Merge pull request #227 from Labelbox/ms/notebook-fix

update notebooks

Author: msokoloff1

examples/README.md

@@ -57,7 +57,7 @@
 | Annotation Type Basics  | [Github](annotation_types/basics.ipynb)               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_types/basics.ipynb) |
 | Converters                 | [Github](annotation_types/converters.ipynb)         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_types/converters.ipynb) |
 | Label Containers           | [Github](annotation_types/label_containers.ipynb)   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_types/label_containers.ipynb) |
-
+| MAL Using Annotation Types | [Github](annotation_types/mal_using_annotation_types.ipynb)   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_types/mal_using_annotation_types.ipynb) |
 
 ## [Model Diagnostics](model_diagnostics)
 | Notebook                    | Github                            | Google Colab |

examples/annotation_types/basics.ipynb

@@ -594,8 +594,7 @@
    "id": "rocky-seeker",
    "metadata": {},
    "source": [
-    "* The Mask class support either RGB or Grayscale masks\n",
-    "* Grayscale masks must have a single color."
+    "* The Mask class support either RGB masks only"
    ]
   },
   {

examples/annotation_types/converters.ipynb

@@ -552,16 +552,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "limited-biography",
+   "id": "bottom-leave",
    "metadata": {},
    "source": [
-    "* See how this is used in the MAL notebooks"
+    "* See how this is used with mal in the `mal_using_annotation_types.ipynb` notebook"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "welcome-designer",
+   "id": "placed-danger",
    "metadata": {},
    "outputs": [],
    "source": []

examples/annotation_types/mal_with_annotation_types.ipynb renamed to examples/annotation_types/mal_using_annotation_types.ipynb

@@ -5,9 +5,9 @@
    "id": "stupid-court",
    "metadata": {},
    "source": [
-    "# MAL With Annotation Types\n",
+    "# MAL Using Annotation Types\n",
     "* Image MAL with subclasses.\n",
-    "* This is the same task as the image mal tutorial but we are going to add a subclass for whether or not the person in the image is holding a bag."
+    "* This is the same task as the image mal tutorial but we are going to use annnotation types"
    ]
   },
   {
@@ -46,8 +46,24 @@
    "source": [
     "#Used this as a reference for the model\n",
     "#https://colab.research.google.com/github/tensorflow/tpu/blob/master/models/official/mask_rcnn/mask_rcnn_demo.ipynb#scrollTo=6lCL-ZcwaJbA\n",
-    "from labelbox.schema.ontology import OntologyBuilder, Tool, Classification, Option\n",
-    "from labelbox import Client, LabelingFrontend\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "import requests\n",
+    "import ndjson\n",
+    "import uuid\n",
+    "from io import BytesIO\n",
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "from labelbox import (\n",
+    "    Client, \n",
+    "    LabelingFrontend, \n",
+    "    OntologyBuilder, \n",
+    "    Tool, \n",
+    "    Classification, \n",
+    "    Option\n",
+    ")\n",
+    "\n",
     "from labelbox.data.annotation_types import (\n",
     "    LabelList,\n",
     "    RasterData,\n",
@@ -61,16 +77,7 @@
     "    Label\n",
     ")\n",
     "from labelbox.data.serialization import NDJsonConverter\n",
-    "from image_model import predict, class_mappings, load_model\n",
-    "from typing import Dict, Any, Tuple, List\n",
-    "import numpy as np\n",
-    "from PIL import Image\n",
-    "import requests\n",
-    "import ndjson\n",
-    "import uuid\n",
-    "from io import BytesIO\n",
-    "import os\n",
-    "from getpass import getpass"
+    "from image_model import predict, class_mappings, load_model"
    ]
   },
   {
@@ -323,6 +330,14 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "prospective-grave",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

examples/model_diagnostics/model_diagnostics_demo.ipynb

@@ -237,19 +237,11 @@
    "source": [
     "# --- setup dataset ---\n",
     "# load mapillary sample\n",
-    "sample_csv_url = \"https://raw.githubusercontent.com/Labelbox/labelbox-python/develop/examples/model_assisted_labeling/mapillary_sample.csv\"\n",
+    "sample_csv_url = \"https://raw.githubusercontent.com/Labelbox/labelbox-python/develop/examples/assets/mapillary_sample.csv\"\n",
     "with requests.get(sample_csv_url, stream=True) as r:\n",
     "    image_data = [row.split(',') for row in (line.decode('utf-8') for line in r.iter_lines())]"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "structural-mandate",
-   "metadata": {},
-   "source": [
-    "* Set up this prediction look to construct Annotation Types"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

examples/model_diagnostics/model_diagnostics_guide.ipynb

@@ -101,11 +101,7 @@
    "outputs": [],
    "source": [
     "# Run these if running in a colab notebook\n",
-    "COLAB = \"google.colab\" in str(get_ipython())\n",
-    "if COLAB:\n",
-    "    !git clone https://github.com/Labelbox/labelbox-python.git\n",
-    "    !cd labelbox-python\n",
-    "    !mv labelbox-python/examples/model_assisted_labeling/*.py ."
+    "COLAB = \"google.colab\" in str(get_ipython())"
    ]
   },
   {
@@ -241,9 +237,7 @@
    },
    "source": [
     "## Create Predictions\n",
-    "\n",
-    "* The prediction format is the same \n",
-    "* Loop over data_rows, make predictions, and create ndjson"
+    "* Loop over data_rows, make predictions, and annotation types"
    ]
   },
   {