super_gradients.training.datasets.segmentation_datasets package
Submodules
super_gradients.training.datasets.segmentation_datasets.cityscape_segmentation module
- class super_gradients.training.datasets.segmentation_datasets.cityscape_segmentation.CityscapesDataset(root_dir: str, list_file: str, labels_csv_path: str, **kwargs)[source]
Bases:
Generic
[torch.utils.data.dataset.T_co
]CityscapesDataset - Segmentation Data Set Class for Cityscapes Segmentation Data Set, main resolution of dataset: (2048 x 1024). Not all the original labels are used for training and evaluation, according to cityscape paper: “Classes that are too rare are excluded from our benchmark, leaving 19 classes for evaluation”. For more details about the dataset labels format see: https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py
- target_loader(label_path: str) → <module ‘PIL.Image’ from ‘/Users/oferbaratz/PycharmProjects/SG/venv/lib/python3.8/site-packages/PIL/Image.py’>[source]
- Override target_loader function, load the labels mask image.
- param label_path
Path to the label image.
- return
The mask image created from the array, with converted class labels.
- static target_transform(target)[source]
target_transform - Transforms the sample image This function overrides the original function from SegmentationDataSet and changes target pixels with value 255 to value = CITYSCAPES_IGNORE_LABEL. This was done since current IoU metric from torchmetrics does not support such a high ignore label value (crashed on OOM)
- param target
The target mask to transform
- return
The transformed target mask
super_gradients.training.datasets.segmentation_datasets.coco_segmentation module
- exception super_gradients.training.datasets.segmentation_datasets.coco_segmentation.EmptyCoCoClassesSelectionException[source]
Bases:
Exception
- class super_gradients.training.datasets.segmentation_datasets.coco_segmentation.CoCoSegmentationDataSet(dataset_classes_inclusion_tuples_list: Optional[list] = None, *args, **kwargs)[source]
Bases:
Generic
[torch.utils.data.dataset.T_co
]CoCoSegmentationDataSet - Segmentation Data Set Class for COCO 2017 Segmentation Data Set
- target_loader(mask_metadata_tuple) → <module ‘PIL.Image’ from ‘/Users/oferbaratz/PycharmProjects/SG/venv/lib/python3.8/site-packages/PIL/Image.py’>[source]
- Parameters
mask_metadata_tuple – A tuple of (coco_image_id, original_image_height, original_image_width)
- Returns
The mask image created from the array
super_gradients.training.datasets.segmentation_datasets.pascal_aug_segmentation module
super_gradients.training.datasets.segmentation_datasets.pascal_voc_segmentation module
- class super_gradients.training.datasets.segmentation_datasets.pascal_voc_segmentation.PascalVOC2012SegmentationDataSet(sample_suffix=None, target_suffix=None, *args, **kwargs)[source]
Bases:
Generic
[torch.utils.data.dataset.T_co
]PascalVOC2012SegmentationDataSet - Segmentation Data Set Class for Pascal VOC 2012 Data Set
super_gradients.training.datasets.segmentation_datasets.segmentation_dataset module
- class super_gradients.training.datasets.segmentation_datasets.segmentation_dataset.SegmentationDataSet(root: str, list_file: str = None, samples_sub_directory: str = None, targets_sub_directory: str = None, img_size: int = 608, crop_size: int = 512, batch_size: int = 16, augment: bool = False, dataset_hyper_params: dict = None, cache_labels: bool = False, cache_images: bool = False, sample_loader: Callable = None, target_loader: Callable = None, collate_fn: Callable = None, target_extension: str = '.png', image_mask_transforms: torchvision.transforms.transforms.Compose = None, image_mask_transforms_aug: torchvision.transforms.transforms.Compose = None)[source]
Bases:
Generic
[torch.utils.data.dataset.T_co
]- static sample_loader(sample_path: str) → <module ‘PIL.Image’ from ‘/Users/oferbaratz/PycharmProjects/SG/venv/lib/python3.8/site-packages/PIL/Image.py’>[source]
- sample_loader - Loads a dataset image from path using PIL
- param sample_path
The path to the sample image
- return
The loaded Image
- static sample_transform(image)[source]
sample_transform - Transforms the sample image
- param image
The input image to transform
- return
The transformed image