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Enable Pyrefly in fbcode/vision/fair

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Summary:
Automated migration to enable Pyrefly type checking for `fbcode/vision/fair`.

- Added `python.set_pyrefly(True)` to PACKAGE file
- Suppressed pre-existing type errors

Pyrefly is Meta's next-generation Python type checker, replacing Pyre.

If you encounter issues, you can revert the PACKAGE change by removing
the `python.set_pyrefly(True)` line.
#pyreupgrade

Differential Revision: D107142434

fbshipit-source-id: 25929bb3d5a310d00dab11a46c5395df94357feb
This commit is contained in:
generatedunixname89002005307016
2026-06-02 06:18:19 -07:00
committed by meta-codesync[bot]
parent b73d735ecf
commit 05025bf005
78 changed files with 336 additions and 12 deletions
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2
projects/implicitron_trainer/experiment.py
View File

@@ -193,6 +193,7 @@ class Experiment(Configurable):
last_epoch=start_epoch,
model=model,
resume=self.model_factory.resume,
# pyrefly: ignore [missing-attribute]
resume_epoch=self.model_factory.resume_epoch,
)
@@ -212,6 +213,7 @@ class Experiment(Configurable):
# Enter the main training loop.
self.training_loop.run(
# pyrefly: ignore [bad-argument-type]
train_loader=train_loader,
val_loader=val_loader,
test_loader=test_loader,

7
projects/implicitron_trainer/impl/optimizer_factory.py
View File

@@ -173,6 +173,7 @@ class ImplicitronOptimizerFactory(OptimizerFactoryBase):
scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer,
lambda epoch: self.gamma ** (epoch / self.exponential_lr_step_size),
# pyrefly: ignore [unexpected-keyword]
verbose=False,
)
elif self.lr_policy.casefold() == "LinearExponential".casefold():
@@ -191,7 +192,11 @@ class ImplicitronOptimizerFactory(OptimizerFactoryBase):
# pyre-fixme[28]: Unexpected keyword argument `verbose`.
scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, _get_lr, verbose=False
# pyrefly: ignore [unexpected-keyword]
optimizer,
_get_lr,
# pyrefly: ignore [unexpected-keyword]
verbose=False,
)
else:
raise ValueError("no such lr policy %s" % self.lr_policy)

2
projects/implicitron_trainer/impl/training_loop.py
View File

@@ -199,6 +199,7 @@ class ImplicitronTrainingLoop(TrainingLoopBase):
and self.test_interval > 0
and epoch % self.test_interval == 0
):
# pyrefly: ignore [missing-attribute]
self.evaluator.run(
device=device,
dataloader=test_loader,
@@ -215,6 +216,7 @@ class ImplicitronTrainingLoop(TrainingLoopBase):
if self.test_when_finished:
if test_loader is not None:
# pyrefly: ignore [missing-attribute]
self.evaluator.run(
device=device,
dump_to_json=True,

1
projects/implicitron_trainer/impl/utils.py
View File

@@ -14,6 +14,7 @@ import torch
def seed_all_random_engines(seed: int) -> None:
# pyrefly: ignore [bad-argument-type]
np.random.seed(seed)
torch.manual_seed(seed)
random.seed(seed)

2
projects/implicitron_trainer/visualize_reconstruction.py
View File

@@ -75,6 +75,7 @@ def visualize_reconstruction(
# Set the random seeds
torch.manual_seed(0)
# pyrefly: ignore [bad-argument-type]
np.random.seed(0)
# Get the config from the experiment_directory,
@@ -135,6 +136,7 @@ def visualize_reconstruction(
"device": device,
**render_flyaround_kwargs,
}
# pyrefly: ignore [bad-argument-type]
render_flyaround(**render_kwargs)

4
pytorch3d/common/workaround/symeig3x3.py
View File

@@ -90,6 +90,7 @@ class _SymEig3x3(nn.Module):
)
p = torch.sqrt(p2 / 6.0)
# pyrefly: ignore [unsupported-operation]
B = (inputs - q[..., None, None] * self._identity) / p[..., None, None]
r = torch.det(B) / 2.0
@@ -174,8 +175,10 @@ class _SymEig3x3(nn.Module):
"""
# Find the eigenvector corresponding to alpha0, its eigenvalue is distinct
# pyrefly: ignore [unsupported-operation]
ev0 = self._get_ev0(inputs - alpha0[..., None, None] * self._identity)
u, v = self._get_uv(ev0)
# pyrefly: ignore [unsupported-operation]
ev1 = self._get_ev1(inputs - alpha1[..., None, None] * self._identity, u, v)
# Third eigenvector is computed as the cross-product of the other two
ev2 = torch.cross(ev0, ev1, dim=-1)
@@ -250,6 +253,7 @@ class _SymEig3x3(nn.Module):
"""
min_idx = w.abs().argmin(dim=-1)
# pyrefly: ignore [bad-index]
rotation_2d = self._rotations_3d[min_idx].to(w)
u = F.normalize((rotation_2d @ w[..., None])[..., 0], dim=-1)

2
pytorch3d/datasets/r2n2/r2n2.py
View File

@@ -200,6 +200,7 @@ class R2N2(ShapeNetBase): # pragma: no cover
) % (shapenet_dir, ", ".join(synset_not_present))
warnings.warn(msg)
# pyrefly: ignore [bad-override]
def __getitem__(self, model_idx, view_idxs: Optional[List[int]] = None) -> Dict:
"""
Read a model by the given index.
@@ -370,6 +371,7 @@ class R2N2(ShapeNetBase): # pragma: no cover
T = RT[3, :3]
return R, T
# pyrefly: ignore [bad-override]
def render(
self,
model_ids: Optional[List[str]] = None,

9
pytorch3d/datasets/r2n2/utils.py
View File

@@ -62,8 +62,10 @@ def collate_batched_R2N2(batch: List[Dict]): # pragma: no cover
# all models have the same number of views V, stack the batches of
# views of each model into a new batch of shape (N, V, H, W, 3).
# Otherwise leave it as a list.
# pyrefly: ignore [not-iterable]
if "images" in collated_dict:
try:
# pyrefly: ignore [unsupported-operation]
collated_dict["images"] = torch.stack(collated_dict["images"])
except RuntimeError:
print(
@@ -75,10 +77,14 @@ def collate_batched_R2N2(batch: List[Dict]): # pragma: no cover
# matrices and that all models have the same number of views V, stack each
# type of matrices into a new batch of shape (N, V, ...).
# Otherwise leave them as lists.
# pyrefly: ignore [not-iterable]
if all(x in collated_dict for x in ["R", "T", "K"]):
try:
# pyrefly: ignore [unsupported-operation]
collated_dict["R"] = torch.stack(collated_dict["R"]) # (N, V, 3, 3)
# pyrefly: ignore [unsupported-operation]
collated_dict["T"] = torch.stack(collated_dict["T"]) # (N, V, 3)
# pyrefly: ignore [unsupported-operation]
collated_dict["K"] = torch.stack(collated_dict["K"]) # (N, V, 4, 4)
except RuntimeError:
print(
@@ -89,8 +95,10 @@ def collate_batched_R2N2(batch: List[Dict]): # pragma: no cover
# If collate_batched_meshes receives voxels and all models have the same
# number of views V, stack the batches of voxels into a new batch of shape
# (N, V, S, S, S), where S is the voxel size.
# pyrefly: ignore [not-iterable]
if "voxels" in collated_dict:
try:
# pyrefly: ignore [unsupported-operation]
collated_dict["voxels"] = torch.stack(collated_dict["voxels"])
except RuntimeError:
print(
@@ -458,6 +466,7 @@ class BlenderCamera(CamerasBase): # pragma: no cover
def get_projection_transform(self, **kwargs) -> Transform3d:
transform = Transform3d(device=self.device)
# pyrefly: ignore [not-callable]
transform._matrix = self.K.transpose(1, 2).contiguous()
return transform

6
pytorch3d/datasets/shapenet_base.py
View File

@@ -52,6 +52,7 @@ class ShapeNetBase(torch.utils.data.Dataset): # pragma: no cover
"""
return len(self.model_ids)
# pyrefly: ignore [bad-override-param-name]
def __getitem__(self, idx) -> Dict:
"""
Read a model by the given index. Need to be implemented for every child class
@@ -147,12 +148,17 @@ class ShapeNetBase(torch.utils.data.Dataset): # pragma: no cover
idxs = self._handle_render_inputs(model_ids, categories, sample_nums, idxs)
# Use the getitem method which loads mesh + texture
models = [self[idx] for idx in idxs]
# pyrefly: ignore [unsupported-operation]
meshes = collate_batched_meshes(models)["mesh"]
# pyrefly: ignore [missing-attribute]
if meshes.textures is None:
# pyrefly: ignore [missing-attribute]
meshes.textures = TexturesVertex(
# pyrefly: ignore [missing-attribute]
verts_features=torch.ones_like(meshes.verts_padded(), device=device)
)
# pyrefly: ignore [missing-attribute]
meshes = meshes.to(device)
cameras = kwargs.get("cameras", FoVPerspectiveCameras()).to(device)
if len(cameras) != 1 and len(cameras) % len(meshes) != 0:

2
pytorch3d/datasets/utils.py
View File

@@ -34,12 +34,14 @@ def collate_batched_meshes(batch: List[Dict]): # pragma: no cover
for k in batch[0].keys():
collated_dict[k] = [d[k] for d in batch]
# pyrefly: ignore [unsupported-operation]
collated_dict["mesh"] = None
if {"verts", "faces"}.issubset(collated_dict.keys()):
textures = None
if "textures" in collated_dict:
textures = TexturesAtlas(atlas=collated_dict["textures"])
# pyrefly: ignore [unsupported-operation]
collated_dict["mesh"] = Meshes(
verts=collated_dict["verts"],
faces=collated_dict["faces"],

1
pytorch3d/implicitron/dataset/dataset_base.py
View File

@@ -101,6 +101,7 @@ class DatasetBase(GenericWorkaround, torch.utils.data.Dataset[FrameData]):
# crashes without overriding __getitem__
sequence_category = self[first_frame_idx].sequence_category
c2seq[sequence_category].append(sequence_name)
# pyrefly: ignore [bad-return]
return dict(c2seq)
def sequence_frames_in_order(

