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apply Black 2024 style in fbcode (4/16)

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Summary:
Formats the covered files with pyfmt.

paintitblack

Reviewed By: aleivag

Differential Revision: D54447727

fbshipit-source-id: 8844b1caa08de94d04ac4df3c768dbf8c865fd2f
This commit is contained in:
Amethyst Reese
2024-03-02 17:31:19 -08:00
committed by Facebook GitHub Bot
parent f34104cf6e
commit 3da7703c5a
31 changed files with 130 additions and 106 deletions
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12
tests/implicitron/test_extending_orm_types.py
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@@ -61,9 +61,9 @@ class ExtendedSqlFrameAnnotation(SqlFrameAnnotation):
class ExtendedSqlIndexDataset(SqlIndexDataset):
frame_annotations_type: ClassVar[
Type[SqlFrameAnnotation]
] = ExtendedSqlFrameAnnotation
frame_annotations_type: ClassVar[Type[SqlFrameAnnotation]] = (
ExtendedSqlFrameAnnotation
)
class CanineFrameData(FrameData):
@@ -96,9 +96,9 @@ class CanineFrameDataBuilder(
class CanineSqlIndexDataset(SqlIndexDataset):
frame_annotations_type: ClassVar[
Type[SqlFrameAnnotation]
] = ExtendedSqlFrameAnnotation
frame_annotations_type: ClassVar[Type[SqlFrameAnnotation]] = (
ExtendedSqlFrameAnnotation
)
frame_data_builder_class_type: str = "CanineFrameDataBuilder"

10
tests/implicitron/test_frame_data_builder.py
View File

@@ -85,11 +85,11 @@ class TestFrameDataBuilder(TestCaseMixin, unittest.TestCase):
camera_quality_score=safe_as_tensor(
self.seq_annotation.viewpoint_quality_score, torch.float
),
point_cloud_quality_score=safe_as_tensor(
point_cloud.quality_score, torch.float
)
if point_cloud is not None
else None,
point_cloud_quality_score=(
safe_as_tensor(point_cloud.quality_score, torch.float)
if point_cloud is not None
else None
),
)
def test_frame_data_builder_args(self):

5
tests/implicitron/test_json_index_dataset_provider_v2.py
View File

@@ -168,7 +168,10 @@ def _make_random_json_dataset_map_provider_v2_data(
mask_path = os.path.join(maskdir, f"frame{i:05d}.png")
mask = np.zeros((H, W))
mask[H // 2 :, W // 2 :] = 1
Image.fromarray((mask * 255.0).astype(np.uint8), mode="L",).convert(
Image.fromarray(
(mask * 255.0).astype(np.uint8),
mode="L",
).convert(
"L"
).save(mask_path)

5
tests/implicitron/test_models_renderer_base.py
View File

@@ -222,10 +222,7 @@ class TestRendererBase(TestCaseMixin, unittest.TestCase):
np.testing.assert_allclose(
(delta**2) / 3
- (4 / 15)
* (
(delta**4 * (12 * mu**2 - delta**2))
/ (3 * mu**2 + delta**2) ** 2
),
* ((delta**4 * (12 * mu**2 - delta**2)) / (3 * mu**2 + delta**2) ** 2),
t_var.numpy(),
)
np.testing.assert_allclose(

8
tests/test_meshes.py
View File

@@ -983,7 +983,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
verts_list = []
faces_list = []
verts_faces = [(10, 100), (20, 200)]
for (V, F) in verts_faces:
for V, F in verts_faces:
verts = torch.rand((V, 3), dtype=torch.float32, device=device)
faces = torch.randint(V, size=(F, 3), dtype=torch.int64, device=device)
verts_list.append(verts)
@@ -1007,7 +1007,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
device = torch.device("cuda:0")
verts_list = []
faces_list = []
for (V, F) in [(10, 100)]:
for V, F in [(10, 100)]:
verts = torch.rand((V, 3), dtype=torch.float32, device=device)
faces = torch.randint(V, size=(F, 3), dtype=torch.int64, device=device)
verts_list.append(verts)
@@ -1025,7 +1025,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
verts_list = []
faces_list = []
verts_faces = [(10, 100), (20, 200), (30, 300)]
for (V, F) in verts_faces:
for V, F in verts_faces:
verts = torch.rand((V, 3), dtype=torch.float32, device=device)
faces = torch.randint(V, size=(F, 3), dtype=torch.int64, device=device)
verts_list.append(verts)
@@ -1047,7 +1047,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
verts_list = []
faces_list = []
verts_faces = [(10, 100), (20, 200), (30, 300)]
for (V, F) in verts_faces:
for V, F in verts_faces:
verts = torch.rand((V, 3), dtype=torch.float32, device=device)
faces = torch.randint(V, size=(F, 3), dtype=torch.int64, device=device)
verts_list.append(verts)

