ Args: index (int): Index Returns: tuple: (image, target) where target is a tuple of all target types if target_type is a list with more than one item. Otherwise target is a json object if target_type="polygon", else the image segmentation. """ image = Image.open(self.images[index]).convert('RGB') targets = [] for i, t in enumerate(self.target_type): if t == 'polygon': target = self._load_json(self.targets[index][i]) else: target = Image.open(self.targets[index][i]) targets.append(target) target = tuple(targets) if len(targets) > 1 else targets[0] if self.transform: image = self.transform(image) if self.target_transform: target = self.target_transform(target) return image, target
def __len__(self): return len(self.images) def __repr__(self): fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) fmt_str += ' Split: {}\n'.format(self.split) fmt_str += ' Mode: {}\n'.format(self.mode) fmt_str += ' Type: {}\n'.format(self.target_type) fmt_str += ' Root Location: {}\n'.format(self.root) tmp = ' Transforms (if any): ' fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) tmp = ' Target Transforms (if any): ' fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) return fmt_str def _load_json(self, path): with open(path, 'r') as file: data = json.load(file) return data def _get_target_suffix(self, mode, target_type): if target_type == 'instance': return '{}_instanceIds.png'.format(mode) elif target_type == 'semantic': return '{}_labelIds.png'.format(mode) elif target_type == 'color': return '{}_color.png'.format(mode) else: return '{}_polygons.json'.format(mode)

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