class PredictionModel(DbObject):
""" A PredictionModel creates a Prediction. Legacy editor only.
Refer to BulkImportRequest if using the new Editor.
Attributes:
updated_at (datetime)
created_at (datetime)
name (str)
slug (str)
version (int)
created_by (Relationship): `ToOne` relationship to User
organization (Relationship): `ToOne` relationship to Organization
"""
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
created_by = Relationship.ToOne("User", False, "created_by")
organization = Relationship.ToOne("Organization", False)
name = Field.String("name")
slug = Field.String("slug")
version = Field.Int("version")
created_predictions = Relationship.ToMany("Prediction", False,
"created_predictions")
class DataRow(DbObject, Updateable, BulkDeletable):
""" A DataRow represents a single piece of data. For example, if you have
a CSV with 100 rows, you will have 1 Dataset and 100 DataRows.
"""
external_id = Field.String("external_id")
row_data = Field.String("row_data")
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
# Relationships
dataset = Relationship.ToOne("Dataset")
created_by = Relationship.ToOne("User", False, "created_by")
organization = Relationship.ToOne("Organization", False)
labels = Relationship.ToMany("Label", True)
metadata = Relationship.ToMany("AssetMetadata", False, "metadata")
predictions = Relationship.ToMany("Prediction", False)
@staticmethod
def bulk_delete(data_rows):
""" Deletes all the given DataRows.
Args:
data_rows (list of DataRow): The DataRows to delete.
"""
BulkDeletable._bulk_delete(data_rows, True)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.metadata.supports_filtering = False
self.metadata.supports_sorting = False
def create_metadata(self, meta_type, meta_value):
""" Creates an asset metadata for this DataRow.
>>> datarow.create_metadata("TEXT", "This is a text message")
Args:
meta_type (str): Asset metadata type, must be one of:
VIDEO, IMAGE, TEXT.
meta_value (str): Asset metadata value.
Returns:
AssetMetadata DB object.
"""
meta_type_param = "metaType"
meta_value_param = "metaValue"
data_row_id_param = "dataRowId"
query_str = """mutation CreateAssetMetadataPyApi(
$%s: AttachmentType!, $%s: String!, $%s: ID!) {
createAssetMetadata(data: {
metaType: $%s metaValue: $%s dataRowId: $%s}) {%s}} """ % (
meta_type_param, meta_value_param, data_row_id_param,
meta_type_param, meta_value_param, data_row_id_param,
query.results_query_part(Entity.AssetMetadata))
res = self.client.execute(
query_str, {
meta_type_param: meta_type,
meta_value_param: meta_value,
data_row_id_param: self.uid
})
return Entity.AssetMetadata(self.client, res["createAssetMetadata"])
class Review(DbObject, Deletable, Updateable):
""" Reviewing labeled data is a collaborative quality assurance technique.
A Review object indicates the quality of the assigned Label. The aggregated
review numbers can be obtained on a Project object.
Attributes:
created_at (datetime)
updated_at (datetime)
score (float)
created_by (Relationship): `ToOne` relationship to User
organization (Relationship): `ToOne` relationship to Organization
project (Relationship): `ToOne` relationship to Project
label (Relationship): `ToOne` relationship to Label
"""
class NetScore(Enum):
""" Negative, Zero, or Positive.
"""
Negative = auto()
Zero = auto()
Positive = auto()
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
score = Field.Float("score")
created_by = Relationship.ToOne("User", False, "created_by")
organization = Relationship.ToOne("Organization", False)
project = Relationship.ToOne("Project", False)
label = Relationship.ToOne("Label", False)
class Prediction(DbObject):
""" A prediction created by a PredictionModel. Legacy editor only.
Refer to BulkImportRequest if using the new Editor.
Attributes:
updated_at (datetime)
created_at (datetime)
label (str)
agreement (float)
organization (Relationship): `ToOne` relationship to Organization
prediction_model (Relationship): `ToOne` relationship to PredictionModel
data_row (Relationship): `ToOne` relationship to DataRow
project (Relationship): `ToOne` relationship to Project
"""
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
organization = Relationship.ToOne("Organization", False)
label = Field.String("label")
agreement = Field.Float("agreement")
prediction_model = Relationship.ToOne("PredictionModel", False)
data_row = Relationship.ToOne("DataRow", False)
project = Relationship.ToOne("Project", False)
class Benchmark(DbObject):
""" Represents a benchmark label.
The Benchmarks tool works by interspersing data to be labeled, for
which there is a benchmark label, to each person labeling. These
labeled data are compared against their respective benchmark and an
accuracy score between 0 and 100 percent is calculated.
Attributes:
created_at (datetime)
last_activity (datetime)
average_agreement (float)
completed_count (int)
created_by (Relationship): `ToOne` relationship to User
reference_label (Relationship): `ToOne` relationship to Label
"""
created_at = Field.DateTime("created_at")
created_by = Relationship.ToOne("User", False, "created_by")
last_activity = Field.DateTime("last_activity")
average_agreement = Field.Float("average_agreement")
completed_count = Field.Int("completed_count")
reference_label = Relationship.ToOne("Label", False, "reference_label")
def delete(self):
label_param = "labelId"
query_str = """mutation DeleteBenchmarkPyApi($%s: ID!) {
deleteBenchmark(where: {labelId: $%s}) {id}} """ % (label_param,
label_param)
self.client.execute(query_str,
{label_param: self.reference_label().uid})
class Prediction(DbObject):
""" A prediction created by a PredictionModel. """
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
organization = Relationship.ToOne("Organization", False)
label = Field.String("label")
agreement = Field.Float("agreement")
prediction_model = Relationship.ToOne("PredictionModel", False)
data_row = Relationship.ToOne("DataRow", False)
project = Relationship.ToOne("Project", False)
class Label(DbObject, Updateable, BulkDeletable):
""" Label represents an assessment on a DataRow. For example one label could
contain 100 bounding boxes (annotations).
