Reformats Colab notebooks.

PiperOrigin-RevId: 479436208
Change-Id: I0d2a7feceb346039af1974ff3418c80ef7d980c5

Author: Johannes Ballé

models/hific/colab.ipynb

@@ -112,17 +112,16 @@
         "File = collections.namedtuple('File', ['full_path', 'num_bytes', 'bpp'])\n",
         "\n",
         "def print_html(html):\n",
-        "  display(HTML(html + '\u003cbr/\u003e'))\n",
+        "  display(HTML(html + '<br/>'))\n",
         "\n",
         "def make_cell_large():\n",
         "  display(Javascript(\n",
         "      '''google.colab.output.setIframeHeight(0, true, {maxHeight: 5000})'''))\n",
         "\n",
         "def get_default_image(output_dir):\n",
         "  output_path = os.path.join(output_dir, os.path.basename(DEFAULT_IMAGE_URL))\n",
-        "  print('Downloading', DEFAULT_IMAGE_URL, '\\n-\u003e', output_path)\n",
-        "  urllib.request.urlretrieve(DEFAULT_IMAGE_URL, output_path)\n",
-        "\n"
+        "  print('Downloading', DEFAULT_IMAGE_URL, '\\n->', output_path)\n",
+        "  urllib.request.urlretrieve(DEFAULT_IMAGE_URL, output_path)\n"
       ]
     },
     {
@@ -162,8 +161,7 @@
         "#@markdown #### Select a model\n",
         "#@markdown Different models target different bitrates.\n",
         "\n",
-        "model = 'hific-lo' #@param [\"hific-lo\", \"hific-mi\", \"hific-hi\"]\n",
-        "\n"
+        "model = 'hific-lo' #@param [\"hific-lo\", \"hific-mi\", \"hific-hi\"]\n"
       ]
     },
     {
@@ -296,10 +294,10 @@
         "make_cell_large()  # Larger output window.\n",
         "\n",
         "for file in all_outputs:\n",
-        "  print_html('\u003chr/\u003e')\n",
+        "  print_html('<hr/>')\n",
         "  print(f'Showing {file.full_path} | {file.num_bytes//1000}kB | {file.bpp:.4f}bpp')\n",
         "  display(Image.open(file.full_path))\n",
-        "  print_html('\u003chr/\u003e')"
+        "  print_html('<hr/>')"
       ]
     },
     {
@@ -342,17 +340,7 @@
     "accelerator": "GPU",
     "colab": {
       "collapsed_sections": [],
-      "last_runtime": {
-        "build_target": "//research/colab/notebook:notebook_backend_py3",
-        "kind": "private"
-      },
-      "name": "HiFiC_Interactive_Colab.ipynb",
-      "provenance": [
-        {
-
-          "timestamp": 1615973093906
-        }
-      ],
+      "name": "colab.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

models/toy_sources/toy_sources.ipynb

@@ -12,25 +12,27 @@
         "\n",
         "The Laplace and Banana sources are described in:\n",
         "\n",
-        "\u003e \"Nonlinear Transform Coding\"\u003cbr /\u003e\n",
-        "\u003e J. Ballé, P. A. Chou, D. Minnen, S. Singh, N. Johnston, E. Agustsson, S. J. Hwang, G. Toderici\u003cbr /\u003e\n",
-        "\u003e https://arxiv.org/abs/2007.03034\n",
+        "> \"Nonlinear Transform Coding\"<br />\n",
+        "> J. Ballé, P. A. Chou, D. Minnen, S. Singh, N. Johnston, E. Agustsson, S. J. Hwang, G. Toderici<br />\n",
+        "> https://arxiv.org/abs/2007.03034\n",
         "\n",
         "The Sawbridge process is described in:\n",
         "\n",
-        "\u003e \"Neural Networks Optimally Compress the Sawbridge\"\u003cbr /\u003e\n",
-        "\u003e A. B. Wagner, J. Ballé\u003cbr /\u003e\n",
-        "\u003e https://arxiv.org/abs/2011.05065\n",
+        "> \"Neural Networks Optimally Compress the Sawbridge\"<br />\n",
+        "> A. B. Wagner, J. Ballé<br />\n",
+        "> https://arxiv.org/abs/2011.05065\n",
         "\n",
         "This notebook requires TFC v2 (`pip install tensorflow-compression==2.*`)\n"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "cellView": "form",
         "id": "wdA5NUZ-fSxG"
       },
+      "outputs": [],
       "source": [
         "#@title Dependencies for Colab\n",
         "\n",
@@ -43,9 +45,7 @@
         "# Downloads the 'models' directory from Github.\n",
         "![[ -e /tfc ]] || git clone https://github.com/tensorflow/compression /tfc\n",
         "%cd /tfc/models\n"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
@@ -322,7 +322,7 @@
         "  # Estimate KLT from samples.\n",
         "  eigv, error = estimate_klt(\n",
         "      source, tf.constant(num_samples), tf.constant(latent_dims))\n",
-        "  assert error \u003c tolerance, error.numpy()\n",
+        "  assert error < tolerance, error.numpy()\n",
         "  eigv = tf.cast(eigv, dtype)\n",
         "\n",
         "  analysis = tf.keras.Sequential([\n",
@@ -417,13 +417,13 @@
         "  \"\"\"Returns a learning rate scheduler function for the given configuration.\"\"\"\n",
         "  def scheduler(epoch, lr):\n",
         "    del lr  # unused\n",
-        "    if epoch \u003c warmup_epochs:\n",
+        "    if epoch < warmup_epochs:\n",
         "      return learning_rate * 10. ** (epoch - warmup_epochs)\n",
-        "    if epoch \u003c 1/2 * epochs:\n",
+        "    if epoch < 1/2 * epochs:\n",
         "      return learning_rate\n",
-        "    if epoch \u003c 3/4 * epochs:\n",
+        "    if epoch < 3/4 * epochs:\n",
         "      return learning_rate * 1e-1\n",
-        "    if epoch \u003c 7/8 * epochs:\n",
+        "    if epoch < 7/8 * epochs:\n",
         "      return learning_rate * 1e-2\n",
         "    return learning_rate * 1e-3\n",
         "  return scheduler\n",
@@ -456,7 +456,7 @@
         "def get_alpha_scheduler(epochs):\n",
         "  \"\"\"Returns an alpha scheduler function for the given configuration.\"\"\"\n",
         "  def scheduler(epoch):\n",
-        "    if epoch \u003c 1/4 * epochs:\n",
+        "    if epoch < 1/4 * epochs:\n",
         "      return 3. * (epoch + 1) / (epochs/4 + 1)\n",
         "    return None\n",
         "  return scheduler\n"
@@ -1160,17 +1160,6 @@
         "    callbacks=tf.keras.callbacks.CallbackList(callback_list, model=model),\n",
         ")\n"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "TmK8AWxQRl6E"
-      },
-      "outputs": [],
-      "source": [
-        ""
-      ]
     }
   ],
   "metadata": {
@@ -1180,11 +1169,8 @@
         "cVgn8T6pySgP",
         "KmoKt709KFAv"
       ],
-      "last_runtime": {
-        "build_target": "",
-        "kind": "local"
-      },
-      "name": "toy_sources.ipynb"
+      "name": "toy_sources.ipynb",
+      "toc_visible": true
     },
     "kernelspec": {
       "display_name": "Python 3",