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Remove unused pyre-ignore or pyre-fixme

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Reviewed By: bottler

Differential Revision: D47223471

fbshipit-source-id: 8bdabf2a69dd7aec7202141122a9c69220ba7ef1
This commit is contained in:
Emilien Garreau 2023-07-05 02:58:47 -07:00 committed by Facebook GitHub Bot
parent f68371d398
commit 4e7715ce66
21 changed files with 16 additions and 133 deletions
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6
pytorch3d/implicitron/dataset/utils.py
View File

@ -316,7 +316,7 @@ def adjust_camera_to_bbox_crop_(
focal_length_px, principal_point_px = _convert_ndc_to_pixels(
camera.focal_length[0],
camera.principal_point[0], # pyre-ignore
camera.principal_point[0],
image_size_wh,
)
principal_point_px_cropped = principal_point_px - clamp_bbox_xywh[:2]
@ -328,7 +328,7 @@ def adjust_camera_to_bbox_crop_(
)
camera.focal_length = focal_length[None]
camera.principal_point = principal_point_cropped[None] # pyre-ignore
camera.principal_point = principal_point_cropped[None]
def adjust_camera_to_image_scale_(
@ -338,7 +338,7 @@ def adjust_camera_to_image_scale_(
) -> PerspectiveCameras:
focal_length_px, principal_point_px = _convert_ndc_to_pixels(
camera.focal_length[0],
camera.principal_point[0], # pyre-ignore
camera.principal_point[0],
original_size_wh,
)

6
pytorch3d/implicitron/models/feature_extractor/resnet_feature_extractor.py
View File

@ -143,7 +143,6 @@ class ResNetFeatureExtractor(FeatureExtractorBase):
return (img - self._resnet_mean) / self._resnet_std
def get_feat_dims(self) -> int:
# pyre-fixme[29]
return sum(self._feat_dim.values())
def forward(
@ -181,13 +180,8 @@ class ResNetFeatureExtractor(FeatureExtractorBase):
imgs_normed = self._resnet_normalize_image(imgs_resized)
else:
imgs_normed = imgs_resized
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.modules.module.Module]`
# is not a function.
feats = self.stem(imgs_normed)
# pyre-fixme[6]: For 1st param expected `Iterable[Variable[_T1]]` but
# got `Union[Tensor, Module]`.
# pyre-fixme[6]: For 2nd param expected `Iterable[Variable[_T2]]` but
# got `Union[Tensor, Module]`.
for stage, (layer, proj) in enumerate(zip(self.layers, self.proj_layers)):
feats = layer(feats)
# just a sanity check below

8
pytorch3d/implicitron/models/generic_model.py
View File

@ -463,10 +463,6 @@ class GenericModel(ImplicitronModelBase): # pyre-ignore: 13
)
custom_args["global_code"] = global_code
# pyre-fixme[29]:
# `Union[BoundMethod[typing.Callable(torch.Tensor.__iter__)[[Named(self,
# torch.Tensor)], typing.Iterator[typing.Any]], torch.Tensor], torch.Tensor,
# torch.nn.Module]` is not a function.
for func in self._implicit_functions:
func.bind_args(**custom_args)
@ -489,10 +485,6 @@ class GenericModel(ImplicitronModelBase): # pyre-ignore: 13
# Unbind the custom arguments to prevent pytorch from storing
# large buffers of intermediate results due to points in the
# bound arguments.
# pyre-fixme[29]:
# `Union[BoundMethod[typing.Callable(torch.Tensor.__iter__)[[Named(self,
# torch.Tensor)], typing.Iterator[typing.Any]], torch.Tensor], torch.Tensor,
# torch.nn.Module]` is not a function.
for func in self._implicit_functions:
func.unbind_args()

