dacapo.experiments.tasks.predictors.predictor
Classes
A predictor is a class that defines how to train a model to predict a |
Module Contents
- class dacapo.experiments.tasks.predictors.predictor.Predictor
A predictor is a class that defines how to train a model to predict a certain output from a certain input.
A predictor is responsible for creating the model, the target, the weight, and the output array type for a given training architecture.
- create_model(self, architecture
“Architecture”) -> “Model”: Given a training architecture, create a model for this predictor.
- create_target(self, gt
“Array”) -> “Array”: Create the target array for training, given a ground-truth array.
- create_weight(self, gt
“Array”, target: “Array”, mask: “Array”, moving_class_counts: Any) -> Tuple[“Array”, Any]: Create the weight array for training, given a ground-truth and associated target array.
- gt_region_for_roi(self, target_spec)
Report how much spatial context this predictor needs to generate a target for the given ROI.
- padding(self, gt_voxel_size
Coordinate) -> Coordinate: Return the padding needed for the ground-truth array.
Notes
This is a subclass of ABC.
- abstract create_model(architecture: dacapo.experiments.architectures.architecture.Architecture) dacapo.experiments.model.Model
Given a training architecture, create a model for this predictor. This is usually done by appending extra layers to the output of the architecture to get the output tensor of the architecture into the right shape for this predictor.
- Parameters:
architecture – The training architecture.
- Returns:
The model.
- Raises:
NotImplementedError – This method is not implemented.
Examples
>>> predictor.create_model(architecture)
- abstract create_target(gt: dacapo.experiments.datasplits.datasets.arrays.Array) dacapo.experiments.datasplits.datasets.arrays.Array
Create the target array for training, given a ground-truth array.
In general, the target is different from the ground-truth.
The target is the array that is passed to the loss, and hence directly compared to the prediction (i.e., the output of the model). Depending on the predictor, the target can therefore be different from the ground-truth (e.g., an instance segmentation ground-truth would have to be converted into boundaries, if the model is predicting boundaries).
By default, it is assumed that the spatial dimensions of ground-truth and target are the same.
If your predictor needs more ground-truth context to create a target (e.g., because it predicts the distance to a boundary, up to a certain threshold), you can request a larger ground-truth region. See method
gt_region_for_roi.- Parameters:
gt – The ground-truth array.
- Returns:
The target array.
- Raises:
NotImplementedError – This method is not implemented.
Examples
>>> predictor.create_target(gt)
- abstract create_weight(gt: dacapo.experiments.datasplits.datasets.arrays.Array, target: dacapo.experiments.datasplits.datasets.arrays.Array, mask: dacapo.experiments.datasplits.datasets.arrays.Array, moving_class_counts: Any) Tuple[dacapo.experiments.datasplits.datasets.arrays.Array, Any]
Create the weight array for training, given a ground-truth and associated target array.
- Parameters:
gt – The ground-truth array.
target – The target array.
mask – The mask array.
moving_class_counts – The moving class counts.
- Returns:
The weight array and the moving class counts.
- Raises:
NotImplementedError – This method is not implemented.
Examples
>>> predictor.create_weight(gt, target, mask, moving_class_counts)
- abstract property output_array_type
- gt_region_for_roi(target_spec)
Report how much spatial context this predictor needs to generate a target for the given ROI. By default, uses the same ROI.
Overwrite this method to request ground-truth in a larger ROI, as needed.
- Parameters:
target_spec – The ROI for which the target is requested.
- Returns:
The ROI for which the ground-truth is requested.
- Raises:
NotImplementedError – This method is not implemented.
Examples
>>> predictor.gt_region_for_roi(target_spec)
- padding(gt_voxel_size: funlib.geometry.Coordinate) funlib.geometry.Coordinate
Return the padding needed for the ground-truth array.
- Parameters:
gt_voxel_size – The voxel size of the ground-truth array.
- Returns:
The padding needed for the ground-truth array.
- Raises:
NotImplementedError – This method is not implemented.
Examples
>>> predictor.padding(gt_voxel_size)