From 7ca8e066c895643574bd49703ea044ab0ea224bc Mon Sep 17 00:00:00 2001
From: brimoor <brimoor@umich.edu>
Date: Thu, 9 Jun 2022 15:57:02 -0400
Subject: [PATCH 1/2] stop silently passing class lists to API methods

---
 docs/source/integrations/coco.rst             |  3 +-
 .../user_guide/dataset_creation/datasets.rst  | 18 +++----
 .../user_guide/dataset_creation/index.rst     | 12 ++---
 docs/source/user_guide/export_datasets.rst    | 37 ++++---------
 fiftyone/core/collections.py                  | 17 ++----
 fiftyone/utils/coco.py                        | 51 ++++++------------
 fiftyone/utils/data/exporters.py              | 51 ++----------------
 fiftyone/utils/eval/classification.py         | 15 +-----
 fiftyone/utils/eval/detection.py              | 14 +----
 fiftyone/utils/eval/segmentation.py           | 13 +----
 fiftyone/utils/labels.py                      | 12 -----
 fiftyone/utils/tf.py                          | 21 ++------
 fiftyone/utils/yolo.py                        | 52 ++-----------------
 tests/unittests/import_export_tests.py        | 12 ++---
 14 files changed, 66 insertions(+), 262 deletions(-)

diff --git a/docs/source/integrations/coco.rst b/docs/source/integrations/coco.rst
index 31b079642e..27ea938245 100644
--- a/docs/source/integrations/coco.rst
+++ b/docs/source/integrations/coco.rst
@@ -325,7 +325,8 @@ to add them to your dataset as follows:
     ]
 
     # Add COCO predictions to `predictions` field of dataset
-    fouc.add_coco_labels(coco_dataset, "predictions", predictions)
+    classes = coco_dataset.default_classes
+    fouc.add_coco_labels(coco_dataset, "predictions", predictions, classes)
 
     # Verify that predictions were added to two images
     print(coco_dataset.count("predictions"))  # 2
diff --git a/docs/source/user_guide/dataset_creation/datasets.rst b/docs/source/user_guide/dataset_creation/datasets.rst
index 463d9df8c5..a5cd6231f0 100644
--- a/docs/source/user_guide/dataset_creation/datasets.rst
+++ b/docs/source/user_guide/dataset_creation/datasets.rst
@@ -1553,16 +1553,16 @@ COCO format:
     dataset.export(
         export_dir="/tmp/coco",
         dataset_type=fo.types.COCODetectionDataset,
-        classes=classes,
         label_field="ground_truth",
+        classes=classes,
     )
 
     # Export predictions
     dataset.export(
         dataset_type=fo.types.COCODetectionDataset,
         labels_path="/tmp/coco/predictions.json",
-        classes=classes,
         label_field="predictions",
+        classes=classes,
     )
 
     # Now load ground truth labels into a new dataset
@@ -1577,7 +1577,7 @@ COCO format:
         dataset2,
         "predictions",
         "/tmp/coco/predictions.json",
-        classes=classes,
+        classes,
     )
 
     # Verify that ground truth and predictions were imported as expected
@@ -2147,16 +2147,16 @@ images-and-labels and labels-only data in YOLO format:
     dataset.export(
         export_dir="/tmp/yolov4",
         dataset_type=fo.types.YOLOv4Dataset,
-        classes=classes,
         label_field="ground_truth",
+        classes=classes,
     )
 
     # Export predictions
     dataset.export(
         dataset_type=fo.types.YOLOv4Dataset,
         labels_path="/tmp/yolov4/predictions",
-        classes=classes,
         label_field="predictions",
+        classes=classes,
     )
 
     # Now load ground truth labels into a new dataset
@@ -2171,7 +2171,7 @@ images-and-labels and labels-only data in YOLO format:
         dataset2,
         "predictions",
         "/tmp/yolov4/predictions",
-        classes=classes,
+        classes,
     )
 
     # Verify that ground truth and predictions were imported as expected
@@ -2327,16 +2327,16 @@ images-and-labels and labels-only data in YOLO format:
         export_dir="/tmp/yolov5",
         dataset_type=fo.types.YOLOv5Dataset,
         split="validation",
-        classes=classes,
         label_field="ground_truth",
+        classes=classes,
     )
 
     # Export predictions
     view.export(
         dataset_type=fo.types.YOLOv5Dataset,
         labels_path="/tmp/yolov5/predictions/validation",
-        classes=classes,
         label_field="predictions",
+        classes=classes,
     )
 
     # Now load ground truth labels into a new dataset
@@ -2352,7 +2352,7 @@ images-and-labels and labels-only data in YOLO format:
         dataset2,
         "predictions",
         "/tmp/yolov5/predictions/validation",
-        classes=classes,
+        classes,
     )
 
     # Verify that ground truth and predictions were imported as expected
diff --git a/docs/source/user_guide/dataset_creation/index.rst b/docs/source/user_guide/dataset_creation/index.rst
index d5fba3045d..626c6d2479 100644
--- a/docs/source/user_guide/dataset_creation/index.rst
+++ b/docs/source/user_guide/dataset_creation/index.rst
@@ -369,16 +369,16 @@ predictions to take advantage of FiftyOne's
         dataset.export(
             export_dir="/tmp/coco",
             dataset_type=fo.types.COCODetectionDataset,
-            classes=classes,
             label_field="ground_truth",
+            classes=classes,
         )
 