13
pytorch3d/implicitron/dataset/frame_data.py
View File

@@ -297,7 +297,11 @@ class FrameData(Mapping[str, Any]):
depth_map = self.depth_map
if depth_map is not None:
clamp_bbox_xyxy_depth = rescale_bbox(
clamp_bbox_xyxy, tuple(depth_map.shape[-2:]), effective_image_size_hw
# pyrefly: ignore [bad-argument-type]
clamp_bbox_xyxy,
# pyrefly: ignore [bad-argument-type]
tuple(depth_map.shape[-2:]),
effective_image_size_hw,
).long()
self.depth_map = crop_around_box(
depth_map,
@@ -308,7 +312,11 @@ class FrameData(Mapping[str, Any]):
depth_mask = self.depth_mask
if depth_mask is not None:
clamp_bbox_xyxy_depth = rescale_bbox(
clamp_bbox_xyxy, tuple(depth_mask.shape[-2:]), effective_image_size_hw
# pyrefly: ignore [bad-argument-type]
clamp_bbox_xyxy,
# pyrefly: ignore [bad-argument-type]
tuple(depth_mask.shape[-2:]),
effective_image_size_hw,
).long()
self.depth_mask = crop_around_box(
depth_mask,
@@ -453,6 +461,7 @@ class FrameDataBuilderBase(ReplaceableBase, Generic[FrameDataSubtype], ABC):
"""
# To be initialised to FrameDataSubtype
# pyrefly: ignore [invalid-annotation]
frame_data_type: ClassVar[Type[FrameDataSubtype]]
@abstractmethod

1
pytorch3d/implicitron/dataset/json_index_dataset.py
View File

@@ -425,6 +425,7 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
raise ValueError("subsets not loaded")
if is_known_frame_scalar(frame_type):
cameras.append(self[frame_idx].camera)
# pyrefly: ignore [bad-argument-type]
return join_cameras_as_batch(cameras)
def __getitem__(self, index) -> FrameData:

3
pytorch3d/implicitron/dataset/json_index_dataset_map_provider_v2.py
View File

@@ -311,6 +311,7 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):
subset_mapping["test"],
) = self._extend_test_data_with_known_views(
subset_mapping,
# pyrefly: ignore [bad-argument-type]
eval_batch_index,
)
@@ -322,6 +323,7 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):
try:
test_dataset.eval_batches = (
test_dataset.seq_frame_index_to_dataset_index(
# pyrefly: ignore [bad-argument-type]
eval_batch_index,
)
)
@@ -335,6 +337,7 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):
)
test_dataset.eval_batches = (
test_dataset.seq_frame_index_to_dataset_index(
# pyrefly: ignore [bad-argument-type]
eval_batch_index,
allow_missing_indices=True,
remove_missing_indices=True,

2
pytorch3d/implicitron/dataset/orm_types.py
View File

@@ -90,6 +90,7 @@ def TupleTypeFactory(dtype=float, shape: Tuple[int, ...] = (2,)):
impl = LargeBinary
_format = format_symbol * math.prod(shape)
# pyrefly: ignore [bad-override-param-name]
def process_bind_param(self, value, _):
if value is None:
return None
@@ -99,6 +100,7 @@ def TupleTypeFactory(dtype=float, shape: Tuple[int, ...] = (2,)):
return struct.pack(TupleType._format, *value)
# pyrefly: ignore [bad-override-param-name]
def process_result_value(self, value, _):
if value is None:
return None

6
pytorch3d/implicitron/dataset/rendered_mesh_dataset_map_provider.py
View File

@@ -121,11 +121,17 @@ class RenderedMeshDatasetMapProvider(DatasetMapProviderBase):
self.poses = poses.cpu()
# pyre-ignore[16]
self.train_dataset = SingleSceneDataset( # pyre-ignore[28]
# pyrefly: ignore [unexpected-keyword]
object_name="cow",
# pyrefly: ignore [unexpected-keyword]
images=list(images.permute(0, 3, 1, 2).cpu()),
# pyrefly: ignore [unexpected-keyword]
fg_probabilities=list(masks[:, None].cpu()),
# pyrefly: ignore [unexpected-keyword]
poses=[self.poses[i] for i in range(len(poses))],
# pyrefly: ignore [unexpected-keyword]
frame_types=[DATASET_TYPE_KNOWN] * len(poses),
# pyrefly: ignore [unexpected-keyword]
eval_batches=None,
)

2
pytorch3d/implicitron/dataset/single_sequence_dataset.py
View File

@@ -131,10 +131,12 @@ class SingleSceneDatasetMapProviderBase(DatasetMapProviderBase):
None
# pyre-ignore[16]
if self.fg_probabilities is None
# pyrefly: ignore [bad-index]
else self.fg_probabilities[split]
)
eval_batches = [[i] for i in range(len(split))]
if split_idx != 0 and self.n_known_frames_for_test is not None:
# pyrefly: ignore [missing-attribute]
train_split = self.i_split[0]
if set_eval_batches:
generator = np.random.default_rng(seed=0)

5
pytorch3d/implicitron/dataset/sql_dataset.py
View File

@@ -221,6 +221,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
def __len__(self) -> int:
return len(self._index)
# pyrefly: ignore [bad-override-param-name]
def __getitem__(self, frame_idx: Union[int, Tuple[str, int]]) -> FrameData:
"""
Fetches FrameData by either iloc in the index or by (sequence, frame_no) pair
@@ -424,6 +425,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
# override
@property
# pyrefly: ignore [bad-override]
def frame_data_type(self) -> Type[FrameData]:
return self.frame_data_builder.frame_data_type
@@ -630,7 +632,9 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
# dev load: 94 s / 23 s (3.1M / 500K)
pick_frames_criteria.append(
sa.or_(
# pyrefly: ignore [missing-attribute]
self.frame_annotations_type._mask_mass.is_(None),
# pyrefly: ignore [missing-attribute]
self.frame_annotations_type._mask_mass != 0,
)
)
@@ -697,6 +701,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
where_conditions.append(
sa.or_(
self.frame_annotations_type._mask_mass.is_(None), # pyre-ignore[16]
# pyrefly: ignore [missing-attribute]
self.frame_annotations_type._mask_mass != 0,
)
)

1
pytorch3d/implicitron/dataset/types.py
View File

@@ -162,6 +162,7 @@ def load_dataclass(f: IO, cls: Type[_X], binary: bool = False) -> _X:
else:
res = _dataclass_from_dict(asdict, cls)
# pyrefly: ignore [bad-return]
return res

1
pytorch3d/implicitron/dataset/utils.py
View File

@@ -117,6 +117,7 @@ def crop_around_box(
# bbox is xyxy, where the upper bound is corrected with +1
bbox = clamp_box_to_image_bounds_and_round(
bbox,
# pyrefly: ignore [bad-argument-type]
image_size_hw=tuple(tensor.shape[-2:]),
)
tensor = tensor[..., bbox[1] : bbox[3], bbox[0] : bbox[2]]

2
pytorch3d/implicitron/evaluation/evaluate_new_view_synthesis.py
View File

@@ -312,6 +312,7 @@ def eval_batch(
)
if visualize:
# pyrefly: ignore [unbound-name]
visualizer.show_rgb(
results[metric_name].item(), metric_name, loss_mask_now
)
@@ -330,6 +331,7 @@ def eval_batch(
results["depth_abs" + name_postfix] = abs_.mean()
if visualize:
# pyrefly: ignore [unbound-name]
visualizer.show_depth(abs_.mean().item(), name_postfix, loss_mask_now)
if break_after_visualising:
breakpoint() # noqa: B601

1
pytorch3d/implicitron/models/generic_model.py
View File

@@ -472,6 +472,7 @@ class GenericModel(ImplicitronModelBase):
sequence_name=safe_slice_targets(sequence_name),
frame_timestamp=safe_slice_targets(frame_timestamp),
)
# pyrefly: ignore [unsupported-operation]
custom_args["global_code"] = global_code
# pyre-fixme[29]: `Union[(self: Tensor) -> Any, Tensor, Module]` is not a

1
pytorch3d/implicitron/models/implicit_function/voxel_grid.py
View File

@@ -907,6 +907,7 @@ class VoxelGridModule(Configurable, torch.nn.Module):
else:
# Torch Module to hold parameters since they can only be registered
# at object level.
# pyrefly: ignore [bad-assignment]
self.params = _RegistratedBufferDict(vars(params))
@staticmethod

3
pytorch3d/implicitron/models/implicit_function/voxel_grid_implicit_function.py
View File

@@ -476,6 +476,7 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
call_epochs = list(
set(self.scaffold_calculating_epochs) | set(self.volume_cropping_epochs)
)
# pyrefly: ignore [bad-return]
return call_epochs, callback
def _crop(self, epoch: int) -> bool:
@@ -581,6 +582,7 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
)
cls = registry.get(DecoderFunctionBase, type_)
# pyrefly: ignore [bad-argument-type]
need_input_dim = any(field.name == "input_dim" for field in fields(cls))
if need_input_dim:
self.decoder_density = cls(input_dim=input_dim, **args)
@@ -621,6 +623,7 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
input_dim = input_dim0 + input_dim1
cls = registry.get(DecoderFunctionBase, type_)
# pyrefly: ignore [bad-argument-type]
need_input_dim = any(field.name == "input_dim" for field in fields(cls))
if need_input_dim:
self.decoder_color = cls(input_dim=input_dim, **args)

2
pytorch3d/implicitron/models/renderer/base.py
View File

@@ -110,6 +110,7 @@ class ImplicitronRayBundle:
# equivalent to: 0.5 * (bins[..., 1:] + bins[..., :-1]) but more efficient
# pyre-ignore
return torch.lerp(self.bins[..., :-1], self.bins[..., 1:], 0.5)
# pyrefly: ignore [bad-return]
return self._lengths
@lengths.setter
@@ -166,6 +167,7 @@ class ImplicitronRayBundle:
)
num_inputs = camera_counts.sum().item()
max_size = torch.max(camera_counts).item()
# pyrefly: ignore [bad-argument-type]
xys = packed_to_padded(self.xys, first_idxs, max_size)
# pyre-ignore [7] pytorch typeshed inaccuracy
return xys, first_idxs, num_inputs

