Small fix to tutorial

Summary: Small fix to `fit_textured_mesh.ipynb` tutorial due to a recent change in numpy

Reviewed By: bottler

Differential Revision: D29219990

fbshipit-source-id: f5feeef9eb952720ea7154d066795fbbe64ce7a1

Author: nikhilaravi

docs/tutorials/deform_source_mesh_to_target_mesh.ipynb

@@ -406,10 +406,10 @@
     "    loop.set_description('total_loss = %.6f' % loss)\n",
     "    \n",
     "    # Save the losses for plotting\n",
-    "    chamfer_losses.append(loss_chamfer)\n",
-    "    edge_losses.append(loss_edge)\n",
-    "    normal_losses.append(loss_normal)\n",
-    "    laplacian_losses.append(loss_laplacian)\n",
+    "    chamfer_losses.append(float(loss_chamfer.detach().cpu()))\n",
+    "    edge_losses.append(float(loss_edge.detach().cpu()))\n",
+    "    normal_losses.append(float(loss_normal.detach().cpu()))\n",
+    "    laplacian_losses.append(float(loss_laplacian.detach().cpu()))\n",
     "    \n",
     "    # Plot mesh\n",
     "    if i % plot_period == 0:\n",

docs/tutorials/fit_textured_mesh.ipynb

@@ -46,8 +46,8 @@
     "id": "okLalbR_g7NS"
    },
    "source": [
-     "Ensure `torch` and `torchvision` are installed. If `pytorch3d` is not installed, install it using the following cell:"
-  ]
+    "Ensure `torch` and `torchvision` are installed. If `pytorch3d` is not installed, install it using the following cell:"
+   ]
   },
   {
    "cell_type": "code",
@@ -645,7 +645,8 @@
     "    sum_loss = torch.tensor(0.0, device=device)\n",
     "    for k, l in loss.items():\n",
     "        sum_loss += l * losses[k][\"weight\"]\n",
-    "        losses[k][\"values\"].append(l)\n",
+    "        losses[k][\"values\"].append(float(l.detach().cpu()))\n",
+    "\n",
     "    \n",
     "    # Print the losses\n",
     "    loop.set_description(\"total_loss = %.6f\" % sum_loss)\n",
@@ -829,7 +830,7 @@
     "    sum_loss = torch.tensor(0.0, device=device)\n",
     "    for k, l in loss.items():\n",
     "        sum_loss += l * losses[k][\"weight\"]\n",
-    "        losses[k][\"values\"].append(l)\n",
+    "        losses[k][\"values\"].append(float(l.detach().cpu()))\n",
     "    \n",
     "    # Print the losses\n",
     "    loop.set_description(\"total_loss = %.6f\" % sum_loss)\n",