         # Export predictions
         dataset.export(
             dataset_type=fo.types.COCODetectionDataset,
             labels_path="/tmp/coco/predictions.json",
-            classes=classes,
             label_field="predictions",
+            classes=classes,
         )
 
         # Now load ground truth labels into a new dataset
@@ -393,7 +393,7 @@ predictions to take advantage of FiftyOne's
             dataset2,
             "predictions",
             "/tmp/coco/predictions.json",
-            classes=classes,
+            classes,
         )
 
         # Verify that ground truth and predictions were imported as expected
@@ -432,16 +432,16 @@ predictions to take advantage of FiftyOne's
         dataset.export(
             export_dir="/tmp/yolov4",
             dataset_type=fo.types.YOLOv4Dataset,
-            classes=classes,
             label_field="ground_truth",
+            classes=classes,
         )
 
         # Export predictions
         dataset.export(
             dataset_type=fo.types.YOLOv4Dataset,
             labels_path="/tmp/yolov4/predictions",
-            classes=classes,
             label_field="predictions",
+            classes=classes,
         )
 
         # Now load ground truth labels into a new dataset
@@ -456,7 +456,7 @@ predictions to take advantage of FiftyOne's
             dataset2,
             "predictions",
             "/tmp/yolov4/predictions",
-            classes=classes,
+            classes,
         )
 
         # Verify that ground truth and predictions were imported as expected
diff --git a/docs/source/user_guide/export_datasets.rst b/docs/source/user_guide/export_datasets.rst
index c3182407ce..78b9e7442e 100644
--- a/docs/source/user_guide/export_datasets.rst
+++ b/docs/source/user_guide/export_datasets.rst
@@ -349,26 +349,16 @@ Class lists
 -----------
 
 Certain labeled image/video export formats such as
-:ref:`COCO <COCODetectionDataset-export>` or
+:ref:`COCO <COCODetectionDataset-export>` and
 :ref:`YOLO <YOLOv5Dataset-export>` store an explicit list of classes for the
 label field being exported.
 
-In such cases, all exporters provided by FiftyOne use the following strategy
-to retrieve the class list when exporting a given `label_field`:
+By convention, all exporters provided by FiftyOne should provide a `classes`
+parameter that allows for manually specifying the classes list to use.
 
-1.  If the exporter provides a parameter like `classes` that allows for
-    manually specifying the classes, this list is used
-2.  If the :meth:`classes <fiftyone.core.dataset.Dataset.classes>` dict of the
-    sample collection contains `label_field`, this class list is used
-3.  If the collection's
-    :meth:`default_classes <fiftyone.core.dataset.Dataset.default_classes>`
-    attribute is non-empty, this class list is used
-4.  If the collection's :meth:`info <fiftyone.core.dataset.Dataset.info>` dict
-    contains a class list under the `classes` key, this list is used
-
-If no explicit class list is available via the above methods, the observed
-classes in the collection being exported are used, which may be a subset of the
-classes in the parent dataset when exporting a view.
+If no explicit class list is provided, the observed classes in the collection
+being exported are used, which may be a subset of the classes in the parent
+dataset when exporting a view.
 
 .. note::
 
@@ -397,26 +387,17 @@ classes in the parent dataset when exporting a view.
     # Create a view that only contains cats and dogs
     view = dataset.filter_labels("ground_truth", F("label").is_in(["cat", "dog"]))
 
-    # By default, `default_classes` will be used to populate the COCO categories
+    # By default, only the observed classes will be stored as COCO categories
     view.export(
         labels_path="/tmp/coco1.json",
         dataset_type=fo.types.COCODetectionDataset,
     )
 
-    # But, since we're only interested in exporting cats and dogs, we can override
-    # this by manually providing the `classes` argument
+    # However, if desired, we can explicitly provide a classes list
     view.export(
         labels_path="/tmp/coco2.json",
         dataset_type=fo.types.COCODetectionDataset,
-        classes=["cat", "dog"],
-    )
-
-    # Equivalently, we can clear `default_classes` first so that the observed
-    # labels (only cats and dogs in this case) will be used
-    dataset.default_classes = None
-    view.export(
-        labels_path="/tmp/coco3.json",
-        dataset_type=fo.types.COCODetectionDataset,
+        classes=dataset.default_classes,
     )
 