4
pytorch3d/implicitron/models/global_encoder/autodecoder.py
View File

@ -69,7 +69,7 @@ class Autodecoder(Configurable, torch.nn.Module):
return key_map
def calculate_squared_encoding_norm(self) -> Optional[torch.Tensor]:
return (self._autodecoder_codes.weight**2).mean() # pyre-ignore[16]
return (self._autodecoder_codes.weight**2).mean()
def get_encoding_dim(self) -> int:
return self.encoding_dim
@ -93,7 +93,6 @@ class Autodecoder(Configurable, torch.nn.Module):
# pyre-fixme[9]: x has type `Union[List[str], LongTensor]`; used as
# `Tensor`.
x = torch.tensor(
# pyre-ignore[29]
[self._key_map[elem] for elem in x],
dtype=torch.long,
device=next(self.parameters()).device,
@ -101,7 +100,6 @@ class Autodecoder(Configurable, torch.nn.Module):
except StopIteration:
raise ValueError("Not enough n_instances in the autodecoder") from None
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
return self._autodecoder_codes(x)
def _load_key_map_hook(

2
pytorch3d/implicitron/models/global_encoder/global_encoder.py
View File

@ -119,7 +119,7 @@ class HarmonicTimeEncoder(GlobalEncoderBase, torch.nn.Module):
if frame_timestamp.shape[-1] != 1:
raise ValueError("Frame timestamp's last dimensions should be one.")
time = frame_timestamp / self.time_divisor
return self._harmonic_embedding(time) # pyre-ignore: 29
return self._harmonic_embedding(time)
def calculate_squared_encoding_norm(self) -> Optional[torch.Tensor]:
return None

5
pytorch3d/implicitron/models/implicit_function/decoding_functions.py
View File

@ -230,14 +230,9 @@ class MLPWithInputSkips(Configurable, torch.nn.Module):
# if the skip tensor is None, we use `x` instead.
z = x
skipi = 0
# pyre-fixme[6]: For 1st param expected `Iterable[Variable[_T]]` but got
# `Union[Tensor, Module]`.
for li, layer in enumerate(self.mlp):
# pyre-fixme[58]: `in` is not supported for right operand type
# `Union[torch._tensor.Tensor, torch.nn.modules.module.Module]`.
if li in self._input_skips:
if self._skip_affine_trans:
# pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C._Te...
y = self._apply_affine_layer(self.skip_affines[skipi], y, z)
else:
y = torch.cat((y, z), dim=-1)

11
pytorch3d/implicitron/models/implicit_function/idr_feature_field.py
View File

@ -141,16 +141,11 @@ class IdrFeatureField(ImplicitFunctionBase, torch.nn.Module):
self.embed_fn is None and fun_viewpool is None and global_code is None
):
return torch.tensor(
[],
device=rays_points_world.device,
dtype=rays_points_world.dtype
# pyre-fixme[6]: For 2nd param expected `int` but got `Union[Module,
# Tensor]`.
[], device=rays_points_world.device, dtype=rays_points_world.dtype
).view(0, self.out_dim)
embeddings = []
if self.embed_fn is not None:
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
embeddings.append(self.embed_fn(rays_points_world))
if fun_viewpool is not None:
@ -169,17 +164,13 @@ class IdrFeatureField(ImplicitFunctionBase, torch.nn.Module):
embedding = torch.cat(embeddings, dim=-1)
x = embedding
# pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C._TensorBase.__s...
for layer_idx in range(self.num_layers - 1):
if layer_idx in self.skip_in:
x = torch.cat([x, embedding], dim=-1) / 2**0.5
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
x = self.linear_layers[layer_idx](x)
# pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C._TensorBase...
if layer_idx < self.num_layers - 2:
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
x = self.softplus(x)
return x

5
pytorch3d/implicitron/models/implicit_function/neural_radiance_field.py
View File

