chore: import upstream snapshot with attribution

tests/datasets/test_save_grids.py

@@ -0,0 +1,62 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+"""Tests for DatasetGridSaver — verifies that annotated grid images are written without OpenCV layout errors across all
+supported OpenCV versions."""
+
+from pathlib import Path
+
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data import DataLoader
+
+
+class _FakeDataset:
+    """Minimal dataset returning a single synthetic image + target."""
+
+    def __init__(self, num_samples: int = 4) -> None:
+        self.num_samples = num_samples
+
+    def __len__(self) -> int:
+        return self.num_samples
+
+    def __getitem__(self, idx):
+        # CHW float tensor in ImageNet-normalised range
+        image = torch.zeros(3, 224, 224)
+        target = {
+            "size": torch.tensor([224, 224]),
+            "boxes": torch.tensor([[0.25, 0.25, 0.5, 0.5], [0.6, 0.6, 0.2, 0.2]]),
+            "labels": torch.tensor([0, 1]),
+        }
+        return image, target
+
+
+def _collate(batch):
+    from rfdetr.utilities import nested_tensor_from_tensor_list
+
+    images, targets = zip(*batch)
+    # NestedTensor expected by DatasetGridSaver
+    nested = nested_tensor_from_tensor_list(list(images))
+    return nested, list(targets)
+
+
+def test_save_grid_writes_files(tmp_path: Path) -> None:
+    """DatasetGridSaver must write JPEG grid files without raising OpenCV errors."""
+    from rfdetr.datasets.save_grids import DatasetGridSaver
+
+    dataset = _FakeDataset(num_samples=4)
+    loader = DataLoader(dataset, batch_size=2, collate_fn=_collate)
+
+    saver = DatasetGridSaver(loader, tmp_path, max_batches=2, dataset_type="train")
+    saver.save_grid()
+
+    grids = list(tmp_path.glob("train_batch*_grid.jpg"))
+    assert len(grids) == 2, f"Expected 2 grid files, got {len(grids)}"
+    for grid_path in grids:
+        with Image.open(grid_path) as pil_img:
+            img = np.array(pil_img)
+        assert img.ndim == 3
+        assert img.shape[2] == 3