do not use dr

app.py

@@ -172,30 +172,30 @@ def save_py3dmesh_with_trimesh_fast(meshes, save_glb_path=TEMP_MESH_ADDRESS, app
 
 @spaces.GPU(duration=120)
 def text_to_detailed(prompt, seed=None):
-    # test nvdiffrast
-    subprocess.run(
-        shlex.split(
-            "rm -rf /home/user/.cache/torch_extensions/py310_cu121/nvdiffrast_plugin/lock"
-        )
-    )
-    os.environ["TORCH_CUDA_ARCH_LIST"] = "9.0"
-    if "CUDA_VISIBLE_DEVICES" in os.environ:
-        del os.environ["CUDA_VISIBLE_DEVICES"]
-    os.environ['CUDA_HOME'] = '/usr/local/cuda-12.1'
-    os.environ['PATH'] += ':/usr/local/cuda-12.1/bin'
-    os.environ['LD_LIBRARY_PATH'] = "/usr/local/cuda-12.1/lib64:" + os.environ.get('LD_LIBRARY_PATH', '')
-    subprocess.run(['nvidia-smi'])  # Test if CUDA is available
-    print(f"torch.cuda.is_available:{torch.cuda.is_available()}")
-    print("Device count:", torch.cuda.device_count()) 
-    subprocess.run(
-        shlex.split(
-            "pip install ./extension/nvdiffrast-0.3.1+torch-py3-none-any.whl --force-reinstall --no-deps"
-        )
-    )
-    import nvdiffrast.torch as dr
-    print("import success")
-    dr.RasterizeCudaContext(device="cuda:0")
-    print("nvdiffrast initialized successfully")
+    # # test nvdiffrast
+    # subprocess.run(
+    #     shlex.split(
+    #         "rm -rf /home/user/.cache/torch_extensions/py310_cu121/nvdiffrast_plugin/lock"
+    #     )
+    # )
+    # os.environ["TORCH_CUDA_ARCH_LIST"] = "9.0"
+    # if "CUDA_VISIBLE_DEVICES" in os.environ:
+    #     del os.environ["CUDA_VISIBLE_DEVICES"]
+    # os.environ['CUDA_HOME'] = '/usr/local/cuda-12.1'
+    # os.environ['PATH'] += ':/usr/local/cuda-12.1/bin'
+    # os.environ['LD_LIBRARY_PATH'] = "/usr/local/cuda-12.1/lib64:" + os.environ.get('LD_LIBRARY_PATH', '')
+    # subprocess.run(['nvidia-smi'])  # Test if CUDA is available
+    # print(f"torch.cuda.is_available:{torch.cuda.is_available()}")
+    # print("Device count:", torch.cuda.device_count()) 
+    # subprocess.run(
+    #     shlex.split(
+    #         "pip install ./extension/nvdiffrast-0.3.1+torch-py3-none-any.whl --force-reinstall --no-deps"
+    #     )
+    # )
+    # import nvdiffrast.torch as dr
+    # print("import success")
+    # dr.RasterizeCudaContext(device="cuda:0")
+    # print("nvdiffrast initialized successfully")
 
     print(f"torch.cuda.is_available():{torch.cuda.is_available()}")
     # print(f"Before text_to_detailed: {torch.cuda.memory_allocated() / 1024**3} GB")

models/DiMeR/models/DiMeR.py

@@ -89,12 +89,12 @@ class DiMeR(nn.Module):
         print(1)
         camera = PerspectiveCamera(fovy=fovy, device=device)
         print(2)
-        renderer = NeuralRender(device, camera_model=camera)
+        # renderer = NeuralRender(device, camera_model=camera)
         print(3)
         self.geometry = FlexiCubesGeometry(
             grid_res=self.grid_res, 
             scale=self.grid_scale, 
-            renderer=renderer, 
+            renderer=None, 
             render_type='neural_render',
             device=device,
         )

models/DiMeR/models/geometry/rep_3d/flexicubes_geometry.py

@@ -67,7 +67,7 @@ class FlexiCubesGeometry(Geometry):
         self.renderer = renderer
         self.render_type = render_type
         print(3.8)
-        self.ctx = dr.RasterizeCudaContext(device=device)
+        # self.ctx = dr.RasterizeCudaContext(device=device)
         print(3.9)
 
