ai4med.components.aux_ops package
-
class
ComputeAccuracy(tag: str, use_sigmoid=False, do_summary=True, do_print=True) Bases:
ai4med.components.aux_ops.aux_op.AuxiliaryOperationThis aux-op computes accuracy by comparing similarity between model predictions and label data.
- Parameters
tag (str) – the key name of the output tensor
use_sigmoid (bool) – whether to apply sigmoid to predictions (Default: False)
do_summary (bool) – whether to aggregate result across training steps for tensorboard reporting (Default: True)
do_print (bool) – whether to aggregate result across training steps for console printing (Default: True)
-
get_output_tensors(predictions, label, build_ctx: ai4med.common.build_ctx.BuildContext) Implements the required method.
- Parameters
predictions – predictions produced by model
label – label of the training subject
build_ctx – build context
Returns: dict of output tensors
-
class
AuxiliaryOperation(do_summary=True, do_print=True) Bases:
ai4med.common.graph_component.GraphComponentThis class defines the interface for auxiliary operations (aux-ops).
An aux-op is a graph building component that adds additional operations to the computation graph. Output from an aux-op can be aggregated for tensorboard recording or printed to the console after each step of training.
Aux-ops are built after all other training graph components (i.e. placeholders, model, loss).
NoteOutput values are aggregated, if so desired, by MetricAccumulator. MetricAccumulator can only process simple output types (scalar and size-1 arrays).
- Parameters
do_summary (bool) – whether output should be summarized for tensorboard recording (Default: True)
do_print (bool) – whether output should be aggregated for printing to the console (Default: True)
-
build(build_ctx: ai4med.common.build_ctx.BuildContext) Implements the required build method of GraphComponent.
NoteAux-op components should never override this method. They should, instead, implement the required get_output_tensors method.
- Parameters
build_ctx – the build context
Returns: dict of output tensors
-
get_output_tensors(predictions, label, build_ctx: ai4med.common.build_ctx.BuildContext) This is the required method of the aux-op. It should add additional elements to the computation graph based on model predictions, label data, and/or any objects in the build context. It can also add more objects into the build context for other aux-ops to use.
- Parameters
predictions – the predictions computed by model architecture
label – label data
build_ctx – the build context
Returns: a dict of tensors
-
class
ComputeBinaryPreds(binary_preds_name='binary_preds', binary_labels_name='binary_labels') Bases:
ai4med.components.aux_ops.aux_op.AuxiliaryOperationThis aux-op processes binary predictions produced by a classification model.
- Parameters
binary_preds_name (str) – name of the prediction processing result
binary_labels_name (str) – name of the label processing result
Returns: a ComputeBinaryPreds object
-
get_output_tensors(predictions, label, build_ctx: ai4med.common.build_ctx.BuildContext) Implements the required method.
- Parameters
predictions – predictions produced by model
label – label of the training subject
build_ctx – build context
Returns: dict of output tensors
-
class
ComputeMulticlassAccuracy(tag, use_sigmoid_for_binary=True, use_softmax_for_multiclass=True, use_sigmoid_for_accuracy=False, do_summary=True, do_print=True) Bases:
ai4med.components.aux_ops.aux_op.AuxiliaryOperationThis aux-op computes accuracy for multi-class predictions produced by a classification model.
- Parameters
tag (str) – the key name of the output tensor
use_sigmoid_for_binary (bool) – whether to apply sigmoid for binary labels (Default: True)
use_softmax_for_multiclass (bool) – whether to apply softmax to multi-class labels (Default: True)
use_sigmoid_for_accuracy (bool) – whether to apply sigmoid to accuracy (Default: False)
do_summary (bool) – whether to aggregate result across training steps for tensorboard reporting (Default: True)
do_print (bool) – whether to aggregate result across training steps for console printing (Default: True)
Returns: a ComputeMulticlassPreds object
-
get_output_tensors(predictions, labels, build_ctx=None) This is the required method of the aux-op. It should add additional elements to the computation graph based on model predictions, label data, and/or any objects in the build context. It can also add more objects into the build context for other aux-ops to use.
- Parameters
predictions – the predictions computed by model architecture
label – label data
build_ctx – the build context
Returns: a dict of tensors
-
class
ComputeMulticlassPreds(preds_name='preds', labels_name='labels', use_sigmoid_for_binary=True, use_softmax_for_multiclass=True) Bases:
ai4med.components.aux_ops.aux_op.AuxiliaryOperationThis aux-op processes multi-class predictions produced by a classification model. The predictions could contain both binary and multi-class labels.
