Model graph and output naming

This page explains the naming scheme behind every outputs_criterions and outputs_dataset key — how KonfAI addresses individual modules inside a model graph. Read it before attaching a loss, metric, or exported prediction to a model output.

KonfAI models are not treated as opaque single-output blocks. A model is a named module graph, and KonfAI lets you attach losses, metrics, and exported datasets to specific named outputs.

Networks

The core abstractions live in konfai.network.network:

• Network

• ModelLoader

• OptimizerLoader

• TargetCriterionsLoader

• Measure

The selected model class is configured under Model.classpath, then further configured under a section named after that class.

Example:

Model:
  classpath: UNet.yml
  UNet:
    parameters:
      dim: 2
      nb_class: 41

Addressing outputs

Losses and metrics are attached through outputs_criterions. Keys in this mapping correspond to named modules or outputs in the model graph.

Example from the segmentation baseline:

outputs_criterions:
  UNetBlock_0:Head:Conv:
    targets_criterions:
      SEG:
        criterions_loader:
          torch:nn:CrossEntropyLoss:
            is_loss: true

Note

An outputs_criterions or outputs_dataset key must match a module’s dotted path exactly — the : separators between graph levels are load-bearing. A key that does not match any module path raises a configuration error at runtime.

Targets and metrics

For each output group you can define one or more target groups, then one or more criteria for each target:

outputs_criterions:
  Head:Tanh:
    targets_criterions:
      CT:
        criterions_loader:
          MAE:
            is_loss: true

This structure lets you express:

• multiple heads

• multiple targets per head

• multiple losses or metrics per target

• independent scheduler weights per criterion

Dataset patching vs model patching

KonfAI supports patching at two different levels:

• dataset patching with Dataset.Patch

• model patching with Model.<Class>.Patch

Dataset patching controls what reaches the model. Model patching controls how a network internally re-processes those tensors.

The examples/Synthesis GAN variant is the clearest example:

• Dataset.Patch provides a 3D chunk to the whole GAN

• Model.Gan.UNetpp5.Patch reprocesses the chunk slice-wise inside the generator

;accu; outputs

The ;accu; marker appears in some advanced workflows, especially when model patching is enabled. Its semantics are inferred from the shipped examples and the network patch/accumulation logic.

In practice it refers to patch-wise outputs before final re-assembly.

This matters in the synthesis GAN example:

• Generator_A_to_B:;accu;Head:Tanh is used for patch-wise reconstruction loss

• Discriminator_pB:Head:Conv is used after the generator output has been re-assembled

Prediction outputs

Inference uses a separate outputs_dataset mapping to decide what should be written to disk.

Example:

outputs_dataset:
  Head:Tanh:
    OutputDataset:
      name_class: OutSameAsGroupDataset
      group: sCT
      reduction: Mean

This lets you control:

• which model output is exported

• how multiple predictions are reduced

• what final transforms are applied before writing files

Next steps

• Declarative YAML model graphs — define the same named graphs entirely in YAML.

• Using custom models, transforms, augmentations, and losses — write your own Network subclass and wire it with add_module.

• Prediction configuration — the full outputs_dataset reference for inference runs.