direct package

direct package#

Subpackages#

Submodules#

direct.checkpointer module#

Checkpointer module.

Handles all logic related to checkpointing.

class direct.checkpointer.Checkpointer(save_directory, save_to_disk=True, model_regex='^.*model$', **checkpointables)[source][source]#

Bases: object

Main Checkpointer module.

Handles writing and restoring from checkpoints of modules and submodules.

load(iteration, checkpointable_objects=None)[source][source]#

Return type:: Dict

load_from_path(checkpoint_path, checkpointable_objects=None, only_models=False)[source][source]#

Load a checkpoint from a path.

Parameters:

checkpoint_path: Path or str: Path to checkpoint, either a path to a file or a path to a URL where the file can be downloaded
checkpointable_objects: dict: Dictionary mapping names to nn.Module’s
only_models: bool: If true will only load the models and no other objects in the checkpoint

Returns:

Dictionary with loaded models.

Return type:

Dict

load_models_from_file(checkpoint_path)[source][source]#

Return type:: None

save(iteration, **kwargs)[source][source]#

Return type:: None

direct.constants module#

direct.engine module#

Main engine of DIRECT.

Implements all the main training, testing and validation logic.

class direct.engine.DataDimensionality[source][source]#

Bases: object

property ndim#

class direct.engine.DoIterationOutput(output_image, sensitivity_map, data_dict)[source]#

Bases: tuple

data_dict#: Alias for field number 2

output_image#: Alias for field number 0

sensitivity_map#: Alias for field number 1

class direct.engine.Engine(cfg, model, device, forward_operator=None, backward_operator=None, mixed_precision=False, **models)[source][source]#

Bases: ABC, DataDimensionality

static build_batch_sampler(dataset, batch_size, sampler_type, **kwargs)[source][source]#

Return type:: Sampler

static build_loader(dataset, batch_sampler=None, num_workers=6)[source][source]#

Return type:: DataLoader

abstract build_loss()[source][source]#

Return type:: Dict

build_metrics(metrics_list)[source][source]#

Return type:: Dict

build_regularizers(regularizers_list)[source][source]#

Return type:: Dict

checkpoint_and_write_to_logs(iter_idx)[source][source]#

checkpoint_model_at_interval(iter_idx, total_iter)[source][source]#

abstract evaluate(*args, **kwargs)[source][source]#

log_first_training_example_and_model(data)[source][source]#

log_process(idx, total)[source][source]#

models_to_device()[source][source]#

models_training_mode()[source][source]#

models_validation_mode()[source][source]#

predict(dataset, experiment_directory, checkpoint=-1, num_workers=6, batch_size=1, crop=None)[source][source]#

Return type:: List[ndarray]

process_slices_for_visualization(visualize_slices, visualize_target)[source][source]#

abstract reconstruct_volumes(*args, **kwargs)[source][source]#

train(optimizer, lr_scheduler, training_datasets, experiment_directory, validation_datasets=None, resume=False, start_with_validation=False, initialization=None, num_workers=6)[source][source]#

Return type:: None

training_loop(training_datasets, start_iter, validation_datasets=None, experiment_directory=None, num_workers=6, start_with_validation=False)[source][source]#

validate_model_at_interval(func, iter_idx, total_iter)[source][source]#

validation_loop(validation_datasets, loss_fns, experiment_directory, iter_idx, num_workers=6)[source][source]#

write_to_logs()[source][source]#

write_to_logs_at_interval(iter_idx, total_iter)[source][source]#

direct.environment module#

class direct.environment.Args(epilog=None, add_help=True, **overrides)[source][source]#

Bases: ArgumentParser

Defines global default arguments.

direct.environment.build_operators(cfg)[source][source]#

Builds operators from configuration.

Return type:: Tuple[Callable, Callable]

direct.environment.extract_names(cfg)[source][source]#

direct.environment.initialize_models_from_config(cfg, models, forward_operator, backward_operator, device)[source][source]#

Creates models from config.

Parameters:

cfg: DictConfig: Configuration.
models: dict: Models dictionary including configurations.
forward_operator: Callable: Forward operator.
backward_operator: Callable: Backward operator.
device: str: Type of device.

Returns:

model: torch.nn.Module: Model.
additional_models: Dict: Additional models.

