direct.config package

Submodules

direct.config.defaults module

class direct.config.defaults.CheckpointerConfig(checkpoint_steps: int = 500)

Bases: BaseConfig

checkpoint_steps: int = 500

class direct.config.defaults.DefaultConfig(model: direct.config.defaults.ModelConfig = '???', additional_models: Optional[Any] = None, physics: direct.config.defaults.PhysicsConfig = PhysicsConfig(forward_operator='fft2', backward_operator='ifft2', use_noise_matrix=False, noise_matrix_scaling=1.0), training: direct.config.defaults.TrainingConfig = TrainingConfig(datasets=[DatasetConfig(name='???', transforms=TransformsConfig(masking=MaskingConfig(name='???', accelerations=(5.0,), center_fractions=(0.1,), uniform_range=False, mode=<MaskFuncMode.STATIC: 'static'>, val_accelerations=(5.0, 10.0), val_center_fractions=(0.1, 0.05)), cropping=CropTransformConfig(crop=None, crop_type='uniform', image_center_crop=False), augmentation=AugmentationTransformConfig(rescale=None, rescale_mode=<RescaleMode.NEAREST: 'nearest'>, rescale_2d_if_3d=False, pad=None), random_augmentations=RandomAugmentationTransformsConfig(random_rotation_degrees=(-90, 90), random_rotation_probability=0.0, random_flip_type=<RandomFlipType.RANDOM: 'random'>, random_flip_probability=0.0, random_reverse_probability=0.0), padding_eps=0.001, estimate_body_coil_image=False, sensitivity_map_estimation=SensitivityMapEstimationTransformConfig(estimate_sensitivity_maps=True, sensitivity_maps_type=<SensitivityMapType.RSS_ESTIMATE: 'rss_estimate'>, sensitivity_maps_espirit_threshold=0.05, sensitivity_maps_espirit_kernel_size=6, sensitivity_maps_espirit_crop=0.95, sensitivity_maps_espirit_max_iters=30, sensitivity_maps_gaussian=0.7), normalization=NormalizationTransformConfig(scaling_key='masked_kspace', scale_percentile=0.99), delete_acs_mask=True, delete_kspace=True, image_recon_type=<ReconstructionType.RSS: 'rss'>, compress_coils=None, pad_coils=None, use_seed=True, transforms_type=<TransformsType.SUPERVISED: 'supervised'>, mask_split_ratio=(0.4,), mask_split_acs_region=(0, 0), mask_split_keep_acs=False, mask_split_type=<MaskSplitterType.GAUSSIAN: 'gaussian'>, mask_split_gaussian_std=3.0, mask_split_half_direction=<HalfSplitType.VERTICAL: 'vertical'>), text_description=None)], model_checkpoint=None, optimizer='Adam', lr=0.0005, weight_decay=1e-06, batch_size=2, lr_step_size=5000, lr_gamma=0.5, lr_warmup_iter=500, swa_start_iter=None, num_iterations=50000, validation_steps=1000, gradient_steps=1, gradient_clipping=0.0, gradient_debug=False, loss=LossConfig(crop=None, losses=[FunctionConfig(function='???', multiplier=1.0)]), checkpointer=CheckpointerConfig(checkpoint_steps=500), metrics=[], regularizers=[]), validation: direct.config.defaults.ValidationConfig = ValidationConfig(datasets=[DatasetConfig(name='???', transforms=TransformsConfig(masking=MaskingConfig(name='???', accelerations=(5.0,), center_fractions=(0.1,), uniform_range=False, mode=<MaskFuncMode.STATIC: 'static'>, val_accelerations=(5.0, 10.0), val_center_fractions=(0.1, 0.05)), cropping=CropTransformConfig(crop=None, crop_type='uniform', image_center_crop=False), augmentation=AugmentationTransformConfig(rescale=None, rescale_mode=<RescaleMode.NEAREST: 'nearest'>, rescale_2d_if_3d=False, pad=None), random_augmentations=RandomAugmentationTransformsConfig(random_rotation_degrees=(-90, 90), random_rotation_probability=0.0, random_flip_type=<RandomFlipType.RANDOM: 'random'>, random_flip_probability=0.0, random_reverse_probability=0.0), padding_eps=0.001, estimate_body_coil_image=False, sensitivity_map_estimation=SensitivityMapEstimationTransformConfig(estimate_sensitivity_maps=True, sensitivity_maps_type=<SensitivityMapType.RSS_ESTIMATE: 'rss_estimate'>, sensitivity_maps_espirit_threshold=0.05, sensitivity_maps_espirit_kernel_size=6, sensitivity_maps_espirit_crop=0.95, sensitivity_maps_espirit_max_iters=30, sensitivity_maps_gaussian=0.7), normalization=NormalizationTransformConfig(scaling_key='masked_kspace', scale_percentile=0.99), delete_acs_mask=True, delete_kspace=True, image_recon_type=<ReconstructionType.RSS: 'rss'>, compress_coils=None, pad_coils=None, use_seed=True, transforms_type=<TransformsType.SUPERVISED: 'supervised'>, mask_split_ratio=(0.4,), mask_split_acs_region=(0, 0), mask_split_keep_acs=False, mask_split_type=<MaskSplitterType.GAUSSIAN: 'gaussian'>, mask_split_gaussian_std=3.0, mask_split_half_direction=<HalfSplitType.VERTICAL: 'vertical'>), text_description=None)], batch_size=8, metrics=[], regularizers=[], crop='training'), inference: Optional[direct.config.defaults.InferenceConfig] = None, logging: direct.config.defaults.LoggingConfig = LoggingConfig(log_as_image=None, tensorboard=TensorboardConfig(num_images=8)))