@ -113,10 +113,8 @@ class NeuralRadianceFieldBase(ImplicitFunctionBase, torch.nn.Module):
# Normalize the ray_directions to unit l2 norm.
rays_directions_normed = torch.nn.functional.normalize(rays_directions, dim=-1)
# Obtain the harmonic embedding of the normalized ray directions.
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
rays_embedding = self.harmonic_embedding_dir(rays_directions_normed)
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
return self.color_layer((self.intermediate_linear(features), rays_embedding))
@staticmethod
@ -171,7 +169,6 @@ class NeuralRadianceFieldBase(ImplicitFunctionBase, torch.nn.Module):
embeds = create_embeddings_for_implicit_function(
xyz_world=rays_points_world,
# pyre-fixme[6]: Expected `Optional[typing.Callable[..., typing.Any]]`
# for 2nd param but got `Union[None, torch.Tensor, torch.nn.Module]`.
xyz_embedding_function=self.harmonic_embedding_xyz
if self.input_xyz
@ -183,14 +180,12 @@ class NeuralRadianceFieldBase(ImplicitFunctionBase, torch.nn.Module):
)
# embeds.shape = [minibatch x n_src x n_rays x n_pts x self.n_harmonic_functions*6+3]
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
features = self.xyz_encoder(embeds)
# features.shape = [minibatch x ... x self.n_hidden_neurons_xyz]
# NNs operate on the flattenned rays; reshaping to the correct spatial size
# TODO: maybe make the transformer work on non-flattened tensors to avoid this reshape
features = features.reshape(*rays_points_world.shape[:-1], -1)
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
raw_densities = self.density_layer(features)
# raw_densities.shape = [minibatch x ... x 1] in [0-1]

11
pytorch3d/implicitron/models/implicit_function/scene_representation_networks.py
View File

@ -101,8 +101,6 @@ class SRNRaymarchFunction(Configurable, torch.nn.Module):
embeds = create_embeddings_for_implicit_function(
xyz_world=rays_points_world,
# pyre-fixme[6]: Expected `Optional[typing.Callable[..., typing.Any]]`
# for 2nd param but got `Union[torch.Tensor, torch.nn.Module]`.
xyz_embedding_function=self._harmonic_embedding,
global_code=global_code,
fun_viewpool=fun_viewpool,
@ -112,7 +110,6 @@ class SRNRaymarchFunction(Configurable, torch.nn.Module):
# Before running the network, we have to resize embeds to ndims=3,
# otherwise the SRN layers consume huge amounts of memory.
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
raymarch_features = self._net(
embeds.view(embeds.shape[0], -1, embeds.shape[-1])
)
@ -167,9 +164,7 @@ class SRNPixelGenerator(Configurable, torch.nn.Module):
# Normalize the ray_directions to unit l2 norm.
rays_directions_normed = torch.nn.functional.normalize(rays_directions, dim=-1)
# Obtain the harmonic embedding of the normalized ray directions.
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
rays_embedding = self._harmonic_embedding(rays_directions_normed)
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
return self._color_layer((features, rays_embedding))
def forward(
@ -198,7 +193,6 @@ class SRNPixelGenerator(Configurable, torch.nn.Module):
denoting the color of each ray point.
"""
# raymarch_features.shape = [minibatch x ... x pts_per_ray x 3]
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
features = self._net(raymarch_features)
# features.shape = [minibatch x ... x self.n_hidden_units]
@ -213,7 +207,6 @@ class SRNPixelGenerator(Configurable, torch.nn.Module):
# NNs operate on the flattenned rays; reshaping to the correct spatial size
features = features.reshape(*raymarch_features.shape[:-1], -1)
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
raw_densities = self._density_layer(features)
rays_colors = self._get_colors(features, directions)
@ -274,7 +267,6 @@ class SRNRaymarchHyperNet(Configurable, torch.nn.Module):
srn_raymarch_function.
"""
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
net = self._hypernet(global_code)
# use the hyper-net generated network to instantiate the raymarch module
@ -310,7 +302,6 @@ class SRNRaymarchHyperNet(Configurable, torch.nn.Module):
# across LSTM iterations for the same global_code.
if self.cached_srn_raymarch_function is None:
# generate the raymarching network from the hypernet
# pyre-fixme[16]: `SRNRaymarchHyperNet` has no attribute
self.cached_srn_raymarch_function = self._run_hypernet(global_code)
(srn_raymarch_function,) = cast(
Tuple[SRNRaymarchFunction], self.cached_srn_raymarch_function
@ -337,7 +328,6 @@ class SRNImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
def create_raymarch_function(self) -> None:
self.raymarch_function = SRNRaymarchFunction(
latent_dim=self.latent_dim,
# pyre-ignore[32]
**self.raymarch_function_args,
)
@ -395,7 +385,6 @@ class SRNHyperNetImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
self.hypernet = SRNRaymarchHyperNet(
latent_dim=self.latent_dim,
latent_dim_hypernet=self.latent_dim_hypernet,
# pyre-ignore[32]
**self.hypernet_args,
)