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.reviews.supports_filtering = False
label = Field.String("label")
seconds_to_label = Field.Float("seconds_to_label")
agreement = Field.Float("agreement")
benchmark_agreement = Field.Float("benchmark_agreement")
is_benchmark_reference = Field.Boolean("is_benchmark_reference")
project = Relationship.ToOne("Project")
data_row = Relationship.ToOne("DataRow")
reviews = Relationship.ToMany("Review", False)
created_by = Relationship.ToOne("User", False, "created_by")
@staticmethod
def bulk_delete(labels):
""" Deletes all the given Labels.
Args:
labels (list of Label): The Labels to delete.
"""
BulkDeletable._bulk_delete(labels, False)
def create_review(self, **kwargs):
""" Creates a Review for this label.
Kwargs:
Review attributes. At a minimum a `Review.score` field
value must be provided.
"""
kwargs[Entity.Review.label.name] = self
kwargs[Entity.Review.project.name] = self.project()
return self.client._create(Entity.Review, kwargs)
def create_benchmark(self):
""" Creates a Benchmark for this Label.
Returns:
The newly created Benchmark.
"""
label_id_param = "labelId"
query_str = """mutation CreateBenchmarkPyApi($%s: ID!) {
createBenchmark(data: {labelId: $%s}) {%s}} """ % (
label_id_param, label_id_param,
query.results_query_part(Entity.Benchmark))
res = self.client.execute(query_str, {label_id_param: self.uid})
return Entity.Benchmark(self.client, res["createBenchmark"])
class PredictionModel(DbObject):
""" A prediction model represents a specific version of a model. """
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
created_by = Relationship.ToOne("User", False, "created_by")
organization = Relationship.ToOne("Organization", False)
name = Field.String("name")
slug = Field.String("slug")
version = Field.Int("version")
created_predictions = Relationship.ToMany("Prediction", False,
"created_predictions")
class Task(DbObject):
""" Represents a server-side process that might take a longer time to process.
Allows the Task state to be updated and checked on the client side.
Attributes:
updated_at (datetime)
created_at (datetime)
name (str)
status (str)
completion_percentage (float)
created_by (Relationship): `ToOne` relationship to User
organization (Relationship): `ToOne` relationship to Organization
"""
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
name = Field.String("name")
status = Field.String("status")
completion_percentage = Field.Float("completion_percentage")
# Relationships
created_by = Relationship.ToOne("User", False, "created_by")
organization = Relationship.ToOne("Organization")
def refresh(self):
""" Refreshes Task data from the server. """
tasks = list(self._user.created_tasks(where=Task.uid == self.uid))
if len(tasks) != 1:
raise ResourceNotFoundError(Task, self.uid)
for field in self.fields():
setattr(self, field.name, getattr(tasks[0], field.name))
def wait_till_done(self, timeout_seconds=60):
""" Waits until the task is completed. Periodically queries the server
to update the task attributes.
Args:
timeout_seconds (float): Maximum time this method can block, in seconds. Defaults to one minute.
"""
check_frequency = 2 # frequency of checking, in seconds
while True:
if self.status != "IN_PROGRESS":
return
sleep_time_seconds = min(check_frequency, timeout_seconds)
logger.debug("Task.wait_till_done sleeping for %.2f seconds" %
sleep_time_seconds)
if sleep_time_seconds <= 0:
break
timeout_seconds -= check_frequency
time.sleep(sleep_time_seconds)
self.refresh()
class LabelingFrontendOptions(DbObject):
""" Label interface options.
Attributes:
customization_options (str)
project (Relationship): `ToOne` relationship to Project
labeling_frontend (Relationship): `ToOne` relationship to LabelingFrontend
organization (Relationship): `ToOne` relationship to Organization
"""
customization_options = Field.String("customization_options")
project = Relationship.ToOne("Project")
labeling_frontend = Relationship.ToOne("LabelingFrontend")
organization = Relationship.ToOne("Organization")
class Prediction(DbObject):
""" A prediction created by a PredictionModel.
NOTE: This is used for the legacy editor [1], if you wish to
import annotations, refer to [2]
[1] https://labelbox.com/docs/legacy/import-model-prediction
[2] https://labelbox.com/docs/automation/model-assisted-labeling
"""
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
organization = Relationship.ToOne("Organization", False)
label = Field.String("label")
agreement = Field.Float("agreement")
prediction_model = Relationship.ToOne("PredictionModel", False)
data_row = Relationship.ToOne("DataRow", False)
project = Relationship.ToOne("Project", False)
class PredictionModel(DbObject):
""" A prediction model represents a specific version of a model.
