diff --git a/prototorch/models/vis.py b/prototorch/models/vis.py
index dfedfe5..4f6b696 100644
--- a/prototorch/models/vis.py
+++ b/prototorch/models/vis.py
@@ -7,6 +7,8 @@ import torchvision
 from matplotlib import pyplot as plt
 from torch.utils.data import DataLoader, Dataset
 
+from ..utils.utils import mesh2d
+
 
 class Vis2DAbstract(pl.Callback):
     def __init__(self,
@@ -73,16 +75,6 @@ class Vis2DAbstract(pl.Callback):
             ax.axis("off")
         return ax
 
-    def get_mesh_input(self, x):
-        x_shift = self.border * np.ptp(x[:, 0])
-        y_shift = self.border * np.ptp(x[:, 1])
-        x_min, x_max = x[:, 0].min() - x_shift, x[:, 0].max() + x_shift
-        y_min, y_max = x[:, 1].min() - y_shift, x[:, 1].max() + y_shift
-        xx, yy = np.meshgrid(np.linspace(x_min, x_max, self.resolution),
-                             np.linspace(y_min, y_max, self.resolution))
-        mesh_input = np.c_[xx.ravel(), yy.ravel()]
-        return mesh_input, xx, yy
-
     def plot_data(self, ax, x, y):
         ax.scatter(
             x[:, 0],
@@ -138,7 +130,7 @@ class VisGLVQ2D(Vis2DAbstract):
         self.plot_data(ax, x_train, y_train)
         self.plot_protos(ax, protos, plabels)
         x = np.vstack((x_train, protos))
-        mesh_input, xx, yy = self.get_mesh_input(x)
+        mesh_input, xx, yy = mesh2d(x, self.border, self.resolution)
         _components = pl_module.proto_layer._components
         mesh_input = torch.from_numpy(mesh_input).type_as(_components)
         y_pred = pl_module.predict(mesh_input)
@@ -173,9 +165,9 @@ class VisSiameseGLVQ2D(Vis2DAbstract):
         if self.show_protos:
             self.plot_protos(ax, protos, plabels)
             x = np.vstack((x_train, protos))
-            mesh_input, xx, yy = self.get_mesh_input(x)
+            mesh_input, xx, yy = mesh2d(x, self.border, self.resolution)
         else:
-            mesh_input, xx, yy = self.get_mesh_input(x_train)
+            mesh_input, xx, yy = mesh2d(x_train, self.border, self.resolution)
         _components = pl_module.proto_layer._components
         mesh_input = torch.Tensor(mesh_input).type_as(_components)
         y_pred = pl_module.predict_latent(mesh_input,
@@ -198,7 +190,7 @@ class VisCBC2D(Vis2DAbstract):
         self.plot_data(ax, x_train, y_train)
         self.plot_protos(ax, protos, "w")
         x = np.vstack((x_train, protos))
-        mesh_input, xx, yy = self.get_mesh_input(x)
+        mesh_input, xx, yy = mesh2d(x, self.border, self.resolution)
         _components = pl_module.components_layer._components
         y_pred = pl_module.predict(
             torch.Tensor(mesh_input).type_as(_components))