Convert directory fbcode/vision to use the Ruff Formatter

Summary:
Converts the directory specified to use the Ruff formatter in pyfmt

ruff_dog

If this diff causes merge conflicts when rebasing, please run
`hg status -n -0 --change . -I '**/*.{py,pyi}' | xargs -0 arc pyfmt`
on your diff, and amend any changes before rebasing onto latest.
That should help reduce or eliminate any merge conflicts.

allow-large-files

Reviewed By: bottler

Differential Revision: D66472063

fbshipit-source-id: 35841cb397e4f8e066e2159550d2f56b403b1bef

Author: Thomas Polasek

.circleci/regenerate.py

@@ -88,7 +88,6 @@ def workflow_pair(
     upload=False,
     filter_branch,
 ):
-
     w = []
     py = python_version.replace(".", "")
     pyt = pytorch_version.replace(".", "")
@@ -127,7 +126,6 @@ def generate_base_workflow(
     btype,
     filter_branch=None,
 ):
-
     d = {
         "name": base_workflow_name,
         "python_version": python_version,

docs/examples/pulsar_basic.py

@@ -10,6 +10,7 @@
 sphere renderer. It renders and saves an image with 10 random spheres.
 Output: basic.png.
 """
+
 import logging
 import math
 from os import path

docs/examples/pulsar_basic_unified.py

@@ -11,6 +11,7 @@
 10 random spheres.
 Output: basic-pt3d.png.
 """
+
 import logging
 from os import path
 

docs/examples/pulsar_cam.py

@@ -14,6 +14,7 @@
 original camera parameters.
 Output: cam.gif.
 """
+
 import logging
 import math
 from os import path

docs/examples/pulsar_cam_unified.py

@@ -14,6 +14,7 @@
 original camera parameters.
 Output: cam-pt3d.gif
 """
+
 import logging
 from os import path
 

docs/examples/pulsar_multiview.py

@@ -18,6 +18,7 @@
 because opacity support has not landed in PyTorch3D for general data
 structures yet.
 """
+
 import logging
 import math
 from os import path

docs/examples/pulsar_optimization.py

@@ -13,6 +13,7 @@
 optimization is used to converge towards a faithful
 scene representation.
 """
+
 import logging
 import math
 

docs/examples/pulsar_optimization_unified.py

@@ -13,6 +13,7 @@
 optimization is used to converge towards a faithful
 scene representation.
 """
+
 import logging
 import math
 

projects/implicitron_trainer/experiment.py

@@ -7,7 +7,7 @@
 
 # pyre-unsafe
 
-""""
+""" "
 This file is the entry point for launching experiments with Implicitron.
 
 Launch Training
@@ -44,6 +44,7 @@
         config file.
 
 """
+
 import logging
 import os
 import warnings

projects/implicitron_trainer/impl/model_factory.py

@@ -26,7 +26,6 @@
 
 
 class ModelFactoryBase(ReplaceableBase):
-
     resume: bool = True  # resume from the last checkpoint
 
     def __call__(self, **kwargs) -> ImplicitronModelBase:

projects/implicitron_trainer/impl/training_loop.py

@@ -161,7 +161,6 @@ def run(
         for epoch in range(start_epoch, self.max_epochs):
             # automatic new_epoch and plotting of stats at every epoch start
             with stats:
-
                 # Make sure to re-seed random generators to ensure reproducibility
                 # even after restart.
                 seed_all_random_engines(seed + epoch)

projects/implicitron_trainer/tests/test_experiment.py

@@ -53,12 +53,8 @@ def test_from_defaults(self):
         cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_class_type = (
             "JsonIndexDatasetMapProvider"
         )
-        dataset_args = (
-            cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_JsonIndexDatasetMapProvider_args
-        )
-        dataloader_args = (
-            cfg.data_source_ImplicitronDataSource_args.data_loader_map_provider_SequenceDataLoaderMapProvider_args
-        )
+        dataset_args = cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_JsonIndexDatasetMapProvider_args
+        dataloader_args = cfg.data_source_ImplicitronDataSource_args.data_loader_map_provider_SequenceDataLoaderMapProvider_args
         dataset_args.category = "skateboard"
         dataset_args.test_restrict_sequence_id = 0
         dataset_args.dataset_root = "manifold://co3d/tree/extracted"
@@ -94,12 +90,8 @@ def test_exponential_lr(self):
         cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_class_type = (
             "JsonIndexDatasetMapProvider"
         )
-        dataset_args = (
-            cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_JsonIndexDatasetMapProvider_args
-        )
-        dataloader_args = (
-            cfg.data_source_ImplicitronDataSource_args.data_loader_map_provider_SequenceDataLoaderMapProvider_args
-        )
+        dataset_args = cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_JsonIndexDatasetMapProvider_args
+        dataloader_args = cfg.data_source_ImplicitronDataSource_args.data_loader_map_provider_SequenceDataLoaderMapProvider_args
         dataset_args.category = "skateboard"
         dataset_args.test_restrict_sequence_id = 0
         dataset_args.dataset_root = "manifold://co3d/tree/extracted"
@@ -111,9 +103,7 @@ def test_exponential_lr(self):
         cfg.training_loop_ImplicitronTrainingLoop_args.max_epochs = 2
         cfg.training_loop_ImplicitronTrainingLoop_args.store_checkpoints = False
         cfg.optimizer_factory_ImplicitronOptimizerFactory_args.lr_policy = "Exponential"
-        cfg.optimizer_factory_ImplicitronOptimizerFactory_args.exponential_lr_step_size = (
-            2
-        )
+        cfg.optimizer_factory_ImplicitronOptimizerFactory_args.exponential_lr_step_size = 2
 