11
pytorch3d/implicitron/models/implicit_function/voxel_grid.py
View File

@ -267,7 +267,6 @@ class VoxelGridBase(ReplaceableBase, torch.nn.Module):
for name, tensor in vars(grid_values_with_wanted_resolution).items()
}
# pyre-ignore[29]
return self.values_type(**params), True
def get_resolution_change_epochs(self) -> Tuple[int, ...]:
@ -881,8 +880,6 @@ class VoxelGridModule(Configurable, torch.nn.Module):
torch.Tensor of shape (..., n_features)
"""
locator = self._get_volume_locator()
# pyre-fixme[29]: `Union[torch._tensor.Tensor,
# torch.nn.modules.module.Module]` is not a function.
grid_values = self.voxel_grid.values_type(**self.params)
# voxel grids operate with extra n_grids dimension, which we fix to one
return self.voxel_grid.evaluate_world(points[None], grid_values, locator)[0]
@ -896,8 +893,6 @@ class VoxelGridModule(Configurable, torch.nn.Module):
replace current parameters
"""
if self.hold_voxel_grid_as_parameters:
# pyre-ignore [16]
# Nones are converted to empty tensors by Parameter()
self.params = torch.nn.ParameterDict(
{
k: torch.nn.Parameter(val)
@ -948,7 +943,6 @@ class VoxelGridModule(Configurable, torch.nn.Module):
Returns:
True if parameter change has happened else False.
"""
# pyre-ignore[29]
grid_values = self.voxel_grid.values_type(**self.params)
grid_values, change = self.voxel_grid.change_resolution(
grid_values, epoch=epoch
@ -996,19 +990,16 @@ class VoxelGridModule(Configurable, torch.nn.Module):
"""
'''
new_params = {}
# pyre-ignore[29]
for name in self.params:
key = prefix + "params." + name
if key in state_dict:
new_params[name] = torch.zeros_like(state_dict[key])
# pyre-ignore[29]
self.set_voxel_grid_parameters(self.voxel_grid.values_type(**new_params))
def get_device(self) -> torch.device:
"""
Returns torch.device on which module parameters are located
"""
# pyre-ignore[29]
return next(val for val in self.params.values() if val is not None).device
def crop_self(self, min_point: torch.Tensor, max_point: torch.Tensor) -> None:
@ -1024,7 +1015,6 @@ class VoxelGridModule(Configurable, torch.nn.Module):
nothing
"""
locator = self._get_volume_locator()
# pyre-fixme[29]: `Union[torch._tensor.Tensor,
# torch.nn.modules.module.Module]` is not a function.
old_grid_values = self.voxel_grid.values_type(**self.params)
new_grid_values = self.voxel_grid.crop_world(
@ -1033,7 +1023,6 @@ class VoxelGridModule(Configurable, torch.nn.Module):
grid_values, _ = self.voxel_grid.change_resolution(
new_grid_values, grid_values_with_wanted_resolution=old_grid_values
)
# pyre-ignore [16]
self.params = torch.nn.ParameterDict(
{
k: torch.nn.Parameter(val)

18
pytorch3d/implicitron/models/implicit_function/voxel_grid_implicit_function.py
View File

