suppress errors in `vision/fair/pytorch3d`

Differential Revision: D37172764

fbshipit-source-id: a2ec367e56de2781a17f5e708eb5832ec9d7e6b4

pytorch3d/common/compat.py

@@ -23,6 +23,7 @@ def solve(A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:  # pragma: no cover
         # PyTorch version >= 1.8.0
         return torch.linalg.solve(A, B)
 
+    # pyre-fixme[16]: `Tuple` has no attribute `solution`.
     return torch.solve(B, A).solution
 
 
@@ -67,9 +68,14 @@ def meshgrid_ij(
     Like torch.meshgrid was before PyTorch 1.10.0, i.e. with indexing set to ij
     """
     if (
+        # pyre-fixme[16]: Callable `meshgrid` has no attribute `__kwdefaults__`.
         torch.meshgrid.__kwdefaults__ is not None
         and "indexing" in torch.meshgrid.__kwdefaults__
     ):
         # PyTorch >= 1.10.0
+        # pyre-fixme[6]: For 1st param expected `Union[List[Tensor], Tensor]` but
+        #  got `Union[Sequence[Tensor], Tensor]`.
         return torch.meshgrid(*A, indexing="ij")
+    # pyre-fixme[6]: For 1st param expected `Union[List[Tensor], Tensor]` but got
+    #  `Union[Sequence[Tensor], Tensor]`.
     return torch.meshgrid(*A)

pytorch3d/common/datatypes.py

@@ -26,7 +26,7 @@ def make_device(device: Device) -> torch.device:
         A matching torch.device object
     """
     device = torch.device(device) if isinstance(device, str) else device
-    if device.type == "cuda" and device.index is None:  # pyre-ignore[16]
+    if device.type == "cuda" and device.index is None:
         # If cuda but with no index, then the current cuda device is indicated.
         # In that case, we fix to that device
         device = torch.device(f"cuda:{torch.cuda.current_device()}")

pytorch3d/common/workaround/symeig3x3.py

@@ -75,12 +75,14 @@ class _SymEig3x3(nn.Module):
         if inputs.shape[-2:] != (3, 3):
             raise ValueError("Only inputs of shape (..., 3, 3) are supported.")
 
-        inputs_diag = inputs.diagonal(dim1=-2, dim2=-1)  # pyre-ignore[16]
+        inputs_diag = inputs.diagonal(dim1=-2, dim2=-1)
         inputs_trace = inputs_diag.sum(-1)
         q = inputs_trace / 3.0
 
         # Calculate squared sum of elements outside the main diagonal / 2
+        # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
         p1 = ((inputs**2).sum(dim=(-1, -2)) - (inputs_diag**2).sum(-1)) / 2
+        # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
         p2 = ((inputs_diag - q[..., None]) ** 2).sum(dim=-1) + 2.0 * p1.clamp(self._eps)
 
         p = torch.sqrt(p2 / 6.0)
@@ -195,8 +197,9 @@ class _SymEig3x3(nn.Module):
             cross_products[..., :1, :]
         )
 
+        # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
         norms_sq = (cross_products**2).sum(dim=-1)
-        max_norms_index = norms_sq.argmax(dim=-1)  # pyre-ignore[16]
+        max_norms_index = norms_sq.argmax(dim=-1)
 
         # Pick only the cross-product with highest squared norm for each input
         max_cross_products = self._gather_by_index(
@@ -227,9 +230,7 @@ class _SymEig3x3(nn.Module):
         index_shape = list(source.shape)
         index_shape[dim] = 1
 
-        return source.gather(dim, index.expand(index_shape)).squeeze(  # pyre-ignore[16]
-            dim
-        )
+        return source.gather(dim, index.expand(index_shape)).squeeze(dim)
 
     def _get_uv(self, w: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
         """
@@ -243,7 +244,7 @@ class _SymEig3x3(nn.Module):
             Tuple of U and V unit-length vector tensors of shape (..., 3)
         """
 
