Vb/depr label list sprnew 4863 2

examples/basics/getting_started.ipynb

@@ -243,7 +243,7 @@
       "metadata": {},
       "source": [
         "queued_data_rows = project.export_queued_data_rows()\n",
-        "ground_truth_list = lb_types.LabelList()\n",
+        "ground_truth_list = list()\n",
         "\n",
         "for datarow in queued_data_rows:\n",
         "  annotations_list = []\n",

examples/integrations/detectron2/coco_object.ipynb

@@ -415,7 +415,7 @@
     {
       "metadata": {},
       "source": [
-        "labels_mea = lb_types.LabelList()\n",
+        "labels_mea = list()\n",
         "\n",
         "with ThreadPoolExecutor(4) as executor:\n",
         "    futures = [executor.submit(get_label, label.data) for label in val_labels]\n",
@@ -527,7 +527,7 @@
         "# This is still a bit slow due to the amount of processing for each data row.\n",
         "# For larger datasets this has to leverage multiprocessing.\n",
         "\n",
-        "labels_mal = lb_types.LabelList()\n",
+        "labels_mal = list()\n",
         "with ThreadPoolExecutor(4) as executor:\n",
         "    data_rows = dataset.data_rows()\n",
         "    images = [lb_types.ImageData(url = data_row.row_data, uid = data_row.uid, external_id = data_row.external_id) for data_row in data_rows]\n",
@@ -581,4 +581,4 @@
       "execution_count": null
     }
   ]
-}
+}

examples/integrations/detectron2/coco_panoptic.ipynb

@@ -194,7 +194,7 @@
         "model_name = \"detectron_panoptic_model\"\n",
         "\n",
         "proj = client.get_project(project_id)\n",
-        "labels = proj.label_generator().as_list()\n"
+        "labels = list(proj.label_generator())\n"
       ],
       "cell_type": "code",
       "outputs": [],
@@ -205,8 +205,8 @@
       "source": [
         "# Set some labels aside for the val set.\n",
         "raw_data = labels._data\n",
-        "labels = lb_types.LabelList(raw_data[100:])\n",
-        "val_labels = lb_types.LabelList(raw_data[:100]) "
+        "labels = list(raw_data[100:])\n",
+        "val_labels = list(raw_data[:100]) "
       ],
       "cell_type": "code",
       "outputs": [],
@@ -1008,12 +1008,13 @@
     {
       "metadata": {},
       "source": [
-        "labels_mea = lb_types.LabelList()\n",
+        "labels = list()\n",
         "with ThreadPoolExecutor(4) as executor:\n",
         "    futures = [executor.submit(get_label,label.data) for label in val_labels]\n",
         "    for future in tqdm(as_completed(futures)):\n",
-        "        labels_mea.append(future.result())\n",
+        "        labels.append(future.result())\n",
         "\n",
+	"labels_mea = LabelGenerator(labels)\n",
         "labels_mea.add_url_to_masks(signer) \\\n",
         "      .add_url_to_data(signer) "
       ],
@@ -1287,7 +1288,7 @@
         "# For larger datasets this has to leverage multiprocessing.\n",
         "\n",
         "\n",
-        "labels_mal = lb_types.LabelList()\n",
+        "labels_mal = list()\n",
         "with ThreadPoolExecutor(4) as executor:\n",
         "    data_rows = dataset.data_rows()\n",
         "    images = [lb_types.ImageData(url = data_row.row_data, uid = data_row.uid, external_id = data_row.external_id) for data_row in data_rows]\n",
@@ -1365,4 +1366,4 @@
       "execution_count": null
     }
   ]
-}
+}

