cellmap_flow.finetune.target_transforms

Target transforms for converting user annotations to training targets.

Each transform takes a raw annotation tensor (B, 1, Z, Y, X) with values:

0 = unannotated (ignored in loss) 1 = background 2 = first foreground object 3 = second foreground object, etc.

And produces:

target: (B, C, Z, Y, X) — training target matching model output channels mask: (B, C, Z, Y, X) or (B, 1, Z, Y, X) — valid loss mask

Classes

TargetTransform	Base class for target transforms.
BinaryTargetTransform	Standard binary segmentation transform (current default behavior).
BroadcastBinaryTargetTransform	Binary target broadcast to N channels.
AffinityTargetTransform	Compute affinity targets from instance labels.

Module Contents

class cellmap_flow.finetune.target_transforms.TargetTransform

Base class for target transforms.

class cellmap_flow.finetune.target_transforms.BinaryTargetTransform

Standard binary segmentation transform (current default behavior).

Produces single-channel binary target: bg=0, fg=1. Mask marks annotated regions.

class cellmap_flow.finetune.target_transforms.BroadcastBinaryTargetTransform(num_channels: int)

Binary target broadcast to N channels.

All output channels receive the same fg/bg target. Useful for treating multi-channel models (affinities, distances) as simple binary segmentation.

num_channels

class cellmap_flow.finetune.target_transforms.AffinityTargetTransform(offsets: List[List[int]], num_channels: int = None)

Compute affinity targets from instance labels.

For each offset, affinity is:

1 if both voxels belong to the same foreground object (same label > 1) 0 if different objects, or either is background

The loss mask requires both voxels in each pair to be annotated (label > 0), producing a per-channel mask since each offset shifts differently.

Parameters:

• offsets – List of [dz, dy, dx] offset tuples defining neighbor relationships.

• num_channels – Total number of model output channels. If greater than len(offsets), extra channels (e.g. LSDs) are masked out (mask=0) so they receive no gradient. If None, defaults to len(offsets).

offsets

num_channels