dsprites_reloading_example.ipynb

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      "source": [
        "Copyright 2017 Google Inc.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "    http://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License.\n",
        "\n",
        "\n",
        "# dSprites - Disentanglement testing Sprites dataset\n",
        "\n",
        "## Description\n",
        "Procedurally generated 2D shapes dataset. This dataset uses 6 latents, controlling the color, shape, scale, rotation and position of a sprite (color isn't varying here, its value is fixed).\n",
        "\n",
        "All possible combinations of the latents are present.\n",
        "\n",
        "The ordering of images in the dataset (i.e. shape[0] in all ndarrays) is fixed and meaningful, see below.\n",
        "\n",
        "We chose the smallest changes in latent values that generated different pixel outputs at our 64x64 resolution after rasterization.\n",
        "\n",
        "No noise added, single image sample for a given latent setting.\n",
        "\n",
        "## Details about the ordering of the dataset\n",
        "\n",
        "The dataset was generated procedurally, and its order is deterministic.\n",
        "For example, the image at index 0 corresponds to the latents (0, 0, 0, 0, 0, 0).\n",
        "\n",
        "Then the image at index 1 increases the least significant \"bit\" of the latent:\n",
        "(0, 0, 0, 0, 0, 1)\n",
        "\n",
        "And similarly, till we reach index 32, where we get (0, 0, 0, 0, 1, 0). \n",
        "\n",
        "Hence the dataset is sequentially addressable using variable bases for every \"bit\".\n",
        "Using dataset['metadata']['latents_sizes'] makes this conversion trivial, see below."
      ]
    },
    {
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          }
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        "colab_type": "code",
        "id": "jJ02BsnqSa96"
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      "outputs": [],
      "source": [
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "# Change figure aesthetics\n",
        "%matplotlib inline\n",
        "sns.set_context('talk', font_scale=1.2, rc={'lines.linewidth': 1.5})\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
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            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 2
            }
          ]
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 10952,
          "status": "ok",
          "timestamp": 1495021223246,
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        },
        "id": "uDL3Iw0WFw1L",
        "outputId": "1a3ce845-1add-41c3-ee3d-6018d09423bc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Keys in the dataset: ['metadata', 'imgs', 'latents_classes', 'latents_values']\n",
            "Metadata: \n",
            " {'description': 'Disentanglement test Sprites dataset.Procedurally generated 2D shapes, from 6 disentangled latent factors.This dataset uses 6 latents, controlling the color, shape, scale, rotation and position of a sprite. All possible variations of the latents are present. Ordering along dimension 1 is fixed and can be mapped back to the exact latent values that generated that image.We made sure that the pixel outputs are different. No noise added.', 'latents_sizes': array([ 1,  3,  6, 40, 32, 32]), 'latents_names': ('color', 'shape', 'scale', 'orientation', 'posX', 'posY'), 'date': 'April 2017', 'version': 1, 'title': 'dSprites dataset', 'latents_possible_values': {'posX': array([ 0.        ,  0.03225806,  0.06451613,  0.09677419,  0.12903226,\n",
            "        0.16129032,  0.19354839,  0.22580645,  0.25806452,  0.29032258,\n",
            "        0.32258065,  0.35483871,  0.38709677,  0.41935484,  0.4516129 ,\n",
            "        0.48387097,  0.51612903,  0.5483871 ,  0.58064516,  0.61290323,\n",
            "        0.64516129,  0.67741935,  0.70967742,  0.74193548,  0.77419355,\n",
            "        0.80645161,  0.83870968,  0.87096774,  0.90322581,  0.93548387,\n",
            "        0.96774194,  1.        ]), 'posY': array([ 0.        ,  0.03225806,  0.06451613,  0.09677419,  0.12903226,\n",
            "        0.16129032,  0.19354839,  0.22580645,  0.25806452,  0.29032258,\n",
            "        0.32258065,  0.35483871,  0.38709677,  0.41935484,  0.4516129 ,\n",
            "        0.48387097,  0.51612903,  0.5483871 ,  0.58064516,  0.61290323,\n",
