{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Camera position optimization using differentiable rendering\n",
    "\n",
    "In this tutorial we will learn the [x, y, z] position of a camera given a reference image using differentiable rendering. \n",
    "\n",
    "We will first initialize a renderer with a starting position for the camera. We will then use this to generate an image, compute a loss with the reference image, and finally backpropagate through the entire pipeline to update the position of the camera. \n",
    "\n",
    "This tutorial shows how to:\n",
    "- load a mesh from an `.obj` file\n",
    "- initialize a `Camera`, `Shader` and `Renderer`,\n",
    "- render a mesh\n",
    "- set up an optimization loop with a loss function and optimizer\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Set up and imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import numpy as np\n",
    "from tqdm import tqdm_notebook\n",
    "import imageio\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage import img_as_ubyte\n",
    "\n",
    "# io utils\n",
    "from pytorch3d.io import load_obj\n",
    "\n",
    "# datastructures\n",
    "from pytorch3d.structures import Meshes, Textures\n",
    "\n",
    "# 3D transformations functions\n",
    "from pytorch3d.transforms import Rotate, Translate\n",
    "\n",
    "# rendering components\n",
    "from pytorch3d.renderer import (\n",
    "    OpenGLPerspectiveCameras, look_at_view_transform, look_at_rotation, \n",
    "    RasterizationSettings, MeshRenderer, MeshRasterizer, BlendParams,\n",
    "    SilhouetteShader, PhongShader, PointLights\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Obj\n",
    "\n",
    "We will load an obj file and create a **Meshes** object. **Meshes** is a unique datastructure provided in PyTorch3d for working with **batches of meshes of different sizes**. It has several useful class methods which are used in the rendering pipeline. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set the cuda device \n",
    "device = torch.device(\"cuda:0\")\n",
    "torch.cuda.set_device(device)\n",
    "\n",
    "# Load the obj and ignore the textures and materials.\n",
    "verts, faces_idx, _ = load_obj(\"./data/teapot.obj\")\n",
    "faces = faces_idx.verts_idx\n",
    "\n",
    "# Initialize each vertex to be white in color.\n",
    "verts_rgb = torch.ones_like(verts)[None]  # (1, V, 3)\n",
    "textures = Textures(verts_rgb=verts_rgb.to(device))\n",
    "\n",
    "# Create a Meshes object for the teapot. Here we have only one mesh in the batch.\n",
    "teapot_mesh = Meshes(\n",
    "    verts=[verts.to(device)],   \n",
    "    faces=[faces.to(device)], \n",
    "    textures=textures\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "##  Optimization setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a renderer\n",
    "\n",
    "A **renderer** in PyTorch3d is composed of a **rasterizer** and a **shader** which each have a number of subcomponents such as a **camera** (orthgraphic/perspective). Here we initialize some of these components and use default values for the rest. \n",
    "\n",
    "For optimizing the camera position we will use a renderer which produces a **silhouette** of the object only and does not apply any **lighting** or **shading**. We will also initialize another renderer which applies full **phong shading** and use this for visualizing the outputs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Initialize an OpenGL perspective camera.\n",
    "cameras = OpenGLPerspectiveCameras(device=device)\n",
    "\n",
    "# To blend the 100 faces we set a few parameters which control the opacity and the sharpness of \n",
    "# edges. Refer to blending.py for more details. \n",
    "blend_params = BlendParams(sigma=1e-4, gamma=1e-4)\n",
    "\n",
    "# Define the settings for rasterization and shading. Here we set the output image to be of size\n",
    "# 256x256. To form the blended image we use 100 faces for each pixel. Refer to rasterize_meshes.py\n",
    "# for an explanation of this parameter. \n",
    "raster_settings = RasterizationSettings(\n",