1
pytorch3d/implicitron/models/renderer/raymarcher.py
View File

@@ -198,6 +198,7 @@ class AccumulativeRaymarcherBase(RaymarcherBase, torch.nn.Module):
depth = (weights * ray_lengths)[..., None].sum(dim=-2)
alpha = opacities if self.blend_output else 1
# pyrefly: ignore [bad-index]
if self._bg_color.shape[-1] not in [1, features.shape[-1]]:
raise ValueError("Wrong number of background color channels.")
# pyre-fixme[58]: `*` is not supported for operand types `int` and

1
pytorch3d/implicitron/models/renderer/sdf_renderer.py
View File

@@ -146,6 +146,7 @@ class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):
# Sample points for the eikonal loss
eik_bounding_box: float = self.object_bounding_sphere
n_eik_points = batch_size * num_pixels // 2
# pyrefly: ignore [no-matching-overload]
eikonal_points = torch.empty(
n_eik_points,
3,

1
pytorch3d/implicitron/models/utils.py
View File

@@ -118,6 +118,7 @@ def weighted_sum_losses(
return None
loss = sum(losses_weighted)
assert torch.is_tensor(loss)
# pyrefly: ignore [bad-return]
return loss

5
pytorch3d/implicitron/tools/config.py
View File

@@ -231,7 +231,9 @@ class Configurable:
return obj
# pyrefly: ignore [invalid-type-var]
_X = TypeVar("X", bound=ReplaceableBase)
# pyrefly: ignore [invalid-type-var]
_Y = TypeVar("Y", bound=Union[ReplaceableBase, Configurable])
@@ -890,10 +892,13 @@ def expand_args_fields(
continue
expand_args_fields(base, _do_not_process=_do_not_process)
if "_creation_functions" in base.__dict__:
# pyrefly: ignore [missing-attribute]
creation_functions.extend(base._creation_functions)
if "_known_implementations" in base.__dict__:
# pyrefly: ignore [missing-attribute]
known_implementations.update(base._known_implementations)
if "_processed_members" in base.__dict__:
# pyrefly: ignore [missing-attribute]
processed_members.update(base._processed_members)
to_process: List[Tuple[str, Type, _ProcessType]] = []

7
pytorch3d/implicitron/tools/rasterize_mc.py
View File

@@ -62,9 +62,14 @@ def rasterize_sparse_ray_bundle(
max_size = torch.max(camera_counts).item()
features_depth_ras = packed_to_padded(
features_depth_ras[:, 0], first_idxs, max_size
# pyrefly: ignore [bad-argument-type]
features_depth_ras[:, 0],
first_idxs,
# pyrefly: ignore [bad-argument-type]
max_size,
)
if masks is not None:
# pyrefly: ignore [bad-argument-type]
padded_mask = packed_to_padded(masks.flatten(1, -1), first_idxs, max_size)
masks_ras = padded_mask * masks_ras

2
pytorch3d/implicitron/tools/video_writer.py
View File

@@ -124,10 +124,12 @@ class VideoWriter:
if im is not None:
if resize is not None:
if isinstance(resize, float):
# pyrefly: ignore [bad-assignment]
resize = [int(resize * s) for s in im.size]
else:
resize = im.size
# make sure size is divisible by 2
# pyrefly: ignore [bad-assignment, bad-index, unsupported-operation]
resize = tuple([resize[i] + resize[i] % 2 for i in (0, 1)])
im = im.resize(resize, Image.ANTIALIAS)

11
pytorch3d/io/experimental_gltf_io.py
View File

@@ -120,6 +120,7 @@ class _TargetType(IntEnum):
class OurEncoder(json.JSONEncoder):
# pyrefly: ignore [bad-override-param-name]
def default(self, obj):
if isinstance(obj, np.int64):
return str(obj)
@@ -242,6 +243,7 @@ class _GLTFLoader:
by _get_texture_map_image which caches it.
"""
# pyrefly: ignore [unsupported-operation]
image_json = self._json_data["images"][image_index]
buffer_view = self._buffer_views[image_json["bufferView"]]
if "byteStride" in buffer_view:
@@ -407,10 +409,12 @@ class _GLTFLoader:
verts_uvs[:, 1] = 1 - verts_uvs[:, -1]
faces_uvs = indices
material_index = primitive.get("material", 0)
# pyrefly: ignore [unsupported-operation]
material = self._json_data["materials"][material_index]
material_roughness = material["pbrMetallicRoughness"]
if "baseColorTexture" in material_roughness:
texture_index = material_roughness["baseColorTexture"]["index"]
# pyrefly: ignore [unsupported-operation]
texture_json = self._json_data["textures"][texture_index]
# Todo - include baseColorFactor when also given
# Todo - look at the sampler
@@ -555,6 +559,7 @@ class _GLTFWriter:
# pyre-fixme[6]: Incompatible parameter type
self._json_data["scene"] = scene_index
self._json_data["scenes"].append({"nodes": [scene_index]})
# pyrefly: ignore [unsupported-operation]
self._json_data["asset"] = {"version": "2.0"}
node = {"name": "Node", "mesh": 0}
self._json_data["nodes"].append(node)
@@ -621,6 +626,7 @@ class _GLTFWriter:
byte_per_element = 3 * _DTYPE_BYTES[_ITEM_TYPES[_ComponentType.FLOAT]]
elif key == "texcoords":
component_type = _ComponentType.FLOAT
# pyrefly: ignore [missing-attribute]
data = self.mesh.textures.verts_uvs_list()[0].cpu().numpy()
data[:, 1] = 1 - data[:, -1] # flip y tex-coordinate
element_type = "VEC2"
@@ -630,6 +636,7 @@ class _GLTFWriter:
byte_per_element = 2 * _DTYPE_BYTES[_ITEM_TYPES[_ComponentType.FLOAT]]
elif key == "texvertices":
component_type = _ComponentType.FLOAT
# pyrefly: ignore [missing-attribute]
data = self.mesh.textures.verts_features_list()[0].cpu().numpy()
element_type = "VEC3"
buffer_view = 2
@@ -700,11 +707,14 @@ class _GLTFWriter:
target = _TargetType.ELEMENT_ARRAY_BUFFER
bufferview["target"] = target
# pyrefly: ignore [bad-typed-dict-key]
bufferview["byteOffset"] = kwargs.get("offset")
# pyrefly: ignore [bad-typed-dict-key]
bufferview["byteLength"] = kwargs.get("byte_length")
self._json_data["bufferViews"].append(bufferview)
def _write_image_buffer(self, **kwargs) -> Tuple[int, bytes]:
# pyrefly: ignore [missing-attribute]
image_np = self.mesh.textures.maps_list()[0].cpu().numpy()
image_array = (image_np * 255.0).astype(np.uint8)
im = Image.fromarray(image_array)
@@ -716,6 +726,7 @@ class _GLTFWriter:
bufferview_image = {
"buffer": 0,
}
# pyrefly: ignore [bad-typed-dict-key]
bufferview_image["byteOffset"] = kwargs.get("offset")
bufferview_image["byteLength"] = image_data_byte_length
self._json_data["bufferViews"].append(bufferview_image)

6
pytorch3d/io/obj_io.py
View File

@@ -226,6 +226,7 @@ def load_obj(
with _open_file(f, path_manager, "r") as f:
return _load_obj(
f,
# pyrefly: ignore [bad-argument-type]
data_dir=data_dir,
load_textures=load_textures,
create_texture_atlas=create_texture_atlas,
@@ -641,6 +642,7 @@ def _load_obj(
material_names.append(next(iter(material_colors.keys())))
# replace all -1 by 0 material idx
if torch.is_tensor(faces_materials_idx):
# pyrefly: ignore [missing-attribute]
faces_materials_idx.clamp_(min=0)
if create_texture_atlas:
@@ -649,14 +651,18 @@ def _load_obj(
# Create an array of strings of material names for each face.
# If faces_materials_idx == -1 then that face doesn't have a material.
# pyrefly: ignore [missing-attribute]
idx = faces_materials_idx.cpu().numpy()
face_material_names = np.array([""] + material_names)[idx + 1] # (F,)
# Construct the atlas.
texture_atlas = make_mesh_texture_atlas(
# pyrefly: ignore [bad-argument-type]
material_colors,
# pyrefly: ignore [bad-argument-type]
texture_images,
face_material_names,
# pyrefly: ignore [bad-argument-type]
faces_textures_idx,
verts_uvs,
texture_atlas_size,

4
pytorch3d/io/ply_io.py
View File

@@ -878,10 +878,14 @@ def _get_verts_column_indices(
):
color_scale = 1.0 / 255
return _VertsColumnIndices(
# pyrefly: ignore [bad-argument-type]
point_idxs=point_idxs,
# pyrefly: ignore [bad-argument-type]
color_idxs=None if None in color_idxs else color_idxs,
color_scale=color_scale,
# pyrefly: ignore [bad-argument-type]
normal_idxs=None if None in normal_idxs else normal_idxs,
# pyrefly: ignore [bad-argument-type]
texture_uv_idxs=None if None in texture_uv_idxs else texture_uv_idxs,
)

4
pytorch3d/loss/point_mesh_distance.py
View File

@@ -81,6 +81,7 @@ class _PointFaceDistance(Function):
@staticmethod
@once_differentiable
# pyrefly: ignore [bad-override]
def backward(ctx, grad_dists):
grad_dists = grad_dists.contiguous()
points, tris, idxs = ctx.saved_tensors
@@ -143,6 +144,7 @@ class _FacePointDistance(Function):
@staticmethod
@once_differentiable
# pyrefly: ignore [bad-override]
def backward(ctx, grad_dists):
grad_dists = grad_dists.contiguous()
points, tris, idxs = ctx.saved_tensors
@@ -194,6 +196,7 @@ class _PointEdgeDistance(Function):
@staticmethod
@once_differentiable
# pyrefly: ignore [bad-override]
def backward(ctx, grad_dists):
grad_dists = grad_dists.contiguous()
points, segms, idxs = ctx.saved_tensors
@@ -244,6 +247,7 @@ class _EdgePointDistance(Function):
@staticmethod
@once_differentiable
# pyrefly: ignore [bad-override]
def backward(ctx, grad_dists):
grad_dists = grad_dists.contiguous()
points, segms, idxs = ctx.saved_tensors

1
pytorch3d/ops/ball_query.py
View File

@@ -36,6 +36,7 @@ class _ball_query(Function):
@staticmethod
@once_differentiable
# pyrefly: ignore [bad-override]
def backward(ctx, grad_dists, grad_idx):
p1, p2, lengths1, lengths2, idx = ctx.saved_tensors
# TODO(gkioxari) Change cast to floats once we add support for doubles.

