Upload pytorch3d_stub/pytorch3d/renderer/__init__.py with huggingface_hub

pytorch3d_stub/pytorch3d/renderer/__init__.py

@@ -1,4 +1,4 @@
-"""pytorch3d.renderer stub – minimal classes for import compatibility."""
+"""pytorch3d.renderer stub – all classes needed by SAM3D."""
 import torch
 import math
 
@@ -12,20 +12,14 @@ def look_at_view_transform(dist=1.0, elev=0.0, azim=0.0, degrees=True, eye=None,
             eye = eye.unsqueeze(0)
     else:
         if degrees:
-            elev_r = math.radians(elev) if isinstance(elev, (int, float)) else torch.deg2rad(torch.tensor(float(elev)))
-            azim_r = math.radians(azim) if isinstance(azim, (int, float)) else torch.deg2rad(torch.tensor(float(azim)))
+            elev_r = math.radians(float(elev))
+            azim_r = math.radians(float(azim))
         else:
-            elev_r, azim_r = elev, azim
-        if isinstance(elev_r, (int, float)):
-            x = dist * math.cos(elev_r) * math.sin(azim_r)
-            y = dist * math.sin(elev_r)
-            z = dist * math.cos(elev_r) * math.cos(azim_r)
-            eye = torch.tensor([[x, y, z]], dtype=torch.float32, device=device)
-        else:
-            x = dist * torch.cos(elev_r) * torch.sin(azim_r)
-            y = dist * torch.sin(elev_r)
-            z = dist * torch.cos(elev_r) * torch.cos(azim_r)
-            eye = torch.stack([x, y, z], dim=-1).unsqueeze(0).to(device)
+            elev_r, azim_r = float(elev), float(azim)
+        x = dist * math.cos(elev_r) * math.sin(azim_r)
+        y = dist * math.sin(elev_r)
+        z = dist * math.cos(elev_r) * math.cos(azim_r)
+        eye = torch.tensor([[x, y, z]], dtype=torch.float32, device=device)
     if not isinstance(at, torch.Tensor):
         at = torch.tensor(at, dtype=torch.float32, device=device)
     if at.dim() == 1:
@@ -44,7 +38,12 @@ def look_at_view_transform(dist=1.0, elev=0.0, azim=0.0, degrees=True, eye=None,
     return R, T
 
 
-class PerspectiveCameras:
+class CamerasBase:
+    """Base camera class stub."""
+    pass
+
+
+class PerspectiveCameras(CamerasBase):
     def __init__(self, focal_length=None, principal_point=None, R=None, T=None,
                  image_size=None, device="cpu", in_ndc=True, **kwargs):
         self.device = device
@@ -84,9 +83,8 @@ class MeshRasterizer(torch.nn.Module):
         super().__init__()
         self.cameras = cameras
         self.raster_settings = raster_settings or RasterizationSettings()
-
     def forward(self, meshes, **kwargs):
-        raise NotImplementedError("pytorch3d.renderer stub: MeshRasterizer.forward not implemented")
+        raise NotImplementedError("MeshRasterizer stub")
 
 
 class MeshRenderer(torch.nn.Module):
@@ -94,14 +92,17 @@ class MeshRenderer(torch.nn.Module):
         super().__init__()
         self.rasterizer = rasterizer
         self.shader = shader
-
     def forward(self, meshes, **kwargs):
-        raise NotImplementedError("pytorch3d.renderer stub: MeshRenderer.forward not implemented")
+        raise NotImplementedError("MeshRenderer stub")
 
 
 class TexturesVertex:
     def __init__(self, verts_features=None):
         self.verts_features_list = verts_features if isinstance(verts_features, list) else [verts_features]
-
     def to(self, device):
         return self
+
+
+class HeterogeneousRayBundle:
+    """Stub for HeterogeneousRayBundle (not used in inference)."""
+    pass