 .. _supported-export-formats:
diff --git a/fiftyone/core/collections.py b/fiftyone/core/collections.py
index 0552db550a..e799ef953d 100644
--- a/fiftyone/core/collections.py
+++ b/fiftyone/core/collections.py
@@ -2010,11 +2010,7 @@ def evaluate_classifications(
                 instances
             eval_key (None): a string key to use to refer to this evaluation
             classes (None): the list of possible classes. If not provided,
-                classes are loaded from
-                :meth:`fiftyone.core.dataset.Dataset.classes` or
-                :meth:`fiftyone.core.dataset.Dataset.default_classes` if
-                possible, or else the observed ground truth/predicted labels
-                are used
+                the observed ground truth/predicted labels are used
             missing (None): a missing label string. Any None-valued labels
                 are given this label for results purposes
             method ("simple"): a string specifying the evaluation method to use.
@@ -2116,11 +2112,7 @@ def evaluate_detections(
                 or :class:`fiftyone.core.labels.TemporalDetections`
             eval_key (None): a string key to use to refer to this evaluation
             classes (None): the list of possible classes. If not provided,
-                classes are loaded from
-                :meth:`fiftyone.core.dataset.Dataset.classes` or
-                :meth:`fiftyone.core.dataset.Dataset.default_classes` if
-                possible, or else the observed ground truth/predicted labels
-                are used
+                the observed ground truth/predicted labels are used
             missing (None): a missing label string. Any unmatched objects are
                 given this label for results purposes
             method (None): a string specifying the evaluation method to use.
@@ -2211,10 +2203,7 @@ def evaluate_segmentations(
                 instances
             eval_key (None): a string key to use to refer to this evaluation
             mask_targets (None): a dict mapping mask values to labels. If not
-                provided, mask targets are loaded from
-                :meth:`fiftyone.core.dataset.Dataset.mask_targets` or
-                :meth:`fiftyone.core.dataset.Dataset.default_mask_targets` if
-                possible, or else the observed pixel values are used
+                provided, the observed pixel values are used
             method ("simple"): a string specifying the evaluation method to
                 use. Supported values are ``("simple")``
             **kwargs: optional keyword arguments for the constructor of the
diff --git a/fiftyone/utils/coco.py b/fiftyone/utils/coco.py
index a1db1e1706..0cc744b754 100644
--- a/fiftyone/utils/coco.py
+++ b/fiftyone/utils/coco.py
@@ -45,9 +45,9 @@ def add_coco_labels(
     sample_collection,
     label_field,
     labels_or_path,
+    classes,
     label_type="detections",
     coco_id_field=None,
-    classes=None,
     extra_attrs=True,
     use_polylines=False,
     tolerance=None,
@@ -128,6 +128,7 @@ def add_coco_labels(
             will be created if necessary
         labels_or_path: a list of COCO annotations or the path to a JSON file
             containing such data on disk
+        classes: the list of class label strings
         label_type ("detections"): the type of labels to load. Supported values
             are ``("detections", "segmentations", "keypoints")``
         coco_id_field (None): this parameter determines how to map the
@@ -140,9 +141,6 @@ def add_coco_labels(
             -   the name of a field of ``sample_collection`` containing the
                 COCO IDs for the samples that correspond to the ``image_id`` of
                 the predictions
-        classes (None): the list of class label strings. If not provided, these
-            must be available from
-            :meth:`fiftyone.core.collections.SampleCollection.get_classes`
         extra_attrs (True): whether to load extra annotation attributes onto
             the imported labels. Supported values are:
 
@@ -155,14 +153,6 @@ def add_coco_labels(
         tolerance (None): a tolerance, in pixels, when generating approximate
             polylines for instance masks. Typical values are 1-3 pixels
     """
-    if classes is None:
-        classes = sample_collection.get_classes(label_field)
-
-    if not classes:
-        raise ValueError(
-            "You must provide `classes` in order to load COCO labels"
-        )
-
     if etau.is_str(labels_or_path):
         labels = etas.load_json(labels_or_path)
         if isinstance(labels, dict):
@@ -646,9 +636,7 @@ class COCODetectionDatasetExporter(
         image_format (None): the image format to use when writing in-memory
             images to disk. By default, ``fiftyone.config.default_image_ext``
             is used
-        classes (None): the list of possible class labels. If not provided,
-            this list will be extracted when :meth:`log_collection` is called,
-            if possible
+        classes (None): the list of possible class labels
         info (None): a dict of info as returned by
             :meth:`load_coco_detection_annotations` to include in the exported
             JSON. If not provided, this info will be extracted when
@@ -709,12 +697,13 @@ def __init__(
         self.num_decimals = num_decimals
         self.tolerance = tolerance
 
-        self._labels_map_rev = None
         self._image_id = None
         self._anno_id = None
         self._images = None
         self._annotations = None
         self._classes = None
+        self._dynamic_classes = classes is None
+        self._labels_map_rev = None
         self._has_labels = None
         self._media_exporter = None
 
@@ -731,7 +720,6 @@ def setup(self):
         self._anno_id = 0
         self._images = []
         self._annotations = []
-        self._classes = set()
         self._has_labels = False
 
         self._parse_classes()
@@ -744,17 +732,6 @@ def setup(self):
         self._media_exporter.setup()
 
     def log_collection(self, sample_collection):
-        if self.classes is None:
-            if sample_collection.default_classes:
-                self.classes = sample_collection.default_classes
-                self._parse_classes()
-            elif sample_collection.classes:
-                self.classes = next(iter(sample_collection.classes.values()))
-                self._parse_classes()
-            elif "classes" in sample_collection.info:
-                self.classes = sample_collection.info["classes"]
-                self._parse_classes()
-
         if self.info is None:
             self.info = sample_collection.info
 
@@ -794,7 +771,10 @@ def export_sample(self, image_or_path, label, metadata=None):
         for label in labels:
             _label = label.label
 