@ -187,21 +187,16 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
def __post_init__(self) -> None:
run_auto_creation(self)
# pyre-ignore[16]
self.voxel_grid_scaffold = self._create_voxel_grid_scaffold()
# pyre-ignore[16]
self.harmonic_embedder_xyz_density = HarmonicEmbedding(
**self.harmonic_embedder_xyz_density_args
)
# pyre-ignore[16]
self.harmonic_embedder_xyz_color = HarmonicEmbedding(
**self.harmonic_embedder_xyz_color_args
)
# pyre-ignore[16]
self.harmonic_embedder_dir_color = HarmonicEmbedding(
**self.harmonic_embedder_dir_color_args
)
# pyre-ignore[16]
self._scaffold_ready = False
def forward(
@ -252,7 +247,6 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
# ########## filter the points using the scaffold ########## #
if self._scaffold_ready and self.scaffold_filter_points:
with torch.no_grad():
# pyre-ignore[29]
non_empty_points = self.voxel_grid_scaffold(points)[..., 0] > 0
points = points[non_empty_points]
if len(points) == 0:
@ -364,7 +358,6 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
feature dimensionality which `decoder_density` returns
"""
embeds_density = self.voxel_grid_density(points)
# pyre-ignore[29]
harmonic_embedding_density = self.harmonic_embedder_xyz_density(embeds_density)
# shape = [..., density_dim]
return self.decoder_density(harmonic_embedding_density)
@ -407,13 +400,11 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
# ########## embed with the harmonic function ########## #
# Obtain the harmonic embedding of the voxel grid output.
# pyre-ignore[29]
harmonic_embedding_color = self.harmonic_embedder_xyz_color(embeds_color)
# Normalize the ray_directions to unit l2 norm.
rays_directions_normed = torch.nn.functional.normalize(directions, dim=-1)
# Obtain the harmonic embedding of the normalized ray directions.
# pyre-ignore[29]
harmonic_embedding_dir = self.harmonic_embedder_dir_color(
rays_directions_normed
)
@ -482,10 +473,8 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
an object inside, else False.
"""
# find bounding box
# pyre-ignore[16]
points = self.voxel_grid_scaffold.get_grid_points(epoch=epoch)
assert self._scaffold_ready, "Scaffold has to be calculated before cropping."
# pyre-ignore[29]
occupancy = self.voxel_grid_scaffold(points)[..., 0] > 0
non_zero_idxs = torch.nonzero(occupancy)
if len(non_zero_idxs) == 0:
@ -517,7 +506,6 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
"""
planes = []
# pyre-ignore[16]
points = self.voxel_grid_scaffold.get_grid_points(epoch=epoch)
chunk_size = (
@ -537,9 +525,7 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
stride=1,
)
occupancy_cube = density_cube > self.scaffold_empty_space_threshold
# pyre-ignore[16]
self.voxel_grid_scaffold.params["voxel_grid"] = occupancy_cube.float()
# pyre-ignore[16]
self._scaffold_ready = True
return False
@ -556,7 +542,6 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
decoding function to this value.
"""
grid_args = self.voxel_grid_density_args
# pyre-ignore[6]
grid_output_dim = VoxelGridModule.get_output_dim(grid_args)
embedder_args = self.harmonic_embedder_xyz_density_args
@ -585,7 +570,6 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
decoding function to this value.
"""
grid_args = self.voxel_grid_color_args
# pyre-ignore[6]
grid_output_dim = VoxelGridModule.get_output_dim(grid_args)
embedder_args = self.harmonic_embedder_xyz_color_args
@ -619,9 +603,7 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
`self.voxel_grid_density`
"""
return VoxelGridModule(
# pyre-ignore[29]
extents=self.voxel_grid_density_args["extents"],
# pyre-ignore[29]
translation=self.voxel_grid_density_args["translation"],
voxel_grid_class_type="FullResolutionVoxelGrid",
hold_voxel_grid_as_parameters=False,

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

@ -134,7 +134,6 @@ class LSTMRenderer(BaseRenderer, torch.nn.Module):
break
# run the lstm marcher
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
state_h, state_c = self._lstm(
raymarch_features.view(-1, raymarch_features.shape[-1]),
states[-1],
@ -142,7 +141,6 @@ class LSTMRenderer(BaseRenderer, torch.nn.Module):
if state_h.requires_grad:
state_h.register_hook(lambda x: x.clamp(min=-10, max=10))
# predict the next step size
# pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
signed_distance = self._out_layer(state_h).view(ray_bundle_t.lengths.shape)
# log the lstm states
states.append((state_h, state_c))

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

@ -171,7 +171,6 @@ class AbstractMaskRaySampler(RaySamplerBase, torch.nn.Module):
"""
sample_mask = None
if (
# pyre-fixme[29]
self._sampling_mode[evaluation_mode] == RenderSamplingMode.MASK_SAMPLE
and mask is not None
):
@ -188,7 +187,6 @@ class AbstractMaskRaySampler(RaySamplerBase, torch.nn.Module):
EvaluationMode.EVALUATION: self._evaluation_raysampler,
}[evaluation_mode]
# pyre-fixme[29]:
ray_bundle = raysampler(
cameras=cameras,
mask=sample_mask,

