minerva.engines.patch_inferencer_engine

Classes

PatchInferencer	This class acts as a normal L.LightningModule that wraps a
PatchInferencerEngine	Main interface for Engine classes. Engines are used to alter the behavior of a model's prediction.

Module Contents

class minerva.engines.patch_inferencer_engine.PatchInferencer(model, input_shape, output_shape=None, weight_function=None, offsets=None, padding=None, return_tuple=None)

Bases: minerva.engines.engine._Inferencer

This class acts as a normal L.LightningModule that wraps a SimpleSupervisedModel model allowing it to perform inference with a custom engine (e.g., PatchInferencerEngine, SlidingWindowInferencerEngine). This is useful when the model’s default input size is smaller than the desired input size (sample size). In this case, the engine split the input tensor into patches, perform inference in each patch, and combine them into a single output of the desired size. The combination of patches can be parametrized by a weight_function allowing a customizable combination of patches (e.g, combining using weighted average). It is important to note that only model’s forward are wrapped, and, thus, any method that requires the forward method (e.g., training_step, predict_step) will be performed in patches, transparently to the user.

Wrap a SimpleSupervisedModel model’s forward method to perform inference in patches, transparently splitting the input tensor into patches, performing inference in each patch, and combining them into a single output of the desired size.

Parameters

modelSimpleSupervisedModel

Model to be wrapped.

input_shapeTuple[int, …]

Expected input shape of the wrapped model.

output_shapeTuple[int, …], optional

Expected output shape of the wrapped model. For models that return logits (e.g., classification models), the output_shape must include an additional dimension at the beginning to accommodate the number of output classes. For example, if the model processes an input tensor of shape (1, 128, 128) and outputs logits for 10 classes, the expected output_shape should be (10, 1, 128, 128). If the model does not return logits (e.g., return a tensor after applying an argmax operation, or a regression models that usually returns a tensor with the same shape as the input tensor), the output_shape should have the same number of dimensions as the input shape. Defaults to None, which assumes the output shape is the same as the input_shape parameter.

weight_function: Callable[[Tuple[int, …]], torch.Tensor], optional

Function that receives a tensor shape and returns the weights for each position of a tensor with the given shape. Useful when regions of the inference present diminishing performance when getting closer to borders, for instance.

offsetsList[Tuple[int, …]], optional

List of tuples with offsets that determine the shift of the initial position of the patch subdivision.

paddingDict[str, Any], optional

Dictionary describing padding strategy. Keys:

• pad (mandatory): tuple with pad width (int) for each

  dimension, e.g.(0, 3, 3) when working with a tensor with 3 dimensions.

• mode (optional): ‘constant’, ‘reflect’, ‘replicate’ or

  ‘circular’. Defaults to ‘constant’.

• value (optional): fill value for ‘constant’. Defaults to 0.

If None, no padding is applied.

return_tuple: int, optional

Some models may return multiple outputs for a single sample (e.g., outputs from multiple auxiliary heads). This parameter is a integer that defines the number of outputs the model generates. By default, it is None, which indicates that the model produces a single output for a single input. When set, it indicates the number of outputs the model produces.

inferencer

model

Parameters:

• model (minerva.models.nets.base.SimpleSupervisedModel)

• input_shape (Tuple[int, Ellipsis])

• output_shape (Optional[Tuple[int, Ellipsis]])

• weight_function (Optional[Callable[[Tuple[int, Ellipsis]], torch.Tensor]])

• offsets (Optional[List[Tuple[int, Ellipsis]]])

• padding (Optional[Dict[str, Any]])

• return_tuple (Optional[int])

class minerva.engines.patch_inferencer_engine.PatchInferencerEngine(input_shape, output_shape=None, offsets=None, padding=None, weight_function=None, return_tuple=None)

Bases: minerva.engines.engine._Engine

Main interface for Engine classes. Engines are used to alter the behavior of a model’s prediction. An engine should be able to take a model and input data x and return a prediction. An use case for Engines is patched inference, where the model’s default input size is smaller them the desired input size. The engine can be used to make predictions in patches and combine this predictions in to a single output.

Parameters

input_shapeTuple[int, …]

Shape of each patch to process.

output_shapeTuple[int, …], optional

Expected output shape of the model. For models that return logits, the output_shape must include an additional dimension at the beginning to accommodate the number of output classes. Else, the output_shape should have the same number of dimensions as the input_shape (i.e., no logits are returned). Defaults to input_shape.

paddingDict[str, Any], optional

Padding configuration with keys:

• ‘pad’: Tuple of padding for each expected final dimension,

  e.g., (0, 512, 512) - (c, h, w).

• ‘mode’: Padding mode, e.g., ‘constant’, ‘reflect’.

• ‘value’: Padding value if mode is ‘constant’.

Defaults to None, which means no padding is applyied.

weight_functionCallable, optional

Function to calculate the weight of each patch. Defaults to None.

return_tupleint, optional

Number of outputs to return. This is useful when the model returns multiple outputs for a single input (e.g., from multiple auxiliary heads). Defaults to None.

__call__(model, x)

Perform inference in patches, from the input tensor x using the model model.

Parameters

model: Union[L.LightningModule, torch.nn.Module]

Model to perform inference.

xtorch.Tensor

Input tensor of the sample. It can be a single sample or a batch of samples.

Parameters:

• model (Union[lightning.LightningModule, torch.nn.Module])

• x (torch.Tensor)

_adjust_patches(arrays, ref_shape, offset, pad_value=0)

Pads reconstructed patches with pad_value to have same shape as the reference shape from the base patch set.

Parameters:

• arrays (List[torch.Tensor])

• ref_shape (Tuple[int])

• offset (Tuple[int])

• pad_value (int)

Return type:

List[torch.Tensor]

_combine_patches(results, offsets, indexes)

Performs the combination of patches based on the weight function.

Parameters:

• results (List[torch.Tensor])

• offsets (List[Tuple[int]])

• indexes (List[Tuple[int]])

Return type:

torch.Tensor

_compute_base_padding(tensor)

Computes the padding for the base patch set based on the input tensor shape and the model’s input shape.

Parameters:

tensor (torch.Tensor)

_compute_output_shape(tensor)

Computes PatchInferencer output shape based on input tensor shape, and model’s input and output shapes.

Parameters:

tensor (torch.Tensor)

Return type:

Tuple[int]

_extract_patches(data, patch_shape)

Patch extraction method. It will be called once for the base patch set and also for the requested offsets (overlapping patch sets).

Parameters:

• data (torch.Tensor)

• patch_shape (Tuple[int])

Return type:

Tuple[torch.Tensor, Tuple[int]]

_reconstruct_patches(patches, index)

Rearranges patches to reconstruct area of interest from patches and weights.

Parameters:

• patches (torch.Tensor)

• index (Tuple[int])

Return type:

Tuple[torch.Tensor, torch.Tensor]

input_shape

logits_dim

output_shape

output_simplified_shape = ()

return_tuple = None

weight_function = None

Parameters:

• input_shape (Tuple[int, Ellipsis])

• output_shape (Optional[Tuple[int, Ellipsis]])

• offsets (Optional[List[Tuple[int, Ellipsis]]])

• padding (Optional[Dict[str, Any]])

• weight_function (Optional[Callable])

• return_tuple (Optional[int])