            "        0.64516129,  0.67741935,  0.70967742,  0.74193548,  0.77419355,\n",
            "        0.80645161,  0.83870968,  0.87096774,  0.90322581,  0.93548387,\n",
            "        0.96774194,  1.        ]), 'scale': array([ 0.5,  0.6,  0.7,  0.8,  0.9,  1. ]), 'orientation': array([ 0.        ,  0.16110732,  0.32221463,  0.48332195,  0.64442926,\n",
            "        0.80553658,  0.96664389,  1.12775121,  1.28885852,  1.44996584,\n",
            "        1.61107316,  1.77218047,  1.93328779,  2.0943951 ,  2.25550242,\n",
            "        2.41660973,  2.57771705,  2.73882436,  2.89993168,  3.061039  ,\n",
            "        3.22214631,  3.38325363,  3.54436094,  3.70546826,  3.86657557,\n",
            "        4.02768289,  4.1887902 ,  4.34989752,  4.51100484,  4.67211215,\n",
            "        4.83321947,  4.99432678,  5.1554341 ,  5.31654141,  5.47764873,\n",
            "        5.63875604,  5.79986336,  5.96097068,  6.12207799,  6.28318531]), 'shape': array([ 1.,  2.,  3.]), 'color': array([ 1.])}, 'author': 'lmatthey@google.com'}\n"
          ]
        }
      ],
      "source": [
        "# Load dataset\n",
        "dataset_zip = np.load('dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz')\n",
        "\n",
        "print('Keys in the dataset:', dataset_zip.keys())\n",
        "imgs = dataset_zip['imgs']\n",
        "latents_values = dataset_zip['latents_values']\n",
        "latents_classes = dataset_zip['latents_classes']\n",
        "metadata = dataset_zip['metadata'][()]\n",
        "\n",
        "print('Metadata: \\n', metadata)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        },
        "colab_type": "code",
        "id": "9RWpIJtiHYUL"
      },
      "outputs": [],
      "source": [
        "# Define number of values per latents and functions to convert to indices\n",
        "latents_sizes = metadata['latents_sizes']\n",
        "latents_bases = np.concatenate((latents_sizes[::-1].cumprod()[::-1][1:],\n",
        "                                np.array([1,])))\n",
        "\n",
        "def latent_to_index(latents):\n",
        "  return np.dot(latents, latents_bases).astype(int)\n",
        "\n",
        "\n",
        "def sample_latent(size=1):\n",
        "  samples = np.zeros((size, latents_sizes.size))\n",
        "  for lat_i, lat_size in enumerate(latents_sizes):\n",
        "    samples[:, lat_i] = np.random.randint(lat_size, size=size)\n",
        "\n",
        "  return samples\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        },
        "colab_type": "code",
        "id": "W8LKpGjGKaiN"
      },
      "outputs": [],
      "source": [
        "# Helper function to show images\n",
        "def show_images_grid(imgs_, num_images=25):\n",
        "  ncols = int(np.ceil(num_images**0.5))\n",
        "  nrows = int(np.ceil(num_images / ncols))\n",
        "  _, axes = plt.subplots(ncols, nrows, figsize=(nrows * 3, ncols * 3))\n",
        "  axes = axes.flatten()\n",
        "\n",
        "  for ax_i, ax in enumerate(axes):\n",
        "    if ax_i \u003c num_images:\n",
        "      ax.imshow(imgs_[ax_i], cmap='Greys_r',  interpolation='nearest')\n",
        "      ax.set_xticks([])\n",
        "      ax.set_yticks([])\n",
        "    else:\n",
        "      ax.axis('off')\n",
        "\n",
        "def show_density(imgs):\n",
        "  _, ax = plt.subplots()\n",
        "  ax.imshow(imgs.mean(axis=0), interpolation='nearest', cmap='Greys_r')\n",
        "  ax.grid('off')\n",
        "  ax.set_xticks([])\n",
        "  ax.set_yticks([])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lXSlqKKAJirL"
      },
      "source": [
        "## Randomly sampling into the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
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        "executionInfo": {
          "elapsed": 1282,
          "status": "ok",
          "timestamp": 1495021397861,
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          "user_tz": -60
        },
        "id": "MFJLYKK5RzbH",
        "outputId": "270d35ee-f376-47a1-9f88-1b279f70c2ca"
      },
      "outputs": [
        {
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9J2mmAAAAAs3UG54TrmOBYTsaDgAY01k+I+/10GDNFAAAQCBMAYtN06RNAgBuR5gCAAAI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\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0xda7bb10\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Sample latents randomly\n",
        "latents_sampled = sample_latent(size=5000)\n",