1
pytorch3d/ops/graph_conv.py
View File

@@ -162,6 +162,7 @@ class GatherScatter(Function):
@staticmethod
@once_differentiable
# pyrefly: ignore [bad-override]
def backward(ctx, grad_output):
grad_output = grad_output.contiguous()
edges = ctx.saved_tensors[0]

1
pytorch3d/ops/interp_face_attrs.py
View File

@@ -72,6 +72,7 @@ class _InterpFaceAttrs(Function):
@staticmethod
@once_differentiable
# pyrefly: ignore [bad-override]
def backward(ctx, grad_pix_attrs):
args = ctx.saved_tensors
args = args + (grad_pix_attrs,)

1
pytorch3d/ops/iou_box3d.py
View File

@@ -106,6 +106,7 @@ class _box3d_overlap(Function):
return vol, iou
@staticmethod
# pyrefly: ignore [bad-override]
def backward(ctx, grad_vol, grad_iou):
raise ValueError("box3d_overlap backward is not supported")

1
pytorch3d/ops/knn.py
View File

@@ -95,6 +95,7 @@ class _knn_points(Function):
@staticmethod
@once_differentiable
# pyrefly: ignore [bad-override]
def backward(ctx, grad_dists, grad_idx):
p1, p2, lengths1, lengths2, idx = ctx.saved_tensors
norm = ctx.norm

1
pytorch3d/ops/marching_cubes.py
View File

@@ -247,6 +247,7 @@ class _marching_cubes(Function):
return verts, faces, ids
@staticmethod
# pyrefly: ignore [bad-override]
def backward(ctx, grad_verts, grad_faces):
raise ValueError("marching_cubes backward is not supported")

1
pytorch3d/ops/mesh_face_areas_normals.py
View File

@@ -50,6 +50,7 @@ class _MeshFaceAreasNormals(Function):
@staticmethod
@once_differentiable
# pyrefly: ignore [bad-override]
def backward(ctx, grad_areas, grad_normals):
grad_areas = grad_areas.contiguous()
grad_normals = grad_normals.contiguous()

2
pytorch3d/ops/packed_to_padded.py
View File

@@ -54,6 +54,7 @@ class _PackedToPadded(Function):
@staticmethod
@once_differentiable
# pyrefly: ignore [bad-override]
def backward(ctx, grad_output):
grad_output = grad_output.contiguous()
first_idxs = ctx.saved_tensors[0]
@@ -143,6 +144,7 @@ class _PaddedToPacked(Function):
@staticmethod
@once_differentiable
# pyrefly: ignore [bad-override]
def backward(ctx, grad_output):
grad_output = grad_output.contiguous()
first_idxs = ctx.saved_tensors[0]

27
pytorch3d/ops/points_alignment.py
View File

@@ -103,14 +103,17 @@ def iterative_closest_point(
Xt, num_points_X = oputil.convert_pointclouds_to_tensor(X)
Yt, num_points_Y = oputil.convert_pointclouds_to_tensor(Y)
# pyrefly: ignore [missing-attribute]
b, size_X, dim = Xt.shape
# pyrefly: ignore [missing-attribute]
if (Xt.shape[2] != Yt.shape[2]) or (Xt.shape[0] != Yt.shape[0]):
raise ValueError(
"Point sets X and Y have to have the same "
+ "number of batches and data dimensions."
)
# pyrefly: ignore [missing-attribute]
if ((num_points_Y < Yt.shape[1]).any() or (num_points_X < Xt.shape[1]).any()) and (
num_points_Y != num_points_X
).any():
@@ -121,6 +124,7 @@ def iterative_closest_point(
< num_points_X[:, None]
).type_as(Xt)
else:
# pyrefly: ignore [missing-attribute]
mask_X = Xt.new_ones(b, size_X)
# clone the initial point cloud
@@ -145,11 +149,15 @@ def iterative_closest_point(
"of scalars of shape (minibatch,)."
) from None
# apply the init transform to the input point cloud
# pyrefly: ignore [bad-argument-type]
Xt = _apply_similarity_transform(Xt, R, T, s)
else:
# initialize the transformation with identity
# pyrefly: ignore [missing-attribute]
R = oputil.eyes(dim, b, device=Xt.device, dtype=Xt.dtype)
# pyrefly: ignore [missing-attribute]
T = Xt.new_zeros((b, dim))
# pyrefly: ignore [missing-attribute]
s = Xt.new_ones(b)
prev_rmse = None
@@ -163,7 +171,14 @@ def iterative_closest_point(
# the main loop over ICP iterations
for iteration in range(max_iterations):
Xt_nn_points = knn_points(
Xt, Yt, lengths1=num_points_X, lengths2=num_points_Y, K=1, return_nn=True
# pyrefly: ignore [bad-argument-type]
Xt,
# pyrefly: ignore [bad-argument-type]
Yt,
lengths1=num_points_X,
lengths2=num_points_Y,
K=1,
return_nn=True,
).knn[:, :, 0, :]
# get the alignment of the nearest neighbors from Yt with Xt_init
@@ -216,6 +231,7 @@ def iterative_closest_point(
if oputil.is_pointclouds(X):
Xt = X.update_padded(Xt) # type: ignore
# pyrefly: ignore [bad-argument-type]
return ICPSolution(converged, rmse, Xt, SimilarityTransform(R, T, s), t_history)
@@ -276,6 +292,7 @@ def corresponding_points_alignment(
Xt, num_points = oputil.convert_pointclouds_to_tensor(X)
Yt, num_points_Y = oputil.convert_pointclouds_to_tensor(Y)
# pyrefly: ignore [missing-attribute]
if (Xt.shape != Yt.shape) or (num_points != num_points_Y).any():
raise ValueError(
"Point sets X and Y have to have the same \
@@ -291,25 +308,33 @@ def corresponding_points_alignment(
weights = [w[..., None] for w in weights]
weights = strutil.list_to_padded(weights)[..., 0]
# pyrefly: ignore [bad-index]
if Xt.shape[:2] != weights.shape:
raise ValueError("weights should have the same first two dimensions as X.")
# pyrefly: ignore [not-iterable]
b, n, dim = Xt.shape
# pyrefly: ignore [bad-index, missing-attribute]
if (num_points < Xt.shape[1]).any() or (num_points < Yt.shape[1]).any():
# in case we got Pointclouds as input, mask the unused entries in Xc, Yc
mask = (
torch.arange(n, dtype=torch.int64, device=Xt.device)[None]
< num_points[:, None]
).type_as(Xt)
# pyrefly: ignore [bad-argument-type]
weights = mask if weights is None else mask * weights.type_as(Xt)
# compute the centroids of the point sets
# pyrefly: ignore [bad-argument-type]
Xmu = oputil.wmean(Xt, weight=weights, eps=eps)
# pyrefly: ignore [bad-argument-type]
Ymu = oputil.wmean(Yt, weight=weights, eps=eps)
# mean-center the point sets
# pyrefly: ignore [unsupported-operation]
Xc = Xt - Xmu
# pyrefly: ignore [unsupported-operation]
Yc = Yt - Ymu
total_weight = torch.clamp(num_points, 1)

15
pytorch3d/ops/points_normals.py
View File

@@ -119,6 +119,7 @@ def estimate_pointcloud_local_coord_frames(
points_padded, num_points = convert_pointclouds_to_tensor(pointclouds)
# pyrefly: ignore [missing-attribute]
ba, N, dim = points_padded.shape
if dim != 3:
raise ValueError(
@@ -133,6 +134,7 @@ def estimate_pointcloud_local_coord_frames(
# undo global mean for stability
# TODO: replace with tutil.wmean once landed
# pyrefly: ignore [missing-attribute]
pcl_mean = points_padded.sum(1) / num_points[:, None]
points_centered = points_padded - pcl_mean[:, None, :]
@@ -154,17 +156,26 @@ def estimate_pointcloud_local_coord_frames(
if disambiguate_directions:
# disambiguate normal
n = _disambiguate_vector_directions(
points_centered, knns, local_coord_frames[:, :, :, 0]
# pyrefly: ignore [unsupported-operation]
points_centered,
knns,
# pyrefly: ignore [unsupported-operation]
local_coord_frames[:, :, :, 0],
)
# disambiguate the main curvature
z = _disambiguate_vector_directions(
points_centered, knns, local_coord_frames[:, :, :, 2]
# pyrefly: ignore [unsupported-operation]
points_centered,
knns,
# pyrefly: ignore [unsupported-operation]
local_coord_frames[:, :, :, 2],
)
# the secondary curvature is just a cross between n and z
y = torch.cross(n, z, dim=2)
# cat to form the set of principal directions
local_coord_frames = torch.stack((n, y, z), dim=3)
# pyrefly: ignore [bad-return]
return curvatures, local_coord_frames

2
pytorch3d/ops/points_to_volumes.py
View File

@@ -141,6 +141,7 @@ class _points_to_volumes_function(Function):
@staticmethod
@once_differentiable
# pyrefly: ignore [bad-override]
def backward(ctx, grad_volume_densities, grad_volume_features):
splat = ctx.splat
N, C = grad_volume_features.shape[:2]
@@ -377,6 +378,7 @@ def add_points_features_to_volume_densities_features(
if grid_sizes is None:
# grid sizes shape (minibatch, 3)
grid_sizes = (
# pyrefly: ignore [bad-assignment]
torch.LongTensor(list(volume_densities.shape[2:]))
.to(volume_densities.device)
.expand(volume_densities.shape[0], 3)

1
pytorch3d/ops/sample_farthest_points.py
View File

@@ -141,6 +141,7 @@ def sample_farthest_points_naive(
for n in range(N):
# Initialize an array for the sampled indices, shape: (max_K,)
# pyrefly: ignore [no-matching-overload]
sample_idx_batch = torch.full(
# pyre-fixme[6]: For 1st param expected `Union[List[int], Size,
# typing.Tuple[int, ...]]` but got `Tuple[Tensor]`.

