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fix: model axis names and min axis size #584

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c032914
fix: model axis names and min axis size
KRiedmiller Jun 25, 2026
ff3a626
fix: validate biio test input/output
KRiedmiller Jun 26, 2026
0cb643d
test: fix training test
KRiedmiller Jun 26, 2026
02d8b79
feat: add PanSeg tag to trained models
KRiedmiller Jun 26, 2026
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2 changes: 1 addition & 1 deletion panseg/core/zoo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -608,7 +608,7 @@ def compute_halo(self, module: Module) -> int:
return halo

def compute_3D_halo_for_pytorch3dunet(
self, module: AbstractUNet
self, module: UNet2D | UNet3D
) -> tuple[int, int, int]:
if isinstance(module, UNet3D):
halo = self.compute_halo(module)
Expand Down
44 changes: 22 additions & 22 deletions panseg/functionals/training/biio.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@ def make_model_description(
in_channels: int,
out_channels: int,
feature_maps: int | list[int] | tuple[int, ...],
patch_size: tuple[int, int, int],
axis_min_sizes: tuple[int, int, int],
dimensionality: Literal["2D", "3D"],
layer_order: str,
modality: str,
Expand All @@ -50,20 +50,20 @@ def make_model_description(
channel_names=[Identifier(f"in_ch_{i}") for i in range(in_channels)]
),
SpaceInputAxis(
id=AxisId("z_in"),
size=ParameterizedSize(min=patch_size[0], step=1),
id=AxisId("z"),
size=ParameterizedSize(min=axis_min_sizes[0], step=1),
scale=resolution[0],
unit="micrometer",
),
SpaceInputAxis(
id=AxisId("y_in"),
size=ParameterizedSize(min=patch_size[1], step=1),
id=AxisId("y"),
size=ParameterizedSize(min=axis_min_sizes[1], step=1),
scale=resolution[1],
unit="micrometer",
),
SpaceInputAxis(
id=AxisId("x_in"),
size=ParameterizedSize(min=patch_size[2], step=1),
id=AxisId("x"),
size=ParameterizedSize(min=axis_min_sizes[2], step=1),
scale=resolution[2],
unit="micrometer",
),
Expand All @@ -75,14 +75,14 @@ def make_model_description(
channel_names=[Identifier(f"in_ch_{i}") for i in range(in_channels)]
),
SpaceInputAxis(
id=AxisId("y_in"),
size=ParameterizedSize(min=patch_size[1], step=1),
id=AxisId("y"),
size=ParameterizedSize(min=axis_min_sizes[1], step=1),
scale=resolution[1],
unit="micrometer",
),
SpaceInputAxis(
id=AxisId("x_in"),
size=ParameterizedSize(min=patch_size[2], step=1),
id=AxisId("x"),
size=ParameterizedSize(min=axis_min_sizes[2], step=1),
scale=resolution[2],
unit="micrometer",
),
Expand All @@ -106,20 +106,20 @@ def make_model_description(
channel_names=[Identifier(f"out_ch_{i}") for i in range(out_channels)]
),
SpaceOutputAxis(
id=AxisId("z_out"),
size=SizeReference(tensor_id=TensorId("input"), axis_id=AxisId("z_in")),
id=AxisId("z"),
size=SizeReference(tensor_id=TensorId("input"), axis_id=AxisId("z")),
scale=resolution[0],
unit="micrometer",
),
SpaceOutputAxis(
id=AxisId("y_out"),
size=SizeReference(tensor_id=TensorId("input"), axis_id=AxisId("y_in")),
id=AxisId("y"),
size=SizeReference(tensor_id=TensorId("input"), axis_id=AxisId("y")),
scale=resolution[1],
unit="micrometer",
),
SpaceOutputAxis(
id=AxisId("x_out"),
size=SizeReference(tensor_id=TensorId("input"), axis_id=AxisId("x_in")),
id=AxisId("x"),
size=SizeReference(tensor_id=TensorId("input"), axis_id=AxisId("x")),
scale=resolution[2],
unit="micrometer",
),
Expand All @@ -131,14 +131,14 @@ def make_model_description(
channel_names=[Identifier(f"out_ch_{i}") for i in range(out_channels)]
),
SpaceOutputAxis(
id=AxisId("y_out"),
size=SizeReference(tensor_id=TensorId("input"), axis_id=AxisId("y_in")),
id=AxisId("y"),
size=SizeReference(tensor_id=TensorId("input"), axis_id=AxisId("y")),
scale=resolution[1],
unit="micrometer",
),
SpaceOutputAxis(
id=AxisId("x_out"),
size=SizeReference(tensor_id=TensorId("input"), axis_id=AxisId("x_in")),
id=AxisId("x"),
size=SizeReference(tensor_id=TensorId("input"), axis_id=AxisId("x")),
scale=resolution[2],
unit="micrometer",
),
Expand Down Expand Up @@ -173,7 +173,7 @@ def make_model_description(
model_desc = ModelDescr(
name=model_name,
description=description,
tags=["UNet", modality, output_type],
tags=["UNet", "PanSeg", modality, output_type],
inputs=[input_desc],
outputs=[output_desc],
weights=WeightsDescr(
Expand Down
14 changes: 13 additions & 1 deletion panseg/functionals/training/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import numpy as np
import torch
import yaml
from bioimageio.core import test_model
from torch import nn
from torch.optim.adam import Adam
from torch.optim.lr_scheduler import ReduceLROnPlateau
Expand Down Expand Up @@ -211,6 +212,9 @@ def unet_training(