8
pytorch3d/implicitron/models/renderer/raymarcher.py
View File

@ -170,9 +170,9 @@ class AccumulativeRaymarcherBase(RaymarcherBase, torch.nn.Module):
rays_densities = torch.relu(rays_densities)
weighted_densities = deltas * rays_densities
capped_densities = self._capping_function(weighted_densities) # pyre-ignore: 29
capped_densities = self._capping_function(weighted_densities)
rays_opacities = self._capping_function( # pyre-ignore: 29
rays_opacities = self._capping_function(
torch.cumsum(weighted_densities, dim=-1)
)
opacities = rays_opacities[..., -1:]
@ -181,9 +181,7 @@ class AccumulativeRaymarcherBase(RaymarcherBase, torch.nn.Module):
)
absorption_shifted[..., : self.surface_thickness] = 1.0
weights = self._weight_function( # pyre-ignore: 29
capped_densities, absorption_shifted
)
weights = self._weight_function(capped_densities, absorption_shifted)
features = (weights[..., None] * rays_features).sum(dim=-2)
depth = (weights * ray_lengths)[..., None].sum(dim=-2)

4
pytorch3d/implicitron/models/renderer/sdf_renderer.py
View File

@ -58,7 +58,6 @@ class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module): # pyre-ign
def create_ray_tracer(self) -> None:
self.ray_tracer = RayTracing(
# pyre-ignore[32]
**self.ray_tracer_args,
object_bounding_sphere=self.object_bounding_sphere,
)
@ -146,7 +145,6 @@ class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module): # pyre-ign
eikonal_points = torch.empty(
n_eik_points,
3,
# pyre-fixme[6]: For 3rd param expected `Union[None, str, device]`
# but got `Union[device, Tensor, Module]`.
device=self._bg_color.device,
).uniform_(-eik_bounding_box, eik_bounding_box)
@ -205,7 +203,7 @@ class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module): # pyre-ign
normals_full.view(-1, 3)[surface_mask] = normals
render_full.view(-1, self.render_features_dimensions)[
surface_mask
] = self._rgb_network( # pyre-fixme[29]:
] = self._rgb_network(
features,
differentiable_surface_points[None],
normals,

5
pytorch3d/implicitron/models/view_pooler/feature_aggregator.py
View File

@ -530,11 +530,6 @@ def _get_ray_dir_dot_prods(camera: CamerasBase, pts: torch.Tensor):
# does not produce nans randomly unlike get_camera_center() below
cam_centers_rep = -torch.bmm(
# pyre-fixme[29]:
# `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
# torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor],
# torch.Tensor, torch.nn.modules.module.Module]` is not a function.
# pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch.Tensor.permute)[[N...
camera_rep.T[:, None],
camera_rep.R.permute(0, 2, 1),
).reshape(-1, *([1] * (pts.ndim - 2)), 3)

15
pytorch3d/ops/cameras_alignment.py
View File

@ -120,16 +120,12 @@ def corresponding_cameras_alignment(
# create a new cameras object and set the R and T accordingly
cameras_src_aligned = cameras_src.clone()
# pyre-fixme[6]: For 2nd param expected `Tensor` but got `Union[Tensor, Module]`.
cameras_src_aligned.R = torch.bmm(align_t_R.expand_as(cameras_src.R), cameras_src.R)
cameras_src_aligned.T = (
torch.bmm(
align_t_T[:, None].repeat(cameras_src.R.shape[0], 1, 1),
# pyre-fixme[6]: For 2nd param expected `Tensor` but got `Union[Tensor,
# Module]`.
cameras_src.R,
)[:, 0]
# pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C._TensorBase.__m...
+ cameras_src.T * align_t_s
)
@ -177,7 +173,6 @@ def _align_camera_extrinsics(
R_A = (U V^T)^T
```
"""
# pyre-fixme[6]: For 1st param expected `Tensor` but got `Union[Tensor, Module]`.
RRcov = torch.bmm(cameras_src.R, cameras_tgt.R.transpose(2, 1)).mean(0)
U, _, V = torch.svd(RRcov)
align_t_R = V @ U.t()
@ -207,17 +202,7 @@ def _align_camera_extrinsics(
T_A = mean(B) - mean(A) * s_A
```
"""
# pyre-fixme[29]:
# `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
# torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor],
# torch.Tensor, torch.nn.Module]` is not a function.
# pyre-fixme[6]: For 1st param expected `Tensor` but got `Union[Tensor, Module]`.
A = torch.bmm(cameras_src.R, cameras_src.T[:, :, None])[:, :, 0]
# pyre-fixme[29]:
# `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
# torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor],
# torch.Tensor, torch.nn.Module]` is not a function.
# pyre-fixme[6]: For 1st param expected `Tensor` but got `Union[Tensor, Module]`.
B = torch.bmm(cameras_src.R, cameras_tgt.T[:, :, None])[:, :, 0]
Amu = A.mean(0, keepdim=True)
Bmu = B.mean(0, keepdim=True)