    "    image_size=256, \n",
    "    blur_radius=np.log(1. / 1e-4 - 1.) * blend_params.sigma, \n",
    "    faces_per_pixel=100, \n",
    "    bin_size=0\n",
    ")\n",
    "\n",
    "# Create a silhouette mesh renderer by composing a rasterizer and a shader. \n",
    "silhouette_renderer = MeshRenderer(\n",
    "    rasterizer=MeshRasterizer(\n",
    "        cameras=cameras, \n",
    "        raster_settings=raster_settings\n",
    "    ),\n",
    "    shader=SilhouetteShader(blend_params=blend_params)\n",
    ")\n",
    "\n",
    "\n",
    "# We will also create a phong renderer. This is simpler and only needs to render one face per pixel.\n",
    "raster_settings = RasterizationSettings(\n",
    "    image_size=256, \n",
    "    blur_radius=0.0, \n",
    "    faces_per_pixel=1, \n",
    "    bin_size=0\n",
    ")\n",
    "# We can add a point light in front of the object. \n",
    "lights = PointLights(device=device, location=((2.0, 2.0, -2.0),))\n",
    "phong_renderer = MeshRenderer(\n",
    "    rasterizer=MeshRasterizer(\n",
    "        cameras=cameras, \n",
    "        raster_settings=raster_settings\n",
    "    ),\n",
    "    shader=PhongShader(device=device, lights=lights)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a reference image\n",
    "\n",
    "We will first position the teapot and generate an image. We use helper functions to rotate the teapot to a desired viewpoint. Then we can use the renderers to produce an image. Here we will use both renderers and visualize the silhouette and full shaded image. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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JiQmCIEBrzebNm9m5cyee55HP5xMhM1Prz1Wh0r4mh7uvEfssnU+qWCzS399Pf39/4uNy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\nAwuph6AHQDgpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140267769944592"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select the viewpoint using spherical angles  \n",
    "distance = 3   # distance from camera to the object\n",
    "elevation = 40.0   # angle of elevation in degrees\n",
    "azimuth = 10.0  # angle of azimuth rotation in degrees\n",
    "\n",
    "# Get the position of the camera based on the spherical angles\n",
    "R, T = look_at_view_transform(distance, elevation, azimuth, device=device)\n",
    "\n",
    "# Render the teapot providing the values of R and T. \n",
    "silhouete = silhouette_renderer(meshes_world=teapot_mesh, R=R, T=T)\n",
    "image_ref = phong_renderer(meshes_world=teapot_mesh, R=R, T=T)\n",
    "\n",
    "silhouete = silhouete.cpu().numpy()\n",
    "image_ref = image_ref.cpu().numpy()\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(silhouete.squeeze()[..., 3])  # only plot the alpha channel of the RGBA image\n",
    "plt.grid(\"off\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(image_ref.squeeze())\n",
    "plt.grid(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up a basic model \n",
    "\n",
    "Here we create a simple model class and initialize a parameter for the camera position. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Model(nn.Module):\n",
    "    def __init__(self, meshes, renderer, image_ref):\n",
    "        super().__init__()\n",
    "        self.meshes = meshes\n",
    "        self.device = meshes.device\n",
    "        self.renderer = renderer\n",
    "        \n",
    "        # Get the silhouette of the reference RGB image by finding all the non zero values. \n",
    "        image_ref = torch.from_numpy((image_ref[..., :3].max(-1) != 0).astype(np.float32))\n",
    "        self.register_buffer('image_ref', image_ref)\n",
    "        \n",
    "        # Create an optimizable parameter for the x, y, z position of the camera. \n",
    "        self.camera_position = nn.Parameter(\n",
    "            torch.from_numpy(np.array([3.0,  6.9, -2.5], dtype=np.float32)).to(meshes.device))\n",
    "\n",
    "    def forward(self):\n",
    "        \n",
    "        # Render the image using the updated camera position. Based on the new position of the \n",
    "        # camer we calculate the rotation and translation matrices\n",
