Make max_windows a required argument for RandomWindowGeoDataset (azav…

…ea#2223)

* Makes max_windows a required argument and make all args other than scene keyword args.

* Updated tutorials to reflect change to RandomWindowGeoDataset.

docs/usage/tutorials/sampling_training_data.ipynb

@@ -517,7 +517,8 @@
     "    # resize chips to 256x256 before returning\n",
     "    out_size=256,\n",
     "    # allow windows to overflow the extent by 100 pixels\n",
-    "    padding=100\n",
+    "    padding=100,\n",
+    "    max_windows=10\n",
     ")\n",
     "\n",
     "img_full = ds.scene.raster_source[:, :]\n",

docs/usage/tutorials/temporal.ipynb

@@ -433,7 +433,7 @@
    "source": [
     "scene = Scene(id='test_scene', raster_source=raster_source)\n",
     "ds = SemanticSegmentationRandomWindowGeoDataset(\n",
-    "    scene=scene, size_lims=(256, 256 + 1), out_size=256, return_window=True)"
+    "    scene=scene, size_lims=(256, 256 + 1), out_size=256, max_windows=10, return_window=True)"
    ]
   },
   {

rastervision_pytorch_learner/rastervision/pytorch_learner/dataset/dataset.py

@@ -283,12 +283,13 @@ class RandomWindowGeoDataset(GeoDataset):
     def __init__(
             self,
             scene: Scene,
+            *,
             out_size: PosInt | tuple[PosInt, PosInt] | None,
             size_lims: tuple[PosInt, PosInt] | None = None,
             h_lims: tuple[PosInt, PosInt] | None = None,
             w_lims: tuple[PosInt, PosInt] | None = None,
             padding: NonNegInt | tuple[NonNegInt, NonNegInt] | None = None,
-            max_windows: NonNegInt | None = None,
+            max_windows: NonNegInt,
             max_sample_attempts: PosInt = 100,
             efficient_aoi_sampling: bool = True,
             within_aoi: bool = True,
@@ -316,8 +317,7 @@ def __init__(
                 sides of the raster source. If ``None``, ``padding = size``.
                 Defaults to ``None``.
             max_windows: Max allowed reads. Will raise ``StopIteration`` on
-                further read attempts. If None, will be set to ``np.inf``.
-                Defaults to ``None``.
+                further read attempts.
             transform: Albumentations
                 transform to apply to the windows. Defaults to ``None``.
                 Each transform in Albumentations takes images of type uint8, and
@@ -384,9 +384,6 @@ def __init__(
                 padding = (max_h // 2, max_w // 2)
         padding: tuple[NonNegInt, NonNegInt] = ensure_tuple(padding)
 
-        if max_windows is None:
-            max_windows = np.iinfo('int').max
-
         self.size_lims = size_lims
         self.h_lims = h_lims
         self.w_lims = w_lims

tests/pytorch_learner/dataset/test_dataset.py

@@ -213,26 +213,29 @@ def test_sample_window_within_aoi(self):
 
         ds = RandomWindowGeoDataset(
             scene,
-            10,
-            (5, 6),
+            out_size=10,
+            size_lims=(5, 6),
+            max_windows=10,
             within_aoi=True,
             transform_type=TransformType.noop,
         )
         self.assertNoError(ds.sample_window)
 
         ds = RandomWindowGeoDataset(
             scene,
-            10,
-            (12, 13),
+            out_size=10,
+            size_lims=(12, 13),
+            max_windows=10,
             within_aoi=True,
             transform_type=TransformType.noop,
         )
         self.assertRaises(StopIteration, ds.sample_window)
 
         ds = RandomWindowGeoDataset(
             scene,
-            10,
-            (12, 13),
+            out_size=10,
+            size_lims=(12, 13),
+            max_windows=10,
             within_aoi=False,
             transform_type=TransformType.noop,
         )
@@ -245,6 +248,7 @@ def test_init_validation(self):
         args = dict(
             scene=scene,
             out_size=10,
+            max_windows=10,
             transform_type=TransformType.noop,
         )
         self.assertRaises(ValueError, lambda: RandomWindowGeoDataset(**args))
@@ -255,6 +259,7 @@ def test_init_validation(self):
             out_size=10,
             size_lims=(10, 11),
             h_lims=(10, 11),
+            max_windows=10,
             transform_type=TransformType.noop,
         )
         self.assertRaises(ValueError, lambda: RandomWindowGeoDataset(**args))
@@ -266,6 +271,7 @@ def test_init_validation(self):
             size_lims=(10, 11),
             h_lims=(10, 11),
             w_lims=(10, 11),
+            max_windows=10,
             transform_type=TransformType.noop,
         )
         self.assertRaises(ValueError, lambda: RandomWindowGeoDataset(**args))
@@ -275,6 +281,7 @@ def test_init_validation(self):
             scene=scene,
             out_size=10,
             w_lims=(10, 11),
+            max_windows=10,
             transform_type=TransformType.noop,
         )
         self.assertRaises(ValueError, lambda: RandomWindowGeoDataset(**args))
@@ -284,6 +291,7 @@ def test_init_validation(self):
             scene,
             out_size=None,
             size_lims=(12, 13),
+            max_windows=10,
             transform_type=TransformType.noop,
         )
         self.assertFalse(ds.normalize)
@@ -295,6 +303,7 @@ def test_init_validation(self):
             out_size=None,
             h_lims=(10, 11),
             w_lims=(10, 11),
+            max_windows=10,
             transform_type=TransformType.noop,
         )
         self.assertTupleEqual(ds.padding, (5, 5))
@@ -305,6 +314,7 @@ def test_min_max_size(self):
             scene,
             out_size=None,
             size_lims=(10, 15),
+            max_windows=10,
             transform_type=TransformType.noop,
         )
         self.assertTupleEqual(ds.min_size, (10, 10))
@@ -315,6 +325,7 @@ def test_min_max_size(self):
             out_size=None,
             h_lims=(10, 15),
             w_lims=(8, 12),
+            max_windows=10,
             transform_type=TransformType.noop,
         )
         self.assertTupleEqual(ds.min_size, (10, 8))
@@ -326,6 +337,7 @@ def test_sample_window_size(self):
             scene,
             out_size=None,
             size_lims=(10, 15),
+            max_windows=10,
             transform_type=TransformType.noop,
         )
         sampled_h, sampled_w = ds.sample_window_size()
@@ -337,6 +349,7 @@ def test_sample_window_size(self):
             out_size=None,
             h_lims=(10, 15),
             w_lims=(8, 12),
+            max_windows=10,
             transform_type=TransformType.noop,
         )
         sampled_h, sampled_w = ds.sample_window_size()
@@ -360,6 +373,7 @@ def test_return_window(self):
             scene,
             out_size=10,
             size_lims=(5, 6),
+            max_windows=10,
             transform_type=TransformType.noop,
             return_window=True,
         )
@@ -376,6 +390,7 @@ def test_triangle_missing(self):
             scene=scene,
             out_size=10,
             size_lims=(5, 6),
+            max_windows=10,
             transform_type=TransformType.noop,
         )
         self.assertNoError(lambda: RandomWindowGeoDataset(**args))