Merge pull request #983 from Labelbox/develop

Release 3.40.0

Author: kkim-labelbox

.github/workflows/python-package.yml

@@ -89,4 +89,4 @@ jobs:
 
           DA_GCP_LABELBOX_API_KEY: ${{ secrets[matrix.da-test-key] }}
         run: |
-          tox -e py -- -svv --reruns 5 --reruns-delay 10
+          tox -e py -- -n 10 -svv --reruns 5 --reruns-delay 10

CHANGELOG.md

@@ -1,22 +1,45 @@
 # Changelog
 
-# Version 3.40.0 (YYYY-MM-DD)
+# Version 3.40.0 (2023-03-10)
 
-## Added 
-* Upsert data rows to model runs using global keys
+## Added
+* Support Global keys to reference data rows in `Project.create_batch()`, `ModelRun.assign_data_rows_to_split()`
+* Support upserting labels via project_id in `model_run.upsert_labels()`
+* `media_type_override` param to export_v2
+* `last_activity_at` and `label_created_at` params to export_v2
+* New client method `is_feature_schema_archived()`
+* New client method `unarchive_feature_schema_node()`
+* New client method `delete_feature_schema_from_ontology()`
+
+## Changed
+* Removed default task names for export_v2
+
+## Fixed
+* process_label() for COCO panoptic dataset
+
+## Notebooks
+* Updated `annotation_import/pdf.ipynb` with more examples
+* Added `integrations/huggingface/huggingface.ipynb`
+* Fixed broken links for detectron notebooks in README
+* Added Dataset QueueMode during project creation in `integrations/detectron2/coco_object.ipynb` 
+* Removed metadata and updated ontology in `annotation_import/text.ipynb`
+* Removed confidence scores in `annotation_import/image.ipynb`
+* Updated custom embedding tutorial links in `basics/data_row_metadata.ipynb`
 
 # Version 3.39.0 (2023-02-28)
 ## Added
 * New method `Project.task_queues()` to obtain the task queues for a project.
 * New method `Project.move_data_rows_to_task_queue()` for moving data rows to a specified task queue.
 * Added more descriptive error messages for metadata operations
 * Added `Task.errors_url` for async tasks that return errors as separate file (e.g. `export_v2`)
+* Upsert data rows to model runs using global keys
 
 ## Changed
 * Updated `ProjectExportParams.labels` to `ProjectExportParams.label_details`
 * Removed `media_attributes` from `DataRowParams`
 * Added deprecation warnings for `LabelList` and removed its usage
 * Removed unused arguments in `Project.export_v2` and `ModelRun.export_v2`
+* In `Project.label_generator()`, we now filter skipped labels for project with videos
 
 ## Notebooks
 * Fixed `examples/label_export/images.ipynb` notebook metadata
@@ -70,7 +93,6 @@
 * Added support for adding metadata by name when creating datarows using `Dataset.create_data_rows()`, `Dataset.create_data_rows_sync()`, and `Dataset.create_data_row()`.
 * Example notebooks for auto metrics in models
 
-
 ### Changed
 * `Dataset.create_data_rows()` max limit of DataRows increased to 150,000
 * Improved error handling for invalid annotation import content

Makefile

@@ -44,4 +44,5 @@ test-custom: build
 		-e DA_GCP_LABELBOX_API_KEY=${DA_GCP_LABELBOX_API_KEY} \
 		-e LABELBOX_TEST_API_KEY_CUSTOM=${LABELBOX_TEST_API_KEY_CUSTOM} \
 		-e LABELBOX_TEST_GRAPHQL_API_ENDPOINT=${LABELBOX_TEST_GRAPHQL_API_ENDPOINT} \
+		-e LABELBOX_TEST_REST_API_ENDPOINT=${LABELBOX_TEST_REST_API_ENDPOINT} \
 		local/labelbox-python:test pytest $(PATH_TO_TEST)

docs/source/conf.py

@@ -21,7 +21,7 @@
 copyright = '2021, Labelbox'
 author = 'Labelbox'
 