-            if self._labels_map_rev is not None:
+            if self._dynamic_classes:
+                category_id = _label  # will be converted to int later
+                self._classes.add(_label)
+            else:
                 if _label not in self._labels_map_rev:
                     msg = (
                         "Ignoring object with label '%s' not in provided "
@@ -804,11 +784,8 @@ def export_sample(self, image_or_path, label, metadata=None):
                     continue
 
                 category_id = self._labels_map_rev[_label]
-            else:
-                category_id = _label  # will be converted to int later
 
             self._anno_id += 1
-            self._classes.add(_label)
 
             obj = COCOObject.from_label(
                 label,
@@ -825,13 +802,13 @@ def export_sample(self, image_or_path, label, metadata=None):
             self._annotations.append(obj.to_anno_dict())
 
     def close(self, *args):
-        if self.classes is not None:
-            classes = self.classes
-        else:
+        if self._dynamic_classes:
             classes = sorted(self._classes)
             labels_map_rev = _to_labels_map_rev(classes)
             for anno in self._annotations:
                 anno["category_id"] = labels_map_rev[anno["category_id"]]
+        else:
+            classes = self.classes
 
         date_created = datetime.now().replace(microsecond=0).isoformat()
         info = {
@@ -874,7 +851,9 @@ def close(self, *args):
         self._media_exporter.close()
 
     def _parse_classes(self):
-        if self.classes is not None:
+        if self._dynamic_classes:
+            self._classes = set()
+        else:
             self._labels_map_rev = _to_labels_map_rev(self.classes)
 
 
diff --git a/fiftyone/utils/data/exporters.py b/fiftyone/utils/data/exporters.py
index 4a835ba078..44c5c337ab 100644
--- a/fiftyone/utils/data/exporters.py
+++ b/fiftyone/utils/data/exporters.py
@@ -1175,8 +1175,7 @@ def log_collection(self, sample_collection):
 
         Subclasses can optionally implement this method if their export format
         can record information such as the
-        :meth:`fiftyone.core.collections.SampleCollection.info` or
-        :meth:`fiftyone.core.collections.SampleCollection.classes` of the
+        :meth:`fiftyone.core.collections.SampleCollection.info` of the
         collection being exported.
 
         By convention, this method must be optional; i.e., if it is not called
@@ -1989,9 +1988,7 @@ class FiftyOneImageClassificationDatasetExporter(
             -   ``True``: always include a (possibly empty) attributes dict
             -   ``None``: include attributes only if they exist
             -   a name or iterable of names of specific attributes to include
-        classes (None): the list of possible class labels. If not provided,
-            this list will be extracted when :meth:`log_collection` is called,
-            if possible
+        classes (None): the list of possible class labels
         image_format (None): the image format to use when writing in-memory
             images to disk. By default, ``fiftyone.config.default_image_ext``
             is used
@@ -2059,18 +2056,6 @@ def setup(self):
         )
         self._media_exporter.setup()
 
-    def log_collection(self, sample_collection):
-        if self.classes is None:
-            if sample_collection.default_classes:
-                self.classes = sample_collection.default_classes
-                self._parse_classes()
-            elif sample_collection.classes:
-                self.classes = next(iter(sample_collection.classes.values()))
-                self._parse_classes()
-            elif "classes" in sample_collection.info:
-                self.classes = sample_collection.info["classes"]
-                self._parse_classes()
-
     def export_sample(self, image_or_path, label, metadata=None):
         _, uuid = self._media_exporter.export(image_or_path)
         self._labels_dict[uuid] = _parse_classifications(
@@ -2339,9 +2324,7 @@ class FiftyOneImageDetectionDatasetExporter(
 
             If None, the default value of this parameter will be chosen based
             on the value of the ``data_path`` parameter
-        classes (None): the list of possible class labels. If not provided,
-            this list will be extracted when :meth:`log_collection` is called,
-            if possible
+        classes (None): the list of possible class labels
         include_confidence (None): whether to include detection confidences in
             the export. The supported values are:
 
@@ -2422,18 +2405,6 @@ def setup(self):
         )
         self._media_exporter.setup()
 
-    def log_collection(self, sample_collection):
-        if self.classes is None:
-            if sample_collection.default_classes:
-                self.classes = sample_collection.default_classes
-                self._parse_classes()
-            elif sample_collection.classes:
-                self.classes = next(iter(sample_collection.classes.values()))
-                self._parse_classes()
-            elif "classes" in sample_collection.info:
-                self.classes = sample_collection.info["classes"]
-                self._parse_classes()
-
     def export_sample(self, image_or_path, detections, metadata=None):
         _, uuid = self._media_exporter.export(image_or_path)
         self._labels_dict[uuid] = _parse_detections(
@@ -2520,9 +2491,7 @@ class FiftyOneTemporalDetectionDatasetExporter(
             on the value of the ``data_path`` parameter
         use_timestamps (False): whether to export the support of each temporal
             detection in seconds rather than frame numbers
-        classes (None): the list of possible class labels. If not provided,
-            this list will be extracted when :meth:`log_collection` is called,
-            if possible
+        classes (None): the list of possible class labels
         include_confidence (None): whether to include detection confidences in
             the export. The supported values are:
 
