From 6fa6c5a84d6144314854589d18d7b1f0dfdc2b63 Mon Sep 17 00:00:00 2001
From: felalex <felalex@gmail.com>
Date: Sun, 3 May 2026 22:24:00 -0700
Subject: [PATCH] test: fail loudly when EnrichmentModelTypeEnum gains an
 unclassified value

The current pattern - hardcoded include-lists in
ONNXModelRunner.has_variable_length_inputs and friends - silently
defaults any unknown model to "fixed-size, simple, single-threaded".
For has_variable_length_inputs that default re-introduces the ORT
mem-pattern leak when the new model is actually variable-length.

Adds test_every_enrichment_model_is_explicitly_classified, which
iterates EnrichmentModelTypeEnum and asserts every member is in one
of two explicit sets the test author maintains.  A new enum value
without classification fails CI with a message naming the classifier
to update.  Also documents the same constraint on the enum itself
with a TODO pointing at the longer-term fix (a co-located MODEL_TRAITS
registry covering all five classifiers, not just this one).

Verified by injecting a fake enum value: the test fails with the
expected pointer to the missing classifier.

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
---
 frigate/embeddings/types.py            | 11 +++++++
 frigate/test/test_detection_runners.py | 43 ++++++++++++++++++++++++++
 2 files changed, 54 insertions(+)

diff --git a/frigate/embeddings/types.py b/frigate/embeddings/types.py
index 32cbe5dd0..c66a6e2ad 100644
--- a/frigate/embeddings/types.py
+++ b/frigate/embeddings/types.py
@@ -7,6 +7,17 @@ class EmbeddingTypeEnum(str, Enum):
 
 
 class EnrichmentModelTypeEnum(str, Enum):
+    # When adding a value, audit every classifier that switches on it:
+    #   - ONNXModelRunner.has_variable_length_inputs
+    #   - ONNXModelRunner.is_cpu_complex_model
+    #   - ONNXModelRunner.is_migraphx_complex_model
+    #   - ONNXModelRunner.is_concurrent_model
+    #   - CudaGraphRunner.is_model_supported
+    # The default for omission is "fixed-size, simple, single-threaded" - which
+    # silently re-introduces the ORT mem-pattern leak if the new model is
+    # actually variable-length (Jina/PaddleOCR-class).
+    # TODO: replace these scattered include-lists with a single MODEL_TRAITS
+    # registry co-located with the enum so adding a value forces classification.
     arcface = "arcface"
     facenet = "facenet"
     jina_v1 = "jina_v1"
diff --git a/frigate/test/test_detection_runners.py b/frigate/test/test_detection_runners.py
index c0d640f0c..85d90ed76 100644
--- a/frigate/test/test_detection_runners.py
+++ b/frigate/test/test_detection_runners.py
@@ -141,6 +141,49 @@ class TestHasVariableLengthInputs(unittest.TestCase):
             )
         )
 
+    def test_every_enrichment_model_is_explicitly_classified(self):
+        """Every EnrichmentModelTypeEnum value must be deliberately classified.
+
+        Adding a new model to the enum without updating has_variable_length_inputs
+        silently defaults it to fixed-size (mem_pattern stays on), which
+        re-introduces the ORT mmap-plan leak if the new model is actually
+        variable-length.  This test fails on any unclassified enum value so the
+        author is forced to make a deliberate decision.
+
+        TODO: replace this guard with a single MODEL_TRAITS registry co-located
+        with EnrichmentModelTypeEnum so adding a value mechanically forces
+        classification across every classifier (variable-length, cpu_complex,
+        migraphx_complex, concurrent, cuda_graph_supported), not just this one.
+        """
+        from frigate.detectors.detection_runners import ONNXModelRunner
+        from frigate.embeddings.types import EnrichmentModelTypeEnum
+
+        VARIABLE_LENGTH = {
+            EnrichmentModelTypeEnum.jina_v1,
+            EnrichmentModelTypeEnum.jina_v2,
+            EnrichmentModelTypeEnum.paddleocr,
+        }
+        FIXED_LENGTH = {
+            EnrichmentModelTypeEnum.arcface,
+            EnrichmentModelTypeEnum.facenet,
+            EnrichmentModelTypeEnum.yolov9_license_plate,
+        }
+        classified = VARIABLE_LENGTH | FIXED_LENGTH
+        for member in EnrichmentModelTypeEnum:
+            self.assertIn(
+                member,
+                classified,
+                f"{member.value} is not explicitly classified — audit "
+                "ONNXModelRunner.has_variable_length_inputs (and the other "
+                "classifiers listed in EnrichmentModelTypeEnum's docstring).",
+            )
+            self.assertEqual(
+                ONNXModelRunner.has_variable_length_inputs(member.value),
+                member in VARIABLE_LENGTH,
+                f"{member.value}: classification disagrees with "
+                "has_variable_length_inputs — update one or the other.",
+            )
+
 
 class TestComputeCudaMemLimit(unittest.TestCase):
     @staticmethod