Merge pull request #393 from bioimage-io/backward_comp

Improve backward compatibility

Author: FynnBe

README.md

@@ -124,6 +124,10 @@ The model specification and its validation tools can be found at <https://github
 
 ## Changelog
 
+### 0.6.6
+
+* add aliases to match previous API more closely
+
 ### 0.6.5
 
 * improve adapter error messages

bioimageio/core/VERSION

@@ -1,3 +1,3 @@
 {
-    "version": "0.6.5"
+    "version": "0.6.6"
 }

bioimageio/core/__init__.py

@@ -27,10 +27,15 @@
 from .axis import AxisId as AxisId
 from .block_meta import BlockMeta as BlockMeta
 from .common import MemberId as MemberId
+from .prediction import predict as predict
+from .prediction import predict_many as predict_many
 from .sample import Sample as Sample
 from .tensor import Tensor as Tensor
 from .utils import VERSION
 
 __version__ = VERSION
 
+# aliases
 test_resource = test_description
+load_resource = load_description
+load_model = load_description

bioimageio/core/commands.py

@@ -11,7 +11,6 @@
     load_description_and_validate_format_only,
     save_bioimageio_package,
 )
-from bioimageio.spec.collection import CollectionDescr
 from bioimageio.spec.dataset import DatasetDescr
 from bioimageio.spec.model import ModelDescr
 from bioimageio.spec.model.v0_5 import WeightsFormat
@@ -94,7 +93,6 @@ def validate_format(
         model RDF {ModelDescr.implemented_format_version}
       dataset RDF {DatasetDescr.implemented_format_version}
      notebook RDF {NotebookDescr.implemented_format_version}
-   collection RDF {CollectionDescr.implemented_format_version}
 
 """
 

bioimageio/core/digest_spec.py

@@ -2,6 +2,7 @@
 
 import importlib.util
 from itertools import chain
+from pathlib import Path
 from typing import (
     Any,
     Callable,
@@ -16,9 +17,15 @@
     Union,
 )
 
+import numpy as np
+import xarray as xr
+from loguru import logger
 from numpy.typing import NDArray
 from typing_extensions import Unpack, assert_never
 
+from bioimageio.core.common import MemberId, PerMember, SampleId
+from bioimageio.core.io import load_tensor
+from bioimageio.core.sample import Sample
 from bioimageio.spec._internal.io_utils import HashKwargs, download
 from bioimageio.spec.common import FileSource
 from bioimageio.spec.model import AnyModelDescr, v0_4, v0_5
@@ -30,7 +37,7 @@
 )
 from bioimageio.spec.utils import load_array
 
-from .axis import AxisId, AxisInfo, PerAxis
+from .axis import AxisId, AxisInfo, AxisLike, PerAxis
 from .block_meta import split_multiple_shapes_into_blocks
 from .common import Halo, MemberId, PerMember, SampleId, TotalNumberOfBlocks
 from .sample import (
@@ -329,12 +336,35 @@ def get_io_sample_block_metas(
     )
 
 
+def get_tensor(
+    src: Union[Tensor, xr.DataArray, NDArray[Any], Path],
+    ipt: Union[v0_4.InputTensorDescr, v0_5.InputTensorDescr],
+):
+    """helper to cast/load various tensor sources"""
+
+    if isinstance(src, Tensor):
+        return src
+
+    if isinstance(src, xr.DataArray):
+        return Tensor.from_xarray(src)
+
+    if isinstance(src, np.ndarray):
+        return Tensor.from_numpy(src, dims=get_axes_infos(ipt))
+
+    if isinstance(src, Path):
+        return load_tensor(src, axes=get_axes_infos(ipt))
+
+    assert_never(src)
+
+
 def create_sample_for_model(
     model: AnyModelDescr,
     *,
     stat: Optional[Stat] = None,
     sample_id: SampleId = None,
-    inputs: Optional[PerMember[NDArray[Any]]] = None,  # TODO: make non-optional
+    inputs: Optional[
+        PerMember[Union[Tensor, xr.DataArray, NDArray[Any], Path]]
+    ] = None,  # TODO: make non-optional
     **kwargs: NDArray[Any],  # TODO: deprecate in favor of `inputs`
 ) -> Sample:
     """Create a sample from a single set of input(s) for a specific bioimage.io model
@@ -359,10 +389,54 @@ def create_sample_for_model(
 