-        min_idx = w.abs().argmin(dim=-1)  # pyre-ignore[16]
+        min_idx = w.abs().argmin(dim=-1)
         rotation_2d = self._rotations_3d[min_idx].to(w)
 
         u = F.normalize((rotation_2d @ w[..., None])[..., 0], dim=-1)

pytorch3d/datasets/r2n2/utils.py

@@ -140,7 +140,6 @@ def compute_extrinsic_matrix(
     # rotates the model 90 degrees about the x axis. To compensate for this quirk we
     # roll that rotation into the extrinsic matrix here
     rot = torch.tensor([[1, 0, 0, 0], [0, 0, -1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])
-    # pyre-fixme[16]: `Tensor` has no attribute `mm`.
     RT = RT.mm(rot.to(RT))
 
     return RT
@@ -180,6 +179,7 @@ def read_binvox_coords(
     size, translation, scale = _read_binvox_header(f)
     storage = torch.ByteStorage.from_buffer(f.read())
     data = torch.tensor([], dtype=torch.uint8)
+    # pyre-fixme[28]: Unexpected keyword argument `source`.
     data.set_(source=storage)
     vals, counts = data[::2], data[1::2]
     idxs = _compute_idxs(vals, counts)

pytorch3d/datasets/shapenet_base.py

@@ -227,6 +227,8 @@ class ShapeNetBase(torch.utils.data.Dataset):  # pragma: no cover
                 sampled_idxs = self._sample_idxs_from_category(
                     sample_num=sample_num, category=category
                 )
+                # pyre-fixme[6]: For 1st param expected `Union[List[Tensor],
+                #  typing.Tuple[Tensor, ...]]` but got `Tuple[Tensor, List[int]]`.
                 idxs_tensor = torch.cat((idxs_tensor, sampled_idxs))
             idxs = idxs_tensor.tolist()
         # Check if the indices are valid if idxs are supplied.
@@ -283,4 +285,5 @@ class ShapeNetBase(torch.utils.data.Dataset):  # pragma: no cover
                 "category " + category if category is not None else "all categories",
             )
             warnings.warn(msg)
+        # pyre-fixme[7]: Expected `List[int]` but got `Tensor`.
         return sampled_idxs

pytorch3d/implicitron/dataset/json_index_dataset.py

@@ -640,14 +640,16 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
         )
         imre = torch.nn.functional.interpolate(
             torch.from_numpy(image)[None],
-            # pyre-ignore[6]
             scale_factor=minscale,
             mode=mode,
             align_corners=False if mode == "bilinear" else None,
             recompute_scale_factor=True,
         )[0]
+        # pyre-fixme[19]: Expected 1 positional argument.
         imre_ = torch.zeros(image.shape[0], self.image_height, self.image_width)
         imre_[:, 0 : imre.shape[1], 0 : imre.shape[2]] = imre
+        # pyre-fixme[6]: For 2nd param expected `int` but got `Optional[int]`.
+        # pyre-fixme[6]: For 3rd param expected `int` but got `Optional[int]`.
         mask = torch.zeros(1, self.image_height, self.image_width)
         mask[:, 0 : imre.shape[1] - 1, 0 : imre.shape[2] - 1] = 1.0
         return imre_, minscale, mask

pytorch3d/implicitron/dataset/utils.py

@@ -23,6 +23,7 @@ def is_known_frame(
     Given a list `frame_type` of frame types in a batch, return a tensor
     of boolean flags expressing whether the corresponding frame is a known frame.
     """
+    # pyre-fixme[7]: Expected `BoolTensor` but got `Tensor`.
     return torch.tensor(
         [ft.endswith(DATASET_TYPE_KNOWN) for ft in frame_type],
         dtype=torch.bool,
@@ -37,6 +38,7 @@ def is_train_frame(
     Given a list `frame_type` of frame types in a batch, return a tensor
     of boolean flags expressing whether the corresponding frame is a training frame.
     """
+    # pyre-fixme[7]: Expected `BoolTensor` but got `Tensor`.
     return torch.tensor(
         [ft.startswith(DATASET_TYPE_TRAIN) for ft in frame_type],
         dtype=torch.bool,

pytorch3d/implicitron/evaluation/evaluate_new_view_synthesis.py

@@ -205,11 +205,7 @@ def eval_batch(
 
         imode = "bilinear" if k == "image_render" else "nearest"
         cloned_render[k] = (
-            # pyre-fixme[6]: For 2nd param expected `Optional[int]` but got
-            #  `Tuple[Any, ...]`.
-            F.interpolate(field[:1], size=image_resol, mode=imode)
-            .detach()
-            .clone()
+            F.interpolate(field[:1], size=image_resol, mode=imode).detach().clone()
         )
 
     frame_data = copy.deepcopy(frame_data)
@@ -408,7 +404,6 @@ def _reduce_camera_iou_overlap(ious: torch.Tensor, topk: int = 2) -> torch.Tenso
     Returns:
         single-element Tensor
     """
-    # pyre-ignore[16]  topk not recognized
     return ious.topk(k=min(topk, len(ious) - 1)).values.mean()
 