    "        R = look_at_rotation(self.camera_position[None, :], device=self.device)  # (1, 3, 3)\n",
    "        T = -torch.bmm(R.transpose(1, 2), self.camera_position[None, :, None])[:, :, 0]   # (1, 3)\n",
    "        \n",
    "        image = self.renderer(meshes_world=self.meshes.clone(), R=R, T=T)\n",
    "        \n",
    "        # Calculate the silhouette loss\n",
    "        loss = torch.sum((image[..., 3] - self.image_ref) ** 2)\n",
    "        return loss, image\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize the model and optimizer\n",
    "\n",
    "Now we can create an instance of the **model** above and set up an **optimizer** for the camera position parameter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We will save images periodically and compose them into a GIF.\n",
    "filename_output = \"./teapot_optimization_demo.gif\"\n",
    "writer = imageio.get_writer(filename_output, mode='I', duration=0.3)\n",
    "\n",
    "# Initialize a model using the renderer, mesh and reference image\n",
    "model = Model(meshes=teapot_mesh, renderer=silhouette_renderer, image_ref=image_ref).to(device)\n",
    "\n",
    "# Create an optimizer. Here we are using Adam and we pass in the parameters of the model\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise the starting position and the reference position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Reference silhouette')"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "bento_obj_id": "140267760329488"
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eApZhqrUNGaxijDOAc4Ebq2adllL6YdVypwN7AhuA22KMV6WUVoxJyyVpCNav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\nWEmSJJXEYCVJklQSg5UkSVJJ/j8aiYrAsm1hZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140267802952144"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "_, image_init = model()\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(image_init.detach().squeeze().cpu().numpy()[..., 3])\n",
    "plt.grid(\"off\")\n",
    "plt.title(\"Starting position\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(model.image_ref.cpu().numpy().squeeze())\n",
    "plt.grid(\"off\")\n",
    "plt.title(\"Reference silhouette\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the optimization \n",
    "\n",
    "We run several iterations of the forward and backward pass and save outputs every 10 iterations. When this has finished take a look at `./teapot_optimization_demo.gif` for a cool gif of the optimization process!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d3e3feb01f74f29a767dea9f99e1315",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=200), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ekkeh2WzywgsvcM011xz0GN/85jd55plnuOqqq4bwDEeLf/tQR+zuu+9mZmZm2RAsLCz0\nVn6tXYZAkn/gRNLgDIEkQyDJEEgyBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIw\nBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIw\nBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIw\nBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIw\nBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIwBJIw\nBJIwBJIwBJIwBJIwBJIAIqU07GWQNGSOCCQZAkmGQJIhkIQhkIQhkIQhkIQhkIQhkIQhkIQhkIQh\nkIQhkIQhkIQhkIQhkIQhkIQhkIQhkIQhkIQhkIQhkATw///mkSj4OiM4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140267769472976"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YmppiamqKmZmZeesbDOeOf/tQffnQhz5EvV6f96fPe4cj0/MY8kJBkA8vzoPgxIkTnDp1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\nBJIwCCRhEEjCIJCEQSAJg0ASBoEkgP8FtdAvPCBp9f4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140267769510800"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PsLQYzcvYQkfqiYkJpqam2vcRyAdG5/ydzYbOWkA+ZLIzDhuNBuVyuX0+QbdJS4c1gmWs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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140267769471504"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nCbPmeFbheivfamMF/dYX9D42HwB5/Z5jkC8+0fPLIBhC+eZ4djs/iJefAcjrt8w40xsC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\nPHIbeOQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140267376690832"