@@ -2603,18 +2572,6 @@ def setup(self):
         )
         self._media_exporter.setup()
 
-    def log_collection(self, sample_collection):
-        if self.classes is None:
-            if sample_collection.default_classes:
-                self.classes = sample_collection.default_classes
-                self._parse_classes()
-            elif sample_collection.classes:
-                self.classes = next(iter(sample_collection.classes.values()))
-                self._parse_classes()
-            elif "classes" in sample_collection.info:
-                self.classes = sample_collection.info["classes"]
-                self._parse_classes()
-
     def export_sample(self, video_path, temporal_detections, _, metadata=None):
         _, uuid = self._media_exporter.export(video_path)
         self._labels_dict[uuid] = _parse_temporal_detections(
diff --git a/fiftyone/utils/eval/classification.py b/fiftyone/utils/eval/classification.py
index fa92da7ba9..7324847be4 100644
--- a/fiftyone/utils/eval/classification.py
+++ b/fiftyone/utils/eval/classification.py
@@ -67,11 +67,8 @@ def evaluate_classifications(
         gt_field ("ground_truth"): the name of the field containing the ground
             truth :class:`fiftyone.core.labels.Classification` instances
         eval_key (None): an evaluation key to use to refer to this evaluation
-        classes (None): the list of possible classes. If not provided, classes
-            are loaded from :meth:`fiftyone.core.dataset.Dataset.classes` or
-            :meth:`fiftyone.core.dataset.Dataset.default_classes` if
-            possible, or else the observed ground truth/predicted labels are
-            used
+        classes (None): the list of possible classes. If not provided, the
+            observed ground truth/predicted labels are used
         missing (None): a missing label string. Any None-valued labels are
             given this label for results purposes
         method ("simple"): a string specifying the evaluation method to use.
@@ -86,14 +83,6 @@ def evaluate_classifications(
         samples, (pred_field, gt_field), fol.Classification, same_type=True
     )
 
-    if classes is None:
-        if pred_field in samples.classes:
-            classes = samples.classes[pred_field]
-        elif gt_field in samples.classes:
-            classes = samples.classes[gt_field]
-        elif samples.default_classes:
-            classes = samples.default_classes
-
     config = _parse_config(pred_field, gt_field, method, **kwargs)
     eval_method = config.build()
     eval_method.ensure_requirements()
diff --git a/fiftyone/utils/eval/detection.py b/fiftyone/utils/eval/detection.py
index 690f521436..e1fdc4a5c3 100644
--- a/fiftyone/utils/eval/detection.py
+++ b/fiftyone/utils/eval/detection.py
@@ -101,10 +101,8 @@ def evaluate_detections(
             :class:`fiftyone.core.labels.Polylines`,
             or :class:`fiftyone.core.labels.TemporalDetections`
         eval_key (None): an evaluation key to use to refer to this evaluation
-        classes (None): the list of possible classes. If not provided, classes
-            are loaded from :meth:`fiftyone.core.dataset.Dataset.classes` or
-            :meth:`fiftyone.core.dataset.Dataset.default_classes` if possible,
-            or else the observed ground truth/predicted labels are used
+        classes (None): the list of possible classes. If not provided, the
+            observed ground truth/predicted labels are used
         missing (None): a missing label string. Any unmatched objects are given
             this label for results purposes
         method (None): a string specifying the evaluation method to use.
@@ -152,14 +150,6 @@ def evaluate_detections(
         **kwargs,
     )
 
-    if classes is None:
-        if pred_field in samples.classes:
-            classes = samples.classes[pred_field]
-        elif gt_field in samples.classes:
-            classes = samples.classes[gt_field]
-        elif samples.default_classes:
-            classes = samples.default_classes
-
     eval_method = config.build()
     eval_method.ensure_requirements()
 
diff --git a/fiftyone/utils/eval/segmentation.py b/fiftyone/utils/eval/segmentation.py
index 91f6e6ab07..f32a0f301f 100644
--- a/fiftyone/utils/eval/segmentation.py
+++ b/fiftyone/utils/eval/segmentation.py
@@ -75,10 +75,7 @@ def evaluate_segmentations(
             truth :class:`fiftyone.core.labels.Segmentation` instances
         eval_key (None): an evaluation key to use to refer to this evaluation
         mask_targets (None): a dict mapping mask values to labels. If not
-            provided, mask targets are loaded from
-            :meth:`fiftyone.core.dataset.Dataset.mask_targets` or
-            :meth:`fiftyone.core.dataset.Dataset.default_mask_targets` if
-            possible, or else the observed pixel values are used
+            provided, the observed pixel values are used
         method ("simple"): a string specifying the evaluation method to use.
             Supported values are ``("simple")``
         **kwargs: optional keyword arguments for the constructor of the
@@ -91,14 +88,6 @@ def evaluate_segmentations(
         samples, (pred_field, gt_field), fol.Segmentation, same_type=True
     )
 
