feat(modelkit): add swin2sr support and canonical cpu recipe

examples/recipes/caidas_swin2SR-classical-sr-x2-64/cpu/cpu/image-to-image_fp16_config.json

@@ -0,0 +1,64 @@
+{
+  "export": {
+    "opset_version": 17,
+    "batch_size": 1,
+    "export_params": true,
+    "do_constant_folding": true,
+    "verbose": false,
+    "dynamo": false,
+    "enable_hierarchy_tags": true,
+    "clean_onnx": false,
+    "hierarchy_tag_format": "full",
+    "input_tensors": [
+      {
+        "name": "pixel_values",
+        "dtype": "float32",
+        "shape": [
+          1,
+          3,
+          64,
+          64
+        ],
+        "value_range": [
+          0,
+          1
+        ]
+      }
+    ],
+    "output_tensors": [
+      {
+        "name": "reconstruction"
+      }
+    ]
+  },
+  "optim": {},
+  "quant": {
+    "mode": "fp16",
+    "samples": 10,
+    "calibration_method": "minmax",
+    "weight_type": "uint8",
+    "activation_type": "uint8",
+    "per_channel": false,
+    "symmetric": false,
+    "weight_symmetric": null,
+    "activation_symmetric": null,
+    "save_calibration": false,
+    "distribution": "uniform",
+    "seed": null,
+    "calibration_load_path": null,
+    "calibration_save_path": null,
+    "op_types_to_quantize": null,
+    "nodes_to_exclude": null,
+    "task": "image-to-image",
+    "model_id": "caidas/swin2SR-classical-sr-x2-64",
+    "model_type": "swin2sr",
+    "fp16_keep_io_types": true,
+    "fp16_op_block_list": null
+  },
+  "compile": null,
+  "loader": {
+    "task": "image-to-image",
+    "model_class": "AutoModelForImageToImage",
+    "model_type": "swin2sr"
+  }
+}

src/winml/modelkit/models/hf/__init__.py

@@ -91,6 +91,8 @@
 from .siglip import SIGLIP_CONFIG
 from .siglip import SiglipTextModelIOConfig as _SiglipTextModelIOConfig  # triggers registration
 from .siglip import SiglipVisionModelIOConfig as _SiglipVisionModelIOConfig  # triggers registration
+from .swin2sr import MODEL_CLASS_MAPPING as _SWIN2SR_CLASS_MAPPING
+from .swin2sr import Swin2SRIOConfig as _Swin2SRIOConfig  # triggers registration
 from .t5 import MODEL_CLASS_MAPPING as _T5_CLASS_MAPPING
 from .t5 import T5_CONFIG
 from .t5 import T5DecoderIOConfig as _T5DecoderIOConfig  # triggers registration
@@ -132,6 +134,7 @@
         _SAM2_CLASS_MAPPING,
         _SEGFORMER_CLASS_MAPPING,
         _SIGLIP_CLASS_MAPPING,
+        _SWIN2SR_CLASS_MAPPING,
         _T5_CLASS_MAPPING,
         _VED_CLASS_MAPPING,
         _VITPOSE_CLASS_MAPPING,

src/winml/modelkit/models/hf/swin2sr.py

@@ -2,12 +2,35 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
 # --------------------------------------------------------------------------
-"""Swin2SR (Swin Transformer V2 for Super-Resolution) HuggingFace Model Patches.
+"""Swin2SR HuggingFace model registration.
 