# Obtain and save test in- and output for biio
test_in, _ = next(iter(loaders["train"]))
# normalize, as this is post-augmentations
test_in = (test_in - test_in.mean()) / (test_in.std() + 1e-6)

if isinstance(model, UNet2D):
# remove the singleton z-dimension from the input
# Extra dim was only added in the panseg loader for the augmentations
Expand All @@ -224,14 +228,21 @@ def unet_training(
np.save(checkpoint_dir / "test_in.npy", test_in.numpy())
np.save(checkpoint_dir / "test_out.npy", test_out.detach().cpu().numpy())

try:
axis_min_sizes = np.min(
(model_zoo.compute_3D_halo_for_pytorch3dunet(model), patch_size), axis=0
)
except ValueError:
axis_min_sizes = patch_size

with chdir(checkpoint_dir):
model_desc = make_model_description(
weights=weights,
model_name=model_name,
in_channels=in_channels,
out_channels=out_channels,
feature_maps=feature_maps,
patch_size=patch_size,
axis_min_sizes=axis_min_sizes,
dimensionality=dimensionality,
layer_order=layer_order,
modality=modality,
Expand All @@ -242,6 +253,7 @@ def unet_training(
test_out=Path("test_out.npy"),
panseg_config=checkpoint_dir / FILE_CONFIG_TRAIN_YAML,
)
test_model(model_desc).display()
model_desc.package(
checkpoint_dir
/ f"biio_model_{''.join(c for c in model_name if c.isalnum())}.zip"
Expand Down
2 changes: 1 addition & 1 deletion tests/functionals/training/test_biio.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ def test_make_model_description(tmp_path):
in_channels=1,
out_channels=1,
feature_maps=64,
patch_size=(16, 32, 64),
axis_min_sizes=(16, 32, 64),
dimensionality="3D",
layer_order="bcr",
modality="mod",
Expand Down
6 changes: 6 additions & 0 deletions tests/functionals/training/test_training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -306,6 +306,7 @@ def test_create_datasets_invalid_phase(self):
class TestUnetTraining:
"""Tests for unet_training function."""

@patch("panseg.functionals.training.train.test_model")
@patch("panseg.functionals.training.train.make_model_description")
@patch("panseg.functionals.training.train.UNetTrainer")
@patch("panseg.functionals.training.train.create_datasets")
Expand All @@ -320,6 +321,7 @@ def test_unet_training_2d(
mock_create_datasets,
mock_trainer,
mock_model_desc,
mock_test_model,
tmp_path,
):
"""Test UNet training for 2D case."""
Expand Down Expand Up @@ -373,6 +375,7 @@ def test_unet_training_2d(
assert (tmp_path / model_name / "test_in.npy").exists()
assert (tmp_path / model_name / "test_out.npy").exists()

@patch("panseg.functionals.training.train.test_model")
@patch("panseg.functionals.training.train.make_model_description")
@patch("panseg.functionals.training.train.UNetTrainer")
@patch("panseg.functionals.training.train.create_datasets")
Expand All @@ -387,6 +390,7 @@ def test_unet_training_3d(
mock_create_datasets,
mock_trainer,
mock_model_desc,
mock_test_model,
tmp_path,
):
"""Test UNet training for 3D case."""
Expand Down Expand Up @@ -484,6 +488,7 @@ def test_unet_training_with_existing_checkpoint_dir(
device="cpu",
)

@patch("panseg.functionals.training.train.test_model")
@patch("panseg.functionals.training.train.isinstance")
@patch("panseg.functionals.training.train.make_model_description")
@patch("panseg.functionals.training.train.DataLoader")
Expand Down Expand Up @@ -512,6 +517,7 @@ def test_unet_training_multi_gpu(
mock_data_loader,
mock_description,
mock_isinstance,
mock_test_model,
tmp_path,
):
"""Test UNet training with multiple GPUs."""
Expand Down
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