chore: import upstream snapshot with attribution

tests/export/test_export_for_roboflow.py

@@ -0,0 +1,92 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+"""Tests for ``RFDETR.export_for_roboflow``.
+
+``export_for_roboflow`` is the extracted, network-free core of ``deploy_to_roboflow``: it writes ``weights.pt`` (a dict
+with ``"model"`` and ``"args"`` keys) and ``class_names.txt`` into a target directory.  A lightweight stub stands in for
+``self.model`` so the file-writing contract is exercised without building a real model or downloading weights.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from types import SimpleNamespace
+
+import torch
+
+from rfdetr.detr import RFDETR
+
+
+def _make_stub_model(class_names: list[str]) -> RFDETR:
+    """Build an RFDETR instance whose model/state are stubbed for export_for_roboflow.
+
+    ``RFDETR.__init__`` is bypassed; only the attributes ``export_for_roboflow`` reads are populated.
+    """
+    instance = RFDETR.__new__(RFDETR)
+    args = SimpleNamespace(resolution=560)
+    inner_module = SimpleNamespace(state_dict=lambda: {"weight": torch.zeros(2, 2)})
+    instance.model = SimpleNamespace(model=inner_module, args=args, class_names=class_names)
+    return instance
+
+
+class TestExportForRoboflow:
+    """export_for_roboflow writes a deploy-ready bundle into a directory."""
+
+    def test_writes_weights_pt_with_model_and_args(self, tmp_path: Path) -> None:
+        """weights.pt is a dict with 'model' and 'args', and args carries resolution."""
+        model = _make_stub_model(["cat", "dog"])
+
+        model.export_for_roboflow(str(tmp_path))
+
+        bundle = torch.load(tmp_path / "weights.pt", map_location="cpu", weights_only=False)
+        assert set(bundle) == {"model", "args"}
+        assert "weight" in bundle["model"]
+        assert bundle["args"].resolution == 560
+
+    def test_writes_class_names_txt(self, tmp_path: Path) -> None:
+        """class_names.txt lists one class name per line."""
+        model = _make_stub_model(["cat", "dog"])
+
+        model.export_for_roboflow(str(tmp_path))
+
+        assert (tmp_path / "class_names.txt").read_text(encoding="utf-8") == "cat\ndog"
+
+    def test_embeds_class_names_in_args(self, tmp_path: Path) -> None:
+        """class_names are embedded in the saved args namespace when absent."""
+        model = _make_stub_model(["cat", "dog"])
+
+        model.export_for_roboflow(str(tmp_path))
+
+        bundle = torch.load(tmp_path / "weights.pt", map_location="cpu", weights_only=False)
+        assert bundle["args"].class_names == ["cat", "dog"]
+
+    def test_does_not_overwrite_existing_args_class_names(self, tmp_path: Path) -> None:
+        """args.class_names already set on the model is preserved in the saved bundle."""
+        model = _make_stub_model(["cat", "dog"])
+        model.model.args.class_names = ["pre_existing"]
+
+        model.export_for_roboflow(str(tmp_path))
+
+        bundle = torch.load(tmp_path / "weights.pt", map_location="cpu", weights_only=False)
+        assert bundle["args"].class_names == ["pre_existing"]
+
+    def test_empty_class_names_writes_empty_file(self, tmp_path: Path) -> None:
+        """Empty class_names list produces an empty class_names.txt (no trailing newline)."""
+        model = _make_stub_model([])
+
+        model.export_for_roboflow(str(tmp_path))
+
+        assert (tmp_path / "class_names.txt").read_text(encoding="utf-8") == ""
+
+    def test_creates_output_dir_when_missing(self, tmp_path: Path) -> None:
+        """output_dir is created if it does not already exist."""
+        model = _make_stub_model(["cat", "dog"])
+        target = tmp_path / "nested" / "bundle"
+
+        model.export_for_roboflow(str(target))
+
+        assert (target / "weights.pt").exists()
+        assert (target / "class_names.txt").exists()