rising Documentation

rising is a highly performant, PyTorch only, framework for efficient data augmentation with native support for volumetric data

Getting Started

• What is rising?
• Installation
• What can I do with rising?
  • rising.loading
  • rising.transforms
• rising MNIST Example with CPU and GPU augmentation
• Dataloading with rising
• Project Organization

Python API

• rising.loading
  • DataLoader
    • DataLoader
    • default_transform_call
    • BatchTransformer
    • patch_worker_init_fn
    • patch_collate_fn
  • Dataset
    • Dataset
    • AsyncDataset
    • dill_helper
    • load_async
  • Collation
    • numpy_collate
    • do_nothing_collate
• rising.ops
  • On Tensors
    • torch_one_hot
    • np_one_hot
• rising.random
  • Random Parameter Injection Base Classes
    • AbstractParameter
  • Continuous Random Parameters
    • ContinuousParameter
    • NormalParameter
    • UniformParameter
  • Discrete Random Parameters
    • DiscreteParameter
    • DiscreteCombinationsParameter
    • combinations_all
• rising.transforms
  • Transformation Base Classes
    • AbstractTransform
    • BaseTransform
    • PerSampleTransform
    • PerChannelTransform
  • Compose Transforms
    • Compose
    • DropoutCompose
    • OneOf
    • dict_call
  • Affine Transforms
    • Affine
    • StackedAffine
    • BaseAffine
    • Rotate
    • Translate
    • Scale
    • Resize
  • Channel Transforms
    • OneHot
    • ArgMax
  • Cropping Transforms
    • CenterCrop
    • RandomCrop
  • Format Transforms
    • MapToSeq
    • SeqToMap
    • PopKeys
    • FilterKeys
    • RenameKeys
  • Intensity Transforms
    • Clamp
    • NormRange
    • NormMinMax
    • NormZeroMeanUnitStd
    • NormMeanStd
    • Noise
    • GaussianNoise
    • ExponentialNoise
    • GammaCorrection
    • RandomValuePerChannel
    • RandomAddValue
    • RandomScaleValue
  • Kernel Transforms
    • KernelTransform
    • GaussianSmoothing
  • Spatial Transforms
    • Mirror
    • Rot90
    • ResizeNative
    • Zoom
    • ProgressiveResize
    • SizeStepScheduler
  • Tensor Transforms
    • ToTensor
    • ToDeviceDtype
    • ToDevice
    • ToDtype
    • TensorOp
    • Permute
  • Utility Transforms
    • DoNothing
    • SegToBox
    • BoxToSeg
    • InstanceToSemantic
• rising.transforms.functional
  • Affine Transforms
    • affine_image_transform
    • affine_point_transform
    • parametrize_matrix
    • create_rotation
    • create_rotation_2d
    • create_rotation_3d
    • create_rotation_3d_0
    • create_rotation_3d_1
    • create_rotation_3d_2
    • create_scale
    • create_translation
    • expand_scalar_param
  • Channel Transforms
    • one_hot_batch
  • Cropping Transforms
    • crop
    • center_crop
    • random_crop
  • Intensity Transforms
    • norm_range
    • norm_min_max
    • norm_zero_mean_unit_std
    • norm_mean_std
    • add_noise
    • add_value
    • gamma_correction
    • scale_by_value
  • Spatial Transforms
    • mirror
    • rot90
    • resize_native
  • Tensor Transforms
    • tensor_op
    • to_device_dtype
  • Utility Transforms
    • box_to_seg
    • seg_to_box
    • instance_to_semantic
    • pop_keys
    • filter_keys
• rising.utils
  • Affines
    • points_to_homogeneous
    • matrix_to_homogeneous
    • matrix_to_cartesian
    • points_to_cartesian
    • matrix_revert_coordinate_order
    • get_batched_eye
    • deg_to_rad
    • unit_box
  • Type Checks
    • check_scalar
  • Reshaping
    • reshape
    • reshape_list

• Tutorials & Examples
  • Segmentation Example With PyTorch Lightning (3D)
  • Classification Example on MedNIST (2D)
  • Using external transformations
  • An Overview on rising transformations

Community

• Contributing to rising

Indices and tables

• Index

• Module Index

• Search Page