use C for #channels in textures

Summary: Comments in textures.py were inconsistent in describing the number of channels, sometimes C, sometimes D, sometimes 3. Now always C.

Reviewed By: patricklabatut

Differential Revision: D29263435

fbshipit-source-id: 7c1260c164c52852dc9e14d0e12da4cfb64af408

Author: bottler

pytorch3d/renderer/mesh/textures.py

@@ -109,17 +109,17 @@ def _pad_texture_maps(
     Pad all texture images so they have the same height and width.
 
     Args:
-        images: list of N tensors of shape (H_i, W_i, 3)
+        images: list of N tensors of shape (H_i, W_i, C)
         align_corners: used for interpolation
 
     Returns:
-        tex_maps: Tensor of shape (N, max_H, max_W, 3)
+        tex_maps: Tensor of shape (N, max_H, max_W, C)
     """
     tex_maps = []
     max_H = 0
     max_W = 0
     for im in images:
-        h, w, _3 = im.shape
+        h, w, _C = im.shape
         if h > max_H:
             max_H = h
         if w > max_W:
@@ -134,7 +134,7 @@ def _pad_texture_maps(
                 image_BCHW, size=max_shape, mode="bilinear", align_corners=align_corners
             )
             tex_maps[i] = new_image_BCHW[0].permute(1, 2, 0)
-    tex_maps = torch.stack(tex_maps, dim=0)  # (num_tex_maps, max_H, max_W, 3)
+    tex_maps = torch.stack(tex_maps, dim=0)  # (num_tex_maps, max_H, max_W, C)
     return tex_maps
 
 
@@ -288,12 +288,12 @@ def Textures(
 
     Args:
         maps: texture map per mesh. This can either be a list of maps
-          [(H, W, 3)] or a padded tensor of shape (N, H, W, 3).
+          [(H, W, C)] or a padded tensor of shape (N, H, W, C).
         faces_uvs: (N, F, 3) tensor giving the index into verts_uvs for each
             vertex in the face. Padding value is assumed to be -1.
         verts_uvs: (N, V, 2) tensor giving the uv coordinate per vertex.
-        verts_rgb: (N, V, 3) tensor giving the rgb color per vertex. Padding
-            value is assumed to be -1.
+        verts_rgb: (N, V, C) tensor giving the color per vertex. Padding
+            value is assumed to be -1. (C=3 for RGB.)
 
 
     Returns:
@@ -327,7 +327,7 @@ def __init__(self, atlas: Union[torch.Tensor, List[torch.Tensor]]):
         This is based on the implementation from SoftRasterizer [1].
 
         Args:
-            atlas: (N, F, R, R, D) tensor giving the per face texture map.
+            atlas: (N, F, R, R, C) tensor giving the per face texture map.
                 The atlas can be created during obj loading with the
                 pytorch3d.io.load_obj function - in the input arguments
                 set `create_texture_atlas=True`. The atlas will be
@@ -354,7 +354,7 @@ def __init__(self, atlas: Union[torch.Tensor, List[torch.Tensor]]):
             )
             if not correct_format:
                 msg = (
-                    "Expected atlas to be a list of tensors of shape (F, R, R, D) "
+                    "Expected atlas to be a list of tensors of shape (F, R, R, C) "
                     "with the same value of R."
                 )
                 raise ValueError(msg)
@@ -373,7 +373,7 @@ def __init__(self, atlas: Union[torch.Tensor, List[torch.Tensor]]):
 
         elif torch.is_tensor(atlas):
             if atlas.ndim != 5:
-                msg = "Expected atlas to be of shape (N, F, R, R, D); got %r"
+                msg = "Expected atlas to be of shape (N, F, R, R, C); got %r"
                 raise ValueError(msg % repr(atlas.ndim))
             self._atlas_padded = atlas
             self._atlas_list = None
@@ -499,7 +499,7 @@ def sample_textures(self, fragments, **kwargs) -> torch.Tensor:
                 representation) which overlap the pixel.
 
         Returns:
-            texels: (N, H, W, K, 3)
+            texels: (N, H, W, K, C)
         """
         N, H, W, K = fragments.pix_to_face.shape
         atlas_packed = self.atlas_packed()
@@ -532,7 +532,7 @@ def faces_verts_textures_packed(self) -> torch.Tensor:
         """
         Samples texture from each vertex for each face in the mesh.
         For N meshes with {Fi} number of faces, it returns a
-        tensor of shape sum(Fi)x3xD (D = 3 for RGB).
+        tensor of shape sum(Fi)x3xC (C = 3 for RGB).
         You can use the utils function in structures.utils to convert the
         packed representation to a list or padded.
         """
@@ -603,7 +603,8 @@ def __init__(
 
         Args:
             maps: texture map per mesh. This can either be a list of maps
-              [(H, W, 3)] or a padded tensor of shape (N, H, W, 3)
+              [(H, W, C)] or a padded tensor of shape (N, H, W, C).
+              For RGB, C = 3.
             faces_uvs: (N, F, 3) LongTensor giving the index into verts_uvs
                         for each face
             verts_uvs: (N, V, 2) tensor giving the uv coordinates per vertex
@@ -708,7 +709,7 @@ def __init__(
 
         if isinstance(maps, torch.Tensor):
             if maps.ndim != 4 or maps.shape[0] != self._N:
-                msg = "Expected maps to be of shape (N, H, W, 3); got %r"
+                msg = "Expected maps to be of shape (N, H, W, C); got %r"
                 raise ValueError(msg % repr(maps.shape))
             self._maps_padded = maps
             self._maps_list = None
@@ -1061,8 +1062,8 @@ def _place_map_into_single_map(
         Used by join_scene.
 
         Args:
-            single_map: (total_H, total_W, 3)
-            map_: (H, W, 3) source data
+            single_map: (total_H, total_W, C)
+            map_: (H, W, C) source data
             location: where to place map
         """
         do_flip = location.flipped
@@ -1246,10 +1247,10 @@ def __init__(
     ):
         """
         Batched texture representation where each vertex in a mesh
-        has a D dimensional feature vector.
+        has a C dimensional feature vector.
 
         Args:
-            verts_features: list of (Vi, D) or (N, V, D) tensor giving a feature
+            verts_features: list of (Vi, C) or (N, V, C) tensor giving a feature
                 vector with arbitrary dimensions for each vertex.
         """
         if isinstance(verts_features, (tuple, list)):
@@ -1258,7 +1259,7 @@ def __init__(
             )
             if not correct_shape:
                 raise ValueError(
-                    "Expected verts_features to be a list of tensors of shape (V, D)."
+                    "Expected verts_features to be a list of tensors of shape (V, C)."
                 )
 
             self._verts_features_list = verts_features
@@ -1276,7 +1277,7 @@ def __init__(
 
         elif torch.is_tensor(verts_features):
             if verts_features.ndim != 3:
-                msg = "Expected verts_features to be of shape (N, V, D); got %r"
+                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
             self._verts_features_list = None