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from ..data_aug import cityscapes_like_image_train_aug, cityscapes_like_image_test_aug, cityscapes_like_label_aug
from torchvision.datasets import Cityscapes as RawCityscapes
from ..ab_dataset import ABDataset
from ..dataset_split import train_val_test_split
import numpy as np
from typing import Dict, List, Optional
from torchvision.transforms import Compose, Lambda
import os
from ..registery import dataset_register
@dataset_register(
name='Cityscapes',
classes=[
'road', 'sidewalk', 'building', 'wall',
'fence', 'pole', 'light', 'sign',
'vegetation', 'terrain', 'sky', 'people', # person
'rider', 'car', 'truck', 'bus', 'train',
'motocycle', 'bicycle'
],
task_type='Semantic Segmentation',
object_type='Autonomous Driving',
# class_aliases=[['automobile', 'car']],
class_aliases=[],
shift_type=None
)
class Cityscapes(ABDataset):
def create_dataset(self, root_dir: str, split: str, transform: Optional[Compose],
classes: List[str], ignore_classes: List[str], idx_map: Optional[Dict[int, int]]):
if transform is None:
x_transform = cityscapes_like_image_train_aug() if split == 'train' else cityscapes_like_image_test_aug()
y_transform = cityscapes_like_label_aug()
self.transform = x_transform
else:
x_transform = transform
y_transform = cityscapes_like_label_aug()
# images_path, labels_path = [], []
# for p in os.listdir(os.path.join(root_dir, 'images')):
# p = os.path.join(root_dir, 'images', p)
# if not p.endswith('png'):
# continue
# images_path += [p]
# labels_path += [p.replace('images', 'labels_gt')]
ignore_label = 255
raw_idx_map_in_y_transform = {-1: ignore_label, 0: ignore_label, 1: ignore_label, 2: ignore_label,
3: ignore_label, 4: ignore_label, 5: ignore_label, 6: ignore_label,
7: 0, 8: 1, 9: ignore_label, 10: ignore_label, 11: 2, 12: 3, 13: 4,
14: ignore_label, 15: ignore_label, 16: ignore_label, 17: 5,
18: ignore_label, 19: 6, 20: 7, 21: 8, 22: 9, 23: 10, 24: 11, 25: 12, 26: 13, 27: 14,
28: 15, 29: ignore_label, 30: ignore_label, 31: 16, 32: 17, 33: 18}
idx_map_in_y_transform = {i: i for i in range(len(classes))}
idx_map_in_y_transform[255] = 255
# dataset.targets = np.asarray(dataset.targets)
if len(ignore_classes) > 0:
for ignore_class in ignore_classes:
# dataset.data = dataset.data[dataset.targets != classes.index(ignore_class)]
# dataset.targets = dataset.targets[dataset.targets != classes.index(ignore_class)]
idx_map_in_y_transform[ignore_class] = 255
if idx_map is not None:
# note: the code below seems correct but has bug!
# for old_idx, new_idx in idx_map.items():
# dataset.targets[dataset.targets == old_idx] = new_idx
# for ti, t in enumerate(dataset.targets):
# dataset.targets[ti] = idx_map[t]
for k, v in idx_map.items():
idx_map_in_y_transform[k] = v
# merge idx map
final_idx_map_in_y_transform = {}
for k1, v1 in raw_idx_map_in_y_transform.items():
final_idx_map_in_y_transform[k1] = idx_map_in_y_transform[v1]
idx_map_in_y_transform = final_idx_map_in_y_transform
def map_class(x):
for k, v in idx_map_in_y_transform.items():
x[x == k] = v
return x
y_transform = Compose([
*y_transform.transforms,
Lambda(lambda x: map_class(x))
])
dataset = RawCityscapes(root_dir, target_type='semantic',
transform=x_transform, target_transform=y_transform)
dataset = train_val_test_split(dataset, split)
return dataset
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