Return type:

Tuple[Module, Dict]

direct.environment.load_dataset_config(dataset_name)[source][source]#

Load specific dataset configuration for dataset based on dataset_name.

Parameters:

dataset_name: str: Name of dataset.

Returns:

dataset_config: Callable: Dataset configuration.

Return type:

Callable

direct.environment.load_model_config_from_name(model_name)[source][source]#

Load specific configuration module for models based on their name.

Parameters:

model_name: str: Path to model relative to direct.nn.

Returns:

model_cfg: Callable: Model configuration.

Return type:

Callable

direct.environment.load_model_from_name(model_name)[source][source]#

Load model based on model_name.

Parameters:

model_name: str: Model name as in direct.nn.

Returns:

model: Callable: Model class.

Return type:

Callable

direct.environment.load_models_into_environment_config(cfg_from_file)[source][source]#

Load the configuration for the models.

Parameters:

cfg_from_file: DictConfig: Omegaconf configuration.

Returns:

(models, models_config): (dict, DictConfig): Models dictionary and models configuration dictionary.

Return type:

Tuple[dict, DictConfig]

direct.environment.setup_common_environment(run_name, base_directory, cfg_pathname, device, machine_rank, mixed_precision, debug=False)[source][source]#

Setup environment.

Parameters:

run_name: str: Run name.
base_directory: pathlib.Path: Base directory path.
cfg_pathname: Union[pathlib.Path, str]: Path or url to configuratio file.
device: str: Device type.
machine_rank: int: Machine rank.
mixed_precision: bool: Whether to enable mixed precision or not. Default: False.
debug: bool: Whether the debug mode is enabled.

Returns:

environment: Common Environment.

direct.environment.setup_engine(cfg, device, model, additional_models, forward_operator=None, backward_operator=None, mixed_precision=False)[source][source]#

Setups engine.

Parameters:

cfg: DictConfig: Configuration.
device: str: Type of device.
model: torch.nn.Module: Model.
additional_models: dict: Additional models.
forward_operator: Callable: Forward operator.
backward_operator: Callable: Backward operator.
mixed_precision: bool: Whether to enable mixed precision or not. Default: False.

Returns:

engine: Experiment Engine.

direct.environment.setup_inference_environment(run_name, base_directory, device, machine_rank, mixed_precision, cfg_file=None, debug=False)[source][source]#

Setup inference environment.

Parameters:

run_name: str: Run name.
base_directory: pathlib.Path: Base directory path.
device: str: Device type.
machine_rank: int: Machine rank.
mixed_precision: bool: Whether to enable mixed precision or not. Default: False.
cfg_file: Union[pathlib.Path, str], optional: Path or url to configuration file.
debug: bool: Whether the debug mode is enabled.

Returns:

environment: Inference Environment.

direct.environment.setup_logging(machine_rank, output_directory, run_name, cfg_filename, cfg, debug)[source][source]#

Logs environment information.

Parameters:

machine_rank: int: Machine rank.
output_directory: pathlib.Path: Path to output directory.
run_name: str: Name of run.
cfg_filename: Union[pathlib.Path, str]: Name of configuration file.
cfg: DefaultConfig: Configuration file.
debug: bool: Whether the debug mode is enabled.

Return type:

None

direct.environment.setup_testing_environment(run_name, base_directory, device, machine_rank, mixed_precision, cfg_pathname=None, debug=False)[source][source]#

Setup testing environment.

Parameters:

run_name: str: Run name.
base_directory: pathlib.Path: Base directory path.
device: str: Device type.
machine_rank: int: Machine rank.
mixed_precision: bool: Whether to enable mixed precision or not. Default: False.
cfg_pathname: Union[pathlib.Path, str], optional: Path or url to configuration file.
debug: bool: Whether the debug mode is enabled.

Returns:

environment: Testing Environment.

direct.environment.setup_training_environment(run_name, base_directory, cfg_filename, device, machine_rank, mixed_precision, debug=False)[source][source]#

Setup training environment.

Parameters:

run_name: str: Run name.
base_directory: pathlib.Path: Base directory path.
cfg_filename: Union[pathlib.Path, str]: Path or url to configuratio file.
device: str: Device type.
machine_rank: int: Machine rank.
mixed_precision: bool: Whether to enable mixed precision or not. Default: False.
debug: bool: Whether the debug mode is enabled.

