chore: import upstream snapshot with attribution

tests/utilities/test_misc.py

@@ -0,0 +1,90 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from rfdetr.utilities.state_dict import strip_checkpoint
+
+
+class TestStripCheckpoint:
+    def test_strip_checkpoint_keeps_only_model_and_args(self, tmp_path):
+        checkpoint_path = tmp_path / "checkpoint_best_total.pth"
+        torch.save(
+            {
+                "model": {"weight": torch.tensor([1.0])},
+                "args": SimpleNamespace(class_names=["a"]),
+                "optimizer": {"lr": 1e-4},
+            },
+            checkpoint_path,
+        )
+
+        strip_checkpoint(str(checkpoint_path))
+
+        stripped = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
+        assert set(stripped.keys()) == {"model", "args"}
+
+    def test_strip_checkpoint_preserves_model_name_when_present(self, tmp_path):
+        checkpoint_path = tmp_path / "checkpoint_best_total.pth"
+        torch.save(
+            {
+                "model": {"weight": torch.tensor([1.0])},
+                "args": SimpleNamespace(class_names=["a"]),
+                "model_name": "RFDETRSmall",
+                "optimizer": {"lr": 1e-4},
+            },
+            checkpoint_path,
+        )
+
+        strip_checkpoint(str(checkpoint_path))
+
+        stripped = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
+        assert set(stripped.keys()) == {"model", "args", "model_name"}
+        assert stripped["model_name"] == "RFDETRSmall"
+
+    def test_strip_checkpoint_omits_model_name_when_absent(self, tmp_path):
+        checkpoint_path = tmp_path / "checkpoint_best_total.pth"
+        torch.save(
+            {
+                "model": {"weight": torch.tensor([1.0])},
+                "args": SimpleNamespace(class_names=["a"]),
+                "optimizer": {"lr": 1e-4},
+            },
+            checkpoint_path,
+        )
+
+        strip_checkpoint(str(checkpoint_path))
+
+        stripped = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
+        assert "model_name" not in stripped
+
+    def test_strip_checkpoint_is_atomic_when_save_fails(self, tmp_path, monkeypatch):
+        checkpoint_path = tmp_path / "checkpoint_best_total.pth"
+        original_checkpoint = {
+            "model": {"weight": torch.tensor([1.0])},
+            "args": SimpleNamespace(class_names=["a"]),
+            "optimizer": {"lr": 1e-4},
+        }
+        torch.save(original_checkpoint, checkpoint_path)
+
+        original_torch_save = torch.save
+
+        def failing_torch_save(obj, destination, *args, **kwargs):
+            if str(destination) != str(checkpoint_path):
+                raise RuntimeError("simulated save failure")
+            return original_torch_save(obj, destination, *args, **kwargs)
+
+        monkeypatch.setattr(torch, "save", failing_torch_save)
+
+        with pytest.raises(RuntimeError, match="simulated save failure"):
+            strip_checkpoint(str(checkpoint_path))
+
+        recovered = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
+        assert set(recovered.keys()) == set(original_checkpoint.keys())
+        assert recovered["model"]["weight"].equal(original_checkpoint["model"]["weight"])
+        assert recovered["optimizer"] == original_checkpoint["optimizer"]