yamle.data.segmentation module

class yamle.data.segmentation.TorchvisionSegmentationDataModule(dataset, **kwargs)

Bases: BaseDataModule

Data module for the torchvision segmentation datasets.

Parameters:

• dataset (str) – Name of the torchvision dataset. Currently supported are cityscapes.

• seed (int) – Seed for the random number generator.

• data_dir (str) – Path to the data directory.

• train_tranform (Callable) – Transformations to apply to the training data. Default: transforms.ToTensor(), transforms.Normalize(mean, str).

• test_transform (Callable) – Transformations to apply to the test data. Default: transforms.ToTensor(), transforms.Normalize(mean, str).

mean = None

std = None

inputs_dim = None

outputs_dim = None

task = 'segmentation'

inputs_dtype = torch.float32

outputs_dtype = torch.int64

available_test_augmentations: List[str]

get_transform(name)

This is a helper function to get the transform by name.

Return type:

Callable

plot(tester, save_path, specific_name='')

Plot random samples from the training and validation set to check if the data is correctly predicted

Return type:

None

prepare_data()

Download and prepare the data, the data is stored in self._train_dataset, self._validation_dataset and self._test_dataset.

Return type:

None

available_transforms: List[str]

test_augmentations: List[str]

class yamle.data.segmentation.TorchvisionSegmentationDataModuleCityscapes(**kwargs)

Bases: TorchvisionSegmentationDataModule

Data module for the Cityscapes dataset.

inputs_dim = (3, 512, 256)

mean = [0.28689554, 0.32513303, 0.28389177]

std = [0.18696375, 0.19017339, 0.18720214]

ignore_indices = [0, 1, 2, 3, 4, 5, 6, 9, 10, 14, 15, 16, 18, 29, 30, 34]

outputs_dim = 35

targets_dim = (35, 512, 256)

plot(tester, save_path, specific_name='')

Plot random samples from the training and validation set to check if the data is correctly predicted

Return type:

None

available_transforms: List[str]

available_test_augmentations: List[str]

test_augmentations: List[str]