         if DEBUG:
             experiment.dump_cfg(cfg)

projects/implicitron_trainer/tests/test_optimizer_factory.py

@@ -81,8 +81,9 @@ def test_self_overrides_member_param_group_assignment(self):
 
     def test_param_overrides_self_param_group_assignment(self):
         pa, pb, pc = [torch.nn.Parameter(data=torch.tensor(i * 1.0)) for i in range(3)]
-        na, nb = Node(params=[pa]), Node(
-            params=[pb], param_groups={"self": "pb_self", "p1": "pb_param"}
+        na, nb = (
+            Node(params=[pa]),
+            Node(params=[pb], param_groups={"self": "pb_self", "p1": "pb_param"}),
         )
         root = Node(children=[na, nb], params=[pc], param_groups={"m1": "pb_member"})
         param_groups = self._get_param_groups(root)

projects/nerf/nerf/stats.py

@@ -194,7 +194,6 @@ def update(self, preds: dict, stat_set: str = "train") -> None:
         it = self.it[stat_set]
 
         for stat in self.log_vars:
-
             if stat not in self.stats[stat_set]:
                 self.stats[stat_set][stat] = AverageMeter()
 

projects/nerf/test_nerf.py

@@ -24,7 +24,6 @@
 
 @hydra.main(config_path=CONFIG_DIR, config_name="lego")
 def main(cfg: DictConfig):
-
     # Device on which to run.
     if torch.cuda.is_available():
         device = "cuda"

projects/nerf/tests/test_raysampler.py

@@ -42,7 +42,6 @@ def test_raysampler_caching(self, batch_size=10):
         cameras, rays = [], []
 
         for _ in range(batch_size):
-
             R = random_rotations(1)
             T = torch.randn(1, 3)
             focal_length = torch.rand(1, 2) + 0.5

projects/nerf/train_nerf.py

@@ -25,7 +25,6 @@
 
 @hydra.main(config_path=CONFIG_DIR, config_name="lego")
 def main(cfg: DictConfig):
-
     # Set the relevant seeds for reproducibility.
     np.random.seed(cfg.seed)
     torch.manual_seed(cfg.seed)
@@ -219,7 +218,6 @@ def lr_lambda(epoch):
 
         # Validation
         if epoch % cfg.validation_epoch_interval == 0 and epoch > 0:
-
             # Sample a validation camera/image.
             val_batch = next(val_dataloader.__iter__())
             val_image, val_camera, camera_idx = val_batch[0].values()

pytorch3d/common/compat.py

@@ -17,7 +17,7 @@
 
 
 def meshgrid_ij(
-    *A: Union[torch.Tensor, Sequence[torch.Tensor]]
+    *A: Union[torch.Tensor, Sequence[torch.Tensor]],
 ) -> Tuple[torch.Tensor, ...]:  # pragma: no cover
     """
     Like torch.meshgrid was before PyTorch 1.10.0, i.e. with indexing set to ij

pytorch3d/datasets/shapenet/shapenet_core.py

@@ -83,7 +83,7 @@ def __init__(
                 ):
                     synset_set.add(synset)
                 elif (synset in self.synset_inv.keys()) and (
-                    (path.isdir(path.join(data_dir, self.synset_inv[synset])))
+                    path.isdir(path.join(data_dir, self.synset_inv[synset]))
                 ):
                     synset_set.add(self.synset_inv[synset])
                 else:

pytorch3d/datasets/utils.py

@@ -36,7 +36,6 @@ def collate_batched_meshes(batch: List[Dict]):  # pragma: no cover
 
     collated_dict["mesh"] = None
     if {"verts", "faces"}.issubset(collated_dict.keys()):
-
         textures = None
         if "textures" in collated_dict:
             textures = TexturesAtlas(atlas=collated_dict["textures"])

