File size: 2,933 Bytes
b84549f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
from ..data_aug import imagenet_like_image_train_aug, imagenet_like_image_test_aug
from ..ab_dataset import ABDataset
from ..dataset_split import train_val_split, train_val_test_split
from torchvision.datasets import ImageFolder
import os
from typing import Dict, List, Optional
from torchvision.transforms import Compose
from ..registery import dataset_register
# with open(os.path.join(os.path.dirname(__file__), 'fruits360_classes.txt'), 'r') as f:
# classes = [line.split(':')[0].strip('"') for line in f.readlines()]
# assert len(classes) == 131
# gta_classes = [
# 'road', 'sidewalk', 'building', 'wall',
# 'fence', 'pole', 'light', 'sign',
# 'vegetation', 'terrain', 'sky', 'people', # person
# 'rider', 'car', 'truck', 'bus', 'train',
# 'motocycle', 'bicycle'
# ]
# cityscapes_classes = []
# ignore_label = 255
# m = {-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}
# for ci, c in enumerate(gta_classes):
# for k, v in m.items():
# if v == ci:
# cityscapes_classes += [c]
# print(cityscapes_classes)
# exit()
@dataset_register(
name='CityscapesCls',
classes=[
'road', 'sidewalk', 'building', 'wall',
'fence', 'pole', 'light', 'sign',
'vegetation', 'terrain', 'sky', 'people', # person
'rider', 'car', 'truck', 'bus', 'train',
'motocycle', 'bicycle'
],
task_type='Image Classification',
object_type='Autonomous Driving',
class_aliases=[],
shift_type=None
)
class CityscapesCls(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:
transform = imagenet_like_image_train_aug() if split == 'train' else imagenet_like_image_test_aug()
self.transform = transform
#root_dir = os.path.join(root_dir, 'train' if split != 'test' else 'val')
dataset = ImageFolder(root_dir, transform=transform)
if len(ignore_classes) > 0:
ignore_classes_idx = [classes.index(c) for c in ignore_classes]
dataset.samples = [s for s in dataset.samples if s[1] not in ignore_classes_idx]
if idx_map is not None:
dataset.samples = [(s[0], idx_map[s[1]]) if s[1] in idx_map.keys() else s for s in dataset.samples]
dataset = train_val_test_split(dataset, split)
return dataset
|