NOTE: This is used for the legacy editor [1], if you wish to
import annotations, refer to [2]
[1] https://labelbox.com/docs/legacy/import-model-prediction
[2] https://labelbox.com/docs/automation/model-assisted-labeling
"""
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
created_by = Relationship.ToOne("User", False, "created_by")
organization = Relationship.ToOne("Organization", False)
name = Field.String("name")
slug = Field.String("slug")
version = Field.Int("version")
created_predictions = Relationship.ToMany("Prediction", False,
"created_predictions")
class Benchmark(DbObject):
""" Benchmarks (also known as Golden Standard) is a quality assurance tool
for training data. Training data quality is the measure of accuracy and
consistency of the training data. Benchmarks works by interspersing data
to be labeled, for which there is a benchmark label, to each person labeling.
These labeled data are compared against their respective benchmark and an
accuracy score between 0 and 100 percent is calculated.
"""
created_at = Field.DateTime("created_at")
created_by = Relationship.ToOne("User", False, "created_by")
last_activity = Field.DateTime("last_activity")
average_agreement = Field.Float("average_agreement")
completed_count = Field.Int("completed_count")
reference_label = Relationship.ToOne("Label", False, "reference_label")
def delete(self):
label_param = "labelId"
query_str = """mutation DeleteBenchmarkPyApi($%s: ID!) {
deleteBenchmark(where: {labelId: $%s}) {id}} """ % (label_param,
label_param)
self.client.execute(query_str,
{label_param: self.reference_label().uid})
class Ontology(DbObject):
"""An ontology specifies which tools and classifications are available
to a project. This is read only for now.
Attributes:
name (str)
description (str)
updated_at (datetime)
created_at (datetime)
normalized (json)
object_schema_count (int)
classification_schema_count (int)
projects (Relationship): `ToMany` relationship to Project
created_by (Relationship): `ToOne` relationship to User
"""
name = Field.String("name")
description = Field.String("description")
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
normalized = Field.Json("normalized")
object_schema_count = Field.Int("object_schema_count")
classification_schema_count = Field.Int("classification_schema_count")
projects = Relationship.ToMany("Project", True)
created_by = Relationship.ToOne("User", False, "created_by")
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self._tools: Optional[List[Tool]] = None
self._classifications: Optional[List[Classification]] = None
def tools(self) -> List[Tool]:
"""Get list of tools (AKA objects) in an Ontology."""
if self._tools is None:
self._tools = [
Tool.from_dict(tool) for tool in self.normalized['tools']
]
return self._tools
def classifications(self) -> List[Classification]:
"""Get list of classifications in an Ontology."""
if self._classifications is None:
self._classifications = [
Classification.from_dict(classification)
for classification in self.normalized['classifications']
]
return self._classifications
class User(DbObject):
""" A User is a registered Labelbox user (for example you) associated with
data they create or import and an Organization they belong to.
"""
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
email = Field.String("email")
name = Field.String("nickname")
nickname = Field.String("name")
intercom_hash = Field.String("intercom_hash")
picture = Field.String("picture")
is_viewer = Field.Boolean("is_viewer")
is_external_user = Field.Boolean("is_external_user")
# Relationships
organization = Relationship.ToOne("Organization")
created_tasks = Relationship.ToMany("Task", False, "created_tasks")
projects = Relationship.ToMany("Project", False)
class Ontology(DbObject):
""" A ontology specifies which tools and classifications are available
to a project.
NOTE: This is read only for now.
>>> project = client.get_project(name="<project_name>")
>>> ontology = project.ontology()
>>> ontology.normalized
"""
name = Field.String("name")
description = Field.String("description")
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
normalized = Field.Json("normalized")
object_schema_count = Field.Int("object_schema_count")
classification_schema_count = Field.Int("classification_schema_count")
projects = Relationship.ToMany("Project", True)
created_by = Relationship.ToOne("User", False, "created_by")
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self._tools: Optional[List[Tool]] = None
self._classifications: Optional[List[Classification]] = None
def tools(self) -> List[Tool]:
if self._tools is None:
self._tools = [
Tool.from_json(tool) for tool in self.normalized['tools']
]
return self._tools # type: ignore
def classifications(self) -> List[Classification]:
if self._classifications is None:
self._classifications = [
Classification.from_json(classification)
for classification in self.normalized['classifications']
]
return self._classifications # type: ignore
class Project(DbObject, Updateable, Deletable):
""" A Project is a container that includes a labeling frontend, an ontology,
datasets and labels.
"""
name = Field.String("name")
description = Field.String("description")
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
setup_complete = Field.DateTime("setup_complete")
last_activity_time = Field.DateTime("last_activity_time")
auto_audit_number_of_labels = Field.Int("auto_audit_number_of_labels")
auto_audit_percentage = Field.Float("auto_audit_percentage")
# Relationships
datasets = Relationship.ToMany("Dataset", True)
created_by = Relationship.ToOne("User", False, "created_by")
organization = Relationship.ToOne("Organization", False)
reviews = Relationship.ToMany("Review", True)
labeling_frontend = Relationship.ToOne("LabelingFrontend")
labeling_frontend_options = Relationship.ToMany(
"LabelingFrontendOptions", False, "labeling_frontend_options")
labeling_parameter_overrides = Relationship.ToMany(
"LabelingParameterOverride", False, "labeling_parameter_overrides")
webhooks = Relationship.ToMany("Webhook", False)
benchmarks = Relationship.ToMany("Benchmark", False)
active_prediction_model = Relationship.ToOne("PredictionModel", False,
"active_prediction_model")
predictions = Relationship.ToMany("Prediction", False)
def create_label(self, **kwargs):
""" Creates a label on this Project.
Kwargs:
Label attributes. At the minimum the label `DataRow`.