1
pytorch3d/ops/utils.py
View File

@@ -143,6 +143,7 @@ def convert_pointclouds_to_tensor(pcl: Union[torch.Tensor, "Pointclouds"]):
elif torch.is_tensor(pcl):
X = pcl
num_points = X.shape[1] * torch.ones( # type: ignore
# pyrefly: ignore [missing-attribute]
X.shape[0],
device=X.device,
dtype=torch.int64,

3
pytorch3d/renderer/blending.py
View File

@@ -101,6 +101,7 @@ class _SigmoidAlphaBlend(torch.autograd.Function):
return alphas
@staticmethod
# pyrefly: ignore [bad-override]
def backward(ctx, grad_alphas):
dists, pix_to_face, alphas = ctx.saved_tensors
sigma = ctx.sigma
@@ -212,8 +213,10 @@ def softmax_rgb_blend(
# Reshape to be compatible with (N, H, W, K) values in fragments
if torch.is_tensor(zfar):
# pyrefly: ignore [bad-index]
zfar = zfar[:, None, None, None]
if torch.is_tensor(znear):
# pyrefly: ignore [bad-index]
znear = znear[:, None, None, None]
# pyre-fixme[6]: Expected `float` but got `Union[float, Tensor]`

1
pytorch3d/renderer/camera_conversions.py
View File

@@ -86,6 +86,7 @@ def _opencv_from_cameras_projection(
scale = scale.expand(-1, 2)
c0 = image_size_wh / 2.0
# pyrefly: ignore [unsupported-operation]
principal_point = -p0_pytorch3d * scale + c0
focal_length = focal_pytorch3d * scale

2
pytorch3d/renderer/camera_utils.py
View File

@@ -202,8 +202,10 @@ def join_cameras_as_batch(cameras_list: Sequence[CamerasBase]) -> CamerasBase:
# In the init, all inputs will be converted to
# batched tensors before set as attributes
# Join as a tensor along the batch dimension
# pyrefly: ignore [unsupported-operation]
kwargs[field] = torch.cat(attrs_list, dim=0)
else:
raise ValueError(f"Field {field} type is not supported for batching")
# pyrefly: ignore [bad-argument-type]
return c0.__class__(**kwargs)

15
pytorch3d/renderer/cameras.py
View File

@@ -362,6 +362,7 @@ class CamerasBase(TensorProperties):
self, with_xyflip=with_xyflip, image_size=image_size
).transform_points(points_ndc, eps=eps)
# pyrefly: ignore [bad-override]
def clone(self):
"""
Returns a copy of `self`.
@@ -390,6 +391,7 @@ class CamerasBase(TensorProperties):
"""
return getattr(self, "image_size", None)
# pyrefly: ignore [bad-override]
def __getitem__(
self, index: Union[int, List[int], torch.BoolTensor, torch.LongTensor]
) -> "CamerasBase":
@@ -455,11 +457,14 @@ class CamerasBase(TensorProperties):
elif isinstance(val, torch.Tensor):
# In the init, all inputs will be converted to
# tensors before setting as attributes
# pyrefly: ignore [unsupported-operation]
kwargs[field] = val[index]
else:
raise ValueError(f"Field {field} type is not supported for indexing")
# pyrefly: ignore [unsupported-operation]
kwargs["device"] = self.device
# pyrefly: ignore [bad-argument-type]
return self.__class__(**kwargs)
@@ -1741,7 +1746,14 @@ def look_at_view_transform(
dist, elev, azim, at, up = broadcasted_args
C = (
camera_position_from_spherical_angles(
dist, elev, azim, degrees=degrees, device=device
# pyrefly: ignore [bad-argument-type]
dist,
# pyrefly: ignore [bad-argument-type]
elev,
# pyrefly: ignore [bad-argument-type]
azim,
degrees=degrees,
device=device,
)
+ at
)
@@ -1787,6 +1799,7 @@ def get_ndc_to_screen_transform(
K = torch.zeros((cameras._N, 4, 4), device=cameras.device, dtype=torch.float32)
if not torch.is_tensor(image_size):
image_size = torch.tensor(image_size, device=cameras.device)
# pyrefly: ignore [missing-attribute]
image_size = image_size.view(-1, 2) # of shape (1 or B)x2
height, width = image_size.unbind(1)

3
pytorch3d/renderer/compositing.py
View File

@@ -51,6 +51,7 @@ class _CompositeAlphaPoints(torch.autograd.Function):
return pt_cld
@staticmethod
# pyrefly: ignore [bad-override]
def backward(ctx, grad_output):
grad_features = None
grad_alphas = None
@@ -130,6 +131,7 @@ class _CompositeNormWeightedSumPoints(torch.autograd.Function):
return pt_cld
@staticmethod
# pyrefly: ignore [bad-override]
def backward(ctx, grad_output):
grad_features = None
grad_alphas = None
@@ -208,6 +210,7 @@ class _CompositeWeightedSumPoints(torch.autograd.Function):
return pt_cld
@staticmethod
# pyrefly: ignore [bad-override]
def backward(ctx, grad_output):
grad_features = None
grad_alphas = None

1
pytorch3d/renderer/fisheyecameras.py
View File

@@ -124,6 +124,7 @@ class FishEyeCameras(CamerasBase):
else:
self.image_size = None
# pyrefly: ignore [bad-assignment]
self.device = device
self.focal = focal_length.to(self.device)
self.principal_point = principal_point.to(self.device)

9
pytorch3d/renderer/implicit/harmonic_embedding.py
View File

@@ -136,13 +136,16 @@ class HarmonicEmbedding(torch.nn.Module):
[..., (n_harmonic_functions * 2 + int(append_input)) * num_points_per_ray]
"""
# [..., dim, n_harmonic_functions]
# pyrefly: ignore [unsupported-operation]
embed = x[..., None] * self._frequencies
# [..., 1, dim, n_harmonic_functions] + [2, 1, 1] => [..., 2, dim, n_harmonic_functions]
# pyrefly: ignore [bad-index]
embed = embed[..., None, :, :] + self._zero_half_pi[..., None, None]
# Use the trig identity cos(x) = sin(x + pi/2)
# and do one vectorized call to sin([x, x+pi/2]) instead of (sin(x), cos(x)).
embed = embed.sin()
if diag_cov is not None:
# pyrefly: ignore [no-matching-overload]
x_var = diag_cov[..., None] * torch.pow(self._frequencies, 2)
exp_var = torch.exp(-0.5 * x_var)
# [..., 2, dim, n_harmonic_functions]
@@ -180,5 +183,9 @@ class HarmonicEmbedding(torch.nn.Module):
so the input might be xyz.
"""
return self.get_output_dim_static(
input_dims, len(self._frequencies), self.append_input
# pyrefly: ignore [bad-argument-type]
input_dims,
# pyrefly: ignore [bad-argument-type]
len(self._frequencies),
self.append_input,
)

1
pytorch3d/renderer/implicit/raymarching.py
View File

@@ -226,6 +226,7 @@ def _check_raymarcher_inputs(
if not z_can_be_none and rays_z.shape != rays_shape:
raise ValueError("rays_z have to be of the same shape as rays_densities.")
# pyrefly: ignore [missing-attribute]
if not features_can_be_none and rays_features.shape[:-1] != rays_shape:
raise ValueError(
"The first to previous to last dimensions of rays_features"

3
pytorch3d/renderer/implicit/raysampling.py
View File

@@ -197,6 +197,7 @@ class MultinomialRaysampler(torch.nn.Module):
"`n_rays_total` and `n_rays_per_image` cannot both be defined."
)
if n_rays_total:
# pyrefly: ignore [bad-assignment]
(
cameras,
mask,
@@ -221,6 +222,7 @@ class MultinomialRaysampler(torch.nn.Module):
if mask is not None and n_rays_per_image is None:
# if num rays not given, sample according to the smallest mask
n_rays_per_image = (
# pyrefly: ignore [bad-assignment]
n_rays_per_image or mask.sum(dim=(1, 2)).min().int().item()
)
@@ -453,6 +455,7 @@ class MonteCarloRaysampler(torch.nn.Module):
# of shape (batch_size, n_rays_per_image, 2)
rays_xy = torch.cat(
[
# pyrefly: ignore [no-matching-overload]
torch.rand(
size=(batch_size, n_rays_per_image, 1),
dtype=torch.float32,

7
pytorch3d/renderer/lighting.py
View File

@@ -190,10 +190,12 @@ class DirectionalLights(TensorProperties):
direction=direction,
)
_validate_light_properties(self)
# pyrefly: ignore [bad-index]
if self.direction.shape[-1] != 3:
msg = "Expected direction to have shape (N, 3); got %r"
raise ValueError(msg % repr(self.direction.shape))
# pyrefly: ignore [bad-override]
def clone(self):
other = self.__class__(device=self.device)
return super().clone(other)
@@ -251,10 +253,12 @@ class PointLights(TensorProperties):
location=location,
)
_validate_light_properties(self)
# pyrefly: ignore [bad-index]
if self.location.shape[-1] != 3:
msg = "Expected location to have shape (N, 3); got %r"
raise ValueError(msg % repr(self.location.shape))
# pyrefly: ignore [bad-override]
def clone(self):
other = self.__class__(device=self.device)
return super().clone(other)
@@ -319,6 +323,7 @@ class AmbientLights(TensorProperties):
ambient_color = ((1.0, 1.0, 1.0),)
super().__init__(ambient_color=ambient_color, device=device)
# pyrefly: ignore [bad-override]
def clone(self):
other = self.__class__(device=self.device)
return super().clone(other)
@@ -330,7 +335,9 @@ class AmbientLights(TensorProperties):
return self._zeros_channels(points)
def _zeros_channels(self, points: torch.Tensor) -> torch.Tensor:
# pyrefly: ignore [bad-index]
ch = self.ambient_color.shape[-1]
# pyrefly: ignore [no-matching-overload]
return torch.zeros(*points.shape[:-1], ch, device=points.device)

2
pytorch3d/renderer/materials.py
View File

@@ -52,6 +52,7 @@ class Materials(TensorProperties):
specular_color=specular_color,
shininess=shininess,
)
# pyrefly: ignore [bad-index]
C = self.ambient_color.shape[-1]
for n in ["ambient_color", "diffuse_color", "specular_color"]:
t = getattr(self, n)
@@ -62,6 +63,7 @@ class Materials(TensorProperties):
msg = "shininess should have shape (N); got %r"
raise ValueError(msg % repr(self.shininess.shape))
# pyrefly: ignore [bad-override]
def clone(self):
other = Materials(device=self.device)
return super().clone(other)

