added test object

MANIFEST.in

@@ -11,6 +11,7 @@ include deepforest/data/2019_YELL_2_528000_4978000_image_crop2.xml
 include deepforest/data/2019_YELL_2_528000_4978000_image_crop2.png
 include deepforest/data/AWPE*
 include deepforest/data/example.csv
+include deepforest/data/test_tiled.tif
 
 include LICENSE
 include dev_requirements.txt

deepforest/dataset.py

@@ -195,6 +195,8 @@ class RasterDataset:
         raster_path (str): Path to raster file
         patch_size (int): Size of windows to predict on
         patch_overlap (float): Overlap between windows as fraction (0-1)
+    Returns:
+        A dataset of raster windows
     """
     def __init__(self, raster_path, patch_size, patch_overlap):
         self.raster_path = raster_path
@@ -206,6 +208,16 @@ def __init__(self, raster_path, patch_size, patch_overlap):
             width = src.shape[0]
             height = src.shape[1]
 
+            # Check is tiled
+            if not src.is_tiled:
+                raise ValueError(
+                    "Out-of-memory dataset is selected, but raster is not tiled, "
+                    "leading to entire raster being read into memory and defeating "
+                    "the purpose of an out-of-memory dataset. "
+                    "\nPlease run: "
+                    "\ngdal_translate -of GTiff -co TILED=YES <input> <output> "
+                    "to create a tiled raster"
+                )
         # Generate sliding windows
         self.windows = slidingwindow.generateForSize(
             height,

deepforest/main.py

@@ -492,7 +492,7 @@ def predict_tile(self,
             iou_threshold: Minimum iou overlap among predictions between
                 windows to be suppressed.
                 Lower values suppress more boxes at edges.
-            in_memory: If true, the entire dataset is loaded into memory. This is useful for small datasets, but not recommended for large datasets since both the tile and the crops are stored in memory.
+            in_memory: If true, the entire dataset is loaded into memory, which increases speed. This is useful for small datasets, but not recommended for very large datasets.
             mosaic: Return a single prediction dataframe (True) or a tuple of image crops and predictions (False)
             sigma: variance of Gaussian function used in Gaussian Soft NMS
             thresh: the score thresh used to filter bboxes after soft-nms performed
@@ -509,6 +509,10 @@ def predict_tile(self,
             - color: Deprecated bounding box color for visualizations.
             - thickness: Deprecated bounding box thickness for visualizations.
 
+        Raises:
+            - ValueError: If `raster_path` is None when `in_memory=False`.
+            - ValueError: If `workers` is greater than 0 when `in_memory=False`. Multiprocessing is not supported when using out-of-memory datasets, rasterio is not threadsafe.
+
         Returns:
             - If `return_plot` is True, returns an image with predictions overlaid (deprecated).
             - If `mosaic` is True, returns a Pandas DataFrame containing the predicted

tests/test_dataset.py

@@ -185,17 +185,16 @@ def test_bounding_box_dataset():
     # Check the shape of the RGB tensor
     assert item.shape == (3, 224, 224)
 
-def test_raster_dataset(raster_path):
+def test_raster_dataset():
     """Test the RasterDataset class"""
     from deepforest.dataset import RasterDataset
     import torch
     from torch.utils.data import DataLoader
 
     # Test initialization and context manager
-    ds = RasterDataset(raster_path, patch_size=256, patch_overlap=0.1)
+    ds = RasterDataset(get_data("test_tiled.tif"), patch_size=256, patch_overlap=0.1)
 
     # Test basic properties
-    assert hasattr(ds, 'raster')
     assert hasattr(ds, 'windows')
 
     # Test first window
@@ -209,8 +208,4 @@ def test_raster_dataset(raster_path):
     dataloader = DataLoader(ds, batch_size=2, num_workers=0)
     batch = next(iter(dataloader))
     assert batch.shape[0] == 2  # Batch size
-    assert batch.shape[1] == 3  # Channels first
-
-    # Test that context manager closed the raster
-    ds.close()
-    assert ds.raster.closed
+    assert batch.shape[1] == 3  # Channels first