"""
# Copy-paste of Client._create code so we can inject
# a connection to Type. Type objects are on their way to being
# deprecated and we don't want the Py client lib user to know
# about them. At the same time they're connected to a Label at
# label creation in a non-standard way (connect via name).
Label = Entity.Label
kwargs[Label.project] = self
kwargs[Label.seconds_to_label] = kwargs.get(
Label.seconds_to_label.name, 0.0)
data = {
Label.attribute(attr) if isinstance(attr, str) else attr:
value.uid if isinstance(value, DbObject) else value
for attr, value in kwargs.items()
}
query_str, params = query.create(Label, data)
# Inject connection to Type
query_str = query_str.replace(
"data: {", "data: {type: {connect: {name: \"Any\"}} ")
res = self.client.execute(query_str, params)
return Label(self.client, res["createLabel"])
def labels(self, datasets=None, order_by=None):
"""
Custom relationship expansion method to support limited filtering.
Args:
datasets (iterable of Dataset): Optional collection of Datasets
whose Labels are sought. If not provided, all Labels in
this Project are returned.
order_by (None or (Field, Field.Order)): Ordering clause.
"""
Label = Entity.Label
if datasets is not None:
where = " where:{dataRow: {dataset: {id_in: [%s]}}}" % ", ".join(
'"%s"' % dataset.uid for dataset in datasets)
else:
where = ""
if order_by is not None:
query.check_order_by_clause(Label, order_by)
order_by_str = "orderBy: %s_%s" % (order_by[0].graphql_name,
order_by[1].name.upper())
else:
order_by_str = ""
id_param = "projectId"
query_str = """query GetProjectLabelsPyApi($%s: ID!)
{project (where: {id: $%s})
{labels (skip: %%d first: %%d%s%s) {%s}}}""" % (
id_param, id_param, where, order_by_str,
query.results_query_part(Label))
return PaginatedCollection(self.client, query_str,
{id_param: self.uid}, ["project", "labels"],
Label)
def export_labels(self, timeout_seconds=60):
""" Calls the server-side Label exporting that generates a JSON
payload, and returns the URL to that payload.
Will only generate a new URL at a max frequency of 30 min.
Args:
timeout_seconds (float): Max waiting time, in seconds.
Returns:
URL of the data file with this Project's labels. If the server
didn't generate during the `timeout_seconds` period, None
is returned.
"""
sleep_time = 2
id_param = "projectId"
query_str = """mutation GetLabelExportUrlPyApi($%s: ID!)
{exportLabels(data:{projectId: $%s }) {downloadUrl createdAt shouldPoll} }
""" % (id_param, id_param)
while True:
res = self.client.execute(query_str, {id_param: self.uid})
res = res["exportLabels"]
if not res["shouldPoll"]:
return res["downloadUrl"]
timeout_seconds -= sleep_time
if timeout_seconds <= 0:
return None
logger.debug("Project '%s' label export, waiting for server...",
self.uid)
time.sleep(sleep_time)
def labeler_performance(self):
""" Returns the labeler performances for this Project.
Returns:
A PaginatedCollection of LabelerPerformance objects.
"""
id_param = "projectId"
query_str = """query LabelerPerformancePyApi($%s: ID!) {
project(where: {id: $%s}) {
labelerPerformance(skip: %%d first: %%d) {
count user {%s} secondsPerLabel totalTimeLabeling consensus
averageBenchmarkAgreement lastActivityTime}
}}""" % (id_param, id_param, query.results_query_part(Entity.User))
def create_labeler_performance(client, result):
result["user"] = Entity.User(client, result["user"])
result["lastActivityTime"] = datetime.fromtimestamp(
result["lastActivityTime"] / 1000, timezone.utc)
return LabelerPerformance(**{
utils.snake_case(key): value
for key, value in result.items()
})
return PaginatedCollection(self.client, query_str,
{id_param: self.uid},
["project", "labelerPerformance"],
create_labeler_performance)
def review_metrics(self, net_score):
""" Returns this Project's review metrics.
Args:
net_score (None or Review.NetScore): Indicates desired metric.
Returns:
int, aggregation count of reviews for given net_score.
"""
if net_score not in (None, ) + tuple(Entity.Review.NetScore):
raise InvalidQueryError(
"Review metrics net score must be either None "
"or one of Review.NetScore values")
id_param = "projectId"
net_score_literal = "None" if net_score is None else net_score.name
query_str = """query ProjectReviewMetricsPyApi($%s: ID!){
project(where: {id:$%s})
{reviewMetrics {labelAggregate(netScore: %s) {count}}}
}""" % (id_param, id_param, net_score_literal)
res = self.client.execute(query_str, {id_param: self.uid})
return res["project"]["reviewMetrics"]["labelAggregate"]["count"]
def setup(self, labeling_frontend, labeling_frontend_options):
""" Finalizes the Project setup.
Args:
labeling_frontend (LabelingFrontend): Which UI to use to label the
data.
labeling_frontend_options (dict or str): Labeling frontend options,
a.k.a. project ontology. If given a `dict` it will be converted
to `str` using `json.dumps`.