- Parameters
preds_name (str) – name of the prediction processing result
labels_name (str) – name of the label processing result
use_sigmoid_for_binary (bool) – whether to apply sigmoid for binary labels (Default: True)
use_softmax_for_multiclass (bool) – whether to apply softmax to multi-class labels (Default: True)
Returns: a ComputeMulticlassPreds object
-
get_output_tensors(predictions, labels, build_ctx=None) Implements the required method.
- Parameters
predictions – predictions produced by model
labels – labels of the training subject
build_ctx – build context
Returns: dict of output tensors
-
class
ComputeDice(tag: str, mask_tag: str = None, data_format='channels_first', skip_background=False, is_onehot_targets=False, is_independent_predictions=False, jaccard=False, threshold=0.5, do_summary=True, do_print=True) Bases:
ai4med.components.aux_ops.aux_op.AuxiliaryOperationThis aux-op computes dice metric from predictions and label.
- Parameters
tag (str) – key name of the dice tensor
mask_tag (str) – key name of the mask tensor. Optional.
data_format (str) – the input format (‘channels_last’ or ‘channels_first’)
skip_background (bool) – skip dice computation on the first channel of the predicted output (basically skipping dice on background class) or not (Default: False)
is_onehot_targets (bool) – targets in One-Hot format or not (Default: False)
is_independent_predictions (bool) – prediction results are independent or not (Default: False)
jaccard (bool) – compute Jaccard Indect (a.k.a. IoU) instead of dice or not (Default: False)
threshold (float) – threshold to convert independent_predictions probabilities into binary
do_summary (bool) – whether to aggregate result across training steps for tensorboard reporting (Default: True)
do_print (bool) – whether to aggregate result across training steps for console printing (Default: True)
-
get_output_tensors(predictions, label, build_ctx: ai4med.common.build_ctx.BuildContext) Implements the required method.
- Parameters
predictions – predictions produced by model
label – label of the training subject
build_ctx – build context
Returns: dict of output tensors
-
class
DiceMaskedOutput(tags, data_format='channels_first', skip_background=False, is_onehot_targets=False, is_independent_predictions=False, jaccard=False, threshold=0.5, do_summary=True, do_print=True) Bases:
ai4med.components.aux_ops.aux_op.AuxiliaryOperationThis aux-op outputs a dict of dice values. The first element in the output dict is the average value across all classes, and if there is more than one class, the remaining elements are per-class values. If there are no ground labels for the class in question, returns nan.
- Parameters
tags – key names of generated outputs for each class
data_format (str) – the input format (‘channels_last’ or ‘channels_first’)
skip_background (bool) – remove background class or not (Default: False)
is_onehot_targets (bool) – targets in One-Hot format or not (Default: False)
is_independent_predictions (bool) – prediction results are independent or not (Default: False)
jaccard (bool) – compute Jaccard Indect (a.k.a. IoU) instead of dice or not (Default: False)
threshold (float) – threshold to convert independent_predictions probabilities into binary
do_summary (bool) – whether to aggregate result across training steps for tensorboard reporting (Default: True)
do_print (bool) – whether to aggregate result across training steps for console printing (Default: True)
-
get_output_tensors(predictions, label, build_ctx: ai4med.common.build_ctx.BuildContext) Implements the required method.
- Parameters
predictions – predictions produced by model
label – label of the training subject
build_ctx – build context
Returns: dict of output tensors
-
class
NegLoss(tag: str, do_summary=True, do_print=True) Bases:
ai4med.components.aux_ops.aux_op.AuxiliaryOperation-
get_output_tensors(predictions, label, build_ctx: ai4med.common.build_ctx.BuildContext) This is the required method of the aux-op. It should add additional elements to the computation graph based on model predictions, label data, and/or any objects in the build context. It can also add more objects into the build context for other aux-ops to use.
- Parameters
predictions – the predictions computed by model architecture
label – label data
build_ctx – the build context
Returns: a dict of tensors
-
-
class
ReduceMeanMasked(tag: str, mask_tag: str = None, axis=None, do_summary=True, do_print=True) Bases:
ai4med.components.aux_ops.aux_op.AuxiliaryOperationThis aux-op computes reduced mean and mask from model predictions and label data.
- Parameters
tag (str) – the key name of the reduced mean tensor
mask_tag (str) – the key name of the mask tensor. Optional.
axis (int) – axis to apply the reduce_sum operation
do_summary (bool) – whether to aggregate result across training steps for tensorboard reporting (Default: True)
do_print (bool) – whether to aggregate result across training steps for console printing (Default: True)
-
get_output_tensors(predictions, label, build_ctx: ai4med.common.build_ctx.BuildContext) Implements the required method.
- Parameters
predictions – predictions produced by model
label – label of the training subject
build_ctx – build context
Returns: dict of 2 output tensors