         # self.verts.requires_grad_(True)
@@ -153,33 +153,33 @@ class FlexiCubesGeometry(Geometry):
         return verts, faces, v_reg_loss, imesh
 
     
-    def render_mesh(self, mesh_v_nx3, mesh_f_fx3, mesh, camera_mv_bx4x4, camera_pos, env, planes, kd_fn, materials, resolution=256, hierarchical_mask=False, gt_albedo_map=None, gt_normal_map=None, gt_depth_map=None, use_PBR=True):
-        return_value = dict()
-        buffer_dict = render.render_mesh(self.ctx, mesh, camera_mv_bx4x4, camera_pos, env, 
-                                         planes, kd_fn, materials, [resolution, resolution], 
-                                         spp=1, num_layers=1, msaa=True, background=None, gt_albedo_map=gt_albedo_map, use_PBR=use_PBR)
+    # def render_mesh(self, mesh_v_nx3, mesh_f_fx3, mesh, camera_mv_bx4x4, camera_pos, env, planes, kd_fn, materials, resolution=256, hierarchical_mask=False, gt_albedo_map=None, gt_normal_map=None, gt_depth_map=None, use_PBR=True):
+    #     return_value = dict()
+    #     buffer_dict = render.render_mesh(self.ctx, mesh, camera_mv_bx4x4, camera_pos, env, 
+    #                                      planes, kd_fn, materials, [resolution, resolution], 
+    #                                      spp=1, num_layers=1, msaa=True, background=None, gt_albedo_map=gt_albedo_map, use_PBR=use_PBR)
 
-        return buffer_dict
+    #     return buffer_dict
     
 
-    def render(self, v_deformed_bxnx3=None, sdf_bxn=None, camera_mv_bxnviewx4x4=None, resolution=256):
-        # Here I assume a batch of meshes (can be different mesh and geometry), for the other shapes, the batch is 1
-        v_list = []
-        f_list = []
-        n_batch = v_deformed_bxnx3.shape[0]
-        all_render_output = []
-        for i_batch in range(n_batch):
-            verts_nx3, faces_fx3 = self.get_mesh(v_deformed_bxnx3[i_batch], sdf_bxn[i_batch])
-            v_list.append(verts_nx3)
-            f_list.append(faces_fx3)
-            render_output = self.render_mesh(verts_nx3, faces_fx3, camera_mv_bxnviewx4x4[i_batch], resolution)
-            all_render_output.append(render_output)
-
-        # Concatenate all render output
-        return_keys = all_render_output[0].keys()
-        return_value = dict()
-        for k in return_keys:
-            value = [v[k] for v in all_render_output]
-            return_value[k] = value
-            # We can do concatenation outside of the render
-        return return_value
+    # def render(self, v_deformed_bxnx3=None, sdf_bxn=None, camera_mv_bxnviewx4x4=None, resolution=256):
+    #     # Here I assume a batch of meshes (can be different mesh and geometry), for the other shapes, the batch is 1
+    #     v_list = []
+    #     f_list = []
+    #     n_batch = v_deformed_bxnx3.shape[0]
+    #     all_render_output = []
+    #     for i_batch in range(n_batch):
+    #         verts_nx3, faces_fx3 = self.get_mesh(v_deformed_bxnx3[i_batch], sdf_bxn[i_batch])
+    #         v_list.append(verts_nx3)
+    #         f_list.append(faces_fx3)
+    #         render_output = self.render_mesh(verts_nx3, faces_fx3, camera_mv_bxnviewx4x4[i_batch], resolution)
+    #         all_render_output.append(render_output)
+
+    #     # Concatenate all render output
+    #     return_keys = all_render_output[0].keys()
+    #     return_value = dict()
+    #     for k in return_keys:
+    #         value = [v[k] for v in all_render_output]
+    #         return_value[k] = value
+    #         # We can do concatenation outside of the render
+    #     return return_value