"""
organization = self.client.get_organization()
if not isinstance(labeling_frontend_options, str):
labeling_frontend_options = json.dumps(labeling_frontend_options)
self.labeling_frontend.connect(labeling_frontend)
LFO = Entity.LabelingFrontendOptions
labeling_frontend_options = self.client._create(
LFO, {
LFO.project: self,
LFO.labeling_frontend: labeling_frontend,
LFO.customization_options: labeling_frontend_options,
LFO.organization: organization
})
timestamp = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
self.update(setup_complete=timestamp)
def set_labeling_parameter_overrides(self, data):
""" Adds labeling parameter overrides to this project. Example:
>>> project.set_labeling_parameter_overrides([
>>> (data_row_1, 2, 3), (data_row_2, 1, 4)])
Args:
data (iterable): An iterable of tuples. Each tuple must contain
(DataRow, priority, numberOfLabels) for the new override.
Returns:
bool, indicates if the operation was a success.
"""
data_str = ",\n".join(
"{dataRow: {id: \"%s\"}, priority: %d, numLabels: %d }" %
(data_row.uid, priority, num_labels)
for data_row, priority, num_labels in data)
id_param = "projectId"
query_str = """mutation SetLabelingParameterOverridesPyApi($%s: ID!){
project(where: { id: $%s }) {setLabelingParameterOverrides
(data: [%s]) {success}}} """ % (id_param, id_param, data_str)
res = self.client.execute(query_str, {id_param: self.uid})
return res["project"]["setLabelingParameterOverrides"]["success"]
def unset_labeling_parameter_overrides(self, data_rows):
""" Removes labeling parameter overrides to this project.
Args:
data_rows (iterable): An iterable of DataRows.
Returns:
bool, indicates if the operation was a success.
"""
id_param = "projectId"
query_str = """mutation UnsetLabelingParameterOverridesPyApi($%s: ID!){
project(where: { id: $%s}) {
unsetLabelingParameterOverrides(data: [%s]) { success }}}""" % (
id_param, id_param, ",\n".join("{dataRowId: \"%s\"}" % row.uid
for row in data_rows))
res = self.client.execute(query_str, {id_param: self.uid})
return res["project"]["unsetLabelingParameterOverrides"]["success"]
def upsert_review_queue(self, quota_factor):
""" Reinitiates the review queue for this project.
Args:
quota_factor (float): Which part (percentage) of the queue
to reinitiate. Between 0 and 1.
"""
id_param = "projectId"
quota_param = "quotaFactor"
query_str = """mutation UpsertReviewQueuePyApi($%s: ID!, $%s: Float!){
upsertReviewQueue(where:{project: {id: $%s}}
data:{quotaFactor: $%s}) {id}}""" % (
id_param, quota_param, id_param, quota_param)
res = self.client.execute(query_str, {
id_param: self.uid,
quota_param: quota_factor
})
def extend_reservations(self, queue_type):
""" Extends all the current reservations for the current user on the given
queue type.
Args:
queue_type (str): Either "LabelingQueue" or "ReviewQueue"
Returns:
int, the number of reservations that were extended.
"""
if queue_type not in ("LabelingQueue", "ReviewQueue"):
raise InvalidQueryError("Unsupported queue type: %s" % queue_type)
id_param = "projectId"
query_str = """mutation ExtendReservationsPyApi($%s: ID!){
extendReservations(projectId:$%s queueType:%s)}""" % (
id_param, id_param, queue_type)
res = self.client.execute(query_str, {id_param: self.uid})
return res["extendReservations"]
def create_prediction_model(self, name, version):
""" Creates a PredictionModel connected to this Project.
Args:
name (str): The new PredictionModel's name.
version (int): The new PredictionModel's version.
Return:
A newly created PredictionModel.
"""
PM = Entity.PredictionModel
model = self.client._create(PM, {
PM.name.name: name,
PM.version.name: version
})
self.active_prediction_model.connect(model)
return model
def create_prediction(self, label, data_row, prediction_model=None):
""" Creates a Prediction within this Project.
Args:
label (str): The `label` field of the new Prediction.
data_row (DataRow): The DataRow for which the Prediction is created.
prediction_model (PredictionModel or None): The PredictionModel
within which the new Prediction is created. If None then this
Project's active_prediction_model is used.
Return:
A newly created Prediction.
Raises:
labelbox.excepions.InvalidQueryError: if given `prediction_model`
is None and this Project's active_prediction_model is also
None.
"""
if prediction_model is None:
prediction_model = self.active_prediction_model()
if prediction_model is None:
raise InvalidQueryError(
"Project '%s' has no active prediction model" % self.name)
label_param = "label"
model_param = "prediction_model_id"
project_param = "project_id"
data_row_param = "data_row_id"
Prediction = Entity.Prediction
query_str = """mutation CreatePredictionPyApi(
$%s: String!, $%s: ID!, $%s: ID!, $%s: ID!) {createPrediction(
data: {label: $%s, predictionModelId: $%s, projectId: $%s,
dataRowId: $%s})
{%s}}""" % (label_param, model_param, project_param,
data_row_param, label_param, model_param,
project_param, data_row_param,
query.results_query_part(Prediction))
params = {
label_param: label,
model_param: prediction_model.uid,
data_row_param: data_row.uid,
project_param: self.uid
}
res = self.client.execute(query_str, params)
return Prediction(self.client, res["createPrediction"])
def upload_annotations(
self,
name: str,
annotations: Union[str, Union[str, Path], Iterable[dict]],
) -> 'BulkImportRequest': # type: ignore
""" Uploads annotations to a project.
