Add inference API and task for all BioImage.IO Model Zoo models

.github/workflows/build-and-publish-docs.yml

@@ -3,7 +3,7 @@ on:
   push:
     branches:
       - master
-      - qy/mask-pred
+      - qy/unify-prediction
 
 permissions:
   contents: write

.github/workflows/build-and-test-package.yml

@@ -62,7 +62,7 @@ jobs:
         shell: bash -l {0}
         run: |
           conda activate plant-seg
-          pytest --cov --cov-report=xml
+          pytest -s --cov --cov-report=xml
           conda deactivate
 
       # Upload Codecov report

.gitignore

@@ -14,3 +14,6 @@ docs/_build/
 
 # Codecov
 .coverage
+
+# macOS
+.DS_Store

conda-recipe/meta.yaml

@@ -17,6 +17,7 @@ requirements:
   build:
     - python
     - pip
+    - setuptools
 
   run:
     - python >=3.9

docs/chapters/getting_started/contributing.md

@@ -17,6 +17,10 @@ To install PlantSeg in development mode, run:
 pip install -e . --no-deps
 ```
 
+## Hierarchical Design of PlantSeg
+
+Please refer to [Python API](../python_api/index.md).
+
 ## Coding Style
 
 PlantSeg uses _Ruff_ for linting and formatting. _Ruff_ is compatible with _Black_ for formatting. Ensure you have _Black_ set as the formatter with a line length of 120.

docs/chapters/python_api/index.md

@@ -0,0 +1,7 @@
+# Hierarchical Design of PlantSeg
+
+PlantSeg is organized into three layers:
+
+ 1. Functionals (Python API): The foundational layer of PlantSeg, providing its core functionality. This layer can be accessed directly in Python scripts or Jupyter notebooks.
+ 2. Tasks: The intermediate layer of PlantSeg, which encapsulates the functionals to handle resource management and support distributed computing.
+ 3. Napari Widgets: The top layer of PlantSeg, which integrates tasks into user-friendly widgets for easy interaction within graphical interfaces.

environment-dev-apple.yaml

@@ -0,0 +1,46 @@
+name: plant-seg-dev
+channels:
+  - pytorch
+  - conda-forge
+  # `defaults` is optional, unless e.g. `conda-forge` has no cudnn 9.* when `defaults` has.
+  # `defaults` of Anaconda is not accessible for many non-profit institutes such as EMBL.
+  # - defaults
+dependencies:
+  - python
+  # Neural Network and GPU
+  - pytorch::pytorch
+  - torchvision
+  # Bioimage and CV
+  - tifffile
+  - h5py
+  - zarr
+  - vigra
+  - python-elf
+  - python-graphviz
+  - scikit-image
+  - bioimageio.core>=0.6.5
+  # GUI
+  - pyqt
+  - napari
+  # Other
+  - requests
+  - pyyaml
+  - pydantic>2,<2.10  # 2.10 cause problem spec-bioimage-io/issues/663
+  # Test
+  - pytest
+  - pytest-qt
+  - pytest-mock
+  - requests-mock
+  # CI/CD
+  - pre-commit
+  - bump-my-version
+  # Docs
+  - mkdocs-material
+  - mkdocs-autorefs
+  - mkdocs-git-revision-date-localized-plugin
+  - mkdocs-git-committers-plugin-2
+  - mkdocstrings-python
+  - pip:
+      - markdown-exec
+      # PlantSeg
+      - -e .

mkdocs.yml

@@ -4,7 +4,7 @@ site_description: Cell instance aware segmentation in densely packed 3D volumetr
 repo_name: kreshuklab/plant-seg
 repo_url: https://github.com/kreshuklab/plant-seg
 edit_uri: edit/main/docs/
-copyright: Copyright © 2019 - 2024 Lorenzo Cerrone, Adrian Wolny, Qin Yu
+copyright: Copyright © 2019 - 2025 Lorenzo Cerrone, Adrian Wolny, Qin Yu
 
 theme:
   name: material
@@ -116,6 +116,7 @@ nav:
       - Training: chapters/plantseg_models/training.md
 
   - API:
+      - chapters/python_api/index.md
       - tasks:
         - plantseg.tasks.io_tasks: chapters/python_api/tasks/io_tasks.md
         - plantseg.tasks.dataprocessing_tasks: chapters/python_api/tasks/dataprocessing_tasks.md

