fbcode/vision/fair/pytorch3d/pytorch3d/transforms/rotation_conversions.py

Reviewed By: bottler

Differential Revision: D93712828

fbshipit-source-id: 3465af450104bb1e5f491e3c0ee0259698cf8ceb

pytorch3d/loss/mesh_laplacian_smoothing.py

@@ -114,9 +114,7 @@ def mesh_laplacian_smoothing(meshes, method: str = "uniform"):
             if method == "cot":
                 norm_w = torch.sparse.sum(L, dim=1).to_dense().view(-1, 1)
                 idx = norm_w > 0
-                # pyre-fixme[58]: `/` is not supported for operand types `float` and
-                #  `Tensor`.
-                norm_w[idx] = 1.0 / norm_w[idx]
+                norm_w[idx] = torch.reciprocal(norm_w[idx])
             else:
                 L_sum = torch.sparse.sum(L, dim=1).to_dense().view(-1, 1)
                 norm_w = 0.25 * inv_areas

pytorch3d/loss/point_mesh_distance.py

@@ -6,6 +6,7 @@
 
 # pyre-unsafe
 
+import torch
 from pytorch3d import _C
 from pytorch3d.structures import Meshes, Pointclouds
 from torch.autograd import Function
@@ -302,8 +303,7 @@ def point_mesh_edge_distance(meshes: Meshes, pcls: Pointclouds):
     point_to_cloud_idx = pcls.packed_to_cloud_idx()  # (sum(P_i), )
     num_points_per_cloud = pcls.num_points_per_cloud()  # (N,)
     weights_p = num_points_per_cloud.gather(0, point_to_cloud_idx)
-    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-    weights_p = 1.0 / weights_p.float()
+    weights_p = torch.reciprocal(weights_p.float())
     point_to_edge = point_to_edge * weights_p
     point_dist = point_to_edge.sum() / N
 
@@ -377,8 +377,7 @@ def point_mesh_face_distance(
     point_to_cloud_idx = pcls.packed_to_cloud_idx()  # (sum(P_i),)
     num_points_per_cloud = pcls.num_points_per_cloud()  # (N,)
     weights_p = num_points_per_cloud.gather(0, point_to_cloud_idx)
-    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-    weights_p = 1.0 / weights_p.float()
+    weights_p = torch.reciprocal(weights_p.float())
     point_to_face = point_to_face * weights_p
     point_dist = point_to_face.sum() / N
 

pytorch3d/ops/laplacian_matrices.py

@@ -55,11 +55,9 @@ def laplacian(verts: torch.Tensor, edges: torch.Tensor) -> torch.Tensor:
     # We construct the Laplacian matrix by adding the non diagonal values
     # i.e. L[i, j] = 1 ./ deg(i) if (i, j) is an edge
     deg0 = deg[e0]
-    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-    deg0 = torch.where(deg0 > 0.0, 1.0 / deg0, deg0)
+    deg0 = torch.where(deg0 > 0.0, torch.reciprocal(deg0), deg0)
     deg1 = deg[e1]
-    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-    deg1 = torch.where(deg1 > 0.0, 1.0 / deg1, deg1)
+    deg1 = torch.where(deg1 > 0.0, torch.reciprocal(deg1), deg1)
     val = torch.cat([deg0, deg1])
     L = torch.sparse_coo_tensor(idx, val, (V, V), dtype=torch.float32)
 
@@ -137,8 +135,7 @@ def cot_laplacian(
     val = torch.stack([area] * 3, dim=1).view(-1)
     inv_areas.scatter_add_(0, idx, val)
     idx = inv_areas > 0
-    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-    inv_areas[idx] = 1.0 / inv_areas[idx]
+    inv_areas[idx] = torch.reciprocal(inv_areas[idx])
     inv_areas = inv_areas.view(-1, 1)
 
     return L, inv_areas

pytorch3d/renderer/cameras.py

@@ -629,10 +629,8 @@ class FoVPerspectiveCameras(CamerasBase):
         # so the so the z sign is 1.0.
         z_sign = 1.0
 