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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N8ebNHXfc0YWjJ56CoAdcccUVjI+PzzobT01NRfcQ+J+0KLjtLg+GYwX8g0oqlQrVajUK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\nIigIRAQFgYigIBARgP8Dgs6M0ev77boAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140267360935888"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140267790171216"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4OEixWMRay9zcHI1Gg3q9TqPRiLL+/ilGpFQ9iOUH4q0RANPT0xhjWLt2LdVqlUqlsqit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\ngg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140267360973264"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EXEpvaA2ktZj+67B/P9xXLBYZHx+fV4ZWVkDSfv9Y2vlJ0Xs3cjAtb6CV+e+PLQiPOZKs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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140267360479440"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IviZRd918GgGwRaBarfbwyRJaBMsMdzyCiARv8LVjBWExgihLISwOELXAiQF0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\nB2h30LjPguQAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140267360183056"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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ZhWcKYwQLEFsIjLlcr9dbugutPtvx6+PW8GH5iar9bTfAWAFmOXLkSN+e+2KA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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140267759762576"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GDjoKSisOSzH2w8/SMMVMpw87jkNLoA6iphhTCOYYg4ODFYN69Nz85qScpigE\njUHwe1V61LEGjRpXEGQ56MqvBeDQoUO49dZbm3xn5ycUgnmOtg6EEEilUt4be6pZBKZABA1ACrMU\n9Dn9hhvDxwKwP/tZALaL4zgOSqUSpqenccMNN8zK/ZyvUAgWADrxyJyu2xSDaqMSq81bMBOLACFW\ngSkAfsFPx3EwMjKC9evXz9q9nK9QCBYQuVzOcxO0IGghsOMGYZZCLTGCMIsAVVwDbQGYCULmwvkE\nGweHIS8gdBejNqn1X9PPdhzHq3hBLXG1pVqEv1rkX88ipMtTLBYrPufzeYyOjrb6di5IaBHMU3K5\nnGch2IFEbSn4BRZr6U2Iiu0K6OzAyclJ3HLLLU28C4SuAakIKJpug9ndWK8Q+LkAqDIvgU4KchyH\n/v8sQSEgFZiikE6nj7AWGmERBCUhabegWCxiw4YNs3rdCx0KAQkll8tV9DRod8Gu7NWEIChF2PzO\nKcRbB4WA1MTmzZt9rYAgMdCvHbdzAO64445WXwoxoBCQhqJzFuLxOFzX9VwN0t5QCAghzCMghESH\nQkAIoRAQQigEhBAKASEEFAJCCCgEhBBQCAghoBAQQkAhIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQ\nCAghoBAQQkAhIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQQkAhIISAQkAIAYWAEAIKASEE\nFAJCCCgEhBBQCAghoBAQQkAhIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQQkAhIISAQkAI\nAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQQkAhIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQ\nQkAhIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQQkAhIISAQkAIAYWAEAIKASEEFAJCCCgE\nhBBQCAghoBAQQkAhIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQQkAhIISAQkAIAYWAEAIK\nASEEFAJCCCgEhBBQCAghACCklK0uAyGkxdAiIIRQCAghFAJCCIWAEAIKASEEFAJCCCgEhBBQCAgh\noBAQQkAhIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQQkAhIISAQkAIAYWAEAIKASEEFAJC\nCAD8H+kNZdOpqJ32AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140267359577808"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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jsgbudiQ85ASe1r9TpsAWI9dFMJW/Uqlw/oBVktYioBBsYGZmZqJK5j7EI5PJ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\nf/2yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140267360608656"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8M1IoBGF8oJNFQBAHyLIICCp/mhA4FycpW/DTn/70Ety91Y+EYA3hxMB4bwL2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\nhAD4/8ra93eprvotAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140266491010064"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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w5MiRXj/OWQktghnGyMgI4PkNTIvA9Q5FV4ShVdcAjqhBFFz+AFsQ1q9fP2nP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\nEAIKASEEFAJCCAD8H19TE6W0UXZdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140266490568400"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BS5cYRgjsab5c0znKtG5nskfRzkKIep90aWcBGAGo1WqoVquYmpqiCMwRdBYu\nQozjzJ7MwxQsg+tAtF/tGtp1LNpE9YB0+wnY292+Bu59+76L+94ImEkfNl2Ib7rppi6fGGkHmwaL\nkLGxMcAqdLlcLhyj324yuPMaxM134DYXzPmT9j6Ep5lgSBIdUFaEwBaA06dP484775zT57mUSdo0\noBAsUkwnJVP4zLj99tx/dvdkVwjc9e18BOhgMhMkFAKfw9P2B9x4443z8CSXNhSCZYLxHYg1c489\n