Args:
name: name of the BulkImportRequest job
annotations:
url that is publicly accessible by Labelbox containing an
ndjson file
OR local path to an ndjson file
OR iterable of annotation rows
Returns:
BulkImportRequest
"""
if isinstance(annotations, str) or isinstance(annotations, Path):
def _is_url_valid(url: Union[str, Path]) -> bool:
""" Verifies that the given string is a valid url.
Args:
url: string to be checked
Returns:
True if the given url is valid otherwise False
"""
if isinstance(url, Path):
return False
parsed = urlparse(url)
return bool(parsed.scheme) and bool(parsed.netloc)
if _is_url_valid(annotations):
return BulkImportRequest.create_from_url(
client=self.client,
project_id=self.uid,
name=name,
url=str(annotations),
)
else:
path = Path(annotations)
if not path.exists():
raise FileNotFoundError(
f'{annotations} is not a valid url nor existing local file'
)
return BulkImportRequest.create_from_local_file(
client=self.client,
project_id=self.uid,
name=name,
file=path,
validate_file=True,
)
elif isinstance(annotations, Iterable):
return BulkImportRequest.create_from_objects(
client=self.client,
project_id=self.uid,
name=name,
predictions=annotations, # type: ignore
)
else:
raise ValueError(
f'Invalid annotations given of type: {type(annotations)}')
class BulkImportRequest(DbObject):
name = Field.String("name")
state = Field.Enum(BulkImportRequestState, "state")
input_file_url = Field.String("input_file_url")
error_file_url = Field.String("error_file_url")
status_file_url = Field.String("status_file_url")
created_at = Field.DateTime("created_at")
project = Relationship.ToOne("Project")
created_by = Relationship.ToOne("User", False, "created_by")
def refresh(self) -> None:
"""
Synchronizes values of all fields with the database.
"""
query_str, params = query.get_single(BulkImportRequest, self.uid)
res = self.client.execute(query_str, params)
res = res[utils.camel_case(BulkImportRequest.type_name())]
self._set_field_values(res)
def wait_until_done(self, sleep_time_seconds: int = 30) -> None:
"""
Blocks until the BulkImportRequest.state changes either to
`BulkImportRequestState.FINISHED` or `BulkImportRequestState.FAILED`,
periodically refreshing object's state.
Args:
sleep_time_seconds (str): a time to block between subsequent API calls
"""
while self.state == BulkImportRequestState.RUNNING:
logger.info(f"Sleeping for {sleep_time_seconds} seconds...")
time.sleep(sleep_time_seconds)
self.__exponential_backoff_refresh()
@backoff.on_exception(
backoff.expo,
(labelbox.exceptions.ApiLimitError, labelbox.exceptions.TimeoutError,
labelbox.exceptions.NetworkError),
max_tries=10,
jitter=None)
def __exponential_backoff_refresh(self) -> None:
self.refresh()
@classmethod
def from_name(cls, client, project_id: str,
name: str) -> 'BulkImportRequest':
""" Fetches existing BulkImportRequest.
Args:
client (Client): a Labelbox client
project_id (str): BulkImportRequest's project id
name (str): name of BulkImportRequest
Returns:
BulkImportRequest object
"""
query_str = """query getBulkImportRequestPyApi(
$projectId: ID!, $name: String!) {
bulkImportRequest(where: {
projectId: $projectId,
name: $name
}) {
%s
}
}
""" % query.results_query_part(cls)
params = {"projectId": project_id, "name": name}
response = client.execute(query_str, params=params)
return cls(client, response['bulkImportRequest'])
@classmethod
def create_from_url(cls, client, project_id: str, name: str,
url: str) -> 'BulkImportRequest':
"""
Creates a BulkImportRequest from a publicly accessible URL
to an ndjson file with predictions.
Args:
client (Client): a Labelbox client
project_id (str): id of project for which predictions will be imported
name (str): name of BulkImportRequest
url (str): publicly accessible URL pointing to ndjson file containing predictions
Returns:
BulkImportRequest object
"""
query_str = """mutation createBulkImportRequestPyApi(
$projectId: ID!, $name: String!, $fileUrl: String!) {
createBulkImportRequest(data: {
projectId: $projectId,
name: $name,
fileUrl: $fileUrl
}) {
%s
}
}
""" % query.results_query_part(cls)
params = {"projectId": project_id, "name": name, "fileUrl": url}
bulk_import_request_response = client.execute(query_str, params=params)
return cls(client,
bulk_import_request_response["createBulkImportRequest"])
@classmethod
def create_from_objects(cls, client, project_id: str, name: str,
predictions: Iterable[dict]) -> 'BulkImportRequest':
"""
Creates a BulkImportRequest from an iterable of dictionaries conforming to
JSON predictions format, e.g.:
``{
"uuid": "9fd9a92e-2560-4e77-81d4-b2e955800092",
"schemaId": "ckappz7d700gn0zbocmqkwd9i",
"dataRow": {
"id": "ck1s02fqxm8fi0757f0e6qtdc"
},
"bbox": {
"top": 48,
"left": 58,
"height": 865,
"width": 1512
}
}``
Args:
client (Client): a Labelbox client
project_id (str): id of project for which predictions will be imported
name (str): name of BulkImportRequest
predictions (Iterable[dict]): iterable of dictionaries representing predictions
Returns:
BulkImportRequest object
"""
_validate_ndjson(predictions)
data_str = ndjson.dumps(predictions)
if not data_str:
raise ValueError('annotations cannot be empty')
data = data_str.encode('utf-8')
file_name = _make_file_name(project_id, name)
request_data = _make_request_data(project_id, name, len(data_str),
file_name)
file_data = (file_name, data, NDJSON_MIME_TYPE)
response_data = _send_create_file_command(client,
request_data=request_data,
file_name=file_name,
file_data=file_data)
return cls(client, response_data["createBulkImportRequest"])
@classmethod
def create_from_local_file(cls,
client,
project_id: str,
name: str,
file: Path,
validate_file=True) -> 'BulkImportRequest':
"""
Creates a BulkImportRequest from a local ndjson file with predictions.
