Merge pull request #271 from Labelbox/develop

3.3.0

Author: msokoloff1

CHANGELOG.md

@@ -1,5 +1,14 @@
 # Changelog
 
+# Version 3.3.0 (2021-09-02)
+## Added
+* `Dataset.create_data_rows_sync()` for synchronous bulk uploads of data rows
+* `Model.delete()`, `ModelRun.delete()`, and `ModelRun.delete_annotation_groups()` to
+    Clean up models, model runs, and annotation groups.
+
+## Fix
+* Increased timeout for label exports since projects with many segmentation masks weren't finishing quickly enough.
+
 # Version 3.2.1 (2021-08-31)
 ## Fix
 * Resolved issue with `create_data_rows()` was not working on amazon linux

labelbox/__init__.py

@@ -1,5 +1,5 @@
 name = "labelbox"
-__version__ = "3.2.1"
+__version__ = "3.3.0"
 
 from labelbox.schema.project import Project
 from labelbox.client import Client

labelbox/data/annotation_types/__init__.py

@@ -29,3 +29,4 @@
 from .collection import LabelGenerator
 
 from .metrics import ScalarMetric
+from .metrics import MetricAggregation

labelbox/data/annotation_types/annotation.py

@@ -1,7 +1,5 @@
 from typing import Any, Dict, List, Union
 
-from pydantic.main import BaseModel
-
 from .classification import Checklist, Dropdown, Radio, Text
 from .feature import FeatureSchema
 from .geometry import Geometry
@@ -16,6 +14,7 @@ class BaseAnnotation(FeatureSchema):
 
 class ClassificationAnnotation(BaseAnnotation):
     """Class representing classification annotations (annotations that don't have a location) """
+
     value: Union[Text, Checklist, Radio, Dropdown]
 
 

labelbox/data/annotation_types/classification/classification.py

@@ -1,10 +1,24 @@
 from typing import Any, Dict, List
 
-from pydantic.main import BaseModel
+try:
+    from typing import Literal
+except:
+    from typing_extensions import Literal
 
+from pydantic import BaseModel, validator
 from ..feature import FeatureSchema
 
 
+# TODO: Replace when pydantic adds support for unions that don't coerce types
+class _TempName(BaseModel):
+    name: str
+
+    def dict(self, *args, **kwargs):
+        res = super().dict(*args, **kwargs)
+        res.pop('name')
+        return res
+
+
 class ClassificationAnswer(FeatureSchema):
     """
     - Represents a classification option.
@@ -19,8 +33,9 @@ class Radio(BaseModel):
     answer: ClassificationAnswer
 
 
-class Checklist(BaseModel):
+class Checklist(_TempName):
     """ A classification with many selected options allowed """
+    name: Literal["checklist"] = "checklist"
     answer: List[ClassificationAnswer]
 
 
@@ -29,9 +44,10 @@ class Text(BaseModel):
     answer: str
 
 
-class Dropdown(BaseModel):
+class Dropdown(_TempName):
     """
     - A classification with many selected options allowed .
     - This is not currently compatible with MAL.
     """
+    name: Literal["dropdown"] = "dropdown"
     answer: List[ClassificationAnswer]

labelbox/data/annotation_types/data/raster.py

@@ -81,7 +81,8 @@ def value(self) -> np.ndarray:
             with open(self.file_path, "rb") as img:
                 im_bytes = img.read()
             self.im_bytes = im_bytes
-            return self.bytes_to_np(im_bytes)
+            arr = self.bytes_to_np(im_bytes)
+            return arr
         elif self.url is not None:
             im_bytes = self.fetch_remote()
             self.im_bytes = im_bytes
@@ -92,7 +93,7 @@ def value(self) -> np.ndarray:
     def set_fetch_fn(self, fn):
         object.__setattr__(self, 'fetch_remote', lambda: fn(self))
 
-    @retry.Retry(deadline=15.)
+    @retry.Retry(deadline=60.)
     def fetch_remote(self) -> bytes:
         """
         Method for accessing url.
@@ -104,7 +105,7 @@ def fetch_remote(self) -> bytes:
         response.raise_for_status()
         return response.content
 
-    @retry.Retry(deadline=15.)
+    @retry.Retry(deadline=30.)
     def create_url(self, signer: Callable[[bytes], str]) -> str:
         """
         Utility for creating a url from any of the other image representations.

