Merge pull request #577 from bioimage-io/rich_md_output

Rich md output for validation summaries

bioimageio/spec/_build_description.py

@@ -1,10 +1,10 @@
-from typing import Any, Callable, Mapping, Optional, Type, TypeVar, Union
+from typing import Any, Callable, List, Mapping, Optional, Type, TypeVar, Union
 
 from ._internal.common_nodes import InvalidDescr, ResourceDescrBase
 from ._internal.io import BioimageioYamlContent
 from ._internal.types import FormatVersionPlaceholder
 from ._internal.validation_context import ValidationContext, validation_context_var
-from .generic import GenericDescr
+from .summary import ErrorEntry, ValidationDetail
 
 ResourceDescrT = TypeVar("ResourceDescrT", bound=ResourceDescrBase)
 
@@ -50,16 +50,48 @@ def build_description_impl(
     get_rd_class: Callable[[Any, Any], Type[ResourceDescrT]],
 ) -> Union[ResourceDescrT, InvalidDescr]:
     context = context or validation_context_var.get()
-    if not isinstance(content, dict):
-        # "Invalid content of type '{type(content)}'"
-        rd_class = GenericDescr
+    errors: List[ErrorEntry] = []
+    if isinstance(content, dict):
+        for minimum in ("type", "format_version"):
+            if minimum not in content:
+                errors.append(
+                    ErrorEntry(
+                        loc=(minimum,), msg=f"Missing field '{minimum}'", type="error"
+                    )
+                )
+            elif not isinstance(content[minimum], str):
+                errors.append(
+                    ErrorEntry(
+                        loc=(minimum,),
+                        msg=f"Invalid type '{type(content[minimum])}'",
+                        type="error",
+                    )
+                )
+    else:
+        errors.append(
+            ErrorEntry(
+                loc=(), msg=f"Invalid content of type '{type(content)}'", type="error"
+            )
+        )
+        content = {}
 
-    typ = content.get("type")
-    rd_class = get_rd_class(typ, content.get("format_version"))
+    if errors:
+        ret = InvalidDescr(**content)  # pyright: ignore[reportArgumentType]
+        ret.validation_summary.add_detail(
+            ValidationDetail(
+                name="extract fields to chose description class",
+                status="failed",
+                errors=errors,
+            )
+        )
+        return ret
 
+    typ = content["type"]
+    rd_class = get_rd_class(typ, content["format_version"])
     rd = rd_class.load(content, context=context)
-    assert rd.validation_summary is not None
+
     if format_version != DISCOVER and not isinstance(rd, InvalidDescr):
+        # load with requested format_version
         discover_details = rd.validation_summary.details
         as_rd_class = get_rd_class(typ, format_version)
         rd = as_rd_class.load(content, context=context)

