Fix float rounding issues (#736)

* Fix rounding bugs

* Mypy fixes

* Undo some changes

* Undo some changes

* mypy fixes

* Fix tests

* Increase size of ETCI2021 fake data

* Undo model changes

* line length fix

tests/data/etci2021/data.py

@@ -15,17 +15,29 @@
     {
         "filename": "train.zip",
         "directory": "train",
-        "subdirs": ["nebraska_20170108t002112", "bangladesh_20170314t115609"],
+        "subdirs": [
+            "nebraska_20170108t002112",
+            "bangladesh_20170314t115609",
+            "northal_20190302t234651",
+        ],
     },
     {
         "filename": "val_with_ref_labels.zip",
         "directory": "test",
-        "subdirs": ["florence_20180510t231343", "florence_20180522t231344"],
+        "subdirs": [
+            "florence_20180510t231343",
+            "florence_20180522t231344",
+            "florence_20190302t234651",
+        ],
     },
     {
         "filename": "test_without_ref_labels.zip",
         "directory": "test_internal",
-        "subdirs": ["redrivernorth_20190104t002247", "redrivernorth_20190116t002247"],
+        "subdirs": [
+            "redrivernorth_20190104t002247",
+            "redrivernorth_20190116t002247",
+            "redrivernorth_20190302t234651",
+        ],
     },
 ]
 

tests/datamodules/test_utils.py

@@ -15,11 +15,11 @@ def test_dataset_split() -> None:
 
     # Test only train/val set split
     train_ds, val_ds = dataset_split(ds, val_pct=1 / 2)
-    assert len(train_ds) == num_samples // 2
-    assert len(val_ds) == num_samples // 2
+    assert len(train_ds) == round(num_samples / 2)
+    assert len(val_ds) == round(num_samples / 2)
 
     # Test train/val/test set split
     train_ds, val_ds, test_ds = dataset_split(ds, val_pct=1 / 3, test_pct=1 / 3)
-    assert len(train_ds) == num_samples // 3
-    assert len(val_ds) == num_samples // 3
-    assert len(test_ds) == num_samples // 3
+    assert len(train_ds) == round(num_samples / 3)
+    assert len(val_ds) == round(num_samples / 3)
+    assert len(test_ds) == round(num_samples / 3)

tests/datasets/test_etci2021.py

@@ -30,19 +30,19 @@ def dataset(
         metadata = {
             "train": {
                 "filename": "train.zip",
-                "md5": "ebbd2e65cd10621bc2e90a230b474b8b",
+                "md5": "bd55f2116e43a35d5b94a765938be2aa",
                 "directory": "train",
                 "url": os.path.join(data_dir, "train.zip"),
             },
             "val": {
                 "filename": "val_with_ref_labels.zip",
-                "md5": "efdd1fe6c90f5dfd267c88b86b237c2b",
+                "md5": "96ed69904043e514c13c14ffd3ec45cd",
                 "directory": "test",
                 "url": os.path.join(data_dir, "val_with_ref_labels.zip"),
             },
             "test": {
                 "filename": "test_without_ref_labels.zip",
-                "md5": "bf1180143de5705fe95fa8490835d6d1",
+                "md5": "1b66d85e22c8f5b0794b3542c5ea09ef",
                 "directory": "test_internal",
                 "url": os.path.join(data_dir, "test_without_ref_labels.zip"),
             },
@@ -67,7 +67,7 @@ def test_getitem(self, dataset: ETCI2021) -> None:
             assert x["mask"].shape[0] == 1
 
     def test_len(self, dataset: ETCI2021) -> None:
-        assert len(dataset) == 2
+        assert len(dataset) == 3
 
     def test_already_downloaded(self, dataset: ETCI2021) -> None:
         ETCI2021(root=dataset.root, download=True)

tests/models/test_changestar.py

@@ -18,7 +18,7 @@
 IN_CHANNELS = [64, 128]
 INNNR_CHANNELS = [16, 32, 64]
 NC = [1, 2, 4]
-SF = [4.0, 8.0, 1.0]
+SF = [4, 8, 1]
 
 
 class TestChangeStar:
@@ -65,15 +65,15 @@ def test_invalid_changestar_farseg_backbone(self) -> None:
         "inc,innerc,nc,sf", list(itertools.product(IN_CHANNELS, INNNR_CHANNELS, NC, SF))
     )
     def test_changemixin_output_size(
-        self, inc: int, innerc: int, nc: int, sf: float
+        self, inc: int, innerc: int, nc: int, sf: int
     ) -> None:
         m = ChangeMixin(
             in_channels=inc, inner_channels=innerc, num_convs=nc, scale_factor=sf
         )
 
         y = m(torch.rand(3, 2, inc // 2, 32, 32))
         assert y[0].shape == y[1].shape
-        assert y[0].shape == (3, 1, int(32 * sf), int(32 * sf))
+        assert y[0].shape == (3, 1, 32 * sf, 32 * sf)
 
