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util/renderer.py

@@ -1,49 +1,49 @@
-
-import torch
-import torch.nn as nn
-import nvdiffrast.torch as dr
-from util.flexicubes_geometry import FlexiCubesGeometry
-
-class Renderer(nn.Module):
-    def __init__(self, tet_grid_size, camera_angle_num, scale, geo_type):
-        super().__init__()
-
-        self.tet_grid_size = tet_grid_size
-        self.camera_angle_num = camera_angle_num
-        self.scale = scale
-        self.geo_type = geo_type
-        # self.glctx = dr.RasterizeCudaContext()
-
-        if self.geo_type == "flex":
-            self.flexicubes = FlexiCubesGeometry(grid_res = self.tet_grid_size)   
-
-    def forward(self, data, sdf, deform, verts, tets, training=False, weight = None):
-
-        results = {}
-
-        deform = torch.tanh(deform) / self.tet_grid_size * self.scale / 0.95
-        if self.geo_type == "flex":
-            deform = deform *0.5
-
-            v_deformed = verts + deform
-
-            verts_list = []
-            faces_list = []
-            reg_list = []
-            n_shape = verts.shape[0]
-            for i in range(n_shape): 
-                verts_i, faces_i, reg_i = self.flexicubes.get_mesh(v_deformed[i], sdf[i].squeeze(dim=-1),
-                with_uv=False, indices=tets, weight_n=weight[i], is_training=training)
-
-                verts_list.append(verts_i)
-                faces_list.append(faces_i)
-                reg_list.append(reg_i)       
-            verts = verts_list
-            faces = faces_list
-
-            flexicubes_surface_reg = torch.cat(reg_list).mean()
-            flexicubes_weight_reg = (weight ** 2).mean()
-            results["flex_surf_loss"] = flexicubes_surface_reg
-            results["flex_weight_loss"] = flexicubes_weight_reg
-
+
+import torch
+import torch.nn as nn
+import nvdiffrast.torch as dr
+from util.flexicubes_geometry import FlexiCubesGeometry
+
+class Renderer(nn.Module):
+    def __init__(self, tet_grid_size, camera_angle_num, scale, geo_type):
+        super().__init__()
+
+        self.tet_grid_size = tet_grid_size
+        self.camera_angle_num = camera_angle_num
+        self.scale = scale
+        self.geo_type = geo_type
+        self.glctx = dr.RasterizeCudaContext()
+
+        if self.geo_type == "flex":
+            self.flexicubes = FlexiCubesGeometry(grid_res = self.tet_grid_size)   
+
+    def forward(self, data, sdf, deform, verts, tets, training=False, weight = None):
+
+        results = {}
+
+        deform = torch.tanh(deform) / self.tet_grid_size * self.scale / 0.95
+        if self.geo_type == "flex":
+            deform = deform *0.5
+
+            v_deformed = verts + deform
+
+            verts_list = []
+            faces_list = []
+            reg_list = []
+            n_shape = verts.shape[0]
+            for i in range(n_shape): 
+                verts_i, faces_i, reg_i = self.flexicubes.get_mesh(v_deformed[i], sdf[i].squeeze(dim=-1),
+                with_uv=False, indices=tets, weight_n=weight[i], is_training=training)
+
+                verts_list.append(verts_i)
+                faces_list.append(faces_i)
+                reg_list.append(reg_i)       
+            verts = verts_list
+            faces = faces_list
+
+            flexicubes_surface_reg = torch.cat(reg_list).mean()
+            flexicubes_weight_reg = (weight ** 2).mean()
+            results["flex_surf_loss"] = flexicubes_surface_reg
+            results["flex_weight_loss"] = flexicubes_weight_reg
+
         return results, verts, faces