Returns:

environment: Training Environment.

direct.exceptions module#

exception direct.exceptions.DirectException(*args, **kwargs)[source][source]#: Bases: BaseException

exception direct.exceptions.ItemNotFoundException(item_name, message=None)[source][source]#: Bases: DirectException

exception direct.exceptions.ProcessKilledException(signal_id, signal_name)[source][source]#

Bases: DirectException

The process received SIGINT signal.

exception direct.exceptions.TrainingException(message=None)[source][source]#: Bases: DirectException

direct.inference module#

direct.inference.build_inference_transforms(env, mask_func, dataset_cfg)[source][source]#

Builds inference transforms.

Return type:: Callable

direct.inference.inference_on_environment(env, data_root, dataset_cfg, transforms, experiment_path, checkpoint, num_workers=0, filenames_filter=None, batch_size=1, crop=None)[source][source]#

Performs inference on environment.

Parameters:

env: Environment.
data_root: Union[PathOrString, None]: Path of the directory of the data if applicable for dataset. Can be None.
dataset_cfg: DictConfig: Configuration containing inference dataset settings.
transforms: Callable: Dataset transformations object.
experiment_path: PathOrString: Path to directory where where inference logs will be stored.
checkpoint: FileOrUrl: Checkpoint to a model. This can be a path to a local file or an URL.
num_workers: int: Number of workers.
filenames_filter: Union[List[PathOrString], None]: List of filenames to include in the dataset (if applicable). Can be None. Default: None.
batch_size: int: Inference batch size. Default: 1.
crop: Optional[str]: Inference crop type. Can be header or None. Default: None.

Returns:

output: Union[Dict, DefaultDict]

Return type:

Union[Dict, DefaultDict]

direct.inference.setup_inference_save_to_h5(get_inference_settings, run_name, data_root, base_directory, output_directory, filenames_filter, checkpoint, device, num_workers, machine_rank, cfg_file=None, process_per_chunk=None, mixed_precision=False, debug=False, is_validation=False)[source][source]#

This function contains most of the logic in DIRECT required to launch a multi-gpu / multi-node inference process.

It saves predictions as .h5 files.

Parameters:

get_inference_settings: Callable: Callable object to create inference dataset and environment.
run_name: str: Experiment run name. Can be an empty string.
data_root: Union[PathOrString, None]: Path of the directory of the data if applicable for dataset. Can be None.
base_directory: PathOrString: Path to directory where where inference logs will be stored. If run_name is not an empty string, base_directory / run_name will be used.
output_directory: PathOrString: Path to directory where output data will be saved.
filenames_filter: Union[List[PathOrString], None]: List of filenames to include in the dataset (if applicable). Can be None.
checkpoint: FileOrUrl: Checkpoint to a model. This can be a path to a local file or an URL.
device: str: Device name.
num_workers: int: Number of workers.
machine_rank: int: Machine rank.
cfg_file: Union[PathOrString, None]: Path to configuration file. If None, will search in base_directory.
process_per_chunk: int: Processes per chunk number.
mixed_precision: bool: If True, mixed precision will be allowed. Default: False.
debug: bool: If True, debug information will be displayed. Default: False.
is_validation: bool: If True, will use settings (e.g. batch_size & crop) of validation in config. Otherwise it will use inference settings. Default: False.

Returns:

None

Return type:

None

direct.launch module#

direct.launch.launch(func, num_machines, num_gpus, machine_rank, dist_url, *args)[source][source]#

Launch the training, in case there is only one GPU available the function can be called directly.

Parameters:

func: Callable: Function to launch.
num_machinesint: The number of machines.
num_gpus: int: The number of GPUs.
machine_rank: int: The machine rank.
dist_url: str: URL to connect to for distributed training, including protocol.
args: Tuple: Arguments to pass to func.

Return type:

None

direct.launch.launch_distributed(main_func, num_gpus_per_machine, num_machines=1, machine_rank=0, dist_url='auto', args=(), timeout=datetime.timedelta(seconds=1800))[source][source]#

Launch multi-gpu or distributed training.

This function must be called on all machines involved in the training and it will spawn child processes (defined by num_gpus_per_machine) on each machine.