pytorch3d/implicitron/dataset/json_index_dataset_map_provider_v2.py

@@ -222,7 +222,6 @@ def __post_init__(self):
         self.dataset_map = dataset_map
 
     def _load_category(self, category: str) -> DatasetMap:
-
         frame_file = os.path.join(self.dataset_root, category, "frame_annotations.jgz")
         sequence_file = os.path.join(
             self.dataset_root, category, "sequence_annotations.jgz"

pytorch3d/implicitron/dataset/load_llff.py

@@ -75,7 +75,6 @@ def _minify(basedir, path_manager, factors=(), resolutions=()):
 def _load_data(
     basedir, factor=None, width=None, height=None, load_imgs=True, path_manager=None
 ):
-
     poses_arr = np.load(
         _local_path(path_manager, os.path.join(basedir, "poses_bounds.npy"))
     )
@@ -164,7 +163,6 @@ def ptstocam(pts, c2w):
 
 
 def poses_avg(poses):
-
     hwf = poses[0, :3, -1:]
 
     center = poses[:, :3, 3].mean(0)
@@ -192,7 +190,6 @@ def render_path_spiral(c2w, up, rads, focal, zdelta, zrate, rots, N):
 
 
 def recenter_poses(poses):
-
     poses_ = poses + 0
     bottom = np.reshape([0, 0, 0, 1.0], [1, 4])
     c2w = poses_avg(poses)
@@ -256,7 +253,6 @@ def min_line_dist(rays_o, rays_d):
     new_poses = []
 
     for th in np.linspace(0.0, 2.0 * np.pi, 120):
-
         camorigin = np.array([radcircle * np.cos(th), radcircle * np.sin(th), zh])
         up = np.array([0, 0, -1.0])
 
@@ -311,7 +307,6 @@ def load_llff_data(
     path_zflat=False,
     path_manager=None,
 ):
-
     poses, bds, imgs = _load_data(
         basedir, factor=factor, path_manager=path_manager
     )  # factor=8 downsamples original imgs by 8x

pytorch3d/implicitron/dataset/utils.py

@@ -201,7 +201,6 @@ def resize_image(
     image_width: Optional[int],
     mode: str = "bilinear",
 ) -> Tuple[torch.Tensor, float, torch.Tensor]:
-
     if isinstance(image, np.ndarray):
         image = torch.from_numpy(image)
 

pytorch3d/implicitron/evaluation/evaluate_new_view_synthesis.py

@@ -299,7 +299,6 @@ def eval_batch(
     )
 
     for loss_fg_mask, name_postfix in zip((mask_crop, mask_fg), ("_masked", "_fg")):
-
         loss_mask_now = mask_crop * loss_fg_mask
 
         for rgb_metric_name, rgb_metric_fun in zip(

pytorch3d/implicitron/models/implicit_function/scene_representation_networks.py

@@ -306,7 +306,6 @@ def forward(
         global_code=None,
         **kwargs,
     ):
-
         if global_code is None:
             raise ValueError("SRN Hypernetwork requires a non-trivial global code.")
 

pytorch3d/implicitron/models/implicit_function/utils.py

@@ -40,7 +40,6 @@ def create_embeddings_for_implicit_function(
     xyz_embedding_function: Optional[Callable],
     diag_cov: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
-
     bs, *spatial_size, pts_per_ray, _ = xyz_world.shape
 
     if xyz_in_camera_coords:
@@ -64,7 +63,6 @@ def create_embeddings_for_implicit_function(
             0,
         )
     else:
-
         embeds = xyz_embedding_function(ray_points_for_embed, diag_cov=diag_cov)
         embeds = embeds.reshape(
             bs,

pytorch3d/implicitron/models/renderer/sdf_renderer.py

@@ -220,8 +220,7 @@ def forward(
             )
             mask_full.view(-1, 1)[~surface_mask] = torch.sigmoid(
                 # pyre-fixme[6]: For 1st param expected `Tensor` but got `float`.
-                -self.soft_mask_alpha
-                * sdf_output[~surface_mask]
+                -self.soft_mask_alpha * sdf_output[~surface_mask]
             )
 
         # scatter points with surface_mask

pytorch3d/implicitron/tools/depth_cleanup.py

@@ -21,7 +21,6 @@ def cleanup_eval_depth(
     sigma: float = 0.01,
     image=None,
 ):
-
     ba, _, H, W = depth.shape
 
     pcl = point_cloud.points_padded()

pytorch3d/implicitron/tools/point_cloud_utils.py

@@ -100,7 +100,6 @@ def render_point_cloud_pytorch3d(
     bin_size: Optional[int] = None,
     **kwargs,
 ):
-
     # feature dimension
     featdim = point_cloud.features_packed().shape[-1]