-    if mask_targets is None:
-        if pred_field in samples.mask_targets:
-            mask_targets = samples.mask_targets[pred_field]
-        elif gt_field in samples.mask_targets:
-            mask_targets = samples.mask_targets[gt_field]
-        elif samples.default_mask_targets:
-            mask_targets = samples.default_mask_targets
-
     config = _parse_config(pred_field, gt_field, method, **kwargs)
     eval_method = config.build()
     eval_method.ensure_requirements()
diff --git a/fiftyone/utils/labels.py b/fiftyone/utils/labels.py
index 369b7c78ab..187792b65f 100644
--- a/fiftyone/utils/labels.py
+++ b/fiftyone/utils/labels.py
@@ -153,12 +153,6 @@ def segmentations_to_detections(
         fol.Segmentation,
     )
 
-    if mask_targets is None:
-        if out_field in sample_collection.mask_targets:
-            mask_targets = sample_collection.mask_targets[out_field]
-        elif sample_collection.default_mask_targets:
-            mask_targets = sample_collection.default_mask_targets
-
     samples = sample_collection.select_fields(in_field)
     in_field, processing_frames = samples._handle_frame_field(in_field)
     out_field, _ = samples._handle_frame_field(out_field)
@@ -278,12 +272,6 @@ def segmentations_to_polylines(
         fol.Segmentation,
     )
 
-    if mask_targets is None:
-        if out_field in sample_collection.mask_targets:
-            mask_targets = sample_collection.mask_targets[out_field]
-        elif sample_collection.default_mask_targets:
-            mask_targets = sample_collection.default_mask_targets
-
     samples = sample_collection.select_fields(in_field)
     in_field, processing_frames = samples._handle_frame_field(in_field)
     out_field, _ = samples._handle_frame_field(out_field)
diff --git a/fiftyone/utils/tf.py b/fiftyone/utils/tf.py
index 13c55e644c..08c6a06a6d 100644
--- a/fiftyone/utils/tf.py
+++ b/fiftyone/utils/tf.py
@@ -846,8 +846,7 @@ class TFObjectDetectionDatasetExporter(TFRecordsDatasetExporter):
             images to disk. By default, ``fiftyone.config.default_image_ext``
             is used
         force_rgb (False): whether to force convert all images to RGB
-        classes (None): the list of possible class labels. If omitted, the
-            class list is dynamically generated as samples are processed
+        classes (None): the list of possible class labels
     """
 
     def __init__(
@@ -873,15 +872,6 @@ def __init__(
     def label_cls(self):
         return fol.Detections
 
-    def log_collection(self, sample_collection):
-        if self.classes is None:
-            if sample_collection.default_classes:
-                self.classes = sample_collection.default_classes
-            elif sample_collection.classes:
-                self.classes = next(iter(sample_collection.classes.values()))
-            elif "classes" in sample_collection.info:
-                self.classes = sample_collection.info["classes"]
-
     def _make_example_generator(self):
         return TFObjectDetectionExampleGenerator(
             force_rgb=self.force_rgb, classes=self.classes
@@ -1019,8 +1009,7 @@ class TFObjectDetectionExampleGenerator(TFExampleGenerator):
 
     Args:
         force_rgb (False): whether to force convert all images to RGB
-        classes (None): the list of possible class labels. If omitted, the
-            class list is dynamically generated as examples are processed
+        classes (None): the list of possible class labels
     """
 
     def __init__(self, force_rgb=False, classes=None):
@@ -1029,14 +1018,14 @@ def __init__(self, force_rgb=False, classes=None):
         self.classes = classes
 
         if classes:
-            labels_map_rev = _to_labels_map_rev(classes)
             dynamic_classes = False
+            labels_map_rev = _to_labels_map_rev(classes)
         else:
-            labels_map_rev = {}
             dynamic_classes = True
+            labels_map_rev = {}
 
-        self._labels_map_rev = labels_map_rev
         self._dynamic_classes = dynamic_classes
+        self._labels_map_rev = labels_map_rev
 
     def make_tf_example(self, image_or_path, detections, filename=None):
         """Makes a ``tf.train.Example`` for the given data.
diff --git a/fiftyone/utils/yolo.py b/fiftyone/utils/yolo.py
index b153e9bd6c..d04ba85734 100644
--- a/fiftyone/utils/yolo.py
+++ b/fiftyone/utils/yolo.py
@@ -25,7 +25,7 @@ def add_yolo_labels(
     sample_collection,
     label_field,
     labels_path,
-    classes=None,
+    classes,
     include_missing=False,
 ):
     """Adds the given YOLO-formatted labels to the collection.
@@ -55,22 +55,12 @@ def add_yolo_labels(
             -   a directory containing YOLO TXT files whose filenames (less
                 extension) correspond to image filenames in
                 ``sample_collection``, in any order
-        classes (None): the list of class label strings. If not provided, these
-            must be available from
-            :meth:`fiftyone.core.collections.SampleCollection.get_classes`
+        classes: the list of class label strings
         include_missing (False): whether to insert empty
             :class:`Detections <fiftyone.core.labels.Detections>` instances for
             any samples in the input collection whose ``label_field`` is
             ``None`` after import
     """
-    if classes is None:
-        classes = sample_collection.get_classes(label_field)
-
-    if not classes:
-        raise ValueError(
-            "You must provide `classes` in order to load YOLO labels"
-        )
-
     if isinstance(labels_path, (list, tuple)):
         # Explicit list of labels files
         labels = [load_yolo_annotations(p, classes) for p in labels_path]
@@ -610,9 +600,7 @@ class YOLOv4DatasetExporter(
 
