chore: import upstream snapshot with attribution

tests/inference/test_inference.py

@@ -0,0 +1,54 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+"""Unit tests for rfdetr.inference weight-adaptation helpers."""
+
+import pytest
+import torch
+
+from rfdetr.inference import _adapt_input_conv
+
+
+@pytest.fixture(autouse=True)
+def reset_random_seeds():
+    """Ensure reproducible random state for every test in this module."""
+    torch.manual_seed(0)
+
+
+class TestAdaptInputConv:
+    @pytest.mark.parametrize(
+        ("num_channels", "expected_shape", "expected_builder"),
+        [
+            pytest.param(3, (8, 3, 3, 3), lambda weight: weight, id="identity_3ch"),
+            pytest.param(1, (8, 1, 3, 3), lambda weight: weight.mean(dim=1, keepdim=True), id="mean_1ch"),
+            pytest.param(
+                4,
+                (8, 4, 3, 3),
+                lambda weight: torch.cat([weight, weight], dim=1)[:, :4] * (3.0 / 4.0),
+                id="tile_4ch",
+            ),
+            pytest.param(
+                6,
+                (8, 6, 3, 3),
+                lambda weight: torch.cat([weight, weight], dim=1)[:, :6] * (3.0 / 6.0),
+                id="tile_6ch",
+            ),
+            pytest.param(
+                2,
+                (8, 2, 3, 3),
+                lambda weight: weight[:, :2] * (3.0 / 2.0),
+                id="tile_2ch",
+            ),
+        ],
+    )
+    def test_adapt_input_conv(self, num_channels, expected_shape, expected_builder):
+        """Verify shape and values for each _adapt_input_conv branch."""
+        conv_weight = torch.randn(8, 3, 3, 3)
+
+        adapted_weight = _adapt_input_conv(num_channels, conv_weight)
+        expected_weight = expected_builder(conv_weight)
+
+        assert adapted_weight.shape == expected_shape
+        torch.testing.assert_close(adapted_weight, expected_weight)