plantseg/core/image.py

@@ -417,7 +417,10 @@ def _check_shape(self, data: np.ndarray, properties: ImageProperties) -> tuple[n
                 return data[:, 0], properties
 
         elif self.image_layout == ImageLayout.ZCYX:
-            raise ValueError(f"Image layout {self.image_layout} not supported, should have been converted to CZYX")
+            logger.warning("Image layout is ZCYX but should have been converted to CZYX. PlantSeg is doing this now.")
+            properties.image_layout = ImageLayout.CZYX
+            data = np.moveaxis(data, 0, 1)
+            return self._check_shape(data, properties)
 
         return data, properties
 

plantseg/core/zoo.py

@@ -1,5 +1,7 @@
 """Model Zoo Singleton"""
 
+# pylint: disable=C0116,C0103
+
 import json
 import logging
 from enum import Enum
@@ -14,7 +16,7 @@
 from bioimageio.spec.model.v0_5 import ModelDescr as ModelDescr_v0_5
 from bioimageio.spec.utils import download
 from pandas import DataFrame, concat
-from pydantic import AliasChoices, BaseModel, Field, model_validator
+from pydantic import AliasChoices, BaseModel, Field, HttpUrl, model_validator
 from torch.nn import Conv2d, Conv3d, MaxPool2d, MaxPool3d, Module
 
 from plantseg import (
@@ -37,18 +39,15 @@
     USER = 'user'
 
 
-BIOIMAGE_IO_COLLECTION_URL = (
-    "https://raw.githubusercontent.com/bioimage-io/collection-bioimage-io/gh-pages/collection.json"
-)
+BIOIMAGE_IO_COLLECTION_URL = "https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/collection.json"
 
 
 class ModelZooRecord(BaseModel):
     """Model Zoo Record"""
 
     name: str
-    url: Optional[str] = Field(None, validation_alias=AliasChoices('model_url', 'url'))
+    url: Optional[HttpUrl] = Field(None, validation_alias=AliasChoices('model_url', 'url'))
     path: Optional[str] = None
-    id: Optional[str] = None
     description: Optional[str] = None
     resolution: Optional[tuple[float, float, float]] = None
     dimensionality: Optional[str] = None
@@ -58,11 +57,25 @@
     doi: Optional[str] = None
     added_by: Optional[str] = None
 
+    # BioImage.IO models specific fields. TODO: unify.
+    id: Optional[str] = None
+    name_display: Optional[str] = None
+    rdf_source: Optional[HttpUrl] = None
+    supported: Optional[bool] = None
+
     @model_validator(mode='after')
     def check_one_id_present(self) -> Self:
         """Check that one of url (zenodo), path (custom/local) or id (bioimage.io) is present"""
         if self.url is None and self.path is None and self.id is None:
-            raise ValueError(f'One of url, path or id must be present: {self}')
+            raise ValueError(f'One of `url`, `path` or `id` must be present: {self}')
+        return self
+
+    @model_validator(mode='after')
+    def check_id_fields_present(self) -> Self:
+        if self.id is not None and (self.name_display is None or self.rdf_source is None or self.supported is None):
+            raise ValueError(
+                f'If `id` exists, then `name_display`, `rdf_source` and `supported` must be present: {self}'
+            )
         return self
 
 
@@ -82,7 +95,6 @@
     _zoo_custom_dict: dict = {}
     _bioimageio_zoo_collection: dict = {}
     _bioimageio_zoo_all_model_url_dict: dict = {}
-    _bioimageio_zoo_plantseg_model_url_dict: dict = {}
 
     path_zoo: Path = PATH_MODEL_ZOO
     path_zoo_custom: Path = PATH_MODEL_ZOO_CUSTOM
@@ -328,13 +340,13 @@
         https://bioimage-io.github.io/collection-bioimage-io/rdfs/10.5281/zenodo.8401064/8429203/rdf.yaml
         """
 
-        if not self._bioimageio_zoo_all_model_url_dict:
