Fix movinet colab references.

PiperOrigin-RevId: 374217229

official/vision/beta/projects/movinet/movinet_tutorial.ipynb

@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "!pip install -q tf-models-nightly\n",
+        "!pip install -q tf-models-nightly tfds-nightly\n",
         "\n",
         "!command -v ffmpeg \u003e/dev/null || (apt update \u0026\u0026 apt install -y ffmpeg)\n",
         "!pip install -q mediapy"
@@ -54,7 +54,6 @@
       },
       "outputs": [],
       "source": [
-        "from io import BytesIO\n",
         "import os\n",
         "from six.moves import urllib\n",
         "\n",
@@ -68,7 +67,7 @@
         "\n",
         "from official.vision.beta.configs import video_classification\n",
         "from official.vision.beta.projects.movinet.configs import movinet as movinet_configs\n",
-        "from official.vision.beta.projects.movinet.modeling import movinet as movinet_backbone\n",
+        "from official.vision.beta.projects.movinet.modeling import movinet\n",
         "from official.vision.beta.projects.movinet.modeling import movinet_layers\n",
         "from official.vision.beta.projects.movinet.modeling import movinet_model"
       ]
@@ -138,7 +137,7 @@
         "image_width = 224\n",
         "\n",
         "with urllib.request.urlopen(image_url) as f:\n",
-        "  image = Image.open(BytesIO(f.read())).resize((image_height, image_width))\n",
+        "  image = Image.open(f).resize((image_height, image_width))\n",
         "video = tf.reshape(np.array(image), [1, 1, image_height, image_width, 3])\n",
         "video = tf.cast(video, tf.float32) / 255.\n",
         "\n",
@@ -200,6 +199,22 @@
         "Calling `download_and_prepare()` will automatically download the dataset. After downloading, this cell will output information about the dataset."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FxM1vNYp_YAM"
+      },
+      "outputs": [],
+      "source": [
+        "dataset_name = 'ucf101'\n",
+        "\n",
+        "builder = tfds.builder(dataset_name)\n",
+        "\n",
+        "config = tfds.download.DownloadConfig(verify_ssl=False)\n",
+        "builder.download_and_prepare(download_config=config)"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -281,11 +296,6 @@
         }
       ],
       "source": [
-        "dataset_name = 'ucf101'\n",
-        "\n",
-        "builder = tfds.builder(dataset_name)\n",
-        "builder.download_and_prepare()\n",
-        "\n",
         "num_classes = builder.info.features['label'].num_classes\n",
         "num_examples = {\n",
         "    name: split.num_examples\n",
@@ -407,12 +417,10 @@
         "tf.keras.backend.clear_session()\n",
         "\n",
         "backbone = movinet.Movinet(\n",
-        "    model_id=model_id,\n",
-        "    stochastic_depth_rate=0.)\n",
+        "    model_id=model_id)\n",
         "model = movinet_model.MovinetClassifier(\n",
         "    backbone=backbone,\n",
-        "    num_classes=600,\n",
-        "    dropout_rate=0.)\n",
+        "    num_classes=600)\n",
         "model.build([batch_size, num_frames, resolution, resolution, 3])\n",
         "\n",
         "# Load pretrained weights from TF Hub\n",