-        # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-        K[:, 0, 0] = 2.0 * znear / (max_x - min_x)
-        # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-        K[:, 1, 1] = 2.0 * znear / (max_y - min_y)
+        K[:, 0, 0] = torch.div(2.0 * znear, max_x - min_x)
+        K[:, 1, 1] = torch.div(2.0 * znear, max_y - min_y)
         K[:, 0, 2] = (max_x + min_x) / (max_x - min_x)
         K[:, 1, 2] = (max_y + min_y) / (max_y - min_y)
         K[:, 3, 2] = z_sign * ones
@@ -1178,9 +1176,7 @@ class PerspectiveCameras(CamerasBase):
         xy_inv_depth = torch.cat(
             # pyre-fixme[6]: For 1st argument expected `Union[List[Tensor],
             #  tuple[Tensor, ...]]` but got `Tuple[Tensor, float]`.
-            # pyre-fixme[58]: `/` is not supported for operand types `float` and
-            #  `Tensor`.
-            (xy_depth[..., :2], 1.0 / xy_depth[..., 2:3]),
+            (xy_depth[..., :2], torch.reciprocal(xy_depth[..., 2:3])),
             dim=-1,  # type: ignore
         )
         return unprojection_transform.transform_points(xy_inv_depth)

pytorch3d/renderer/mesh/clip.py

@@ -434,13 +434,7 @@ def clip_faces(
     # These will then be filled in for each case.
     ###########################################
     F_clipped = (
-        F
-        # pyre-fixme[58]: `+` is not supported for operand types `int` and
-        #  `Union[bool, float, int]`.
-        + faces_delta_cum[-1].item()
-        # pyre-fixme[58]: `+` is not supported for operand types `int` and
-        #  `Union[bool, float, int]`.
-        + faces_delta[-1].item()
+        F + int(faces_delta_cum[-1].item()) + int(faces_delta[-1].item())
     )  # Total number of faces in the new Meshes
     face_verts_clipped = torch.zeros(
         (F_clipped, 3, 3), dtype=face_verts_unclipped.dtype, device=device

pytorch3d/renderer/mesh/textures.py

@@ -71,9 +71,7 @@ def _list_to_padded_wrapper(
         # pyre-fixme[6]: For 2nd param expected `int` but got `Union[bool, float, int]`.
         x_reshaped.append(y.reshape(-1, D))
     x_padded = list_to_padded(x_reshaped, pad_size=pad_size, pad_value=pad_value)
-    # pyre-fixme[58]: `+` is not supported for operand types `Tuple[int, int]` and
-    #  `Size`.
-    return x_padded.reshape((N, -1) + reshape_dims)
+    return x_padded.reshape((N, -1) + tuple(reshape_dims))
 
 
 def _padded_to_list_wrapper(
@@ -104,9 +102,7 @@ def _padded_to_list_wrapper(
     # pyre-fixme[6]: For 3rd param expected `int` but got `Union[bool, float, int]`.
     x_reshaped = x.reshape(N, M, D)
     x_list = padded_to_list(x_reshaped, split_size=split_size)
-    # pyre-fixme[58]: `+` is not supported for operand types `Tuple[typing.Any]` and
-    #  `Size`.
-    x_list = [xl.reshape((xl.shape[0],) + reshape_dims) for xl in x_list]
+    x_list = [xl.reshape((xl.shape[0],) + tuple(reshape_dims)) for xl in x_list]
     return x_list
 
 

pytorch3d/transforms/rotation_conversions.py

@@ -52,8 +52,7 @@ def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor:
         Rotation matrices as tensor of shape (..., 3, 3).
     """
     r, i, j, k = torch.unbind(quaternions, -1)
-    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-    two_s = 2.0 / (quaternions * quaternions).sum(-1)
+    two_s = torch.div(2.0, (quaternions * quaternions).sum(-1))
 
     o = torch.stack(
         (
@@ -137,18 +136,18 @@ def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
     # we produce the desired quaternion multiplied by each of r, i, j, k
     quat_by_rijk = torch.stack(
         [
-            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
-            #  `int`.
-            torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1),
-            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
-            #  `int`.
-            torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1),
-            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
-            #  `int`.
-            torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1),
-            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
-            #  `int`.
-            torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1),
+            torch.stack(
+                [torch.square(q_abs[..., 0]), m21 - m12, m02 - m20, m10 - m01], dim=-1
+            ),
+            torch.stack(
+                [m21 - m12, torch.square(q_abs[..., 1]), m10 + m01, m02 + m20], dim=-1
+            ),
+            torch.stack(
+                [m02 - m20, m10 + m01, torch.square(q_abs[..., 2]), m12 + m21], dim=-1
+            ),
+            torch.stack(
+                [m10 - m01, m20 + m02, m21 + m12, torch.square(q_abs[..., 3])], dim=-1
+            ),
         ],
         dim=-2,
     )

pytorch3d/transforms/transform3d.py

@@ -623,9 +623,7 @@ class Scale(Transform3d):
         Return the inverse of self._matrix.
         """
         xyz = torch.stack([self._matrix[:, i, i] for i in range(4)], dim=1)
-        # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-        ixyz = 1.0 / xyz
-        # pyre-fixme[6]: For 1st param expected `Tensor` but got `float`.
+        ixyz = torch.reciprocal(xyz)
         imat = torch.diag_embed(ixyz, dim1=1, dim2=2)
         return imat