bioimageio/spec/model/v0_5.py

@@ -2271,49 +2271,59 @@ def get_axis_sizes(
         outputs: Dict[Tuple[TensorId, AxisId], Union[int, _DataDepSize]] = {}
         input_ids = {d.id for d in self.inputs}
         output_ids = {d.id for d in self.outputs}
-        for t_descr in chain(self.inputs, self.outputs):
-            for a in t_descr.axes:
-                if isinstance(a, BatchAxis):
-                    if (t_descr.id, a.id) in ns:
-                        raise ValueError(
-                            f"No size increment factor (n) for batch axis of tensor {t_descr.id} expected."
-                        )
-                    s = batch_size
-                elif isinstance(a.size, int):
-                    if (t_descr.id, a.id) in ns:
-                        raise ValueError(
-                            f"No size increment factor (n) for fixed size axis {a.id} of tensor {t_descr.id} expected."
-                        )
-                    s = a.size
-                elif isinstance(a.size, ParameterizedSize):
-                    if (t_descr.id, a.id) not in ns:
-                        raise ValueError(
-                            f"Size increment factor (n) not given for parametrized axis {a.id} of tensor {t_descr.id}."
-                        )
-                    s = a.size.get_size(ns[(t_descr.id, a.id)])
-                elif isinstance(a.size, SizeReference):
-                    assert not isinstance(a, BatchAxis)
-                    ref_axis = all_axes[a.size.tensor_id][a.size.axis_id]
-                    assert not isinstance(ref_axis, BatchAxis)
-                    if (a.size.tensor_id, a.size.axis_id) not in ns:
-                        raise ValueError(
-                            f"No increment (n) provided for axis {a.id} of tensor {t_descr.id}. Expected reference from tensor {a.size.tensor_id} and axis {a.size.axis_id}."
-                        )
-                    s = a.size.get_size(
-                        axis=a, ref_axis=ref_axis, n=ns[(t_descr.id, a.id)]
+
+        def get_axis_size(a: Union[InputAxis, OutputAxis]):
+            if isinstance(a, BatchAxis):
+                if (t_descr.id, a.id) in ns:
+                    raise ValueError(
+                        "No size increment factor (n) for batch axis of tensor"
+                        + f" '{t_descr.id}' expected."
                     )
-                elif isinstance(a.size, DataDependentSize):
-                    if (t_descr.id, a.id) in ns:
-                        raise ValueError(
-                            f"No size increment factor (n) for data dependent size axis {a.id} of tensor {t_descr.id} expected."
-                        )
-                    assert t_descr.id in output_ids
-                    outputs[t_descr.id, a.id] = _DataDepSize(a.size.min, a.size.max)
-                    continue
-                else:
-                    assert_never(a.size)
+                return batch_size
+            elif isinstance(a.size, int):
+                if (t_descr.id, a.id) in ns:
+                    raise ValueError(
+                        "No size increment factor (n) for fixed size axis"
+                        + f" '{a.id}' of tensor '{t_descr.id}' expected."
+                    )
+                return a.size
+            elif isinstance(a.size, ParameterizedSize):
+                if (t_descr.id, a.id) not in ns:
+                    raise ValueError(
+                        "Size increment factor (n) missing for parametrized axis"
+                        + f" '{a.id}' of tensor '{t_descr.id}'."
+                    )
+                return a.size.get_size(ns[(t_descr.id, a.id)])
+            elif isinstance(a.size, SizeReference):
+                if (t_descr.id, a.id) in ns:
+                    raise ValueError(
+                        f"No size increment factor (n) for axis '{a.id}' of tensor"
+                        + f" '{t_descr.id}' with size reference expected."
+                    )
+                assert not isinstance(a, BatchAxis)
+                ref_axis = all_axes[a.size.tensor_id][a.size.axis_id]
+                assert not isinstance(ref_axis, BatchAxis)
+                return a.size.get_size(
+                    axis=a,
+                    ref_axis=ref_axis,
+                    n=ns.get((a.size.tensor_id, a.size.axis_id), 0),
+                )
+            elif isinstance(a.size, DataDependentSize):
+                if (t_descr.id, a.id) in ns:
+                    raise ValueError(
+                        "No size increment factor (n) for data dependent size axis"
+                        + f" '{a.id}' of tensor '{t_descr.id}' expected."
+                    )
+                assert t_descr.id in output_ids
+                return _DataDepSize(a.size.min, a.size.max)
+            else:
+                assert_never(a.size)
 
+        for t_descr in chain(self.inputs, self.outputs):
+            for a in t_descr.axes:
+                s = get_axis_size(a)
                 if t_descr.id in input_ids:
+                    assert not isinstance(s, _DataDepSize)
                     inputs[t_descr.id, a.id] = s
                 else:
                     assert t_descr.id in output_ids

bioimageio/spec/summary.py

@@ -3,6 +3,8 @@
 from types import MappingProxyType
 from typing import Any, Iterable, List, Literal, Mapping, Tuple, Union, no_type_check
 
+import rich.console
+import rich.markdown
 from pydantic import BaseModel, Field, model_validator
 from pydantic_core.core_schema import ErrorType
 from typing_extensions import TypedDict, assert_never
@@ -279,12 +281,15 @@ def display(self) -> None:
             from IPython.core.getipython import get_ipython
             from IPython.display import Markdown, display
         except ImportError:
-            print(formatted)
+            pass
         else:
-            if get_ipython() is None:
-                print(formatted)
-            else:
+            if get_ipython() is not None:
                 _ = display(Markdown(formatted))
+                return
+
+        rich_markdown = rich.markdown.Markdown(formatted)
+        console = rich.console.Console()
+        console.print(rich_markdown)
 
     def add_detail(self, detail: ValidationDetail):
         if detail.status == "failed":

setup.py

@@ -40,6 +40,7 @@
         "pydantic>=2.6.3",
         "python-dateutil",
         "python-dotenv",
+        "rich",
         "ruyaml",
         "tqdm",
         "typing-extensions",

tests/test_model/test_v0_5.py

@@ -379,15 +379,15 @@ def test_get_axis_sizes_raises_with_surplus_n(model_data: Dict[str, Any]):
 
 
 def test_get_axis_sizes_raises_with_missing_n(model_data: Dict[str, Any]):
-    model_data["outputs"][0]["axes"][2] = {
+    model_data["inputs"][0]["axes"][2] = {
         "type": "space",
         "id": "x",
-        "size": {"tensor_id": "input_1", "axis_id": "x"},
-        "halo": 0,
+        "size": {"min": 10, "step": 5},
     }
 
     with ValidationContext(perform_io_checks=False):
         model = ModelDescr(**model_data)
+
     with pytest.raises(ValueError):
         _ = model.get_axis_sizes(ns={}, batch_size=1)