Args:
client (Client): a Labelbox client
project_id (str): id of project for which predictions will be imported
name (str): name of BulkImportRequest
file (Path): local ndjson file with predictions
validate_file (bool): a flag indicating if there should be a validation
if `file` is a valid ndjson file
Returns:
BulkImportRequest object
"""
file_name = _make_file_name(project_id, name)
content_length = file.stat().st_size
request_data = _make_request_data(project_id, name, content_length,
file_name)
with file.open('rb') as f:
if validate_file:
reader = ndjson.reader(f)
# ensure that the underlying json load call is valid
# https://github.com/rhgrant10/ndjson/blob/ff2f03c56b21f28f7271b27da35ca4a8bf9a05d0/ndjson/api.py#L53
# by iterating through the file so we only store
# each line in memory rather than the entire file
try:
_validate_ndjson(reader)
except ValueError:
raise ValueError(f"{file} is not a valid ndjson file")
else:
f.seek(0)
file_data = (file.name, f, NDJSON_MIME_TYPE)
response_data = _send_create_file_command(client, request_data,
file_name, file_data)
return cls(client, response_data["createBulkImportRequest"])
class User(DbObject):
""" A User is a registered Labelbox user (for example you) associated with
data they create or import and an Organization they belong to.
Attributes:
updated_at (datetime)
created_at (datetime)
email (str)
name (str)
nickname (str)
intercom_hash (str)
picture (str)
is_viewer (bool)
is_external_viewer (bool)
organization (Relationship): `ToOne` relationship to Organization
created_tasks (Relationship): `ToMany` relationship to Task
projects (Relationship): `ToMany` relationship to Project
"""
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
email = Field.String("email")
name = Field.String("nickname")
nickname = Field.String("name")
intercom_hash = Field.String("intercom_hash")
picture = Field.String("picture")
is_viewer = Field.Boolean("is_viewer")
is_external_user = Field.Boolean("is_external_user")
# Relationships
organization = Relationship.ToOne("Organization")
created_tasks = Relationship.ToMany("Task", False, "created_tasks")
projects = Relationship.ToMany("Project", False)
org_role = Relationship.ToOne("OrgRole", False)
def update_org_role(self, role: Role):
""" Updated the `User`s organization role.
See client.get_roles() to get all valid roles
If you a user is converted from project level permissions to org level permissions and then convert back, their permissions will remain for each individual project
Args:
role (Role): The role that you want to set for this user.
"""
user_id_param = "userId"
role_id_param = "roleId"
query_str = """mutation SetOrganizationRolePyApi($%s: ID!, $%s: ID!) {
setOrganizationRole(data: {userId: $userId, roleId: $roleId}) { id name }}
""" % (user_id_param, role_id_param)
self.client.execute(query_str, {
user_id_param: self.uid,
role_id_param: role.uid
})
def remove_from_project(self, project: Project):
""" Removes a User from a project. Only used for project based users.
Project based user means their org role is "NONE"
Args:
project (Project): Project to remove user from
"""
self.upsert_project_role(project, self.client.get_roles()['NONE'])
def upsert_project_role(self, project: Project, role: Role):
""" Updates or replaces a User's role in a project.
Args:
project (Project): The project to update the users permissions for
role (Role): The role to assign to this user in this project.
"""
org_role = self.org_role()
if org_role.name.upper() != 'NONE':
raise ValueError(
"User is not project based and has access to all projects")
project_id_param = "projectId"
user_id_param = "userId"
role_id_param = "roleId"
query_str = """mutation SetProjectMembershipPyApi($%s: ID!, $%s: ID!, $%s: ID!) {
setProjectMembership(data: {%s: $userId, roleId: $%s, projectId: $%s}) {id}}
""" % (user_id_param, role_id_param, project_id_param, user_id_param,
role_id_param, project_id_param)
self.client.execute(
query_str, {
project_id_param: project.uid,
user_id_param: self.uid,
role_id_param: role.uid
})
class Webhook(DbObject, Updateable):
""" Represents a server-side rule for sending notifications to a web-server
whenever one of several predefined actions happens within a context of
a Project or an Organization.
Attributes:
updated_at (datetime)
created_at (datetime)
url (str)
topics (str): LABEL_CREATED, LABEL_UPDATED, LABEL_DELETED
REVIEW_CREATED, REVIEW_UPDATED, REVIEW_DELETED
status (str): ACTIVE, INACTIVE, REVOKED
"""
class Status(Enum):
ACTIVE = "ACTIVE"
INACTIVE = "INACTIVE"
REVOKED = "REVOKED"
class Topic(Enum):
LABEL_CREATED = "LABEL_CREATED"
LABEL_UPDATED = "LABEL_UPDATED"
LABEL_DELETED = "LABEL_DELETED"
REVIEW_CREATED = "REVIEW_CREATED"
REVIEW_UPDATED = "REVIEW_UPDATED"
REVIEW_DELETED = "REVIEW_DELETED"
# For backwards compatibility
for topic in Status:
vars()[topic.name] = topic.value
for status in Topic:
vars()[status.name] = status.value
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
url = Field.String("url")
topics = Field.String("topics")
status = Field.String("status")
created_by = Relationship.ToOne("User", False, "created_by")
organization = Relationship.ToOne("Organization")
project = Relationship.ToOne("Project")
@staticmethod
def create(client, topics, url, secret, project):
""" Creates a Webhook.