-release = '3.39.0'
+release = '3.40.0'
 
 # -- General configuration ---------------------------------------------------
 

examples/README.md

@@ -36,8 +36,8 @@ Train a model using data annotated on Labelbox
 
 | Notebook                    | Github                            | Google Colab |
 | --------------------------- | --------------------------------- | ------------ |
-| Object Detection (Detectron2)            | [Github](coco_object.ipynb)  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/master/examples/integrations/detectron2/coco_object.ipynb) |
-| Panoptic Detection (Detectron2)          | [Github](coco_panoptic.ipynb)    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/master/examples/integrations/detectron2/coco_panoptic.ipynb)
+| Object Detection (Detectron2)            | [Github](integrations/detectron2/coco_object.ipynb)  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/master/examples/integrations/detectron2/coco_object.ipynb) |
+| Panoptic Detection (Detectron2)          | [Github](integrations/detectron2/coco_panoptic.ipynb)    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/master/examples/integrations/detectron2/coco_panoptic.ipynb)
 ------
 
 ## [Annotation Import (Ground Truth & MAL)](annotation_import)

examples/annotation_import/image.ipynb

@@ -271,15 +271,14 @@
         "# Bounding box with nested classification\n",
         "bbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n",
         "    name=\"bbox_with_radio_subclass\",\n",
-        "    confidence=0.5, # must match your ontology feature's name\n",
         "    value=lb_types.Rectangle(\n",
         "        start=lb_types.Point(x=933, y=541), # Top left\n",
         "        end=lb_types.Point(x=191, y=330), # Bottom right\n",
         "    ),\n",
         "    classifications=[\n",
         "    \tlb_types.ClassificationAnnotation(\n",
         "        \tname=\"sub_radio_question\",\n",
-        "      \t\tvalue=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"first_sub_radio_answer\", confidence=0.5))\n",
+        "      \t\tvalue=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"first_sub_radio_answer\"))\n",
         "    )\n",
         "  ]\n",
         ")\n",
@@ -313,7 +312,6 @@
         "# Python AnotationTypes \n",
         "polygon_annotation = lb_types.ObjectAnnotation(\n",
         "  name = \"polygon\",  # must match your ontology feature's name \n",
-        "  confidence = 0.5, \n",
         "  value=lb_types.Polygon( # Coordinates for the verticies of your polygon\n",
         "        points=[lb_types.Point(x=1489.581,y=183.934), lb_types.Point(x=2278.306,y=256.885), lb_types.Point(x=2428.197,y=200.437), lb_types.Point(x=2560.0,y=335.419),\n",
         "                lb_types.Point(x=2557.386,y=503.165), lb_types.Point(x=2320.596,y=503.103), lb_types.Point(x=2156.083, y=628.943), lb_types.Point(x=2161.111,y=785.519),\n",
@@ -401,7 +399,6 @@
         "# Python Annotation\n",
         "point_annotation = lb_types.ObjectAnnotation(\n",
         "  name = \"point\",  # must match your ontology feature's name\n",
-        "  confidence=0.5,\n",
         "  value = lb_types.Point(x=1166.606, y=1441.768),\n",
         ")\n",
         "\n",
@@ -629,7 +626,10 @@
         "      name=\"point\"), \n",
         "    lb.Tool( # Polyline tool given the name \"line\"\n",
         "      tool=lb.Tool.Type.LINE,\n",
-        "      name=\"polyline\")]\n",
+        "      name=\"polyline\"),\n",
+        "    lb.Tool( # Relationship tool given the name \"relationship\"\n",
+        "      tool=lb.Tool.Type.RELATIONSHIP,\n",
+        "      name=\"relationship\")]\n",
         ")\n",
         "\n",
         "ontology = client.create_ontology(\"Image Prediction Import Demo\", ontology_builder.asdict(), media_type=lb.MediaType.Image)"
@@ -865,4 +865,4 @@
       "execution_count": null
     }
   ]
-}
+}