CPU device for tutorials

Reviewed By: nikhilaravi

Differential Revision: D22357376

fbshipit-source-id: c103f9b0c798d4425d642781b5bfbb1a27310270

docs/tutorials/bundle_adjustment.ipynb

@@ -114,7 +114,12 @@
     "sys.path.append(os.path.abspath(''))\n",
     "\n",
     "# set for reproducibility\n",
-    "torch.manual_seed(42)"
+    "torch.manual_seed(42)\n",
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device(\"cuda:0\")\n",
+    "else:\n",
+    "    device = torch.device(\"cpu\")\n",
+    "    print(\"WARNING: CPU only, this will be slow!\")"
    ]
   },
   {
@@ -200,16 +205,16 @@
     "\n",
     "# create the relative cameras\n",
     "cameras_relative = SfMPerspectiveCameras(\n",
-    "    R = R_relative.cuda(),\n",
-    "    T = T_relative.cuda(),\n",
-    "    device = \"cuda\",\n",
+    "    R = R_relative.to(device),\n",
+    "    T = T_relative.to(device),\n",
+    "    device = device,\n",
     ")\n",
     "\n",
     "# create the absolute ground truth cameras\n",
     "cameras_absolute_gt = SfMPerspectiveCameras(\n",
-    "    R = R_absolute_gt.cuda(),\n",
-    "    T = T_absolute_gt.cuda(),\n",
-    "    device = \"cuda\",\n",
+    "    R = R_absolute_gt.to(device),\n",
+    "    T = T_absolute_gt.to(device),\n",
+    "    device = device,\n",
     ")\n",
     "\n",
     "# the number of absolute camera positions\n",
@@ -270,7 +275,7 @@
     "        SfMPerspectiveCameras(\n",
     "            R = cams.R[edges[:, i]],\n",
     "            T = cams.T[edges[:, i]],\n",
-    "            device = \"cuda\",\n",
+    "            device = device,\n",
     "        ).get_world_to_view_transform()\n",
     "         for i in (0, 1)\n",
     "    ]\n",
@@ -283,7 +288,7 @@
     "    cams_relative = SfMPerspectiveCameras(\n",
     "                        R = matrix_rel[:, :3, :3],\n",
     "                        T = matrix_rel[:, 3, :3],\n",
-    "                        device = \"cuda\",\n",
+    "                        device = device,\n",
     "                    )\n",
     "    return cams_relative"
    ]
@@ -320,8 +325,8 @@
    "outputs": [],
    "source": [
     "# initialize the absolute log-rotations/translations with random entries\n",
-    "log_R_absolute_init = torch.randn(N, 3).float().cuda()\n",
-    "T_absolute_init = torch.randn(N, 3).float().cuda()\n",
+    "log_R_absolute_init = torch.randn(N, 3, dtype=torch.float32, device=device)\n",
+    "T_absolute_init = torch.randn(N, 3, dtype=torch.float32, device=device)\n",
     "\n",
     "# furthermore, we know that the first camera is a trivial one \n",
     "#    (see the description above)\n",
@@ -337,7 +342,7 @@
     "# the mask the specifies which cameras are going to be optimized\n",
     "#     (since we know the first camera is already correct, \n",
     "#      we only optimize over the 2nd-to-last cameras)\n",
-    "camera_mask = torch.ones(N, 1).float().cuda()\n",
+    "camera_mask = torch.ones(N, 1, dtype=torch.float32, device=device)\n",
     "camera_mask[0] = 0.\n",
     "\n",
     "# init the optimizer\n",
@@ -358,7 +363,7 @@
     "    cameras_absolute = SfMPerspectiveCameras(\n",
     "        R = R_absolute,\n",
     "        T = T_absolute * camera_mask,\n",
-    "        device = \"cuda\",\n",
+    "        device = device,\n",
     "    )\n",
     "\n",
     "    # compute the relative cameras as a compositon of the absolute cameras\n",

docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb

@@ -84,7 +84,7 @@
     "import os\n",
     "import torch\n",
     "import numpy as np\n",
-    "from tqdm import tqdm_notebook\n",
+    "from tqdm.notebook import tqdm\n",
     "import imageio\n",
     "import torch.nn as nn\n",
     "import torch.nn.functional as F\n",
@@ -159,8 +159,11 @@
    "outputs": [],
    "source": [
     "# Set the cuda device \n",
-    "device = torch.device(\"cuda:0\")\n",
-    "torch.cuda.set_device(device)\n",
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device(\"cuda:0\")\n",
+    "    torch.cuda.set_device(device)\n",
+    "else:\n",
+    "    device = torch.device(\"cpu\")\n",
     "\n",
     "# Load the obj and ignore the textures and materials.\n",
     "verts, faces_idx, _ = load_obj(\"./data/teapot.obj\")\n",
@@ -433,7 +436,7 @@
     "plt.subplot(1, 2, 2)\n",
     "plt.imshow(model.image_ref.cpu().numpy().squeeze())\n",
     "plt.grid(False)\n",
-    "plt.title(\"Reference silhouette\")\n"
+    "plt.title(\"Reference silhouette\");\n"
    ]
   },
   {
@@ -472,7 +475,7 @@
    },
    "outputs": [],
    "source": [
-    "loop = tqdm_notebook(range(200))\n",
+    "loop = tqdm(range(200))\n",
     "for i in loop:\n",
     "    optimizer.zero_grad()\n",
     "    loss, _ = model()\n",

docs/tutorials/deform_source_mesh_to_target_mesh.ipynb

@@ -108,7 +108,7 @@
     "    mesh_normal_consistency,\n",
     ")\n",
     "import numpy as np\n",
-    "from tqdm import tqdm_notebook\n",
+    "from tqdm.notebook import tqdm\n",
     "%matplotlib notebook \n",
     "from mpl_toolkits.mplot3d import Axes3D\n",
     "import matplotlib.pyplot as plt\n",
@@ -117,7 +117,11 @@
     "mpl.rcParams['figure.dpi'] = 80\n",
     "\n",
     "# Set the device\n",
-    "device = torch.device(\"cuda:0\")"
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device(\"cuda:0\")\n",
+    "else:\n",
+    "    device = torch.device(\"cpu\")\n",
+    "    print(\"WARNING: CPU only, this will be slow!\")"
    ]
   },
   {
@@ -342,7 +346,7 @@
     "w_laplacian = 0.1 \n",
     "# Plot period for the losses\n",
     "plot_period = 250\n",
-    "loop = tqdm_notebook(range(Niter))\n",
+    "loop = tqdm(range(Niter))\n",
     "\n",
     "chamfer_losses = []\n",
     "laplacian_losses = []\n",
@@ -428,7 +432,7 @@
     "ax.legend(fontsize=\"16\")\n",
     "ax.set_xlabel(\"Iteration\", fontsize=\"16\")\n",
     "ax.set_ylabel(\"Loss\", fontsize=\"16\")\n",
-    "ax.set_title(\"Loss vs iterations\", fontsize=\"16\")"
+    "ax.set_title(\"Loss vs iterations\", fontsize=\"16\");"
    ]
   },
   {

docs/tutorials/render_colored_points.ipynb

@@ -52,7 +52,6 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "os.chdir('../..')\n",
     "import torch\n",
     "import torch.nn.functional as F\n",
     "import matplotlib.pyplot as plt\n",
@@ -110,8 +109,11 @@
    "outputs": [],
    "source": [
     "# Setup\n",
-    "device = torch.device(\"cuda:0\")\n",
-    "torch.cuda.set_device(device)\n",
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device(\"cuda:0\")\n",
+    "    torch.cuda.set_device(device)\n",
+    "else:\n",
+    "    device = torch.device(\"cpu\")\n",
     "\n",
     "# Set paths\n",
     "DATA_DIR = \"./data\"\n",
@@ -177,7 +179,7 @@
     "plt.figure(figsize=(10, 10))\n",
     "plt.imshow(images[0, ..., :3].cpu().numpy())\n",
     "plt.grid(\"off\")\n",
-    "plt.axis(\"off\")"
+    "plt.axis(\"off\");"
    ]
   },
   {
@@ -225,7 +227,7 @@
     "plt.figure(figsize=(10, 10))\n",
     "plt.imshow(images[0, ..., :3].cpu().numpy())\n",
     "plt.grid(\"off\")\n",
-    "plt.axis(\"off\")"
+    "plt.axis(\"off\");"
    ]
   }
  ],

docs/tutorials/render_textured_meshes.ipynb

@@ -217,8 +217,11 @@
    "outputs": [],
    "source": [
     "# Setup\n",
-    "device = torch.device(\"cuda:0\")\n",
-    "torch.cuda.set_device(device)\n",
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device(\"cuda:0\")\n",
+    "    torch.cuda.set_device(device)\n",
+    "else:\n",
+    "    device = torch.device(\"cpu\")\n",
     "\n",
     "# Set paths\n",
     "DATA_DIR = \"./data\"\n",