             If None, the default value of this parameter will be chosen based
             on the value of the ``data_path`` parameter
-        classes (None): the list of possible class labels. If not provided,
-            this list will be extracted when :meth:`log_collection` is called,
-            if possible
+        classes (None): the list of possible class labels
         include_confidence (False): whether to include detection confidences in
             the export, if they exist
         image_format (None): the image format to use when writing in-memory
@@ -707,21 +695,6 @@ def setup(self):
         )
         self._media_exporter.setup()
 
-    def log_collection(self, sample_collection):
-        if self.classes is None:
-            if sample_collection.default_classes:
-                self.classes = sample_collection.default_classes
-                self._parse_classes()
-                self._dynamic_classes = False
-            elif sample_collection.classes:
-                self.classes = next(iter(sample_collection.classes.values()))
-                self._parse_classes()
-                self._dynamic_classes = False
-            elif "classes" in sample_collection.info:
-                self.classes = sample_collection.info["classes"]
-                self._parse_classes()
-                self._dynamic_classes = False
-
     def export_sample(self, image_or_path, detections, metadata=None):
         out_image_path, uuid = self._media_exporter.export(image_or_path)
 
@@ -821,9 +794,7 @@ class YOLOv5DatasetExporter(
 
             If None, the default value of this parameter will be chosen based
             on the value of the ``data_path`` parameter
-        classes (None): the list of possible class labels. If not provided,
-            this list will be extracted when :meth:`log_collection` is called,
-            if possible
+        classes (None): the list of possible class labels
         include_confidence (False): whether to include detection confidences in
             the export, if they exist
         image_format (None): the image format to use when writing in-memory
@@ -906,21 +877,6 @@ def setup(self):
         )
         self._media_exporter.setup()
 
-    def log_collection(self, sample_collection):
-        if self.classes is None:
-            if sample_collection.default_classes:
-                self.classes = sample_collection.default_classes
-                self._parse_classes()
-                self._dynamic_classes = False
-            elif sample_collection.classes:
-                self.classes = next(iter(sample_collection.classes.values()))
-                self._parse_classes()
-                self._dynamic_classes = False
-            elif "classes" in sample_collection.info:
-                self.classes = sample_collection.info["classes"]
-                self._parse_classes()
-                self._dynamic_classes = False
-
     def export_sample(self, image_or_path, detections, metadata=None):
         _, uuid = self._media_exporter.export(image_or_path)
 
diff --git a/tests/unittests/import_export_tests.py b/tests/unittests/import_export_tests.py
index 8c94deed7d..527a2b0cc5 100644
--- a/tests/unittests/import_export_tests.py
+++ b/tests/unittests/import_export_tests.py
@@ -1263,9 +1263,7 @@ def test_add_yolo_labels(self):
 
         # Standard
 
-        fouy.add_yolo_labels(
-            dataset, "yolo", labels_path=yolo_labels_path, classes=classes
-        )
+        fouy.add_yolo_labels(dataset, "yolo", yolo_labels_path, classes)
         self.assertEqual(
             dataset.count_values("predictions.detections.label"),
             dataset.count_values("yolo.detections.label"),
@@ -1277,8 +1275,8 @@ def test_add_yolo_labels(self):
         fouy.add_yolo_labels(
             dataset,
             "yolo_inclusive",
-            labels_path=yolo_labels_path,
-            classes=classes,
+            yolo_labels_path,
+            classes,
             include_missing=True,
         )
         self.assertEqual(
@@ -1300,9 +1298,7 @@ def test_add_coco_labels(self):
         )
         coco_labels_path = os.path.join(export_dir, "labels.json")
 
-        fouc.add_coco_labels(
-            dataset, "coco", coco_labels_path, classes=classes
-        )
+        fouc.add_coco_labels(dataset, "coco", coco_labels_path, classes)
         self.assertEqual(
             dataset.count_values("predictions.detections.label"),
             dataset.count_values("coco.detections.label"),

From 14b086748154a11b38be2aab82f4f62e14cc1228 Mon Sep 17 00:00:00 2001
From: brimoor <brimoor@umich.edu>
Date: Thu, 9 Jun 2022 21:30:11 -0400
Subject: [PATCH 2/2] removing default classes/mask targets from annotation API
 too

---
 docs/source/integrations/cvat.rst     | 17 ++++-------------
 docs/source/user_guide/annotation.rst | 17 ++++-------------
 fiftyone/utils/annotations.py         | 16 ++--------------
 3 files changed, 10 insertions(+), 40 deletions(-)

diff --git a/docs/source/integrations/cvat.rst b/docs/source/integrations/cvat.rst
index 3d94669abe..b819a44625 100644
--- a/docs/source/integrations/cvat.rst
+++ b/docs/source/integrations/cvat.rst
@@ -431,9 +431,8 @@ details:
     `label_field` or all fields in `label_schema` without classes specified.
     All new label fields must have a class list provided via one of the
     supported methods. For existing label fields, if classes are not provided
-    by this argument nor `label_schema`, they are retrieved from
-    :meth:`Dataset.get_classes() <fiftyone.core.dataset.Dataset.get_classes>`
-    if possible, or else the observed labels on your dataset are used
+    by this argument nor `label_schema`, the observed labels on your dataset
+    are used
 -   **attributes** (*True*): specifies the label attributes of each label field
     to include (other than their `label`, which is always included) in the
     annotation export. Can be any of the following:
@@ -631,16 +630,8 @@ FiftyOne can infer the appropriate values to use:
 