 

pytorch3d/implicitron/models/autodecoder.py

@@ -99,6 +99,8 @@ class Autodecoder(Configurable, torch.nn.Module):
 
         if isinstance(x[0], str):
             try:
+                # pyre-fixme[9]: x has type `Union[List[str], LongTensor]`; used as
+                #  `Tensor`.
                 x = torch.tensor(
                     # pyre-ignore[29]
                     [self._sequence_map[elem] for elem in x],

pytorch3d/implicitron/models/feature_extractor/resnet_feature_extractor.py

@@ -169,8 +169,6 @@ class ResNetFeatureExtractor(FeatureExtractorBase):
         if self.image_rescale != 1.0 and imgs_input is not None:
             imgs_resized = Fu.interpolate(
                 imgs_input,
-                # pyre-fixme[6]: For 2nd param expected `Optional[List[float]]` but
-                #  got `float`.
                 scale_factor=self.image_rescale,
                 mode="bilinear",
             )

pytorch3d/implicitron/models/implicit_function/idr_feature_field.py

@@ -103,7 +103,11 @@ 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
+                [],
+                device=rays_points_world.device,
+                dtype=rays_points_world.dtype
+                # pyre-fixme[6]: For 2nd param expected `int` but got `Union[Module,
+                #  Tensor]`.
             ).view(0, self.out_dim)
 
         embedding = None
@@ -128,6 +132,7 @@ class IdrFeatureField(ImplicitFunctionBase, torch.nn.Module):
             )
 
         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
@@ -135,6 +140,7 @@ class IdrFeatureField(ImplicitFunctionBase, torch.nn.Module):
             # 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)

pytorch3d/implicitron/models/implicit_function/neural_radiance_field.py

@@ -406,6 +406,8 @@ class TransformerWithInputSkips(torch.nn.Module):
         self.last = torch.nn.Linear(dimout, output_dim)
         _xavier_init(self.last)
 
+        # pyre-fixme[8]: Attribute has type `Tuple[ModuleList, ModuleList]`; used as
+        #  `ModuleList`.
         self.layers_pool, self.layers_ray = (
             torch.nn.ModuleList(layers_pool),
             torch.nn.ModuleList(layers_ray),

pytorch3d/implicitron/models/model_dbir.py

@@ -93,6 +93,8 @@ class ModelDBIR(ImplicitronModelBase, torch.nn.Module):
         mask_fg = (fg_probability > 0.5).type_as(image_rgb)
 
         point_cloud = get_rgbd_point_cloud(
+            # pyre-fixme[6]: For 1st param expected `Union[List[int], int,
+            #  LongTensor]` but got `Tensor`.
             camera[is_known_idx],
             image_rgb[is_known_idx],
             depth_map[is_known_idx],
@@ -101,6 +103,8 @@ class ModelDBIR(ImplicitronModelBase, torch.nn.Module):
 
         pcl_size = point_cloud.num_points_per_cloud().item()
         if (self.max_points > 0) and (pcl_size > self.max_points):
+            # pyre-fixme[6]: For 1st param expected `int` but got `Union[bool,
+            #  float, int]`.
             prm = torch.randperm(pcl_size)[: self.max_points]
             point_cloud = Pointclouds(
                 point_cloud.points_padded()[:, prm, :],

pytorch3d/implicitron/models/renderer/lstm_renderer.py

@@ -117,13 +117,7 @@ class LSTMRenderer(BaseRenderer, torch.nn.Module):
                 msg = (
                     f"{t}: mu={float(signed_distance.mean()):1.2e};"
                     + f" std={float(signed_distance.std()):1.2e};"
-                    # pyre-fixme[6]: Expected `Union[bytearray, bytes, str,
-                    #  typing.SupportsFloat, typing_extensions.SupportsIndex]` for 1st
-                    #  param but got `Tensor`.
                     + f" mu_d={float(ray_bundle_t.lengths.mean()):1.2e};"
-                    # pyre-fixme[6]: Expected `Union[bytearray, bytes, str,
-                    #  typing.SupportsFloat, typing_extensions.SupportsIndex]` for 1st
-                    #  param but got `Tensor`.
                     + f" std_d={float(ray_bundle_t.lengths.std()):1.2e};"
                 )
                 logger.info(msg)

