recipe(vitpose): add SynthPose Base CPU recipes

examples/recipes/stanfordmimi_synthpose-vitpose-base-hf/cpu/cpu/keypoint-detection_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,
+          256,
+          192
+        ],
+        "value_range": [
+          -2.1179039478302,
+          2.640000343322754
+        ]
+      }
+    ],
+    "output_tensors": [
+      {
+        "name": "heatmaps"
+      }
+    ]
+  },
+  "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": "keypoint-detection",
+    "model_id": "stanfordmimi/synthpose-vitpose-base-hf",
+    "model_type": "vitpose",
+    "fp16_keep_io_types": true,
+    "fp16_op_block_list": null
+  },
+  "compile": null,
+  "loader": {
+    "task": "keypoint-detection",
+    "model_class": "VitPoseForPoseEstimation",
+    "model_type": "vitpose"
+  }
+}

examples/recipes/stanfordmimi_synthpose-vitpose-base-hf/cpu/cpu/keypoint-detection_fp32_config.json

@@ -0,0 +1,42 @@
+{
+  "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,
+          256,
+          192
+        ],
+        "value_range": [
+          -2.1179039478302,
+          2.640000343322754
+        ]
+      }
+    ],
+    "output_tensors": [
+      {
+        "name": "heatmaps"
+      }
+    ]
+  },
+  "optim": {},
+  "quant": null,
+  "compile": null,
+  "loader": {
+    "task": "keypoint-detection",
+    "model_class": "VitPoseForPoseEstimation",
+    "model_type": "vitpose"
+  }
+}

src/winml/modelkit/commands/perf.py

@@ -262,6 +262,7 @@ def generate_random_inputs(
     symbolic_shapes = io_config.get("input_symbolic_shapes") or [
         [None] * len(s) for s in io_config["input_shapes"]
     ]
+    value_ranges = io_config.get("input_value_ranges") or {}
     overrides = shape_config or {}
 
     specs: dict[str, dict[str, Any]] = {}
@@ -290,6 +291,11 @@ def generate_random_inputs(
             "dtype": gen_dtype,
             "shape": list(resolved_shape),
         }
+        if name in value_ranges:
+            low, high = value_ranges[name]
+            # Persisted InputTensorSpec ranges are [low, high), while the
+            # legacy NumPy generator treats integer maxima as inclusive.
+            specs[name]["range"] = [int(low), int(high) - 1] if gen_dtype == "int" else [low, high]
 
     return generate_dummy_inputs_from_specs(specs)
 

tests/unit/commands/test_perf_cli.py

@@ -849,6 +849,117 @@ def test_symbolic_override_rejects_non_integer_float_value(self) -> None:
             )
 
 
+class TestGenerateRandomInputs:
+    def test_honors_negative_float_value_range(self) -> None:
+        """A configured range outside [0, 1) must replace the float fallback."""
+        import numpy as np
+
+        from winml.modelkit.commands.perf import generate_random_inputs
+
+        inputs = generate_random_inputs(
+            {
+                "input_names": ["features"],
+                "input_shapes": [[4, 32]],
+                "input_types": ["float32"],
+                "input_value_ranges": {"features": [-9.0, -8.0]},
+            }
+        )
+
+        assert np.all(inputs["features"] >= -9.0)
+        assert np.all(inputs["features"] < -8.0)
+
+    def test_honors_singleton_exclusive_integer_value_range(self) -> None:
+        """The canonical [7, 8) range must generate only the value seven."""
+        import numpy as np
+
+        from winml.modelkit.commands.perf import generate_random_inputs
+
+        inputs = generate_random_inputs(
+            {
+                "input_names": ["category_ids"],
+                "input_shapes": [[2, 512]],
+                "input_types": ["int64"],
+                "input_value_ranges": {"category_ids": [7, 8]},
+            }
+        )
+
+        assert inputs["category_ids"].dtype == np.int64
+        assert np.all(inputs["category_ids"] == 7)
+
+    def test_adapts_integer_high_exclusive_for_legacy_generator(self) -> None:
+        """The persisted exclusive high must not become a legal integer value."""
+        from winml.modelkit.commands.perf import generate_random_inputs
+
+        with patch(
+            "winml.modelkit.core.generate_dummy_inputs_from_specs",
+            return_value={"category_ids": MagicMock()},
+        ) as mock_generate:
+            generate_random_inputs(
+                {
+                    "input_names": ["category_ids"],
+                    "input_shapes": [[1, 8]],
+                    "input_types": ["int32"],
+                    "input_value_ranges": {"category_ids": [3, 7]},
+                }
+            )
+
+        assert mock_generate.call_args.args[0]["category_ids"]["range"] == [3, 6]
+
+    def test_unspecified_float_input_keeps_legacy_fallback(self) -> None:
+        """Ranges are per input; unspecified floats remain in [0, 1)."""
+        import numpy as np
+
+        from winml.modelkit.commands.perf import generate_random_inputs
+
+        inputs = generate_random_inputs(
+            {
+                "input_names": ["ranged", "default"],
+                "input_shapes": [[4, 32], [4, 32]],
+                "input_types": ["float32", "float32"],
+                "input_value_ranges": {"ranged": [-2.0, -1.0]},
+            }
+        )
+
+        assert np.all(inputs["ranged"] >= -2.0)
+        assert np.all(inputs["ranged"] < -1.0)
+        assert np.all(inputs["default"] >= 0.0)
+        assert np.all(inputs["default"] < 1.0)
+
+    def test_persisted_build_config_range_reaches_perf_generation(self, tmp_path: Path) -> None:
+        """WinMLSession must carry a co-located build range into perf inputs."""
+        import numpy as np
+        from onnx import TensorProto, helper, save
+
+        from winml.modelkit.commands.perf import generate_random_inputs
+        from winml.modelkit.session import WinMLSession
+
+        input_info = helper.make_tensor_value_info("features", TensorProto.FLOAT, [1, 16])
+        output_info = helper.make_tensor_value_info("output", TensorProto.FLOAT, [1, 16])
+        graph = helper.make_graph(
+            [helper.make_node("Identity", ["features"], ["output"])],
+            "range_forwarding",
+            [input_info],
+            [output_info],
+        )
+        model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 17)])
+        model.ir_version = 9
+        model_path = tmp_path / "model.onnx"
+        save(model, model_path)
+        (tmp_path / "winml_build_config.json").write_text(
+            json.dumps(
+                {"export": {"input_tensors": [{"name": "features", "value_range": [-9.0, -8.0]}]}}
+            ),
+            encoding="utf-8",
+        )
+
+        session = WinMLSession(model_path, device="cpu")
+        assert session.io_config["input_value_ranges"] == {"features": [-9.0, -8.0]}
+
+        inputs = generate_random_inputs(session.io_config)
+        assert np.all(inputs["features"] >= -9.0)
+        assert np.all(inputs["features"] < -8.0)
+
+
 class TestEffectiveBatchSize:
     """Throughput must scale by the batch the session actually ran.