Parameters:

main_func: Callable: A function that will be called by main_func(*args).
num_gpus_per_machine: int: The number of GPUs per machine.
num_machinesint: The number of machines.
machine_rank: int: The rank of this machine (one per machine).
dist_url: str: URL to connect to for distributed training, including protocol e.g. “tcp://127.0.0.1:8686”. Can be set to auto to automatically select a free port on localhost
args: Tuple: arguments passed to main_func.
timeout: timedelta: Timeout of the distributed workers.

Return type:

None

direct.predict module#

direct.predict.predict_from_argparse(args)[source][source]#

direct.train module#

direct.train.build_training_datasets_from_environment(env, datasets_config, lists_root=None, data_root=None, initial_images=None, initial_kspaces=None, pass_text_description=True, pass_dictionaries=None)[source][source]#

direct.train.build_transforms_from_environment(env, dataset_config)[source][source]#

Return type:: Callable

direct.train.get_root_of_file(filename)[source][source]#

Get the root directory of the file or URL to file.

Parameters:

filename: pathlib.Path or str

Returns:

pathlib.Path or str

Examples

>>> get_root_of_file('/mnt/archive/data.txt')
>>> /mnt/archive
>>> get_root_of_file('https://aiforoncology.nl/people')
>>> https://aiforoncology.nl/

direct.train.parse_noise_dict(noise_dict, percentile=1.0, multiplier=1.0)[source][source]#

direct.train.setup_train(run_name, training_root, validation_root, base_directory, cfg_filename, force_validation, initialization_checkpoint, initial_images, initial_kspace, noise, device, num_workers, resume, machine_rank, mixed_precision, debug)[source][source]#

direct.train.train_from_argparse(args)[source][source]#

direct.types module#

direct.types module.

class direct.types.DirectEnum(value)[source][source]#

Bases: str, Enum

Type of any enumerator with allowed comparison to string invariant to cases.

classmethod from_str(value)[source][source]#

Return type:: DirectEnum | None

class direct.types.IntegerListOrTupleString(string)[source][source]#

Bases: object

IntegerListOrTupleString class represents a list or tuple of integers based on a string representation.

Examples

s1 = “[1, 2, 45, -1, 0]” print(isinstance(s1, IntegerListOrTupleString)) # True print(IntegerListOrTupleString(s1)) # [1, 2, 45, -1, 0] print(type(IntegerListOrTupleString(s1))) # <class ‘list’> print(type(IntegerListOrTupleString(s1)[0])) # <class ‘int’>

s2 = “(10, -9, 20)” print(isinstance(s2, IntegerListOrTupleString)) # True print(IntegerListOrTupleString(s2)) # (10, -9, 20) print(type(IntegerListOrTupleString(s2))) # <class ‘tuple’> print(type(IntegerListOrTupleString(s2)[0])) # <class ‘int’>

s3 = “[a, we, 2]” print(isinstance(s3, IntegerListOrTupleString)) # False

s4 = “(1, 2, 3]” print(isinstance(s4 IntegerListOrTupleString)) # False

class direct.types.IntegerListOrTupleStringMeta[source][source]#

Bases: type

Metaclass for the IntegerListOrTupleString class.

Returns:

bool: True if the instance is a valid representation of IntegerListOrTupleString, False otherwise.

class direct.types.KspaceKey(value)[source][source]#

Bases: DirectEnum

An enumeration.

KSPACE = 'kspace'#

MASKED_KSPACE = 'masked_kspace'#

class direct.types.MaskFuncMode(value)[source][source]#

Bases: DirectEnum

An enumeration.

DYNAMIC = 'dynamic'#

MULTISLICE = 'multislice'#

STATIC = 'static'#

class direct.types.TransformKey(value)[source][source]#

Bases: DirectEnum

An enumeration.

ACS_MASK = 'acs_mask'#

KSPACE = 'kspace'#

MASKED_KSPACE = 'masked_kspace'#

SAMPLING_MASK = 'sampling_mask'#

SCALING_FACTOR = 'scaling_factor'#

SENSITIVITY_MAP = 'sensitivity_map'#

TARGET = 'target'#

direct package

Contents

direct package#

Subpackages#

Submodules#

direct.checkpointer module#

direct.constants module#

direct.engine module#

direct.environment module#

direct.exceptions module#

direct.inference module#

direct.launch module#

direct.predict module#

direct.train module#

direct.types module#

Module contents#