Transforms are now in the Clara Train API package. Compared to how they were before in TLT, they have been designed to be more data-driven rather than configuration driven, and now use a more standardized transform context (transform_ctx) that typically expects MedicalImages with ShapeFormat data. With ShapeFormat readily available, each transform can easily know what are the data dimensions, whether the data is channel first or channels last, and whether it is batched without having to manually configure the data format as a parameter.
Many transforms have been renamed so they are purpose instead of mechanism based in order to help the user better understand what is happening through the transform chain and why. For example, instead of VolumeTo4DArray previously, ConvertToChannelsFirst makes it clear what the purpose of the transform is (and also generalizes to work for 2D and 3D image data, all without having to specify a data format manually).
Below is a table providing a mapping of previous TLT transforms to their equivalent classes in ai4med.components.transforms.
It is possible that parameters have been added, removed, renamed, or reordered in the new versions of transforms in Clara Train API.
Old |
New |
Notes |
|---|---|---|
LoadNifty |
LoadNifti |
|
LoadPng |
LoadPng |
|
TransformVolumeCropROIFastPosNegRatio |
FastPosNegRatioCropROI |
|
SymmetricPadderDiv |
||
CropSubVolumeBatchPosNegRatio without fast_crop |
CropByPosNegRatio |
|
CropSubVolumeBatchPosNegRatio with fast_crop |
FastCropByPosNegRatio |
image_field -> fields |
CropByPosNegRatioLabelOnly |
||
CropForegroundObject |
CropForegroundObject |
|
CropSubVolumeCenter |
CropSubVolumeCenter |
|
CropRandomSubImageInRange |
CropRandomSizeWithDisplacement |
image_field -> fields, no more data_format |
CropSubVolumeRandomWithinBounds |
CropFixedSizeRandomCenter |
|
ResampleVolume |
||
ScaleByFactor |
||
ScaleByResolution |
||
ScaleBySpacing |
||
NPResize3D |
ScaleToShape |
|
NPResizeImage |
ScaleToShape |
applied_keys -> fields, output_shape -> target_shape, no more data_format |
RestoreOriginalShape |
||
NPRandomZoom3D |
RandomZoom |
|
FlipAxisRandom |
RandomSpatialFlip |
no more data_format, no more axis because all spatial axes are now known and used |
NPRandomFlip3D |
RandomAxisFlip |
applied_keys -> fields |
NPRandomRot90XY |
RandomRotate3D |
applied_keys -> fields |
NP2DRotate |
RandomRotate2D |
applied_keys -> fields, random -> is_random, no more data_format |
NormalizeNonzeroIntensities |
NormalizeNonzeroIntensities |
|
CenterData |
CenterData |
applied_keys -> fields |
AdjustContrast |
AdjustContrast |
field -> fields |
AddGaussianNoise |
AddGaussianNoise |
field -> fields |
ScaleIntensityOscillation |
ScaleIntensityOscillation |
field -> fields |
ScaleIntensityRange |
ScaleIntensityRange |
field -> fields |
AugmentIntensityRandomScaleShift |
ScaleShiftIntensity |
|
FetchExtremePoints |
AddExtremePointsChannel |
|
BratsConvertLabels |
ConvertToMultiChannelBasedOnBratsClasses |
|
ConvertToMultiChannelBasedOnLabel |
||
SplitAcrossChannels |
SplitAcrossChannels |
no more squeeze |
SplitBasedOnLabel |
SplitBasedOnLabel |
applied_key -> field, channel_names -> label_names |
ArgmaxAcrossChannels |
ArgmaxAcrossChannels |
applied_keys -> fields |
ThresholdValues |
ApplyThreshold |
|
LabelSelection |
ReplaceLabels |
|
SplitBasedOnBratsClasses |
SplitBasedOnBratsClasses |
|
NPRepChannels |
RepeatChannel |
applied_keys -> fields, repeat -> repeat_times, no more channel_axis |
VolumeTo4DArray (depending on usage) |
ConvertToChannelsFirst |
|
NPExpandDims (depending on usage) |
ConvertToChannelsLast |
LoadResolutionFromNifty is no longer needed as LoadNifti now saves the original resolution as a property of the MedicalImage.
Load3DShapeFromNumpy is no longer needed as LoadNifti now saves the original shape as a property of the MedicalImage.
See ImagePipeline for details about using subclasses instead of ImagePipeline directly.
task is no longer needed becauses the subclasses are task specific. Other parameters have been renamed and modified as well to be applicable to each subclass.
determinism - See determinism.
use_amp - See Automatic Mixed Precision.
dynamic_input_shape - This used to be set by having crop_size be [-1, -1, -1] in the ImagePipeline, but now ImagePipeline’s subclasses should be used and they use output_crop_size to specify the crop size of the output from the image pipeline. dynamic_input_shape being set to true is now what controls whether a model is constructed with dynamic input shape (so inference can be performed on images of a different shape). See Improving inference performance for details.
The data conversion tool has been renamed from tlt-dataconvert to nvmidl-dataconvert.
Component |
Old |
New |
Notes |
|---|---|---|---|
lr_policy |
DecayLRonStep |
DecayOnStep |
|
ReduceLRPoly |
ReducePoly |
||
model |
SegmAhnet3D |
SegAhnet |
add n_spatial_dim, no more if_from_scratch |
metrics |
MetricAverageFromArrayDice |
ComputeAverageDice |
applied_key -> field, label_key -> label_field, stopping_metric -> is_key_metric |
MetricAverage |
ComputeAverage |
applied_key -> field |
|
MetricAUC |
ComputeAUC |
applied_key -> field, label_key -> label_field, stopping_metric -> is_key_metric |
|
inferer |
ScanWindowInferer |
TFScanWindowInferer |
no more is_channels_first |
SimpleInferer |
TFSimpleInferer |
||
writer |
NiftyWriter |
WriteNifti |
applied_key -> field |
loss |
ClassificationLoss |
BinaryClassificationLoss |
like in the classification config example, some of what used to be implemented as metrics should now be implemented as aux_ops:
AccuracyComputer -> ComputeAccuracy: tags -> tag
ClassificationMetric -> ComputeBinaryPreds