labelbox/data/annotation_types/label.py

@@ -1,4 +1,5 @@
 from collections import defaultdict
+from labelbox.data.annotation_types.metrics.scalar import ScalarMetric
 
 from typing import Any, Callable, Dict, List, Union, Optional
 
@@ -21,7 +22,8 @@ class Label(BaseModel):
     data: Union[VideoData, ImageData, TextData]
     annotations: List[Union[ClassificationAnnotation, ObjectAnnotation,
                             VideoObjectAnnotation,
-                            VideoClassificationAnnotation, ScalarMetric]] = []
+                            VideoClassificationAnnotation, ScalarMetric,
+                            ScalarMetric]] = []
     extra: Dict[str, Any] = {}
 
     def object_annotations(self) -> List[ObjectAnnotation]:

labelbox/data/annotation_types/metrics.py

@@ -0,0 +1,2 @@
+from .scalar import ScalarMetric
+from .aggregations import MetricAggregation

labelbox/data/annotation_types/metrics/__init__.py

@@ -0,0 +1,8 @@
+from enum import Enum
+
+
+class MetricAggregation(Enum):
+    ARITHMETIC_MEAN = "ARITHMETIC_MEAN"
+    GEOMETRIC_MEAN = "GEOMETRIC_MEAN"
+    HARMONIC_MEAN = "HARMONIC_MEAN"
+    SUM = "SUM"

labelbox/data/annotation_types/metrics/aggregations.py

@@ -0,0 +1,26 @@
+from labelbox.data.annotation_types.metrics.aggregations import MetricAggregation
+from typing import Any, Dict, Optional
+from pydantic import BaseModel
+
+
+class ScalarMetric(BaseModel):
+    """ Class representing metrics
+
+    # For backwards compatibility, metric_name is optional. This will eventually be deprecated
+    # The metric_name will be set to a default name in the editor if it is not set.
+
+    # aggregation will be ignored wihtout providing a metric name.
+    # Not providing a metric name is deprecated.
+    """
+    value: float
+    metric_name: Optional[str] = None
+    feature_name: Optional[str] = None
+    subclass_name: Optional[str] = None
+    aggregation: MetricAggregation = MetricAggregation.ARITHMETIC_MEAN
+    extra: Dict[str, Any] = {}
+
+    def dict(self, *args, **kwargs):
+        res = super().dict(*args, **kwargs)
+        if res['metric_name'] is None:
+            res.pop('aggregation')
+        return {k: v for k, v in res.items() if v is not None}