     @torch.no_grad()  # type: ignore[misc]
     def test_changestar(self) -> None:

tests/samplers/test_single.py

@@ -182,8 +182,8 @@ def test_iter(self, sampler: GridGeoSampler) -> None:
             )
 
     def test_len(self, sampler: GridGeoSampler) -> None:
-        rows = int((100 - sampler.size[0]) // sampler.stride[0]) + 1
-        cols = int((100 - sampler.size[1]) // sampler.stride[1]) + 1
+        rows = ((100 - sampler.size[0]) // sampler.stride[0]) + 1
+        cols = ((100 - sampler.size[1]) // sampler.stride[1]) + 1
         length = rows * cols * 2
         assert len(sampler) == length
 

torchgeo/datamodules/chesapeake.py

@@ -82,7 +82,7 @@ def __init__(
         self.patch_size = patch_size
         # This is a rough estimate of how large of a patch we will need to sample in
         # EPSG:3857 in order to guarantee a large enough patch in the local CRS.
-        self.original_patch_size = int(patch_size * 2.0)
+        self.original_patch_size = patch_size * 2
         self.batch_size = batch_size
         self.num_workers = num_workers
         self.class_set = class_set
@@ -151,8 +151,8 @@ def center_crop(
         def center_crop_inner(sample: Dict[str, Tensor]) -> Dict[str, Tensor]:
             _, height, width = sample["image"].shape
 
-            y1 = (height - size) // 2
-            x1 = (width - size) // 2
+            y1 = round((height - size) / 2)
+            x1 = round((width - size) / 2)
             sample["image"] = sample["image"][:, y1 : y1 + size, x1 : x1 + size]
             sample["mask"] = sample["mask"][:, y1 : y1 + size, x1 : x1 + size]
 

torchgeo/datamodules/etci2021.py

@@ -101,7 +101,7 @@ def setup(self, stage: Optional[str] = None) -> None:
         )
 
         size_train_val = len(train_val_dataset)
-        size_train = int(0.8 * size_train_val)
+        size_train = round(0.8 * size_train_val)
         size_val = size_train_val - size_train
 
         self.train_dataset, self.val_dataset = random_split(

torchgeo/datamodules/utils.py

@@ -28,11 +28,11 @@ def dataset_split(
         a list of the subset datasets. Either [train, val] or [train, val, test]
     """
     if test_pct is None:
-        val_length = int(len(dataset) * val_pct)
+        val_length = round(len(dataset) * val_pct)
         train_length = len(dataset) - val_length
         return random_split(dataset, [train_length, val_length])
     else:
-        val_length = int(len(dataset) * val_pct)
-        test_length = int(len(dataset) * test_pct)
+        val_length = round(len(dataset) * val_pct)
+        test_length = round(len(dataset) * test_pct)
         train_length = len(dataset) - (val_length + test_length)
         return random_split(dataset, [train_length, val_length, test_length])

torchgeo/datasets/geo.py

@@ -449,8 +449,8 @@ def _merge_files(self, filepaths: Sequence[str], query: BoundingBox) -> Tensor:
         bounds = (query.minx, query.miny, query.maxx, query.maxy)
         if len(vrt_fhs) == 1:
             src = vrt_fhs[0]
-            out_width = int(round((query.maxx - query.minx) / self.res))
-            out_height = int(round((query.maxy - query.miny) / self.res))
+            out_width = round((query.maxx - query.minx) / self.res)
+            out_height = round((query.maxy - query.miny) / self.res)
             out_shape = (src.count, out_height, out_width)
             dest = src.read(
                 out_shape=out_shape, window=from_bounds(*bounds, src.transform)

torchgeo/datasets/openbuildings.py

@@ -324,11 +324,11 @@ def __getitem__(self, query: BoundingBox) -> Dict[str, Any]:
         )
         if shapes:
             masks = rasterio.features.rasterize(
-                shapes, out_shape=(int(height), int(width)), transform=transform
+                shapes, out_shape=(round(height), round(width)), transform=transform
             )
             masks = torch.tensor(masks).unsqueeze(0)
         else:
-            masks = torch.zeros(size=(1, int(height), int(width)))
+            masks = torch.zeros(size=(1, round(height), round(width)))
 
         sample = {"mask": masks, "crs": self.crs, "bbox": query}
 

torchgeo/datasets/so2sat.py

@@ -197,7 +197,7 @@ def __init__(
             raise RuntimeError("Dataset not found or corrupted.")
 
         with h5py.File(self.fn, "r") as f:
-            self.size = int(f["label"].shape[0])
+            self.size: int = f["label"].shape[0]
 
     def __getitem__(self, index: int) -> Dict[str, Tensor]:
         """Return an index within the dataset.

torchgeo/datasets/spacenet.py

@@ -703,7 +703,7 @@ def _load_mask(
         speed_arr_bin = np.arange(min_speed_bin, max_speed_bin + 1)
         bin_size_mph = 10.0
         speed_cls_arr: "np.typing.NDArray[np.int_]" = np.array(
-            [int(math.ceil(s / bin_size_mph)) for s in speed_arr_bin]
+            [math.ceil(s / bin_size_mph) for s in speed_arr_bin]
         )
 
         try:

torchgeo/samplers/single.py

@@ -205,7 +205,7 @@ def __init__(
             ):
                 self.hits.append(hit)
 
-        self.length: int = 0
+        self.length = 0
         for hit in self.hits:
             bounds = BoundingBox(*hit.bounds)