Joining mismatched texture maps on CUDA #175

Summary:
Use nn.functional.interpolate instead of a TorchVision transform to resize texture maps to a common value. This works on all devices. This fixes issue #175.

Also fix the condition so it only happens when needed.

Reviewed By: nikhilaravi

Differential Revision: D21324510

fbshipit-source-id: c50eb06514984995bd81f2c44079be6e0b4098e4

pytorch3d/structures/textures.py

@@ -3,7 +3,7 @@
 from typing import List, Optional, Union
 
 import torch
-import torchvision.transforms as T
+from torch.nn.functional import interpolate
 
 from .utils import padded_to_list, padded_to_packed
 
@@ -18,10 +18,10 @@ def _pad_texture_maps(images: List[torch.Tensor]) -> torch.Tensor:
     Pad all texture images so they have the same height and width.
 
     Args:
-        images: list of N tensors of shape (H, W)
+        images: list of N tensors of shape (H, W, 3)
 
     Returns:
-        tex_maps: Tensor of shape (N, max_H, max_W)
+        tex_maps: Tensor of shape (N, max_H, max_W, 3)
     """
     tex_maps = []
     max_H = 0
@@ -35,15 +35,13 @@ def _pad_texture_maps(images: List[torch.Tensor]) -> torch.Tensor:
         tex_maps.append(im)
     max_shape = (max_H, max_W)
 
-    # If all texture images are not the same size then resize to the
-    # largest size.
-    resize = T.Compose([T.ToPILImage(), T.Resize(size=max_shape), T.ToTensor()])
-
     for i, image in enumerate(tex_maps):
-        if image.shape != max_shape:
-            # ToPIL takes and returns a C x H x W tensor
-            image = resize(image.permute(2, 0, 1)).permute(1, 2, 0)
-            tex_maps[i] = image
+        if image.shape[:2] != max_shape:
+            image_BCHW = image.permute(2, 0, 1)[None]
+            new_image_BCHW = interpolate(
+                image_BCHW, size=max_shape, mode="bilinear", align_corners=False
+            )
+            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)
     return tex_maps