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

pytorch3d_stub/pytorch3d/renderer/__init__.py

@@ -1,10 +1,10 @@
-"""pytorch3d.renderer stub – look_at_view_transform only."""
+"""pytorch3d.renderer stub – minimal classes for import compatibility."""
 import torch
 import math
 
+
 def look_at_view_transform(dist=1.0, elev=0.0, azim=0.0, degrees=True, eye=None,
                             at=((0, 0, 0),), up=((0, 1, 0),), device="cpu"):
-    """Compute R, T for look-at camera transform (pytorch3d convention)."""
     if eye is not None:
         if not isinstance(eye, torch.Tensor):
             eye = torch.tensor(eye, dtype=torch.float32, device=device)
@@ -12,11 +12,10 @@ 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(elev, dtype=torch.float32))
-            azim_r = math.radians(azim) if isinstance(azim, (int, float)) else torch.deg2rad(torch.tensor(azim, dtype=torch.float32))
+            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)))
         else:
-            elev_r = elev
-            azim_r = azim
+            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)
@@ -27,7 +26,6 @@ def look_at_view_transform(dist=1.0, elev=0.0, azim=0.0, degrees=True, eye=None,
             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)
-
     if not isinstance(at, torch.Tensor):
         at = torch.tensor(at, dtype=torch.float32, device=device)
     if at.dim() == 1:
@@ -36,13 +34,74 @@ def look_at_view_transform(dist=1.0, elev=0.0, azim=0.0, degrees=True, eye=None,
         up = torch.tensor(up, dtype=torch.float32, device=device)
     if up.dim() == 1:
         up = up.unsqueeze(0)
-
     z_axis = eye - at
     z_axis = z_axis / z_axis.norm(dim=-1, keepdim=True).clamp(min=1e-8)
     x_axis = torch.cross(up.expand_as(z_axis), z_axis, dim=-1)
     x_axis = x_axis / x_axis.norm(dim=-1, keepdim=True).clamp(min=1e-8)
     y_axis = torch.cross(z_axis, x_axis, dim=-1)
-
-    R = torch.stack([x_axis, y_axis, z_axis], dim=-1)  # (N, 3, 3)
-    T = -torch.bmm(R.transpose(1, 2), eye.unsqueeze(-1)).squeeze(-1)  # (N, 3)
+    R = torch.stack([x_axis, y_axis, z_axis], dim=-1)
+    T = -torch.bmm(R.transpose(1, 2), eye.unsqueeze(-1)).squeeze(-1)
     return R, T
+
+
+class PerspectiveCameras:
+    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
+        self.focal_length = focal_length
+        self.principal_point = principal_point
+        self.R = R if R is not None else torch.eye(3, device=device).unsqueeze(0)
+        self.T = T if T is not None else torch.zeros(1, 3, device=device)
+        self.image_size = image_size
+        self.in_ndc = in_ndc
+
+    def to(self, device):
+        self.device = device
+        return self
+
+
+class RasterizationSettings:
+    def __init__(self, image_size=256, blur_radius=0.0, faces_per_pixel=1, **kwargs):
+        self.image_size = image_size
+        self.blur_radius = blur_radius
+        self.faces_per_pixel = faces_per_pixel
+
+
+class BlendParams:
+    def __init__(self, sigma=1e-4, gamma=1e-4, background_color=(0.0, 0.0, 0.0)):
+        self.sigma = sigma
+        self.gamma = gamma
+        self.background_color = background_color
+
+
+class SoftSilhouetteShader:
+    def __init__(self, blend_params=None):
+        self.blend_params = blend_params or BlendParams()
+
+
+class MeshRasterizer(torch.nn.Module):
+    def __init__(self, cameras=None, raster_settings=None):
+        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")
+
+
+class MeshRenderer(torch.nn.Module):
+    def __init__(self, rasterizer=None, shader=None):
+        super().__init__()
+        self.rasterizer = rasterizer
+        self.shader = shader
+
+    def forward(self, meshes, **kwargs):
+        raise NotImplementedError("pytorch3d.renderer stub: MeshRenderer.forward not implemented")
+
+
+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