     return Sample(
         members={
-            m: Tensor.from_numpy(inputs[m], dims=get_axes_infos(ipt))
+            m: get_tensor(inputs[m], ipt)
             for m, ipt in model_inputs.items()
             if m in inputs
         },
         stat={} if stat is None else stat,
         id=sample_id,
     )
+
+
+def load_sample_for_model(
+    *,
+    model: AnyModelDescr,
+    paths: PerMember[Path],
+    axes: Optional[PerMember[Sequence[AxisLike]]] = None,
+    stat: Optional[Stat] = None,
+    sample_id: Optional[SampleId] = None,
+):
+    """load a single sample from `paths` that can be processed by `model`"""
+
+    if axes is None:
+        axes = {}
+
+    # make sure members are keyed by MemberId, not string
+    paths = {MemberId(k): v for k, v in paths.items()}
+    axes = {MemberId(k): v for k, v in axes.items()}
+
+    model_inputs = {get_member_id(d): d for d in model.inputs}
+
+    if unknown := {k for k in paths if k not in model_inputs}:
+        raise ValueError(f"Got unexpected paths for {unknown}")
+
+    if unknown := {k for k in axes if k not in model_inputs}:
+        raise ValueError(f"Got unexpected axes hints for: {unknown}")
+
+    members: Dict[MemberId, Tensor] = {}
+    for m, p in paths.items():
+        if m not in axes:
+            axes[m] = get_axes_infos(model_inputs[m])
+            logger.warning(
+                "loading paths with {}'s default input axes {} for input '{}'",
+                axes[m],
+                model.id or model.name,
+                m,
+            )
+        members[m] = load_tensor(p, axes[m])
+
+    return Sample(
+        members=members,
+        stat={} if stat is None else stat,
+        id=sample_id or tuple(sorted(paths.values())),
+    )

bioimageio/core/io.py

@@ -1,18 +1,13 @@
 from pathlib import Path
-from typing import Any, Dict, Optional, Sequence
+from typing import Any, Optional, Sequence, Union
 
 import imageio
-from loguru import logger
 from numpy.typing import NDArray
 
-from bioimageio.spec.model import AnyModelDescr
-from bioimageio.spec.utils import load_array
+from bioimageio.spec.utils import load_array, save_array
 
 from .axis import Axis, AxisLike
-from .common import MemberId, PerMember, SampleId
-from .digest_spec import get_axes_infos, get_member_id
 from .sample import Sample
-from .stat_measures import Stat
 from .tensor import Tensor
 
 
@@ -26,6 +21,7 @@ def load_image(path: Path, is_volume: bool) -> NDArray[Any]:
 
 
 def load_tensor(path: Path, axes: Optional[Sequence[AxisLike]] = None) -> Tensor:
+    # TODO: load axis meta data
     array = load_image(
         path,
         is_volume=(
@@ -36,45 +32,24 @@ def load_tensor(path: Path, axes: Optional[Sequence[AxisLike]] = None) -> Tensor
     return Tensor.from_numpy(array, dims=axes)
 
 
-def load_sample_for_model(
-    *,
-    model: AnyModelDescr,
-    paths: PerMember[Path],
-    axes: Optional[PerMember[Sequence[AxisLike]]] = None,
-    stat: Optional[Stat] = None,
-    sample_id: Optional[SampleId] = None,
-):
-    """load a single sample from `paths` that can be processed by `model`"""
-
-    if axes is None:
-        axes = {}
-
-    # make sure members are keyed by MemberId, not string
-    paths = {MemberId(k): v for k, v in paths.items()}
-    axes = {MemberId(k): v for k, v in axes.items()}
-
-    model_inputs = {get_member_id(d): d for d in model.inputs}
+def save_tensor(path: Path, tensor: Tensor) -> None:
+    # TODO: save axis meta data
+    data: NDArray[Any] = tensor.data.to_numpy()
+    if path.suffix == ".npy":
+        save_array(path, data)
+    else:
+        imageio.volwrite(path, data)
 
-    if unknown := {k for k in paths if k not in model_inputs}:
-        raise ValueError(f"Got unexpected paths for {unknown}")
 
-    if unknown := {k for k in axes if k not in model_inputs}:
-        raise ValueError(f"Got unexpected axes hints for: {unknown}")
+def save_sample(path: Union[Path, str], sample: Sample) -> None:
+    """save a sample to path
 
-    members: Dict[MemberId, Tensor] = {}
-    for m, p in paths.items():
-        if m not in axes:
-            axes[m] = get_axes_infos(model_inputs[m])
-            logger.warning(
-                "loading paths with {}'s default input axes {} for input '{}'",
-                axes[m],
-                model.id or model.name,
-                m,
-            )
-        members[m] = load_tensor(p, axes[m])
+    `path` must contain `{member_id}` and may contain `{sample_id}`,
+    which are resolved with the `sample` object.
+    """
+    path = str(path).format(sample_id=sample.id)
+    if "{member_id}" not in path:
+        raise ValueError(f"missing `{{member_id}}` in path {path}")
 
-    return Sample(
-        members=members,
-        stat={} if stat is None else stat,
-        id=sample_id or tuple(sorted(paths.values())),
-    )
+    for m, t in sample.members.items():
+        save_tensor(Path(path.format(member_id=m)), t)