simulation.utils.machine_learning.data package

Submodules:

• simulation.utils.machine_learning.data.base_dataset module
• simulation.utils.machine_learning.data.data_loader module
• simulation.utils.machine_learning.data.extract_simulated_images module
• simulation.utils.machine_learning.data.image_folder module
• simulation.utils.machine_learning.data.image_operations module
• simulation.utils.machine_learning.data.image_pool module
• simulation.utils.machine_learning.data.images_to_video module
• simulation.utils.machine_learning.data.labeled_dataset module
• simulation.utils.machine_learning.data.record_simulated_rosbag module
• simulation.utils.machine_learning.data.rosbag_to_images module
• simulation.utils.machine_learning.data.rosbag_to_labels module
• simulation.utils.machine_learning.data.unlabeled_dataset module
• simulation.utils.machine_learning.data.visualizer module

Summary

Reference

load_unpaired_unlabeled_datasets(dir_a: Union[str, List[str]], dir_b: Union[str, List[str]], max_dataset_size: int, batch_size: int, sequential: bool, num_threads: int, grayscale_a: bool, grayscale_b: bool, transform_properties: Dict[str, Any]) → Tuple[simulation.utils.machine_learning.data.data_loader.DataLoader, simulation.utils.machine_learning.data.data_loader.DataLoader]

Create dataloader for two unpaired and unlabeled datasets.

E.g. used by cycle gan with data from two domains.

Parameters

• dir_a – path to images of domain a

• dir_b – path to images of domain b

• max_dataset_size (int) – maximum amount of images to load; -1 means infinity

• batch_size (int) – input batch size

• sequential (bool) – if true, takes images in order, otherwise takes them randomly

• num_threads (int) – threads for loading data

• grayscale_a (bool) – transform domain a to gray images

• grayscale_b (bool) – transform domain b to gray images

• transform_properties – dict containing properties for transforming images

sample_generator(dataloader: simulation.utils.machine_learning.data.data_loader.DataLoader, n_samples: int = inf)

Generator that samples from a dataloader.

Parameters

• dataloader – Dataloader.

• n_samples – Number of batches of samples.

unpaired_sample_generator(dataloader_a: simulation.utils.machine_learning.data.data_loader.DataLoader, dataloader_b: simulation.utils.machine_learning.data.data_loader.DataLoader, n_samples: int = inf)

Generator that samples pairwise from both dataloaders.

Parameters

• dataloader_a – Domain a dataloader.

• dataloader_b – Domain b dataloader.

• n_samples – Number of batches of samples.

load_labeled_dataset(label_file: str, max_dataset_size: int, batch_size: int, sequential: bool, num_threads: int, transform_properties: Dict[str, Any]) → simulation.utils.machine_learning.data.data_loader.DataLoader

Create dataloader for a labeled dataset.

Parameters

• label_file – Path to a file containing all labels

• max_dataset_size – Maximum amount of images to load; -1 means infinity

• batch_size – Batch size

• sequential – If true, takes images in order, otherwise takes them randomly

• num_threads – Threads for loading data