Bases: BaseConfig

additional_models: Optional[Any] = None

inference: Optional[InferenceConfig] = None

logging: LoggingConfig = LoggingConfig(log_as_image=None, tensorboard=TensorboardConfig(num_images=8))

model: ModelConfig = '???'

physics: PhysicsConfig = PhysicsConfig(forward_operator='fft2', backward_operator='ifft2', use_noise_matrix=False, noise_matrix_scaling=1.0)

training: TrainingConfig = TrainingConfig(datasets=[DatasetConfig(name='???', transforms=TransformsConfig(masking=MaskingConfig(name='???', accelerations=(5.0,), center_fractions=(0.1,), uniform_range=False, mode=<MaskFuncMode.STATIC: 'static'>, val_accelerations=(5.0, 10.0), val_center_fractions=(0.1, 0.05)), cropping=CropTransformConfig(crop=None, crop_type='uniform', image_center_crop=False), augmentation=AugmentationTransformConfig(rescale=None, rescale_mode=<RescaleMode.NEAREST: 'nearest'>, rescale_2d_if_3d=False, pad=None), random_augmentations=RandomAugmentationTransformsConfig(random_rotation_degrees=(-90, 90), random_rotation_probability=0.0, random_flip_type=<RandomFlipType.RANDOM: 'random'>, random_flip_probability=0.0, random_reverse_probability=0.0), padding_eps=0.001, estimate_body_coil_image=False, sensitivity_map_estimation=SensitivityMapEstimationTransformConfig(estimate_sensitivity_maps=True, sensitivity_maps_type=<SensitivityMapType.RSS_ESTIMATE: 'rss_estimate'>, sensitivity_maps_espirit_threshold=0.05, sensitivity_maps_espirit_kernel_size=6, sensitivity_maps_espirit_crop=0.95, sensitivity_maps_espirit_max_iters=30, sensitivity_maps_gaussian=0.7), normalization=NormalizationTransformConfig(scaling_key='masked_kspace', scale_percentile=0.99), delete_acs_mask=True, delete_kspace=True, image_recon_type=<ReconstructionType.RSS: 'rss'>, compress_coils=None, pad_coils=None, use_seed=True, transforms_type=<TransformsType.SUPERVISED: 'supervised'>, mask_split_ratio=(0.4,), mask_split_acs_region=(0, 0), mask_split_keep_acs=False, mask_split_type=<MaskSplitterType.GAUSSIAN: 'gaussian'>, mask_split_gaussian_std=3.0, mask_split_half_direction=<HalfSplitType.VERTICAL: 'vertical'>), text_description=None)], model_checkpoint=None, optimizer='Adam', lr=0.0005, weight_decay=1e-06, batch_size=2, lr_step_size=5000, lr_gamma=0.5, lr_warmup_iter=500, swa_start_iter=None, num_iterations=50000, validation_steps=1000, gradient_steps=1, gradient_clipping=0.0, gradient_debug=False, loss=LossConfig(crop=None, losses=[FunctionConfig(function='???', multiplier=1.0)]), checkpointer=CheckpointerConfig(checkpoint_steps=500), metrics=[], regularizers=[])

