torch_geometric.datasets.PascalVOCKeypoints
- class PascalVOCKeypoints(root: str, category: str, train: bool = True, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, pre_filter: Optional[Callable] = None, force_reload: bool = False, device: Optional[str] = None)[source]
Bases:
InMemoryDataset
The Pascal VOC 2011 dataset with Berkely annotations of keypoints from the “Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations” paper, containing 0 to 23 keypoints per example over 20 categories. The dataset is pre-filtered to exclude difficult, occluded and truncated objects. The keypoints contain interpolated features from a pre-trained VGG16 model on ImageNet (
relu4_2
andrelu5_1
).- Parameters:
root (str) – Root directory where the dataset should be saved.
category (str) – The category of the images (one of
"Aeroplane"
,"Bicycle"
,"Bird"
,"Boat"
,"Bottle"
,"Bus"
,"Car"
,"Cat"
,"Chair"
,"Diningtable"
,"Dog"
,"Horse"
,"Motorbike"
,"Person"
,"Pottedplant"
,"Sheep"
,"Sofa"
,"Train"
,"TVMonitor"
)train (bool, optional) – If
True
, loads the training dataset, otherwise the test dataset. (default:True
)transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
)pre_transform (callable, optional) – A function/transform that takes in an
torch_geometric.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
)pre_filter (callable, optional) – A function that takes in an
torch_geometric.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)force_reload (bool, optional) – Whether to re-process the dataset. (default:
False
)device (str or torch.device, optional) – The device to use for processing the raw data. If set to
None
, will utilize GPU-processing if available. (default:None
)