16
pytorch3d/renderer/mesh/clip.py
View File

@@ -496,7 +496,12 @@ def clip_faces(
# Solve for the points p4, p5 that intersect the clipping plane
p, p_barycentric = _find_verts_intersecting_clipping_plane(
faces_case3, p1_face_ind, z_clip_value, perspective_correct
# pyrefly: ignore [bad-argument-type]
faces_case3,
p1_face_ind,
# pyrefly: ignore [bad-argument-type]
z_clip_value,
perspective_correct,
)
p1, _, _, p4, p5 = p
@@ -540,7 +545,12 @@ def clip_faces(
# Solve for the points p4, p5 that intersect the clipping plane
p, p_barycentric = _find_verts_intersecting_clipping_plane(
faces_case4, p1_face_ind, z_clip_value, perspective_correct
# pyrefly: ignore [bad-argument-type]
faces_case4,
p1_face_ind,
# pyrefly: ignore [bad-argument-type]
z_clip_value,
perspective_correct,
)
_, p2, p3, p4, p5 = p
_, p2_barycentric, p3_barycentric, p4_barycentric, p5_barycentric = p_barycentric
@@ -682,6 +692,7 @@ def convert_clipped_rasterization_to_original_faces(
# rasterized pixel.
pix_to_conversion_idx = torch.where(
pix_to_face_clipped != -1,
# pyrefly: ignore [unsupported-operation]
faces_clipped_to_conversion_idx[pix_to_face_clipped],
empty,
)
@@ -709,6 +720,7 @@ def convert_clipped_rasterization_to_original_faces(
bary_coords_clipped_subset
)
# pyrefly: ignore [no-matching-overload]
bary_coords_unclipped_subset = bary_coords_unclipped_subset.reshape([N * 3])
bary_coords_unclipped[faces_to_convert_mask_expanded] = (
bary_coords_unclipped_subset

1
pytorch3d/renderer/mesh/rasterize_meshes.py
View File

@@ -316,6 +316,7 @@ class _RasterizeFaceVerts(torch.autograd.Function):
return pix_to_face, zbuf, barycentric_coords, dists
@staticmethod
# pyrefly: ignore [bad-override]
def backward(ctx, grad_pix_to_face, grad_zbuf, grad_barycentric_coords, grad_dists):
grad_face_verts = None
grad_mesh_to_face_first_idx = None

3
pytorch3d/renderer/mesh/rasterizer.py
View File

@@ -161,6 +161,7 @@ class MeshRasterizer(nn.Module):
self.cameras = cameras
self.raster_settings = raster_settings
# pyrefly: ignore [bad-override]
def to(self, device):
# Manually move to device cameras as it is not a subclass of nn.Module
if self.cameras is not None:
@@ -238,10 +239,12 @@ class MeshRasterizer(nn.Module):
if raster_settings.perspective_correct is not None:
perspective_correct = raster_settings.perspective_correct
else:
# pyrefly: ignore [missing-attribute]
perspective_correct = cameras.is_perspective()
if raster_settings.z_clip_value is not None:
z_clip = raster_settings.z_clip_value
else:
# pyrefly: ignore [missing-attribute]
znear = cameras.get_znear()
if isinstance(znear, torch.Tensor):
znear = znear.min().item()

2
pytorch3d/renderer/mesh/renderer.py
View File

@@ -41,6 +41,7 @@ class MeshRenderer(nn.Module):
self.rasterizer = rasterizer
self.shader = shader
# pyrefly: ignore [bad-override]
def to(self, device):
# Rasterizer and shader have submodules which are not of type nn.Module
self.rasterizer.to(device)
@@ -85,6 +86,7 @@ class MeshRendererWithFragments(nn.Module):
self.rasterizer = rasterizer
self.shader = shader
# pyrefly: ignore [bad-override]
def to(self, device):
# Rasterizer and shader have submodules which are not of type nn.Module
self.rasterizer.to(device)

1
pytorch3d/renderer/mesh/shader.py
View File

@@ -72,6 +72,7 @@ class ShaderBase(nn.Module):
cameras = self.cameras
if cameras is not None:
self.cameras = cameras.to(device)
# pyrefly: ignore [bad-assignment]
self.materials = self.materials.to(device)
self.lights = self.lights.to(device)
return self

34
pytorch3d/renderer/mesh/textures.py
View File

@@ -261,6 +261,7 @@ class TexturesBase:
f"Property {p} has unsupported type {type(t)}."
"Only tensors and lists are supported."
)
# pyrefly: ignore [bad-return]
return new_props
def _getitem(self, index: Union[int, slice], props: List[str]):
@@ -275,6 +276,7 @@ class TexturesBase:
t = t() # class method
new_props[p] = t[index] if t is not None else None
elif isinstance(index, list):
# pyrefly: ignore [bad-assignment]
index = torch.tensor(index)
if isinstance(index, torch.Tensor):
if index.dtype == torch.bool:
@@ -451,6 +453,7 @@ class TexturesAtlas(TexturesBase):
msg = "Expected atlas to be of shape (N, F, R, R, C); got %r"
raise ValueError(msg % repr(atlas.ndim))
self._atlas_padded = atlas
# pyrefly: ignore [bad-assignment]
self._atlas_list = None
self.device = atlas.device
@@ -474,6 +477,7 @@ class TexturesAtlas(TexturesBase):
if self._atlas_list is not None:
tex._atlas_list = [atlas.clone() for atlas in self._atlas_list]
num_faces = (
# pyrefly: ignore [missing-attribute]
self._num_faces_per_mesh.clone()
if torch.is_tensor(self._num_faces_per_mesh)
else self._num_faces_per_mesh
@@ -487,6 +491,7 @@ class TexturesAtlas(TexturesBase):
if self._atlas_list is not None:
tex._atlas_list = [atlas.detach() for atlas in self._atlas_list]
num_faces = (
# pyrefly: ignore [missing-attribute]
self._num_faces_per_mesh.detach()
if torch.is_tensor(self._num_faces_per_mesh)
else self._num_faces_per_mesh
@@ -504,9 +509,11 @@ class TexturesAtlas(TexturesBase):
new_tex = self.__class__(atlas=atlas)
elif torch.is_tensor(atlas):
# single element
# pyrefly: ignore [bad-argument-type]
new_tex = self.__class__(atlas=[atlas])
else:
raise ValueError("Not all values are provided in the correct format")
# pyrefly: ignore [bad-assignment]
new_tex._num_faces_per_mesh = new_props["_num_faces_per_mesh"]
return new_tex
@@ -528,6 +535,7 @@ class TexturesAtlas(TexturesBase):
self._atlas_padded = [
torch.empty((0, 0, 0, 3), dtype=torch.float32, device=self.device)
] * self._N
# pyrefly: ignore [bad-assignment]
self._atlas_list = _padded_to_list_wrapper(
self._atlas_padded, split_size=self._num_faces_per_mesh
)
@@ -544,6 +552,7 @@ class TexturesAtlas(TexturesBase):
def extend(self, N: int) -> "TexturesAtlas":
new_props = self._extend(N, ["atlas_padded", "_num_faces_per_mesh"])
new_tex = self.__class__(atlas=new_props["atlas_padded"])
# pyrefly: ignore [bad-assignment]
new_tex._num_faces_per_mesh = new_props["_num_faces_per_mesh"]
return new_tex
@@ -790,6 +799,7 @@ class TexturesUV(TexturesBase):
msg = "Expected faces_uvs to be of shape (N, F, 3); got %r"
raise ValueError(msg % repr(faces_uvs.shape))
self._faces_uvs_padded = faces_uvs
# pyrefly: ignore [bad-assignment]
self._faces_uvs_list = None
self.device = faces_uvs.device
@@ -826,6 +836,7 @@ class TexturesUV(TexturesBase):
msg = "Expected verts_uvs to be of shape (N, V, 2); got %r"
raise ValueError(msg % repr(verts_uvs.shape))
self._verts_uvs_padded = verts_uvs
# pyrefly: ignore [bad-assignment]
self._verts_uvs_list = None
if verts_uvs.device != self.device:
@@ -838,6 +849,7 @@ class TexturesUV(TexturesBase):
if isinstance(maps, (list, tuple)):
self._maps_list = maps
else:
# pyrefly: ignore [bad-assignment]
self._maps_list = None
self._maps_padded = self._format_maps_padded(maps)
@@ -966,6 +978,7 @@ class TexturesUV(TexturesBase):
if self._maps_ids_list is not None:
tex._maps_ids_list = [f.clone() for f in self._maps_ids_list]
num_faces = (
# pyrefly: ignore [missing-attribute]
self._num_faces_per_mesh.clone()
if torch.is_tensor(self._num_faces_per_mesh)
else self._num_faces_per_mesh
@@ -997,6 +1010,7 @@ class TexturesUV(TexturesBase):
if self._maps_ids_list is not None:
tex._maps_ids_list = [mi.detach() for mi in self._maps_ids_list]
num_faces = (
# pyrefly: ignore [missing-attribute]
self._num_faces_per_mesh.detach()
if torch.is_tensor(self._num_faces_per_mesh)
else self._num_faces_per_mesh
@@ -1026,8 +1040,11 @@ class TexturesUV(TexturesBase):
"Maps ids are not in the correct format expected list or tuple"
)
new_tex = self.__class__(
# pyrefly: ignore [bad-argument-type]
faces_uvs=faces_uvs,
# pyrefly: ignore [bad-argument-type]
verts_uvs=verts_uvs,
# pyrefly: ignore [bad-argument-type]
maps=maps,
maps_ids=maps_ids,
padding_mode=self.padding_mode,
@@ -1040,8 +1057,11 @@ class TexturesUV(TexturesBase):
"Maps ids are not in the correct format expected tensor"
)
new_tex = self.__class__(
# pyrefly: ignore [bad-argument-type]
faces_uvs=[faces_uvs],
# pyrefly: ignore [bad-argument-type]
verts_uvs=[verts_uvs],
# pyrefly: ignore [bad-argument-type]
maps=[maps],
maps_ids=[maps_ids] if maps_ids is not None else None,
padding_mode=self.padding_mode,
@@ -1050,6 +1070,7 @@ class TexturesUV(TexturesBase):
)
else:
raise ValueError("Not all values are provided in the correct format")
# pyrefly: ignore [bad-assignment]
new_tex._num_faces_per_mesh = new_props["_num_faces_per_mesh"]
return new_tex
@@ -1072,9 +1093,11 @@ class TexturesUV(TexturesBase):
torch.empty((0, 3), dtype=torch.float32, device=self.device)
] * self._N
else:
# pyrefly: ignore [bad-assignment]
self._faces_uvs_list = padded_to_list(
self._faces_uvs_padded, split_size=self._num_faces_per_mesh
)
# pyrefly: ignore [bad-return]
return self._faces_uvs_list
def verts_uvs_padded(self) -> torch.Tensor:
@@ -1099,7 +1122,9 @@ class TexturesUV(TexturesBase):
# The number of vertices in the mesh and in verts_uvs can differ
# e.g. if a vertex is shared between 3 faces, it can
# have up to 3 different uv coordinates.
# pyrefly: ignore [bad-assignment]
self._verts_uvs_list = list(self._verts_uvs_padded.unbind(0))
# pyrefly: ignore [bad-return]
return self._verts_uvs_list
def maps_ids_padded(self) -> Optional[torch.Tensor]:
@@ -1107,8 +1132,10 @@ class TexturesUV(TexturesBase):
def maps_ids_list(self) -> Optional[List[torch.Tensor]]:
if self._maps_ids_list is not None:
# pyrefly: ignore [bad-return]
return self._maps_ids_list
elif self._maps_ids_padded is not None:
# pyrefly: ignore [bad-return]
return self._maps_ids_padded.unbind(0)
else:
return None
@@ -1143,6 +1170,7 @@ class TexturesUV(TexturesBase):
sampling_mode=self.sampling_mode,
)
# pyrefly: ignore [bad-assignment]
new_tex._num_faces_per_mesh = new_props["_num_faces_per_mesh"]
return new_tex
@@ -1716,6 +1744,7 @@ class TexturesVertex(TexturesBase):
msg = "Expected verts_features to be of shape (N, V, C); got %r"
raise ValueError(msg % repr(verts_features.shape))
self._verts_features_padded = verts_features
# pyrefly: ignore [bad-assignment]
self._verts_features_list = None
self.device = verts_features.device
@@ -1763,9 +1792,11 @@ class TexturesVertex(TexturesBase):
)
new_tex = self.__class__(verts_features=verts_features)
elif torch.is_tensor(verts_features):
# pyrefly: ignore [bad-argument-type]
new_tex = self.__class__(verts_features=[verts_features])
else:
raise ValueError("Not all values are provided in the correct format")
# pyrefly: ignore [bad-assignment]
new_tex._num_verts_per_mesh = new_props["_num_verts_per_mesh"]
return new_tex
@@ -1788,9 +1819,11 @@ class TexturesVertex(TexturesBase):
torch.empty((0, 3), dtype=torch.float32, device=self.device)
] * self._N
else:
# pyrefly: ignore [bad-assignment]
self._verts_features_list = padded_to_list(
self._verts_features_padded, split_size=self._num_verts_per_mesh
)
# pyrefly: ignore [bad-return]
return self._verts_features_list
def verts_features_packed(self) -> torch.Tensor:
@@ -1802,6 +1835,7 @@ class TexturesVertex(TexturesBase):
def extend(self, N: int) -> "TexturesVertex":
new_props = self._extend(N, ["verts_features_padded", "_num_verts_per_mesh"])
new_tex = self.__class__(verts_features=new_props["verts_features_padded"])
# pyrefly: ignore [bad-assignment]
new_tex._num_verts_per_mesh = new_props["_num_verts_per_mesh"]
return new_tex