examples/label_export/images.ipynb

@@ -1,18 +1,16 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
   "cells": [
     {
+      "cell_type": "markdown",
       "metadata": {},
       "source": [
         "<td>\n",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
         "</td>"
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {},
       "source": [
         "<td>\n",
@@ -24,70 +22,72 @@
         "<a href=\"https://github.com/Labelbox/labelbox-python/tree/master/examples/label_export/images.ipynb\" target=\"_blank\"><img\n",
         "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
         "</td>"
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {},
       "source": [
         "# Image Data Export\n",
         "* Export labels from image annotation projects"
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
         "!pip install \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
+      ]
     },
     {
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
         "import labelbox as lb\n",
         "import labelbox.types as lb_types\n",
         "\n",
         "from PIL import Image\n",
         "import numpy as np\n",
         "import os"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
+      ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {},
       "source": [
         "# API Key and Client\n",
         "Provide a valid api key below in order to properly connect to the Labelbox Client."
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
         "# Add your api key and project\n",
         "API_KEY = None\n",
         "client = lb.Client(api_key=API_KEY)\n",
         "project = client.get_project(PROJECT_ID)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
+      ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {},
       "source": [
         "### Export the labels\n",
         "* Data can be exported to annotation objects or raw_json (old export format)"
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
         "labels = project.label_generator()\n",
         "\n",
@@ -104,7 +104,7 @@
     {
       "metadata": {},
       "source": [
-        "* Optionally convert to a `LabelList` for small to medium sized datasets\n",
+        "* Optionally convert to a `list` for small to medium sized datasets\n",
         "* This is more convenient than the `LabelGenerator` but less memory efficient\n",
         "* Read more about the differences [here](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/master/examples/annotation_types/label_containers.ipynb)"
       ],
@@ -113,7 +113,7 @@
     {
       "metadata": {},
       "source": [
-        "labels = labels.as_list()"
+        "labels = list(labels)"
       ],
       "cell_type": "code",
       "outputs": [],
@@ -169,4 +169,4 @@
       "execution_count": null
     }
   ]
-}
+}

examples/label_export/text.ipynb

@@ -110,7 +110,7 @@
     {
       "metadata": {},
       "source": [
-        "Optionally, convert to a `LabelList` for small to medium-sized datasets.\n",
+        "Optionally, convert to a `list` for small to medium-sized datasets.\n",
         "\n",
         "This is more convenient than the LabelGenerator, but less memory efficient. Read more about the differences [here](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/master/examples/annotation_types/label_containers.ipynb)."
       ],
@@ -119,7 +119,7 @@
     {
       "metadata": {},
       "source": [
-        "labels = labels.as_list()"
+        "labels = list(labels)"
       ],
       "cell_type": "code",
       "outputs": [],

examples/model_diagnostics/custom_metrics_demo.ipynb

@@ -262,7 +262,7 @@
     {
       "metadata": {},
       "source": [
-        "predictions = lb_types.LabelList()\n",
+        "predictions = list()\n",
         "for (image_url, external_id) in notebook.tqdm(image_data):\n",
         "    image = lb_types.ImageData(url=image_url, external_id=external_id)\n",
         "    height, width = image.value.shape[:2]\n",
@@ -580,4 +580,4 @@
       "execution_count": null
     }
   ]
-}
+}

examples/model_diagnostics/model_diagnostics_demo.ipynb

@@ -269,7 +269,7 @@
     {
       "metadata": {},
       "source": [
-        "predictions = lb_types.LabelList()\n",
+        "predictions = list()\n",
         "for (image_url, external_id) in notebook.tqdm(image_data[:10]):\n",
         "    image = lb_types.ImageData(url=image_url, external_id=external_id)\n",
         "    height, width = image.value.shape[:2]\n",
@@ -549,4 +549,4 @@
       "execution_count": null
     }
   ]
-}
+}

examples/model_diagnostics/model_diagnostics_guide.ipynb

@@ -207,7 +207,7 @@
     {
       "metadata": {},
       "source": [
-        "predictions = lb_types.LabelList()\n",
+        "predictions = list()\n",
         "for label in notebook.tqdm(labels):\n",
         "    annotations = []\n",
         "    image = label.data\n",
@@ -352,4 +352,4 @@
       "execution_count": null
     }
   ]
-}
+}

labelbox/data/annotation_types/collection.py

@@ -22,6 +22,9 @@ class LabelList:
     Use on smaller datasets.
     """
 
+    warnings.warn("LabelList is deprecated and will be "
+                  "removed in a future release.")
+
     def __init__(self, data: Optional[Iterable[Label]] = None):
         if data is None:
             self._data = []
@@ -187,6 +190,9 @@ def __init__(self, data: Generator[Label, None, None], *args, **kwargs):
         super().__init__(data, *args, **kwargs)
 
     def as_list(self) -> "LabelList":
+        warnings.warn("This method is deprecated and will be "
+                      "removed in a future release. LabeList"
+                      " class will be deprecated.")
         return LabelList(data=list(self))
 
     def assign_feature_schema_ids(