-This module will provide Swin2SR-specific patches for ONNX export compatibility.
-Currently empty — patches will be added as needed (see issue #236).
-
-Note:
-    No model-specific build config is needed. The analyzer autoconf loop in the
-    build pipeline discovers optimization flags automatically. See issue #232.
+Swin2SR checkpoints default to image super-resolution (image-to-image). Optimum
+ships a built-in ``Swin2srOnnxConfig``, but registering it here ensures WinML's
+``get_supported_tasks("swin2sr")`` is populated as soon as
+``winml.modelkit.models`` is imported, even if
+``optimum.exporters.onnx.model_configs`` has not been imported yet.
 """
+
+from __future__ import annotations
+
+from optimum.exporters.onnx.model_configs import Swin2srOnnxConfig
+from transformers import AutoModelForImageToImage
+
+from ...export import register_onnx_overwrite
+
+
+# (model_type, task) -> HuggingFace model class
+#
+# The (swin2sr, None) sentinel declares image-to-image as the default task for
+# task auto-detection when --task is omitted.
+MODEL_CLASS_MAPPING: dict[tuple[str, str | None], type] = {
+    ("swin2sr", "image-to-image"): AutoModelForImageToImage,
+    ("swin2sr", None): AutoModelForImageToImage,
+}
+
+
+@register_onnx_overwrite("swin2sr", "feature-extraction", library_name="transformers")
+@register_onnx_overwrite("swin2sr", "image-to-image", library_name="transformers")
+class Swin2SRIOConfig(Swin2srOnnxConfig):  # type: ignore[misc]  # optimum base is untyped
+    """Local registration shim for Swin2SR ONNX export tasks."""
+

tests/unit/loader/test_get_supported_tasks.py

@@ -29,6 +29,7 @@ class TestGetSupportedTasks:
             ("whisper", "automatic-speech-recognition"),
             ("clip", "zero-shot-image-classification"),
             ("llama", "text-generation"),
+            ("swin2sr", "image-to-image"),
         ],
         ids=[
             "bert",
@@ -41,6 +42,7 @@ class TestGetSupportedTasks:
             "whisper",
             "clip",
             "llama",
+            "swin2sr",
         ],
     )
     def test_known_model_type_contains_expected_task(self, model_type, expected_task):

tests/unit/models/test_swin2sr_support.py

@@ -0,0 +1,58 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+"""Tests for Swin2SR model support registration.
+
+Swin2SR should resolve to image-to-image by default and expose ONNX export
+registrations through WinML's local registry import path.
+"""
+
+from __future__ import annotations
+
+from transformers import Swin2SRConfig
+
+import winml.modelkit.models  # noqa: F401  # trigger registrations
+from winml.modelkit.export.io import _get_onnx_config
+from winml.modelkit.loader import get_supported_tasks, resolve_task
+from winml.modelkit.models.hf import MODEL_CLASS_MAPPING
+from winml.modelkit.models.hf.swin2sr import (
+    MODEL_CLASS_MAPPING as SWIN2SR_MAPPING,
+)
+from winml.modelkit.models.hf.swin2sr import (
+    Swin2SRIOConfig,
+)
+
+
+class TestSwin2SRSupport:
+    """Swin2SR is discoverable and resolvable as image-to-image."""
+
+    def test_get_supported_tasks_includes_image_to_image(self):
+        """swin2sr task list includes image-to-image after local registrations."""
+        tasks = get_supported_tasks("swin2sr")
+        assert "image-to-image" in tasks
+
+    def test_default_resolution_is_image_to_image(self):
+        """Task auto-detection defaults Swin2SR to image-to-image."""
+        config = Swin2SRConfig()
+        config.architectures = ["Swin2SRForImageSuperResolution"]
+
+        resolution = resolve_task(config)
+
+        assert resolution.task == "image-to-image"
+        assert resolution.model_class.__name__ == "AutoModelForImageToImage"
+
+    def test_onnx_config_registration(self):
+        """ONNX config lookup for swin2sr/image-to-image resolves to local shim."""
+        onnx_config = _get_onnx_config("swin2sr", "image-to-image", Swin2SRConfig())
+        assert isinstance(onnx_config, Swin2SRIOConfig)
+
+    def test_mapping_is_merged_and_has_default_sentinel(self):
+        """Model-class mapping includes swin2sr task key and default sentinel."""
+        assert SWIN2SR_MAPPING.items() <= MODEL_CLASS_MAPPING.items()
+        assert ("swin2sr", "image-to-image") in MODEL_CLASS_MAPPING
+        assert ("swin2sr", None) in MODEL_CLASS_MAPPING
+        assert (
+            MODEL_CLASS_MAPPING[("swin2sr", None)]
+            is MODEL_CLASS_MAPPING[("swin2sr", "image-to-image")]
+        )