 -   **label_type**: if omitted, the |Label| type of the field will be used to
     infer the appropriate value for this parameter
--   **classes**: if omitted for a non-semantic segmentation field, the class
-    lists from the :meth:`classes <fiftyone.core.dataset.Dataset.classes>` or
-    :meth:`default_classes <fiftyone.core.dataset.Dataset.default_classes>`
-    properties of your dataset will be used, if available. Otherwise, the
-    observed labels on your dataset will be used to construct a classes list
--   **mask_targets**: if omitted for a semantic segmentation field, the mask
-    targets from the
-    :meth:`mask_targets <fiftyone.core.dataset.Dataset.mask_targets>` or
-    :meth:`default_mask_targets <fiftyone.core.dataset.Dataset.default_mask_targets>`
-    properties of your dataset will be used, if available
+-   **classes**: if omitted for a non-semantic segmentation field, the observed
+    labels on your dataset will be used to construct a classes list
 
 .. _cvat-label-attributes:
 
diff --git a/docs/source/user_guide/annotation.rst b/docs/source/user_guide/annotation.rst
index 9e26f1402e..666a82bc2f 100644
--- a/docs/source/user_guide/annotation.rst
+++ b/docs/source/user_guide/annotation.rst
@@ -472,9 +472,8 @@ more details:
     `label_field` or all fields in `label_schema` without classes specified.
     All new label fields must have a class list provided via one of the
     supported methods. For existing label fields, if classes are not provided
-    by this argument nor `label_schema`, they are retrieved from
-    :meth:`Dataset.get_classes() <fiftyone.core.dataset.Dataset.get_classes>`
-    if possible, or else the observed labels on your dataset are used
+    by this argument nor `label_schema`, the observed labels on your dataset
+    are used
 -   **attributes** (*True*): specifies the label attributes of each label field
     to include (other than their `label`, which is always included) in the
     annotation export. Can be any of the following:
@@ -644,16 +643,8 @@ FiftyOne can infer the appropriate values to use:
 
 -   **label_type**: if omitted, the |Label| type of the field will be used to
     infer the appropriate value for this parameter
--   **classes**: if omitted for a non-semantic segmentation field, the class
-    lists from the :meth:`classes <fiftyone.core.dataset.Dataset.classes>` or
-    :meth:`default_classes <fiftyone.core.dataset.Dataset.default_classes>`
-    properties of your dataset will be used, if available. Otherwise, the
-    observed labels on your dataset will be used to construct a classes list
--   **mask_targets**: if omitted for a semantic segmentation field, the mask
-    targets from the
-    :meth:`mask_targets <fiftyone.core.dataset.Dataset.mask_targets>` or
-    :meth:`default_mask_targets <fiftyone.core.dataset.Dataset.default_mask_targets>`
-    properties of your dataset will be used, if available
+-   **classes**: if omitted for a non-semantic segmentation field, the observed
+    labels on your dataset will be used to construct a classes list
 
 .. _annotation-label-attributes:
 
diff --git a/fiftyone/utils/annotations.py b/fiftyone/utils/annotations.py
index 42e72f01f8..61d79a64c5 100644
--- a/fiftyone/utils/annotations.py
+++ b/fiftyone/utils/annotations.py
@@ -115,10 +115,8 @@ def annotate(
             ``label_field`` or all fields in ``label_schema`` without classes
             specified. All new label fields must have a class list provided via
             one of the supported methods. For existing label fields, if classes
-            are not provided by this argument nor ``label_schema``, they are
-            parsed from
-            :meth:`fiftyone.core.collections.SampleCollection.get_classes` if
-            possible, or else the observed labels on your dataset are used
+            are not provided by this argument nor ``label_schema``, the
+            observed labels on your dataset are used
         attributes (True): specifies the label attributes of each label
             field to include (other than their ``label``, which is always
             included) in the annotation export. Can be any of the
@@ -772,10 +770,6 @@ def _get_classes(
             % label_field
         )
 
-    classes = samples.get_classes(label_field)
-    if classes:
-        return classes
-
     _, label_path = samples._get_label_field_path(label_field, "label")
     return sorted(
         set(samples._dataset.distinct(label_path))
@@ -815,12 +809,6 @@ def _get_mask_targets(samples, mask_targets, label_field, label_info):
     if "mask_targets" in label_info:
         mask_targets = label_info["mask_targets"]
 
-    if mask_targets is None and label_field in samples.mask_targets:
-        mask_targets = samples.mask_targets[label_field]
-
-    if mask_targets is None and samples.default_mask_targets:
-        mask_targets = samples.default_mask_targets
-
     if mask_targets is None:
         mask_targets = {i: str(i) for i in range(1, 256)}
         mask_targets[0] = "background"