+        if not hasattr(self, 'models_bioimageio'):
             self.refresh_bioimageio_zoo_urls()
 
-        if model_id not in self._bioimageio_zoo_all_model_url_dict:
+        if model_id not in self.models_bioimageio.index:
             raise ValueError(f"Model ID {model_id} not found in BioImage.IO Model Zoo")
 
-        rdf_url = self._bioimageio_zoo_all_model_url_dict[model_id]
+        rdf_url = self.models_bioimageio.at[model_id, 'rdf_source']
         model_description = load_description(rdf_url)
 
         # Check if description is `ResourceDescr`
@@ -360,6 +372,8 @@
         elif isinstance(model_description, ModelDescr_v0_5):  # then it is `ArchitectureDescr` with `callable`
             architecture_callable = model_description.weights.pytorch_state_dict.architecture.callable
             architecture_kwargs = model_description.weights.pytorch_state_dict.architecture.kwargs
+        else:
+            raise ValueError(f"Unsupported model description format: {type(model_description).__name__}")
         logger_zoo.info(f"Got {architecture_callable} model with kwargs {architecture_kwargs}.")
 
         # Create model from architecture and kwargs
@@ -382,25 +396,54 @@
         logger_zoo.info(f"Loaded model from BioImage.IO Model Zoo: {model_id}")
         return model, model_config, model_weights_path
 
+    def _init_bioimageio_zoo_df(self) -> None:
+        records = []
+        for _, model in self._bioimageio_zoo_all_model_url_dict.items():
+            records.append(ModelZooRecord(**model, added_by=Author.BIOIMAGEIO).model_dump())
+
+        self.models_bioimageio = DataFrame(
+            records,
+            columns=list(ModelZooRecord.model_fields.keys()),
+        ).set_index('id')
+
     def refresh_bioimageio_zoo_urls(self):
         """Initialize the BioImage.IO Model Zoo collection and URL dictionaries.
 
         The BioImage.IO Model Zoo collection is not downloaded during ModelZoo initialization to avoid unnecessary
         network requests. This method downloads the collection and extracts the model URLs for all models.
+
+        Note that `models_bioimageio` doesn't exist until this method is called.
         """
         logger_zoo.info(f"Fetching BioImage.IO Model Zoo collection from {BIOIMAGE_IO_COLLECTION_URL}")
         collection_path = Path(pooch.retrieve(BIOIMAGE_IO_COLLECTION_URL, known_hash=None))
         with collection_path.open(encoding='utf-8') as f:
             collection = json.load(f)
+
+        def get_id(entry):
+            return entry["id"] if "nickname" not in entry else entry["nickname"]
+
+        models = [entry for entry in collection["collection"] if entry["type"] == "model"]
+        max_nickname_length = max(len(get_id(entry)) for entry in models)
+
+        def truncate_name(name, length=100):
+            return name[:length] + '...' if len(name) > length else name
+
+        def build_model_url_dict(filter_func=None):
+            filtered_models = filter(filter_func, models) if filter_func else models
+            return {
+                entry['name']: {
+                    "id": get_id(entry),
+                    "name": entry["name"],
+                    "name_display": f"{get_id(entry):<{max_nickname_length}}: {truncate_name(entry['name'])}",
+                    "rdf_source": entry["rdf_source"],
+                    "supported": self._is_plantseg_model(entry),
+                }
+                for entry in filtered_models
+            }
+
         self._bioimageio_zoo_collection = collection
-        self._bioimageio_zoo_all_model_url_dict = {
