cleanup spatial_size -> canvas_size

test/common_utils.py

@@ -423,7 +423,7 @@ def load(self, device="cpu"):
 )
 
 
-def _parse_canvas_size(size, *, name="size"):
+def _parse_size(size, *, name="size"):
     if size == "random":
         raise ValueError("This should never happen")
     elif isinstance(size, int) and size > 0:
@@ -478,13 +478,13 @@ def load(self, device):
 
 @dataclasses.dataclass
 class ImageLoader(TensorLoader):
-    canvas_size: Tuple[int, int] = dataclasses.field(init=False)
+    spatial_size: Tuple[int, int] = dataclasses.field(init=False)
     num_channels: int = dataclasses.field(init=False)
     memory_format: torch.memory_format = torch.contiguous_format
     canvas_size: Tuple[int, int] = dataclasses.field(init=False)
 
     def __post_init__(self):
-        self.canvas_size = self.canvas_size = self.shape[-2:]
+        self.spatial_size = self.canvas_size = self.shape[-2:]
         self.num_channels = self.shape[-3]
 
     def load(self, device):
@@ -550,7 +550,7 @@ def make_image_loader(
 ):
     if not constant_alpha:
         raise ValueError("This should never happen")
-    size = _parse_canvas_size(size)
+    size = _parse_size(size)
     num_channels = get_num_channels(color_space)
 
     def fn(shape, dtype, device, memory_format):
@@ -590,7 +590,7 @@ def make_image_loaders(
 def make_image_loader_for_interpolation(
     size=(233, 147), *, color_space="RGB", dtype=torch.uint8, memory_format=torch.contiguous_format
 ):
-    size = _parse_canvas_size(size)
+    size = _parse_size(size)
     num_channels = get_num_channels(color_space)
 
     def fn(shape, dtype, device, memory_format):
@@ -687,33 +687,33 @@ def sample_position(values, max_value):
     )
 
 
-def make_bounding_box_loader(*, extra_dims=(), format, canvas_size=DEFAULT_PORTRAIT_SPATIAL_SIZE, dtype=torch.float32):
+def make_bounding_box_loader(*, extra_dims=(), format, spatial_size=DEFAULT_PORTRAIT_SPATIAL_SIZE, dtype=torch.float32):
     if isinstance(format, str):
         format = datapoints.BoundingBoxFormat[format]
 
-    canvas_size = _parse_canvas_size(canvas_size, name="canvas_size")
+    spatial_size = _parse_size(spatial_size, name="canvas_size")
 
     def fn(shape, dtype, device):
         *batch_dims, num_coordinates = shape
         if num_coordinates != 4:
             raise pytest.UsageError()
 
         return make_bounding_box(
-            format=format, canvas_size=canvas_size, batch_dims=batch_dims, dtype=dtype, device=device
+            format=format, canvas_size=spatial_size, batch_dims=batch_dims, dtype=dtype, device=device
         )
 
-    return BoundingBoxesLoader(fn, shape=(*extra_dims, 4), dtype=dtype, format=format, spatial_size=canvas_size)
+    return BoundingBoxesLoader(fn, shape=(*extra_dims, 4), dtype=dtype, format=format, spatial_size=spatial_size)
 
 
 def make_bounding_box_loaders(
     *,
     extra_dims=DEFAULT_EXTRA_DIMS,
     formats=tuple(datapoints.BoundingBoxFormat),
-    canvas_size=DEFAULT_PORTRAIT_SPATIAL_SIZE,
+    spatial_size=DEFAULT_PORTRAIT_SPATIAL_SIZE,
     dtypes=(torch.float32, torch.float64, torch.int64),
 ):
     for params in combinations_grid(extra_dims=extra_dims, format=formats, dtype=dtypes):
-        yield make_bounding_box_loader(**params, canvas_size=canvas_size)
+        yield make_bounding_box_loader(**params, spatial_size=spatial_size)
 
 
 make_bounding_boxes = from_loaders(make_bounding_box_loaders)
@@ -738,7 +738,7 @@ def make_detection_mask(size=DEFAULT_SIZE, *, num_objects=5, batch_dims=(), dtyp
 
 def make_detection_mask_loader(size=DEFAULT_PORTRAIT_SPATIAL_SIZE, *, num_objects=5, extra_dims=(), dtype=torch.uint8):
     # This produces "detection" masks, i.e. `(*, N, H, W)`, where `N` denotes the number of objects
-    size = _parse_canvas_size(size)
+    size = _parse_size(size)
 
     def fn(shape, dtype, device):
         *batch_dims, num_objects, height, width = shape
@@ -779,15 +779,15 @@ def make_segmentation_mask_loader(
     size=DEFAULT_PORTRAIT_SPATIAL_SIZE, *, num_categories=10, extra_dims=(), dtype=torch.uint8
 ):
     # This produces "segmentation" masks, i.e. `(*, H, W)`, where the category is encoded in the values
-    canvas_size = _parse_canvas_size(size)
+    size = _parse_size(size)
 
     def fn(shape, dtype, device):
         *batch_dims, height, width = shape
         return make_segmentation_mask(
             (height, width), num_categories=num_categories, batch_dims=batch_dims, dtype=dtype, device=device
         )
 
-    return MaskLoader(fn, shape=(*extra_dims, *canvas_size), dtype=dtype)
+    return MaskLoader(fn, shape=(*extra_dims, *size), dtype=dtype)
 
 
 def make_segmentation_mask_loaders(
@@ -841,7 +841,7 @@ def make_video_loader(
     extra_dims=(),
     dtype=torch.uint8,
 ):
-    size = _parse_canvas_size(size)
+    size = _parse_size(size)
 
     def fn(shape, dtype, device, memory_format):
         *batch_dims, num_frames, _, height, width = shape

test/test_transforms_v2_functional.py

@@ -884,7 +884,7 @@ def _compute_expected_bbox(bbox, pcoeffs_):
     pcoeffs = _get_perspective_coeffs(startpoints, endpoints)
     inv_pcoeffs = _get_perspective_coeffs(endpoints, startpoints)
 
-    for bboxes in make_bounding_boxes(canvas_size=canvas_size, extra_dims=((4,),)):
+    for bboxes in make_bounding_boxes(spatial_size=canvas_size, extra_dims=((4,),)):
         bboxes = bboxes.to(device)
 
         output_bboxes = F.perspective_bounding_boxes(