1
pytorch3d/renderer/opengl/opengl_utils.py
View File

@@ -114,6 +114,7 @@ def _get_cuda_device(requested_device_id: int):
# Iterate over all the EGL devices, and check if their CUDA ID matches the request.
for device in devices:
# pyrefly: ignore [bad-argument-type]
available_device_id = egl.EGLAttrib(ctypes.c_int(-1))
# pyre-ignore Undefined attribute [16]
egl.eglQueryDeviceAttribEXT(device, EGL_CUDA_DEVICE_NV, available_device_id)

7
pytorch3d/renderer/opengl/rasterizer_opengl.py
View File

@@ -213,6 +213,7 @@ class MeshRasterizerOpenGL(nn.Module):
dists=None,
).detach()
# pyrefly: ignore [bad-override]
def to(self, device):
# Manually move to device cameras as it is not a subclass of nn.Module
if self.cameras is not None:
@@ -276,6 +277,7 @@ class _OpenGLMachinery:
"""
self.initialize_device_data(meshes_gl_ndc.device)
# pyrefly: ignore [missing-attribute]
with self.egl_context.active_and_locked():
# Perspective projection happens in OpenGL. Move the matrix over if there's only
# a single camera shared by all the meshes.
@@ -370,11 +372,15 @@ class _OpenGLMachinery:
"""
# Finish all current operations.
torch.cuda.synchronize()
# pyrefly: ignore [missing-attribute]
self.cuda_context.synchronize()
# Free pycuda resources.
# pyrefly: ignore [missing-attribute]
self.cuda_context.push()
# pyrefly: ignore [missing-attribute]
self.cuda_buffer.unregister()
# pyrefly: ignore [missing-attribute]
self.cuda_context.pop()
# Free GL resources.
@@ -391,6 +397,7 @@ class _OpenGLMachinery:
del self.mesh_buffer_object
gl.glDeleteProgram(self.program)
# pyrefly: ignore [missing-attribute]
self.egl_context.release()
def _projection_matrix_to_opengl(self, projection_matrix: torch.Tensor) -> None:

1
pytorch3d/renderer/points/pulsar/renderer.py
View File

@@ -171,6 +171,7 @@ class _Render(torch.autograd.Function):
return image
@staticmethod
# pyrefly: ignore [bad-override]
def backward(ctx, grad_im, *args):
global GAMMA_WARNING_EMITTED
(

16
pytorch3d/renderer/points/pulsar/unified.py
View File

@@ -121,7 +121,9 @@ class PulsarPointsRenderer(nn.Module):
"gamma is a required keyword argument for the PulsarPointsRenderer!"
)
if (
# pyrefly: ignore [bad-argument-type]
len(point_clouds) != len(self.rasterizer.cameras)
# pyrefly: ignore [bad-argument-type]
and len(self.rasterizer.cameras) != 1
):
raise ValueError(
@@ -132,6 +134,7 @@ class PulsarPointsRenderer(nn.Module):
)
% (
len(point_clouds),
# pyrefly: ignore [bad-argument-type]
len(self.rasterizer.cameras),
)
)
@@ -141,6 +144,7 @@ class PulsarPointsRenderer(nn.Module):
self.rasterizer.cameras, (FoVOrthographicCameras, OrthographicCameras)
)
if orthogonal_projection != self.renderer._renderer.orthogonal:
# pyrefly: ignore [unsupported-operation]
raise ValueError(
"The camera type can not be changed after renderer initialization! "
"Current camera orthogonal: %r. Original orthogonal: %r."
@@ -219,6 +223,7 @@ class PulsarPointsRenderer(nn.Module):
)
else:
# Currently, this means it must be an 'OrthographicCameras' object.
# pyrefly: ignore [missing-attribute]
focal_length_conf = kwargs.get("focal_length", cameras.focal_length)[
cloud_idx
]
@@ -249,11 +254,13 @@ class PulsarPointsRenderer(nn.Module):
znear = kwargs["znear"][cloud_idx]
zfar = kwargs["zfar"][cloud_idx]
principal_point_x = (
# pyrefly: ignore [missing-attribute]
kwargs.get("principal_point", cameras.principal_point)[cloud_idx][0]
* 0.5
* self.renderer._renderer.width
)
principal_point_y = (
# pyrefly: ignore [missing-attribute]
kwargs.get("principal_point", cameras.principal_point)[cloud_idx][1]
* 0.5
* self.renderer._renderer.height
@@ -261,20 +268,26 @@ class PulsarPointsRenderer(nn.Module):
else:
if not isinstance(cameras, PerspectiveCameras):
# Create a virtual focal length that is closer than znear.
# pyrefly: ignore [missing-attribute]
znear = kwargs.get("znear", cameras.znear)[cloud_idx]
# pyrefly: ignore [missing-attribute]
zfar = kwargs.get("zfar", cameras.zfar)[cloud_idx]
focal_length = znear - 1e-6
# Create a sensor size that matches the expected fov assuming this f.
# pyrefly: ignore [missing-attribute]
afov = kwargs.get("fov", cameras.fov)[cloud_idx]
# pyrefly: ignore [missing-attribute]
if kwargs.get("degrees", cameras.degrees):
afov *= math.pi / 180.0
sensor_width = math.tan(afov / 2.0) * 2.0 * focal_length
if not (
# pyrefly: ignore [missing-attribute]
kwargs.get("aspect_ratio", cameras.aspect_ratio)[cloud_idx]
- self.renderer._renderer.width / self.renderer._renderer.height
< 1e-6
):
raise ValueError(
# pyrefly: ignore [missing-attribute]
"The aspect ratio ("
f"{kwargs.get('aspect_ratio', cameras.aspect_ratio)[cloud_idx]}) "
"must agree with the resolution width / height ("
@@ -361,7 +374,9 @@ class PulsarPointsRenderer(nn.Module):
"""
# Shorthand:
cameras = self.rasterizer.cameras
# pyrefly: ignore [missing-attribute]
R = kwargs.get("R", cameras.R)[cloud_idx]
# pyrefly: ignore [missing-attribute]
T = kwargs.get("T", cameras.T)[cloud_idx]
tmp_cams = PerspectiveCameras(
R=R.unsqueeze(0), T=T.unsqueeze(0), device=R.device
@@ -388,6 +403,7 @@ class PulsarPointsRenderer(nn.Module):
# or itself a tensor.
raster_rad = self.rasterizer.raster_settings.radius
if kwargs.get("radius_world", False):
# pyrefly: ignore [bad-return]
return raster_rad
if (
isinstance(raster_rad, torch.Tensor)

1
pytorch3d/renderer/points/rasterize_points.py
View File

@@ -216,6 +216,7 @@ class _RasterizePoints(torch.autograd.Function):
return idx, zbuf, dists
@staticmethod
# pyrefly: ignore [bad-override]
def backward(ctx, grad_idx, grad_zbuf, grad_dists):
grad_points = None
grad_cloud_to_packed_first_idx = None