2
tests/test_render_implicit.py
View File

@@ -284,7 +284,7 @@ class TestRenderImplicit(TestCaseMixin, unittest.TestCase):
os.makedirs(outdir, exist_ok=True)
frames = []
for (image_opacity, image_opacity_mesh) in zip(
for image_opacity, image_opacity_mesh in zip(
images_opacities, images_opacities_meshes
):
image, opacity = image_opacity.split([3, 1], dim=-1)

3
tests/test_render_meshes.py
View File

@@ -303,7 +303,6 @@ class TestRenderMeshes(TestCaseMixin, unittest.TestCase):
self.test_simple_sphere(check_depth=True)
def test_simple_sphere_screen(self):
"""
Test output when rendering with PerspectiveCameras & OrthographicCameras
in NDC vs screen space.
@@ -1221,7 +1220,7 @@ class TestRenderMeshes(TestCaseMixin, unittest.TestCase):
"flat": HardFlatShader,
"splatter": SplatterPhongShader,
}
for (name, shader_init) in shaders.items():
for name, shader_init in shaders.items():
if rasterizer_type == MeshRasterizerOpenGL and name != "splatter":
continue
if rasterizer_type == MeshRasterizer and name == "splatter":

4
tests/test_render_meshes_clipped.py
View File

@@ -620,7 +620,7 @@ class TestRenderMeshesClipping(TestCaseMixin, unittest.TestCase):
plane into a quadrilateral, there shouldn't be duplicates indices of
the face in the pix_to_face output of rasterization.
"""
for (device, bin_size) in [("cpu", 0), ("cuda:0", 0), ("cuda:0", None)]:
for device, bin_size in [("cpu", 0), ("cuda:0", 0), ("cuda:0", None)]:
verts = torch.tensor(
[[0.0, -10.0, 1.0], [-1.0, 2.0, -2.0], [1.0, 5.0, -10.0]],
dtype=torch.float32,
@@ -673,7 +673,7 @@ class TestRenderMeshesClipping(TestCaseMixin, unittest.TestCase):
device = "cuda:0"
mesh1 = torus(20.0, 85.0, 32, 16, device=device)
mesh2 = torus(2.0, 3.0, 32, 16, device=device)
for (mesh, z_clip) in [(mesh1, None), (mesh2, 5.0)]:
for mesh, z_clip in [(mesh1, None), (mesh2, 5.0)]:
tex = TexturesVertex(verts_features=torch.rand_like(mesh.verts_padded()))
mesh.textures = tex
raster_settings = RasterizationSettings(

4
tests/test_render_points.py
View File

@@ -384,7 +384,7 @@ class TestRenderPoints(TestCaseMixin, unittest.TestCase):
(AlphaCompositor, alpha_composite),
]
for (compositor_class, composite_func) in compositor_funcs:
for compositor_class, composite_func in compositor_funcs:
compositor = compositor_class(background_color)
@@ -435,7 +435,7 @@ class TestRenderPoints(TestCaseMixin, unittest.TestCase):
(AlphaCompositor, alpha_composite),
]
for (compositor_class, composite_func) in compositor_funcs:
for compositor_class, composite_func in compositor_funcs:
compositor = compositor_class(background_color)

2
tests/test_render_volumes.py
View File

@@ -392,7 +392,7 @@ class TestRenderVolumes(TestCaseMixin, unittest.TestCase):
os.makedirs(outdir, exist_ok=True)
frames = []
for (image, image_pts) in zip(images, images_pts):
for image, image_pts in zip(images, images_pts):
diff_image = (
((image - image_pts) * 0.5 + 0.5)
.mean(dim=2, keepdim=True)

2
tests/test_vert_align.py
View File

@@ -100,7 +100,7 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
def init_feats(batch_size: int = 10, num_channels: int = 256, device: str = "cuda"):
H, W = [14, 28], [14, 28]
feats = []
for (h, w) in zip(H, W):
for h, w in zip(H, W):
feats.append(torch.rand((batch_size, num_channels, h, w), device=device))
return feats