Optimize generate3d function for performance and clarity

- Streamlined the color and coordinate processing by simplifying tensor operations and enhancing readability.
- Improved the handling of denoising logic with clearer condition checks and timing measurements.
- Added debug prints to monitor processing times for unet and UV mapping, aiding in performance analysis.

inference.py

@@ -1,55 +1,58 @@
 import numpy as np
 import torch
 import time
+import nvdiffrast.torch as dr
 from util.utils import get_tri
 import tempfile
+from mesh import Mesh
+import zipfile
 from util.renderer import Renderer
-import os
-from PIL import Image
-import trimesh
-from scipy.spatial import cKDTree
+def generate3d(model, rgb, ccm, device):
 
+    model.renderer = Renderer(tet_grid_size=model.tet_grid_size, camera_angle_num=model.camera_angle_num,
+                                scale=model.input.scale, geo_type = model.geo_type)
+
+    color_tri = torch.from_numpy(rgb)/255
+    xyz_tri = torch.from_numpy(ccm[:,:,(2,1,0)])/255
+    color = color_tri.permute(2,0,1)
+    xyz = xyz_tri.permute(2,0,1)
 
-def generate3d(model, rgb, ccm, device):
-    model.renderer = Renderer(
-        tet_grid_size=model.tet_grid_size,
-        camera_angle_num=model.camera_angle_num,
-        scale=model.input.scale,
-        geo_type=model.geo_type
-    )
-
-    color_tri = torch.from_numpy(rgb) / 255
-    xyz_tri = torch.from_numpy(ccm[:, :, (2, 1, 0)]) / 255
-    color = color_tri.permute(2, 0, 1)
-    xyz = xyz_tri.permute(2, 0, 1)
 
     def get_imgs(color):
+        # color : [C, H, W*6]
         color_list = []
-        color_list.append(color[:, :, 256 * 5:256 * (1 + 5)])
-        for i in range(0, 5):
-            color_list.append(color[:, :, 256 * i:256 * (1 + i)])
-        return torch.stack(color_list, dim=0)
-
-    triplane_color = get_imgs(color).permute(0, 2, 3, 1).unsqueeze(0).to(device)
+        color_list.append(color[:,:,256*5:256*(1+5)])
+        for i in range(0,5):
+            color_list.append(color[:,:,256*i:256*(1+i)])
+        return torch.stack(color_list, dim=0)# [6, C, H, W]
+    
+    triplane_color = get_imgs(color).permute(0,2,3,1).unsqueeze(0).to(device)# [1, 6, H, W, C]
 
     color = get_imgs(color)
     xyz = get_imgs(xyz)
 
-    color = get_tri(color, dim=0, blender=True, scale=1).unsqueeze(0)
-    xyz = get_tri(xyz, dim=0, blender=True, scale=1, fix=True).unsqueeze(0)
+    color = get_tri(color, dim=0, blender= True, scale = 1).unsqueeze(0)
+    xyz = get_tri(xyz, dim=0, blender= True, scale = 1, fix= True).unsqueeze(0)
 
-    triplane = torch.cat([color, xyz], dim=1).to(device)
+    triplane = torch.cat([color,xyz],dim=1).to(device)
+    # 3D visualize
     model.eval()
+    
 
-    if model.denoising:
+    if model.denoising == True:
         tnew = 20
-        tnew = torch.randint(tnew, tnew + 1, [triplane.shape[0]], dtype=torch.long, device=triplane.device)
-        noise_new = torch.randn_like(triplane) * 0.5 + 0.5
-        triplane = model.scheduler.add_noise(triplane, noise_new, tnew)
+        tnew = torch.randint(tnew, tnew+1, [triplane.shape[0]], dtype=torch.long, device=triplane.device)
+        noise_new = torch.randn_like(triplane) *0.5+0.5
+        triplane = model.scheduler.add_noise(triplane, noise_new, tnew)    
+        start_time = time.time()
         with torch.no_grad():
-            triplane_feature2 = model.unet2(triplane, tnew)
+            triplane_feature2 = model.unet2(triplane,tnew)
+        end_time = time.time()
+        elapsed_time = end_time - start_time
+        print(f"unet takes {elapsed_time}s")
     else:
         triplane_feature2 = model.unet2(triplane)
+        
 
     with torch.no_grad():
         data_config = {
@@ -58,8 +61,10 @@ def generate3d(model, rgb, ccm, device):
         }
 
         verts, faces = model.decode(data_config, triplane_feature2)
+
         data_config['verts'] = verts[0]
         data_config['faces'] = faces
+        
 
     from kiui.mesh_utils import clean_mesh
     verts, faces = clean_mesh(data_config['verts'].squeeze().cpu().numpy().astype(np.float32), data_config['faces'].squeeze().cpu().numpy().astype(np.int32), repair = False, remesh=True, remesh_size=0.005, remesh_iters=1)
@@ -70,25 +75,25 @@ def generate3d(model, rgb, ccm, device):
     with torch.no_grad():
         mesh_path_glb = tempfile.NamedTemporaryFile(suffix=f"", delete=False).name
         model.export_mesh(data_config, mesh_path_glb, tri_fea_2 = triplane_feature2)
-    # # Build KDTree from original verts
-    # tree = cKDTree(orig_verts)
-
-    # # For each new vertex, find the nearest old vertex and copy its color
-    # k = 3
-    # dists, idxs = tree.query(verts, k=k)
-    # # Use only the nearest neighbor for color assignment
-    # new_colors = orig_colors[idxs[:, 0]]
 
-    # Create the new mesh with colors
-    # mesh = trimesh.Trimesh(vertices=verts, faces=faces, vertex_colors=orig_colors)
+        # glctx = dr.RasterizeGLContext()#dr.RasterizeCudaContext()
+        # mesh_path_obj = tempfile.NamedTemporaryFile(suffix=f"", delete=False).name
+        # model.export_mesh_wt_uv(glctx, data_config, mesh_path_obj, "", device, res=(1024,1024), tri_fea_2=triplane_feature2)
 
-    # === Export OBJ/MTL/PNG ===
-    obj_path = tempfile.NamedTemporaryFile(suffix=".obj", delete=False).name
-    base_path = obj_path[:-4]  # remove .obj
+        # mesh = Mesh.load(mesh_path_obj+".obj", bound=0.9, front_dir="+z")
+        # mesh_path_glb = tempfile.NamedTemporaryFile(suffix=f"", delete=False).name
+        # mesh.write(mesh_path_glb+".glb")
 
-    texture_path = base_path + ".png"
-    mtl_path = base_path + ".mtl"
+        # # mesh_obj2 = trimesh.load(mesh_path_glb+".glb", file_type='glb')
+        # # mesh_path_obj2 = tempfile.NamedTemporaryFile(suffix=f"", delete=False).name
+        # # mesh_obj2.export(mesh_path_obj2+".obj")
 
-    model.export_mesh(data_config, base_path, tri_fea_2=triplane_feature2)  # writes .obj
+        # with zipfile.ZipFile(mesh_path_obj+'.zip', 'w') as myzip:
+        #     myzip.write(mesh_path_obj+'.obj', mesh_path_obj.split("/")[-1]+'.obj')
+        #     myzip.write(mesh_path_obj+'.png', mesh_path_obj.split("/")[-1]+'.png')
+        #     myzip.write(mesh_path_obj+'.mtl', mesh_path_obj.split("/")[-1]+'.mtl')
 
-    return obj_path
+    end_time = time.time()
+    elapsed_time = end_time - start_time
+    print(f"uv takes {elapsed_time}s")
+    return mesh_path_glb+".obj"