labelbox/data/annotation_types/metrics/scalar.py

@@ -0,0 +1,160 @@
+"""
+Tools for grouping features and labels so that we can compute metrics on the individual groups
+"""
+from collections import defaultdict
+from typing import Dict, List, Tuple, Union
+try:
+    from typing import Literal
+except ImportError:
+    from typing_extensions import Literal
+
+from labelbox.data.annotation_types import Label
+from labelbox.data.annotation_types.collection import LabelList
+from labelbox.data.annotation_types.feature import FeatureSchema
+
+
+def get_identifying_key(
+    features_a: List[FeatureSchema], features_b: List[FeatureSchema]
+) -> Union[Literal['name'], Literal['feature_schema_id']]:
+    """
+    Checks to make sure that features in both sets contain the same type of identifying keys.
+    This can either be the feature name or feature schema id.
+
+    Args:
+        features_a : List of FeatureSchemas (usually ObjectAnnotations or ClassificationAnnotations)
+        features_b : List of FeatureSchemas (usually ObjectAnnotations or ClassificationAnnotations)
+    Returns:
+        The field name that is present in both feature lists.
+    """
+
+    all_schema_ids_defined_pred, all_names_defined_pred = all_have_key(
+        features_a)
+    if (not all_schema_ids_defined_pred and not all_names_defined_pred):
+        raise ValueError("All data must have feature_schema_ids or names set")
+
+    all_schema_ids_defined_gt, all_names_defined_gt = all_have_key(features_b)
+
+    # Prefer name becuse the user will be able to know what it means
+    # Schema id incase that doesn't exist.
+    if (all_names_defined_pred and all_names_defined_gt):
+        return 'name'
+    elif all_schema_ids_defined_pred and all_schema_ids_defined_gt:
+        return 'feature_schema_id'
+    else:
+        raise ValueError(
+            "Ground truth and prediction annotations must have set all name or feature ids. "
+            "Otherwise there is no key to match on. Please update.")
+
+
+def all_have_key(features: List[FeatureSchema]) -> Tuple[bool, bool]:
+    """
+    Checks to make sure that all FeatureSchemas have names set or feature_schema_ids set.
+
+    Args:
+        features (List[FeatureSchema]) :
+
+    """
+    all_names = True
+    all_schemas = True
+    for feature in features:
+        if feature.name is None:
+            all_names = False
+        if feature.feature_schema_id is None:
+            all_schemas = False
+    return all_schemas, all_names
+
+
+def get_label_pairs(labels_a: LabelList,
+                    labels_b: LabelList,
+                    match_on="uid",
+                    filter=False) -> Dict[str, Tuple[Label, Label]]:
+    """
+    This is a function to pairing a list of prediction labels and a list of ground truth labels easier.
+    There are a few potentiall problems with this function.
+    We are assuming that the data row `uid` or `external id` have been provided by the user.
+    However, these particular fields are not required and can be empty.
+    If this assumption fails, then the user has to determine their own matching strategy.
+
+    Args:
+        labels_a (LabelList): A collection of labels to match with labels_b
+        labels_b (LabelList): A collection of labels to match with labels_a
+        match_on ('uid' or 'external_id'): The data row key to match labels by. Can either be uid or external id.
+        filter (bool): Whether or not to ignore mismatches
+
+    Returns:
+        A dict containing the union of all either uids or external ids and values as a tuple of the matched labels
+
+    """
+
+    if match_on not in ['uid', 'external_id']:
+        raise ValueError("Can only match on  `uid` or `exteranl_id`.")
+
+    label_lookup_a = {
+        getattr(label.data, match_on, None): label for label in labels_a
+    }
+    label_lookup_b = {
+        getattr(label.data, match_on, None): label for label in labels_b
+    }
+    all_keys = set(label_lookup_a.keys()).union(label_lookup_b.keys())
+    if None in label_lookup_a or None in label_lookup_b:
+        raise ValueError(
+            f"One or more of the labels has a data row without the required key {match_on}."
+            " It cannot be determined which labels match without this information."
+            f" Either assign {match_on} to each Label or create your own pairing function."
+        )
+    pairs = defaultdict(list)
+    for key in all_keys:
+        a, b = label_lookup_a.pop(key, None), label_lookup_b.pop(key, None)
+        if a is None or b is None:
+            if not filter:
+                raise ValueError(
+                    f"{match_on} {key} is not available in both LabelLists. "
+                    "Set `filter = True` to filter out these examples, assign the ids manually, or create your own matching function."
+                )
+            else:
+                continue
+        pairs[key].append([a, b])
+    return pairs
+
+
+def get_feature_pairs(
+    features_a: List[FeatureSchema], features_b: List[FeatureSchema]
+) -> Dict[str, Tuple[List[FeatureSchema], List[FeatureSchema]]]:
+    """
+    Matches features by schema_ids
+
+    Args:
+        labels_a (List[FeatureSchema]): A list of features to match with features_b
+        labels_b (List[FeatureSchema]): A list of features to match with features_a
+    Returns:
+        The matched features as dict. The key will be the feature name and the value will be
+        two lists each containing the matched features from each set.
+
+    """
+    identifying_key = get_identifying_key(features_a, features_b)
+    lookup_a, lookup_b = _create_feature_lookup(
+        features_a,
+        identifying_key), _create_feature_lookup(features_b, identifying_key)
+
+    keys = set(lookup_a.keys()).union(set(lookup_b.keys()))
+    result = defaultdict(list)
+    for key in keys:
+        result[key].extend([lookup_a[key], lookup_b[key]])
+    return result
+
+
+def _create_feature_lookup(features: List[FeatureSchema],
+                           key: str) -> Dict[str, List[FeatureSchema]]:
+    """
+    Groups annotation by name (if available otherwise feature schema id).
+
+    Args:
+        annotations: List of annotations to group
+    Returns:
+        a dict where each key is the feature_schema_id (or name)
+        and the value is a list of annotations that have that feature_schema_id (or name)
+    """
+    grouped_features = defaultdict(list)
+    for feature in features:
+        grouped_features[getattr(feature, key)].append(feature)
+    return grouped_features

labelbox/data/metrics/group.py

@@ -0,0 +1,2 @@
+from .calculation import *
+from .iou import *