feat: Remove special cases from SegmenationMethod.from_dict

pyproject.toml

@@ -94,7 +94,7 @@ addopts = ["-ra", "--showlocals", "--strict-markers", "--strict-config"]
 xfail_strict = true
 filterwarnings = [
   "error",
-  "ignore:Unsupported Windows version.*ONNX Runtime supports Windows 10 and above, only.:UserWarning",
+  "ignore:Unsupported Windows version.*ONNX Runtime supports Windows 10 and above, only.:UserWarning"
 ]
 log_cli_level = "INFO"
 testpaths = [

src/openlifu/seg/seg_method.py

@@ -1,9 +1,11 @@
 from __future__ import annotations
 
 import copy
+import inspect
+import logging
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
-from typing import Annotated, Any
+from typing import Annotated, Any, Literal
 
 import numpy as np
 import pandas as pd
@@ -42,7 +44,7 @@ def to_dict(self) -> dict[str, Any]:
         return d
 
     @staticmethod
-    def from_dict(d: dict) -> SegmentationMethod:
+    def from_dict(d: dict, on_keyword_mismatch: Literal['warn', 'raise', 'ignore'] = 'warn') -> SegmentationMethod:
         from openlifu.seg import seg_methods
         if not isinstance(d, dict):  # previous implementations might pass str
             raise TypeError(f"Expected dict for from_dict, got {type(d).__name__}")
@@ -58,10 +60,21 @@ def from_dict(d: dict) -> SegmentationMethod:
                 for k, v in materials_dict.items()
             }
 
-        # Ignore ref_material if class is `UniformWater` or `UniformTissue`
-        if short_classname in ["UniformWater", "UniformTissue"]:
-            d.pop("ref_material")
         class_constructor = getattr(seg_methods, short_classname)
+
+        # Filter out unexpected keywords
+        sig = inspect.signature(class_constructor)
+        expected_keywords = [p.name for p in sig.parameters.values() if p.kind == p.POSITIONAL_OR_KEYWORD]
+        unexpected_keywords = [k for k in d if k not in expected_keywords]
+
+        if unexpected_keywords:
+            if on_keyword_mismatch == 'raise':
+                raise TypeError(f"Unexpected keyword arguments for {short_classname}: {unexpected_keywords}")
+            elif on_keyword_mismatch == 'warn':
+                logging.warning(f"Ignoring unexpected keyword arguments for {short_classname}: {unexpected_keywords}")
+            for k in unexpected_keywords:
+                d.pop(k)
+
         return class_constructor(**d)
 
     def _material_indices(self, materials: dict | None = None):

src/openlifu/seg/seg_methods/uniform.py

@@ -37,6 +37,12 @@ def to_table(self) -> pd.DataFrame:
         records = [{"Name": "Type", "Value": "Uniform Tissue", "Unit": ""}]
         return pd.DataFrame.from_records(records)
 
+    def to_dict(self):
+        d = super().to_dict()
+        d.pop("ref_material")
+        return d
+
+
 class UniformWater(UniformSegmentation):
     """ Assigns the water material to all voxels in the volume. """
     def __init__(self, materials: dict[str, Material] | None = None):
@@ -52,3 +58,8 @@ def to_table(self) -> pd.DataFrame:
         """
         records = [{"Name": "Type", "Value": "Uniform Water", "Unit": ""}]
         return pd.DataFrame.from_records(records)
+
+    def to_dict(self):
+        d = super().to_dict()
+        d.pop("ref_material")
+        return d

tests/test_seg_method.py

@@ -70,3 +70,25 @@ def test_uniformwater_errors_when_specify_ref_material():
 def test_materials_as_none_gets_default_materials():
     seg_method = seg_methods.UniformSegmentation(materials=None)  # pyright: ignore[reportArgumentType]
     assert seg_method.materials == MATERIALS.copy()
+
+def test_from_dict_on_keyword_mismatch():
+    d = {
+        "class": "UniformWater",
+        "materials": {
+            "water": {
+                "name": "water",
+                "sound_speed": 1500,
+                "density": 1000,
+                "attenuation": 0.0022,
+                "specific_heat": 4182,
+                "thermal_conductivity": 0.598
+            }
+        },
+        "ref_material": "water"
+    }
+
+    with pytest.raises(TypeError, match=r"Unexpected keyword arguments for UniformWater: \['ref_material'\]"):
+        SegmentationMethod.from_dict(d, on_keyword_mismatch='raise')
+
+    # This should not raise any warning or exception
+    SegmentationMethod.from_dict(d, on_keyword_mismatch='ignore')