pytorch3d/implicitron/models/renderer/ray_sampler.py

@@ -164,8 +164,6 @@ class AbstractMaskRaySampler(RaySamplerBase, torch.nn.Module):
         ):
             sample_mask = torch.nn.functional.interpolate(
                 mask,
-                # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got
-                #  `List[int]`.
                 size=[self.image_height, self.image_width],
                 mode="nearest",
             )[:, 0]

pytorch3d/implicitron/models/renderer/ray_tracing.py

@@ -123,12 +123,12 @@ class RayTracing(Configurable, nn.Module):
 
         ray_directions = ray_directions.reshape(-1, 3)
         mask_intersect = mask_intersect.reshape(-1)
+        # pyre-fixme[9]: object_mask has type `BoolTensor`; used as `Tensor`.
         object_mask = object_mask.reshape(-1)
 
         in_mask = ~network_object_mask & object_mask & ~sampler_mask
         out_mask = ~object_mask & ~sampler_mask
 
-        # pyre-fixme[16]: `Tensor` has no attribute `__invert__`.
         mask_left_out = (in_mask | out_mask) & ~mask_intersect
         if (
             mask_left_out.sum() > 0
@@ -410,10 +410,17 @@ class RayTracing(Configurable, nn.Module):
         if n_p_out > 0:
             out_pts_idx = torch.argmin(sdf_val[p_out_mask, :], -1)
             sampler_pts[mask_intersect_idx[p_out_mask]] = points[p_out_mask, :, :][
-                torch.arange(n_p_out), out_pts_idx, :
+                # pyre-fixme[6]: For 1st param expected `Union[bool, float, int]`
+                #  but got `Tensor`.
+                torch.arange(n_p_out),
+                out_pts_idx,
+                :,
             ]
             sampler_dists[mask_intersect_idx[p_out_mask]] = pts_intervals[
-                p_out_mask, :
+                p_out_mask,
+                :
+                # pyre-fixme[6]: For 1st param expected `Union[bool, float, int]` but
+                #  got `Tensor`.
             ][torch.arange(n_p_out), out_pts_idx]
 
         # Get Network object mask
@@ -434,10 +441,16 @@ class RayTracing(Configurable, nn.Module):
                 secant_pts
             ]
             z_low = pts_intervals[secant_pts][
-                torch.arange(n_secant_pts), sampler_pts_ind[secant_pts] - 1
+                # pyre-fixme[6]: For 1st param expected `Union[bool, float, int]`
+                #  but got `Tensor`.
+                torch.arange(n_secant_pts),
+                sampler_pts_ind[secant_pts] - 1,
             ]
             sdf_low = sdf_val[secant_pts][
-                torch.arange(n_secant_pts), sampler_pts_ind[secant_pts] - 1
+                # pyre-fixme[6]: For 1st param expected `Union[bool, float, int]`
+                #  but got `Tensor`.
+                torch.arange(n_secant_pts),
+                sampler_pts_ind[secant_pts] - 1,
             ]
             cam_loc_secant = cam_loc.reshape(-1, 3)[mask_intersect_idx[secant_pts]]
             ray_directions_secant = ray_directions.reshape((-1, 3))[
@@ -514,6 +527,7 @@ class RayTracing(Configurable, nn.Module):
         mask_max_dis = max_dis[mask].unsqueeze(-1)
         mask_min_dis = min_dis[mask].unsqueeze(-1)
         steps = (
+            # pyre-fixme[6]: For 1st param expected `int` but got `Tensor`.
             steps.unsqueeze(0).repeat(n_mask_points, 1) * (mask_max_dis - mask_min_dis)
             + mask_min_dis
         )
@@ -533,8 +547,13 @@ class RayTracing(Configurable, nn.Module):
         mask_sdf_all = torch.cat(mask_sdf_all).reshape(-1, n)
         min_vals, min_idx = mask_sdf_all.min(-1)
         min_mask_points = mask_points_all.reshape(-1, n, 3)[
-            torch.arange(0, n_mask_points), min_idx
+            # pyre-fixme[6]: For 2nd param expected `Union[bool, float, int]` but
+            #  got `Tensor`.
+            torch.arange(0, n_mask_points),
+            min_idx,
         ]
+        # pyre-fixme[6]: For 2nd param expected `Union[bool, float, int]` but got
+        #  `Tensor`.
         min_mask_dist = steps.reshape(-1, n)[torch.arange(0, n_mask_points), min_idx]
 