validation: ValidationConfig = ValidationConfig(datasets=[DatasetConfig(name='???', transforms=TransformsConfig(masking=MaskingConfig(name='???', accelerations=(5.0,), center_fractions=(0.1,), uniform_range=False, mode=<MaskFuncMode.STATIC: 'static'>, val_accelerations=(5.0, 10.0), val_center_fractions=(0.1, 0.05)), cropping=CropTransformConfig(crop=None, crop_type='uniform', image_center_crop=False), augmentation=AugmentationTransformConfig(rescale=None, rescale_mode=<RescaleMode.NEAREST: 'nearest'>, rescale_2d_if_3d=False, pad=None), random_augmentations=RandomAugmentationTransformsConfig(random_rotation_degrees=(-90, 90), random_rotation_probability=0.0, random_flip_type=<RandomFlipType.RANDOM: 'random'>, random_flip_probability=0.0, random_reverse_probability=0.0), padding_eps=0.001, estimate_body_coil_image=False, sensitivity_map_estimation=SensitivityMapEstimationTransformConfig(estimate_sensitivity_maps=True, sensitivity_maps_type=<SensitivityMapType.RSS_ESTIMATE: 'rss_estimate'>, sensitivity_maps_espirit_threshold=0.05, sensitivity_maps_espirit_kernel_size=6, sensitivity_maps_espirit_crop=0.95, sensitivity_maps_espirit_max_iters=30, sensitivity_maps_gaussian=0.7), normalization=NormalizationTransformConfig(scaling_key='masked_kspace', scale_percentile=0.99), delete_acs_mask=True, delete_kspace=True, image_recon_type=<ReconstructionType.RSS: 'rss'>, compress_coils=None, pad_coils=None, use_seed=True, transforms_type=<TransformsType.SUPERVISED: 'supervised'>, mask_split_ratio=(0.4,), mask_split_acs_region=(0, 0), mask_split_keep_acs=False, mask_split_type=<MaskSplitterType.GAUSSIAN: 'gaussian'>, mask_split_gaussian_std=3.0, mask_split_half_direction=<HalfSplitType.VERTICAL: 'vertical'>), text_description=None)], batch_size=8, metrics=[], regularizers=[], crop='training')

class direct.config.defaults.FunctionConfig(function: str = '???', multiplier: float = 1.0)

Bases: BaseConfig

function: str = '???'

multiplier: float = 1.0

class direct.config.defaults.InferenceConfig(dataset: direct.data.datasets_config.DatasetConfig = DatasetConfig(name='???', transforms=TransformsConfig(masking=MaskingConfig(name='???', accelerations=(5.0, ), center_fractions=(0.1, ), uniform_range=False, mode=<MaskFuncMode.STATIC: 'static'>, val_accelerations=(5.0, 10.0), val_center_fractions=(0.1, 0.05)), cropping=CropTransformConfig(crop=None, crop_type='uniform', image_center_crop=False), augmentation=AugmentationTransformConfig(rescale=None, rescale_mode=<RescaleMode.NEAREST: 'nearest'>, rescale_2d_if_3d=False, pad=None), random_augmentations=RandomAugmentationTransformsConfig(random_rotation_degrees=(-90, 90), random_rotation_probability=0.0, random_flip_type=<RandomFlipType.RANDOM: 'random'>, random_flip_probability=0.0, random_reverse_probability=0.0), padding_eps=0.001, estimate_body_coil_image=False, sensitivity_map_estimation=SensitivityMapEstimationTransformConfig(estimate_sensitivity_maps=True, sensitivity_maps_type=<SensitivityMapType.RSS_ESTIMATE: 'rss_estimate'>, sensitivity_maps_espirit_threshold=0.05, sensitivity_maps_espirit_kernel_size=6, sensitivity_maps_espirit_crop=0.95, sensitivity_maps_espirit_max_iters=30, sensitivity_maps_gaussian=0.7), normalization=NormalizationTransformConfig(scaling_key='masked_kspace', scale_percentile=0.99), delete_acs_mask=True, delete_kspace=True, image_recon_type=<ReconstructionType.RSS: 'rss'>, compress_coils=None, pad_coils=None, use_seed=True, transforms_type=<TransformsType.SUPERVISED: 'supervised'>, mask_split_ratio=(0.4, ), mask_split_acs_region=(0, 0), mask_split_keep_acs=False, mask_split_type=<MaskSplitterType.GAUSSIAN: 'gaussian'>, mask_split_gaussian_std=3.0, mask_split_half_direction=<HalfSplitType.VERTICAL: 'vertical'>), text_description=None), batch_size: int = 1, crop: Optional[str] = None)