-            entry["nickname"]: entry["rdf_source"] for entry in collection["collection"] if entry["type"] == "model"
-        }
-        self._bioimageio_zoo_plantseg_model_url_dict = {
-            entry["nickname"]: entry["rdf_source"]
-            for entry in collection["collection"]
-            if entry["type"] == "model" and self._is_plantseg_model(entry)
-        }
+        self._bioimageio_zoo_all_model_url_dict = build_model_url_dict()
+        self._init_bioimageio_zoo_df()
 
     def _is_plantseg_model(self, collection_entry: dict) -> bool:
         """Determines if the 'tags' field in a collection entry contains the keyword 'plantseg'."""
@@ -414,23 +457,26 @@
         normalized_tags = ["".join(filter(str.isalnum, tag.lower())) for tag in tags]
         return 'plantseg' in normalized_tags
 
-    def get_bioimageio_zoo_plantseg_model_names(self) -> list[str]:
-        """Return a list of model names in the BioImage.IO Model Zoo tagged with 'plantseg'."""
-        if not self._bioimageio_zoo_plantseg_model_url_dict:
+    def get_bioimageio_zoo_all_model_names(self) -> list[tuple[str, str]]:
+        """Return a list of (model id, model display name) in the BioImage.IO Model Zoo."""
+        if not hasattr(self, 'models_bioimageio'):
             self.refresh_bioimageio_zoo_urls()
-        return sorted(list(self._bioimageio_zoo_plantseg_model_url_dict.keys()))
+        id_name = self.models_bioimageio[['name_display']]
+        return sorted([(name, id) for id, name in id_name.itertuples()])
 
-    def get_bioimageio_zoo_all_model_names(self) -> list[str]:
-        """Return a list of all model names in the BioImage.IO Model Zoo."""
-        if not self._bioimageio_zoo_all_model_url_dict:
+    def get_bioimageio_zoo_plantseg_model_names(self) -> list[tuple[str, str]]:
+        """Return a list of (model id, model display name) in the BioImage.IO Model Zoo tagged with 'plantseg'."""
+        if not hasattr(self, 'models_bioimageio'):
             self.refresh_bioimageio_zoo_urls()
-        return sorted(list(self._bioimageio_zoo_all_model_url_dict.keys()))
+        id_name = self.models_bioimageio[self.models_bioimageio["supported"]][['name_display']]
+        return sorted([(name, id) for id, name in id_name.itertuples()])
 
-    def get_bioimageio_zoo_other_model_names(self) -> list[str]:
-        """Return a list of model names in the BioImage.IO Model Zoo not tagged with 'plantseg'."""
-        return sorted(
-            list(set(self.get_bioimageio_zoo_all_model_names()) - set(self.get_bioimageio_zoo_plantseg_model_names()))
-        )
+    def get_bioimageio_zoo_other_model_names(self) -> list[tuple[str, str]]:
+        """Return a list of (model id, model display name) in the BioImage.IO Model Zoo not tagged with 'plantseg'."""
+        if not hasattr(self, 'models_bioimageio'):
+            self.refresh_bioimageio_zoo_urls()
+        id_name = self.models_bioimageio[~self.models_bioimageio["supported"]][['name_display']]
+        return sorted([(name, id) for id, name in id_name.itertuples()])
 
     def _flatten_module(self, module: Module) -> list[Module]:
         """Recursively flatten a PyTorch nn.Module into a list of its elemental layers."""

plantseg/functionals/prediction/__init__.py

@@ -1,6 +1,7 @@
-from plantseg.functionals.prediction.prediction import unet_prediction
+from plantseg.functionals.prediction.prediction import biio_prediction, unet_prediction
 
 # Use __all__ to let type checkers know what is part of the public API.
 __all__ = [
     "unet_prediction",
+    "biio_prediction",
 ]