         return min_mask_points, min_mask_dist
@@ -553,6 +572,7 @@ def _get_sphere_intersection(
     # cam_loc = cam_loc.unsqueeze(-1)
     # ray_cam_dot = torch.bmm(ray_directions, cam_loc).squeeze()
     ray_cam_dot = (ray_directions * cam_loc).sum(-1)  # n_images x n_rays
+    # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
     under_sqrt = ray_cam_dot**2 - (cam_loc.norm(2, dim=-1) ** 2 - r**2)
 
     under_sqrt = under_sqrt.reshape(-1)

pytorch3d/implicitron/models/renderer/sdf_renderer.py

@@ -132,7 +132,11 @@ class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):
             eik_bounding_box: float = self.object_bounding_sphere
             n_eik_points = batch_size * num_pixels // 2
             eikonal_points = torch.empty(
-                n_eik_points, 3, device=self._bg_color.device
+                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)
             eikonal_pixel_points = points.clone()
             eikonal_pixel_points = eikonal_pixel_points.detach()
@@ -196,7 +200,9 @@ class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):
                 pooling_fn=None,  # TODO
             )
             mask_full.view(-1, 1)[~surface_mask] = torch.sigmoid(
-                -self.soft_mask_alpha * sdf_output[~surface_mask]
+                # pyre-fixme[6]: For 1st param expected `Tensor` but got `float`.
+                -self.soft_mask_alpha
+                * sdf_output[~surface_mask]
             )
 
         # scatter points with surface_mask

pytorch3d/implicitron/models/view_pooler/feature_aggregator.py

@@ -550,7 +550,6 @@ def _get_ray_dir_dot_prods(camera: CamerasBase, pts: 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],
-        # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch.Tensor.permute)[[N...
         camera_rep.R.permute(0, 2, 1),
     ).reshape(-1, *([1] * (pts.ndim - 2)), 3)
     # cam_centers_rep = camera_rep.get_camera_center().reshape(
@@ -649,6 +648,7 @@ def _avgmaxstd_reduction_function(
     x_aggr = torch.cat(pooled_features, dim=-1)
 
     # zero out features that were all masked out
+    # pyre-fixme[16]: `bool` has no attribute `type_as`.
     any_active = (w.max(dim=dim, keepdim=True).values > 1e-4).type_as(x_aggr)
     x_aggr = x_aggr * any_active[..., None]
 
@@ -676,6 +676,7 @@ def _std_reduction_function(
 ):
     if mu is None:
         mu = _avg_reduction_function(x, w, dim=dim)
+    # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
     std = wmean((x - mu) ** 2, w, dim=dim, eps=1e-2).clamp(1e-4).sqrt()
     # FIXME: somehow this is extremely heavy in mem?
     return std

pytorch3d/implicitron/models/view_pooler/view_sampler.py

@@ -205,7 +205,11 @@ def handle_seq_id(
     if not torch.is_tensor(seq_id):
         if isinstance(seq_id[0], str):
             seq_id = [hash(s) for s in seq_id]
+        # pyre-fixme[9]: seq_id has type `Union[List[int], List[str], LongTensor]`;
+        #  used as `Tensor`.
         seq_id = torch.tensor(seq_id, dtype=torch.long, device=device)
+    # pyre-fixme[16]: Item `List` of `Union[List[int], List[str], LongTensor]` has
+    #  no attribute `to`.
     return seq_id.to(device)
 
 
@@ -287,5 +291,7 @@ def cameras_points_cartesian_product(
         )
         .reshape(batch_pts * n_cameras)
     )
+    # pyre-fixme[6]: For 1st param expected `Union[List[int], int, LongTensor]` but
+    #  got `Tensor`.
     camera_rep = camera[idx_cams]
     return camera_rep, pts_rep

pytorch3d/implicitron/third_party/hyperlayers.py

@@ -215,7 +215,6 @@ class BatchLinear(nn.Module):
 def last_hyper_layer_init(m) -> None:
     if type(m) == nn.Linear:
         nn.init.kaiming_normal_(m.weight, a=0.0, nonlinearity="relu", mode="fan_in")
-        # pyre-fixme[41]: `data` cannot be reassigned. It is a read-only property.
         m.weight.data *= 1e-1