Bases: BaseConfig

batch_size: int = 1

crop: Optional[str] = None

dataset: DatasetConfig = DatasetConfig(name='???', transforms=TransformsConfig(masking=MaskingConfig(name='???', accelerations=(5.0,), center_fractions=(0.1,), uniform_range=False, mode=<MaskFuncMode.STATIC: 'static'>, val_accelerations=(5.0, 10.0), val_center_fractions=(0.1, 0.05)), cropping=CropTransformConfig(crop=None, crop_type='uniform', image_center_crop=False), augmentation=AugmentationTransformConfig(rescale=None, rescale_mode=<RescaleMode.NEAREST: 'nearest'>, rescale_2d_if_3d=False, pad=None), random_augmentations=RandomAugmentationTransformsConfig(random_rotation_degrees=(-90, 90), random_rotation_probability=0.0, random_flip_type=<RandomFlipType.RANDOM: 'random'>, random_flip_probability=0.0, random_reverse_probability=0.0), padding_eps=0.001, estimate_body_coil_image=False, sensitivity_map_estimation=SensitivityMapEstimationTransformConfig(estimate_sensitivity_maps=True, sensitivity_maps_type=<SensitivityMapType.RSS_ESTIMATE: 'rss_estimate'>, sensitivity_maps_espirit_threshold=0.05, sensitivity_maps_espirit_kernel_size=6, sensitivity_maps_espirit_crop=0.95, sensitivity_maps_espirit_max_iters=30, sensitivity_maps_gaussian=0.7), normalization=NormalizationTransformConfig(scaling_key='masked_kspace', scale_percentile=0.99), delete_acs_mask=True, delete_kspace=True, image_recon_type=<ReconstructionType.RSS: 'rss'>, compress_coils=None, pad_coils=None, use_seed=True, transforms_type=<TransformsType.SUPERVISED: 'supervised'>, mask_split_ratio=(0.4,), mask_split_acs_region=(0, 0), mask_split_keep_acs=False, mask_split_type=<MaskSplitterType.GAUSSIAN: 'gaussian'>, mask_split_gaussian_std=3.0, mask_split_half_direction=<HalfSplitType.VERTICAL: 'vertical'>), text_description=None)

class direct.config.defaults.LoggingConfig(log_as_image: List[str] | None = None, tensorboard: direct.config.defaults.TensorboardConfig = TensorboardConfig(num_images=8))

Bases: BaseConfig

log_as_image: Optional[List[str]] = None

tensorboard: TensorboardConfig = TensorboardConfig(num_images=8)

class direct.config.defaults.LossConfig(crop: Optional[str] = None, losses: List[Any] = <factory>)

Bases: BaseConfig

crop: Optional[str] = None

losses: List[Any]

class direct.config.defaults.ModelConfig(model_name: str = '???', engine_name: str | None = None)

Bases: BaseConfig

engine_name: Optional[str] = None

model_name: str = '???'

class direct.config.defaults.PhysicsConfig(forward_operator: str = 'fft2', backward_operator: str = 'ifft2', use_noise_matrix: bool = False, noise_matrix_scaling: float | None = 1.0)

Bases: BaseConfig

backward_operator: str = 'ifft2'

forward_operator: str = 'fft2'

noise_matrix_scaling: Optional[float] = 1.0

use_noise_matrix: bool = False

class direct.config.defaults.TensorboardConfig(num_images: int = 8)

Bases: BaseConfig

num_images: int = 8

class direct.config.defaults.TrainingConfig(datasets: List[Any] = <factory>, model_checkpoint: Optional[str] = None, optimizer: str = 'Adam', lr: float = 0.0005, weight_decay: float = 1e-06, batch_size: int = 2, lr_step_size: int = 5000, lr_gamma: float = 0.5, lr_warmup_iter: int = 500, swa_start_iter: Optional[int] = None, num_iterations: int = 50000, validation_steps: int = 1000, gradient_steps: int = 1, gradient_clipping: float = 0.0, gradient_debug: bool = False, loss: direct.config.defaults.LossConfig = LossConfig(crop=None, losses=[FunctionConfig(function='???', multiplier=1.0)]), checkpointer: direct.config.defaults.CheckpointerConfig = CheckpointerConfig(checkpoint_steps=500), metrics: List[str] = <factory>, regularizers: List[str] = <factory>)

Bases: BaseConfig

batch_size: int = 2

checkpointer: CheckpointerConfig = CheckpointerConfig(checkpoint_steps=500)

datasets: List[Any]

gradient_clipping: float = 0.0

gradient_debug: bool = False

gradient_steps: int = 1

loss: LossConfig = LossConfig(crop=None, losses=[FunctionConfig(function='???', multiplier=1.0)])

lr: float = 0.0005

lr_gamma: float = 0.5

lr_step_size: int = 5000

lr_warmup_iter: int = 500

metrics: List[str]

model_checkpoint: Optional[str] = None

num_iterations: int = 50000

optimizer: str = 'Adam'

regularizers: List[str]

swa_start_iter: Optional[int] = None

validation_steps: int = 1000

weight_decay: float = 1e-06

class direct.config.defaults.ValidationConfig(datasets: List[Any] = <factory>, batch_size: int = 8, metrics: List[str] = <factory>, regularizers: List[str] = <factory>, crop: Optional[str] = 'training')

Bases: BaseConfig

batch_size: int = 8

crop: Optional[str] = 'training'

datasets: List[Any]

metrics: List[str]

regularizers: List[str]

Module contents

class direct.config.BaseConfig

Bases: object