7Zc7K1KUEzGJszCJRYAORCAqRDg+Po7bb799zp/dcoBCsIwwYqCUarEMfFOkmanTXBGIEoJ2CxIM\nQIKIqIDt9DQ5AkEQ4Oabb57nJ7h0oRAsQ8w8iiISTvMVJQRxVsJsmgY+7P+YbQGYLsRGAG699dZ5\neErLCwoBaRGGuCZDEiGIE4Eo3IiEUios9EEQ0Ak4D1AISIgdZRCRMMpgOxQ7tQjsvgs+3CaAEYE7\n7rhjXr4zeRUKAYlkbGwsLPxmyWazkULgWgWwnIW+JCH3c7VaxQc/+MF+f+1lCYWAJKZYLM4QgiTR\nBHhyAGq1GoIgAAB8+MMf7vM3IxQC0hVun4Z6vR5uc52L9XodqVQK999/fx/vmMRBISCEsNMRISQ5\nFAJCCIWAEEIhIIRQCAghoBAQQkAhIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQQkAhIISA\nQkAIAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQQkAhIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQCAgh\noBAQQkAhIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQQkAhIISAQkAIAYWAEAIKASEEFAJC\nCCgEhBBQCAghoBAQQkAhIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQQkAhIISAQkAIAYWA\nEAIKASEEFAJCCCgEhBBQCAghoBAQQkAhIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQQkAh\nIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQQkAhIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQ\nCAghoBAQQkAhIISAQkAIAYWAEAIAopTq9z0QQvoMLQJCCIWAEEIhIIRQCAghoBAQQkAhIISAQkAI\nAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQQkAhIISAQkAIAYWAEAIKASEEFAJCCCgEhBBQCAghoBAQ\nQkAhIIQAwP8BFvVSR4ODOyEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140266490416912"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iUKvVUKlUUC6XMTo6ShGYJegazEOktSSXHgCkCxIcAUTtVtgLsZpuhE3YKMYk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\nhBBQCAghoBAQQkAhIISAQkAIAYD/B2EIkQdyVavyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140266490120848"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bMLGJIwRxtptzEScT0RWGZoS5Ce531y2xr8+kH5t1mzdvxhVXXBGrfNIcCkEbFAqFIJ/f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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140266489824208"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PYhI1MUk71oCNGbw0g57aEmAsoLtQCJYp2kLQj/lqJQQuK6Ed1wAtKr6JnRtg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\nQCEghIBCQAgBhYAQAgD/D/gMYMHD6wYuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140266489511376"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Pv6cdUa0YjvdPszaSau40cRhU2/tR/3BbHkFwx0xqNQi/h26J/921jrhtDz74INddd12u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\nQiCEQEIghEBCIIRAQiCEAPj/H6FflwRh8HAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140266489284432"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140266489631952"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loop = tqdm_notebook(range(200))\n",
    "for i in loop:\n",
    "    optimizer.zero_grad()\n",
    "    loss, _ = model()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    loop.set_description('Optimizing (loss %.4f)' % loss.data)\n",
    "    \n",
    "    if loss.item() < 200:\n",
    "        break\n",
    "    \n",
    "    # Save outputs to create a GIF. \n",
    "    if i % 10 == 0:\n",
    "        R = look_at_rotation(model.camera_position[None, :], device=model.device)\n",
    "        T = -torch.bmm(R.transpose(1, 2), model.camera_position[None, :, None])[:, :, 0]   # (1, 3)\n",
    "        image = phong_renderer(meshes_world=model.meshes.clone(), R=R, T=T)\n",
    "        image = image[0, ..., :3].detach().squeeze().cpu().numpy()\n",
    "        image = img_as_ubyte(image)\n",
    "        writer.append_data(image)\n",
    "        \n",
    "        plt.figure()\n",
    "        plt.imshow(image[..., :3])\n",
    "        plt.title(\"iter: %d, loss: %0.2f\" % (i, loss.data))\n",
    "        plt.grid(\"off\")\n",
    "        plt.axis(\"off\")\n",
    "    \n",
    "writer.close()"
   ]
  }
 ],
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  "bento_stylesheets": {
   "bento/extensions/flow/main.css": true,
   "bento/extensions/kernel_selector/main.css": true,
   "bento/extensions/kernel_ui/main.css": true,
   "bento/extensions/new_kernel/main.css": true,
   "bento/extensions/system_usage/main.css": true,
   "bento/extensions/theme/main.css": true
  },
  "kernelspec": {
   "display_name": "pytorch3d (local)",
   "language": "python",
   "name": "pytorch3d_local"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