4
pytorch3d/renderer/camera_conversions.py
View File

@ -63,8 +63,8 @@ def _opencv_from_cameras_projection(
cameras: PerspectiveCameras,
image_size: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
R_pytorch3d = cameras.R.clone() # pyre-ignore
T_pytorch3d = cameras.T.clone() # pyre-ignore
R_pytorch3d = cameras.R.clone()
T_pytorch3d = cameras.T.clone()
focal_pytorch3d = cameras.focal_length
p0_pytorch3d = cameras.principal_point
T_pytorch3d[:, :2] *= -1

8
pytorch3d/renderer/cameras.py
View File

@ -201,8 +201,8 @@ class CamerasBase(TensorProperties):
"""
R: torch.Tensor = kwargs.get("R", self.R)
T: torch.Tensor = kwargs.get("T", self.T)
self.R = R # pyre-ignore[16]
self.T = T # pyre-ignore[16]
self.R = R
self.T = T
world_to_view_transform = get_world_to_view_transform(R=R, T=T)
return world_to_view_transform
@ -226,8 +226,8 @@ class CamerasBase(TensorProperties):
a Transform3d object which represents a batch of transforms
of shape (N, 3, 3)
"""
self.R: torch.Tensor = kwargs.get("R", self.R) # pyre-ignore[16]
self.T: torch.Tensor = kwargs.get("T", self.T) # pyre-ignore[16]
self.R: torch.Tensor = kwargs.get("R", self.R)
self.T: torch.Tensor = kwargs.get("T", self.T)
world_to_view_transform = self.get_world_to_view_transform(R=self.R, T=self.T)
view_to_proj_transform = self.get_projection_transform(**kwargs)
return world_to_view_transform.compose(view_to_proj_transform)

2
pytorch3d/renderer/lighting.py
View File

@ -264,9 +264,7 @@ class PointLights(TensorProperties):
shape (P, 3) or (N, H, W, K, 3).
"""
if self.location.ndim == points.ndim:
# pyre-fixme[7]
return self.location
# pyre-fixme[29]
return self.location[:, None, None, None, :]
def diffuse(self, normals, points) -> torch.Tensor:

12
pytorch3d/vis/plotly_vis.py
View File

@ -585,21 +585,9 @@ def _add_struct_from_batch(
if isinstance(batched_struct, CamerasBase):
# we can't index directly into camera batches
R, T = batched_struct.R, batched_struct.T
# pyre-fixme[6]: Expected `Sized` for 1st param but got `Union[torch.Tensor,
# torch.nn.Module]`.
r_idx = min(scene_num, len(R) - 1)
# pyre-fixme[6]: Expected `Sized` for 1st param but got `Union[torch.Tensor,
# torch.nn.Module]`.
t_idx = min(scene_num, len(T) - 1)
# pyre-fixme[29]:
# `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
# torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor],
# torch.Tensor, torch.nn.Module]` is not a function.
R = R[r_idx].unsqueeze(0)
# pyre-fixme[29]:
# `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
# torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor],
# torch.Tensor, torch.nn.Module]` is not a function.
T = T[t_idx].unsqueeze(0)
struct = CamerasBase(device=batched_struct.device, R=R, T=T)
elif _is_ray_bundle(batched_struct) and not _is_heterogeneous_ray_bundle(