File size: 1,998 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 |
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 .common_dataset import VideoDataset
from ..registery import dataset_register
@dataset_register(
name='IXMAS',
classes=['check_watch', 'cross_arms', 'get_up', 'kick', 'pick_up', 'point', 'punch', 'scratch_head', 'sit_down', 'turn_around', 'walk', 'wave'],
task_type='Action Recognition',
object_type='Web Video',
# class_aliases=[['automobile', 'car']],
class_aliases=[],
shift_type=None
)
class IXMAS(ABDataset): # just for demo now
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, y_transform = transform
dataset = VideoDataset([root_dir], mode='train')
if len(ignore_classes) > 0:
for ignore_class in ignore_classes:
ci = classes.index(ignore_class)
dataset.fnames = [img for img, label in zip(dataset.fnames, dataset.label_array) if label != ci]
dataset.label_array = [label for label in dataset.label_array if label != ci]
if idx_map is not None:
dataset.label_array = [idx_map[label] for label in dataset.label_array]
dataset = train_val_test_split(dataset, split)
return dataset
|