        "\n",
        "# Select images\n",
        "indices_sampled = latent_to_index(latents_sampled)\n",
        "imgs_sampled = imgs[indices_sampled]\n",
        "\n",
        "# Show images\n",
        "show_images_grid(imgs_sampled)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ]
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 125,
          "status": "ok",
          "timestamp": 1495021398201,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": -60
        },
        "id": "IygFe_LtLoUg",
        "outputId": "8ce35c72-f502-4f8f-d0c6-71ae645662c4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0xda7ba90\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Compute the density of the data to show that no pixel ever goes out of\n",
        "# the boundary. Obviously it also means that the main support of the pixels is in the center\n",
        "# half. \n",
        "# Locations cover a square, which make the aligned X-Y latents more likely for\n",
        "# models to discover.\n",
        "\n",
        "show_density(imgs_sampled)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "peJaYLHyLKDu"
      },
      "source": [
        "## Conditional sampling of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            },
            {
              "item_id": 2
            }
          ]
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 551,
          "status": "ok",
          "timestamp": 1495021412038,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": -60
        },
        "id": "6DYLZkFJQjb9",
        "outputId": "601edffd-fa13-474b-e61c-0c476f00b52d"
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAIECAYAAABmAjaWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDOSLs4jOAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0xe13c850\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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/VPFOlel0JPUYuTL95Mn2vyOk350q5LPrSsdVp2O6U8V6jHzf03WpRsPj50kHgBqhA0CN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\nf3G3dER6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0xe13c790\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "## Fix posX latent to left\n",
        "latents_sampled = sample_latent(size=5000)\n",
        "latents_sampled[:, -2] = 0\n",
        "indices_sampled = latent_to_index(latents_sampled)\n",
        "imgs_sampled = imgs[indices_sampled]\n",
        "\n",
        "# Samples\n",
        "show_images_grid(imgs_sampled, 9)\n",
        "\n",
        "# Show the density too to check\n",
        "show_density(imgs_sampled)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            },
            {
              "item_id": 2
            }
          ]
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 708,
          "status": "ok",
          "timestamp": 1495021438003,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": -60
        },
        "id": "l2g-xquoTJaG",
        "outputId": "3e3df869-eab7-4428-9749-81440506f0d6"
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAIECAYAAABmAjaWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x107de190\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x134157d0\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "## Fix orientation to 0.8 rad\n",
        "latents_sampled = sample_latent(size=5000)\n",
        "latents_sampled[:, 3] = 5\n",
        "indices_sampled = latent_to_index(latents_sampled)\n",
        "imgs_sampled = imgs[indices_sampled]\n",
        "\n",
        "# Samples\n",
        "show_images_grid(imgs_sampled, 9)\n",
        "\n",
        "# Density should not be different than for all orientations\n",
        "show_density(imgs_sampled)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        },
        "colab_type": "code",
        "id": "-FCACtAlqKTA"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "colab": {
      "default_view": {},
      "last_runtime": {
        "build_target": "",
        "kind": "local"
      },
      "name": "deepmind_2d_shapes_dataset_public.ipynb",
      "provenance": [
        {
          "file_id": "/piper/depot/google3/experimental/deepmind/concepts/dataset2dshapes/public/deepmind_2d_shapes_dataset.ipynb?workspaceId=lmatthey:lmatthey-2dshapes-dataset:580:citc",
          "timestamp": 1493149332589
        },
        {
          "file_id": "0BxLiVtkN33-wbmVnbVQwcUhjY0U",
          "timestamp": 1493149291483
        }
      ],
      "version": "0.3.2",
      "views": {}
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}