1
pytorch3d/renderer/points/rasterizer.py
View File

@@ -143,6 +143,7 @@ class PointsRasterizer(nn.Module):
point_clouds = point_clouds.update_padded(pts_ndc)
return point_clouds
# pyrefly: ignore [bad-override]
def to(self, device):
# Manually move to device cameras as it is not a subclass of nn.Module
if self.cameras is not None:

1
pytorch3d/renderer/points/renderer.py
View File

@@ -45,6 +45,7 @@ class PointsRenderer(nn.Module):
self.rasterizer = rasterizer
self.compositor = compositor
# pyrefly: ignore [bad-override]
def to(self, device):
# Manually move to device rasterizer as the cameras
# within the class are not of type nn.Module

1
pytorch3d/renderer/splatter_blend.py
View File

@@ -464,6 +464,7 @@ class SplatterBlender(torch.nn.Module):
input_shape, device
)
# pyrefly: ignore [bad-override]
def to(self, device):
self.offsets = self.offsets.to(device)
self.crop_ids_h = self.crop_ids_h.to(device)

5
pytorch3d/renderer/utils.py
View File

@@ -67,12 +67,15 @@ class TensorAccessor(nn.Module):
if (
v.dim() == 0
and isinstance(self.index, slice)
# pyrefly: ignore [bad-argument-type]
and len(value) != len(self.index)
):
msg = "Expected value to have len %r; got %r"
# pyrefly: ignore [bad-argument-type]
raise ValueError(msg % (len(self.index), len(value)))
self.class_object.__dict__[name][self.index] = value
# pyrefly: ignore [bad-override]
def __getattr__(self, name: str):
"""
Return the value of the attribute given by "name" on self.class_object
@@ -85,6 +88,7 @@ class TensorAccessor(nn.Module):
return self.class_object.__dict__[name][self.index]
else:
msg = "Attribute %s not found on %r"
# pyrefly: ignore [missing-attribute]
return AttributeError(msg % (name, self.class_object.__name__))
@@ -456,4 +460,5 @@ def parse_image_size(
raise ValueError("Image sizes must be greater than 0; got %d, %d" % image_size)
if not all(isinstance(i, int) for i in image_size):
raise ValueError("Image sizes must be integers; got %f, %f" % image_size)
# pyrefly: ignore [bad-return]
return tuple(image_size)

17
pytorch3d/structures/meshes.py
View File

@@ -432,13 +432,17 @@ class Meshes:
# Set the num verts/faces on the textures if present.
if textures is not None:
# pyrefly: ignore [missing-attribute]
shape_ok = self.textures.check_shapes(self._N, self._V, self._F)
if not shape_ok:
msg = "Textures do not match the dimensions of Meshes."
raise ValueError(msg)
# pyrefly: ignore [missing-attribute]
self.textures._num_faces_per_mesh = self._num_faces_per_mesh.tolist()
# pyrefly: ignore [missing-attribute]
self.textures._num_verts_per_mesh = self._num_verts_per_mesh.tolist()
# pyrefly: ignore [missing-attribute]
self.textures.valid = self.valid
if verts_normals is not None:
@@ -449,6 +453,7 @@ class Meshes:
if len(verts_normals) != self._N:
raise ValueError("Invalid verts_normals input")
# pyrefly: ignore [bad-argument-type]
for item, n_verts in zip(verts_normals, self._num_verts_per_mesh):
if (
not isinstance(item, torch.Tensor)
@@ -466,7 +471,10 @@ class Meshes:
):
raise ValueError("Vertex normals tensor has incorrect dimensions.")
self._verts_normals_packed = struct_utils.padded_to_packed(
verts_normals, split_size=self._num_verts_per_mesh.tolist()
# pyrefly: ignore [missing-attribute]
verts_normals,
# pyrefly: ignore [missing-attribute]
split_size=self._num_verts_per_mesh.tolist(),
)
else:
raise ValueError("verts_normals must be a list or tensor")
@@ -497,8 +505,11 @@ class Meshes:
# NOTE consider converting index to cpu for efficiency
if index.dtype == torch.bool:
# advanced indexing on a single dimension
# pyrefly: ignore [bad-assignment]
index = index.nonzero()
# pyrefly: ignore [missing-attribute]
index = index.squeeze(1) if index.numel() > 0 else index
# pyrefly: ignore [missing-attribute]
index = index.tolist()
verts = [self.verts_list()[i] for i in index]
faces = [self.faces_list()[i] for i in index]
@@ -521,6 +532,7 @@ class Meshes:
Returns:
bool indicating whether there is any data.
"""
# pyrefly: ignore [missing-attribute]
return self._N == 0 or self.valid.eq(False).all()
def verts_list(self):
@@ -1058,6 +1070,7 @@ class Meshes:
# All edges including duplicates.
edges = torch.cat([e12, e20, e01], dim=0) # (sum(F_n)*3, 2)
# pyrefly: ignore [no-matching-overload]
edge_to_mesh = torch.cat(
[
self._faces_packed_to_mesh_idx,
@@ -1082,6 +1095,7 @@ class Meshes:
# unique_edges[inverse_idxs] == edges
# i.e. inverse_idxs[i] == j means that edges[i] == unique_edges[j]
# pyrefly: ignore [missing-attribute]
V = self._verts_packed.shape[0]
edges_hash = V * edges[:, 0] + edges[:, 1]
u, inverse_idxs = torch.unique(edges_hash, return_inverse=True)
@@ -1699,6 +1713,7 @@ def join_meshes_as_batch(meshes: List[Meshes], include_textures: bool = True) ->
if not tex_types_same:
raise ValueError("All meshes in the batch must have the same type of texture.")
# pyrefly: ignore [missing-attribute]
tex = first.join_batch(all_textures[1:])
return Meshes(verts=verts, faces=faces, textures=tex)

12
pytorch3d/structures/pointclouds.py
View File

@@ -319,6 +319,7 @@ class Pointclouds:
if len(aux_input) != self._N:
raise ValueError("Points and auxiliary input must be the same length.")
# pyrefly: ignore [bad-argument-type]
for p, d in zip(self._num_points_per_cloud, aux_input):
valid_but_empty = p == 0 and d is not None and d.ndim == 2
if p > 0 or valid_but_empty:
@@ -350,6 +351,7 @@ class Pointclouds:
if good_empty is None:
good_empty = torch.zeros((0, aux_input_C), device=self.device)
aux_input_out = []
# pyrefly: ignore [bad-argument-type]
for p, d in zip(self._num_points_per_cloud, aux_input):
valid_but_empty = p == 0 and d is not None and d.ndim == 2
if p > 0 or valid_but_empty:
@@ -403,8 +405,11 @@ class Pointclouds:
# NOTE consider converting index to cpu for efficiency
if index.dtype == torch.bool:
# advanced indexing on a single dimension
# pyrefly: ignore [bad-assignment]
index = index.nonzero()
# pyrefly: ignore [missing-attribute]
index = index.squeeze(1) if index.numel() > 0 else index
# pyrefly: ignore [missing-attribute]
index = index.tolist()
points = [self.points_list()[i] for i in index]
if normals_list is not None:
@@ -423,6 +428,7 @@ class Pointclouds:
Returns:
bool indicating whether there is any data.
"""
# pyrefly: ignore [missing-attribute]
return self._N == 0 or self.valid.eq(False).all()
def points_list(self) -> List[torch.Tensor]:
@@ -486,6 +492,7 @@ class Pointclouds:
tensor of points of shape (sum(P_n), 3).
"""
self._compute_packed()
# pyrefly: ignore [bad-return]
return self._points_packed
def normals_packed(self) -> Optional[torch.Tensor]:
@@ -541,6 +548,7 @@ class Pointclouds:
Returns:
1D tensor of sizes.
"""
# pyrefly: ignore [bad-return]
return self._num_points_per_cloud
def points_padded(self) -> torch.Tensor:
@@ -551,6 +559,7 @@ class Pointclouds:
tensor of points of shape (N, max(P_n), 3).
"""
self._compute_padded()
# pyrefly: ignore [bad-return]
return self._points_padded
def normals_padded(self) -> Optional[torch.Tensor]:
@@ -636,6 +645,7 @@ class Pointclouds:
if features_list is not None:
self._features_padded = struct_utils.list_to_padded(
features_list,
# pyrefly: ignore [bad-argument-type]
(self._P, self._C),
pad_value=0.0,
equisized=self.equisized,
@@ -686,6 +696,7 @@ class Pointclouds:
points_list_to_packed = struct_utils.list_to_packed(points_list)
self._points_packed = points_list_to_packed[0]
# pyrefly: ignore [bad-argument-type]
if not torch.allclose(self._num_points_per_cloud, points_list_to_packed[1]):
raise ValueError("Inconsistent list to packed conversion")
self._cloud_to_packed_first_idx = points_list_to_packed[2]
@@ -1066,6 +1077,7 @@ class Pointclouds:
self.normals_list()
if self._points_packed is not None:
# update self._normals_packed
# pyrefly: ignore [no-matching-overload]
self._normals_packed = torch.cat(self._normals_list, dim=0)
return normals_est

2
pytorch3d/structures/volumes.py
View File

@@ -1010,6 +1010,7 @@ class VolumeLocator:
Defaults to all items (`:`).
"""
device = device if device is not None else self.device
# pyrefly: ignore [bad-assignment]
other._grid_sizes = self._grid_sizes[index].to(device)
other._local_to_world_transform = self.get_local_to_world_coords_transform()[
# pyre-fixme[6]: For 1st param expected `Union[List[int], int, slice,
@@ -1114,6 +1115,7 @@ class VolumeLocator:
return other
other.device = device_
# pyrefly: ignore [bad-assignment]
other._grid_sizes = self._grid_sizes.to(device_)
other._local_to_world_transform = self.get_local_to_world_coords_transform().to(
device

1
pytorch3d/vis/plotly_vis.py
View File

@@ -628,6 +628,7 @@ def _add_struct_from_batch(
# pyre-ignore[16]
struct = batched_struct[struct_idx]
trace_name = "trace{}-{}".format(scene_num + 1, trace_idx)
# pyrefly: ignore [unsupported-operation]
scene_dictionary[subplot_title][trace_name] = struct