Args:
client (Client): The Labelbox client used to connect
to the server.
topics (list of str): A list of topics this Webhook should
get notifications for. Must be one of Webhook.Topic
url (str): The URL to which notifications should be sent
by the Labelbox server.
secret (str): A secret key used for signing notifications.
project (Project or None): The project for which notifications
should be sent. If None notifications are sent for all
events in your organization.
Returns:
A newly created Webhook.
Raises:
ValueError: If the topic is not one of Topic or status is not one of Status
Information on configuring your server can be found here (this is where the url points to and the secret is set).
https://docs.labelbox.com/en/configure-editor/webhooks-setup#setup-steps
"""
Webhook.validate_topics(topics)
project_str = "" if project is None \
else ("project:{id:\"%s\"}," % project.uid)
query_str = """mutation CreateWebhookPyApi {
createWebhook(data:{%s topics:{set:[%s]}, url:"%s", secret:"%s" }){%s}
} """ % (project_str, " ".join(topics), url, secret,
query.results_query_part(Entity.Webhook))
return Webhook(client, client.execute(query_str)["createWebhook"])
@staticmethod
def validate_topics(topics):
if isinstance(topics, str) or not isinstance(topics, Iterable):
raise TypeError(
f"Topics must be List[Webhook.Topic]. Found `{topics}`")
for topic in topics:
Webhook.validate_value(topic, Webhook.Topic)
@staticmethod
def validate_value(value, enum):
supported_values = {item.value for item in enum}
if value not in supported_values:
raise ValueError(
f"Value `{value}` does not exist in supported values. Expected one of {supported_values}"
)
def delete(self):
"""
Deletes the webhook
"""
self.update(status=self.Status.INACTIVE)
def update(self, topics=None, url=None, status=None):
""" Updates the Webhook.
Args:
topics (Optional[List[Topic]]): The new topics.
url Optional[str): The new URL value.
status (Optional[Status]): The new status.
If an argument is set to None then no updates will be made to that field.
"""
# Webhook has a custom `update` function due to custom types
# in `status` and `topics` fields.
if topics is not None:
self.validate_topics(topics)
if status is not None:
self.validate_value(status, self.Status)
topics_str = "" if topics is None \
else "topics: {set: [%s]}" % " ".join(topics)
url_str = "" if url is None else "url: \"%s\"" % url
status_str = "" if status is None else "status: %s" % status
query_str = """mutation UpdateWebhookPyApi {
updateWebhook(where: {id: "%s"} data:{%s}){%s}} """ % (
self.uid, ", ".join(filter(None,
(topics_str, url_str, status_str))),
query.results_query_part(Entity.Webhook))
self._set_field_values(self.client.execute(query_str)["updateWebhook"])
class LabelingFrontendOptions(DbObject):
customization_options = Field.String("customization_options")
project = Relationship.ToOne("Project")
labeling_frontend = Relationship.ToOne("LabelingFrontend")
organization = Relationship.ToOne("Organization")
class DataRow(DbObject, Updateable, BulkDeletable):
""" Internal Labelbox representation of a single piece of data (e.g. image, video, text).
Attributes:
external_id (str): User-generated file name or identifier
row_data (str): Paths to local files are uploaded to Labelbox's server.
Otherwise, it's treated as an external URL.
updated_at (datetime)
created_at (datetime)
dataset (Relationship): `ToOne` relationship to Dataset
created_by (Relationship): `ToOne` relationship to User
organization (Relationship): `ToOne` relationship to Organization
labels (Relationship): `ToMany` relationship to Label
metadata (Relationship): `ToMany` relationship to AssetMetadata
predictions (Relationship): `ToMany` relationship to Prediction
"""
external_id = Field.String("external_id")
row_data = Field.String("row_data")
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
# Relationships
dataset = Relationship.ToOne("Dataset")
created_by = Relationship.ToOne("User", False, "created_by")
organization = Relationship.ToOne("Organization", False)
labels = Relationship.ToMany("Label", True)
metadata = Relationship.ToMany("AssetMetadata", False, "metadata")
predictions = Relationship.ToMany("Prediction", False)
supported_meta_types = {
meta_type.value for meta_type in AssetMetadata.MetaType
}
@staticmethod
def bulk_delete(data_rows):
""" Deletes all the given DataRows.
Args:
data_rows (list of DataRow): The DataRows to delete.
"""
BulkDeletable._bulk_delete(data_rows, True)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.metadata.supports_filtering = False
self.metadata.supports_sorting = False
def create_metadata(self, meta_type, meta_value):
""" Attaches asset metadata to a DataRow.
>>> datarow.create_metadata("TEXT", "This is a text message")
Args:
meta_type (str): Asset metadata type, must be one of:
VIDEO, IMAGE, TEXT, IMAGE_OVERLAY (AssetMetadata.MetaType)
meta_value (str): Asset metadata value.
Returns:
`AssetMetadata` DB object.
Raises:
ValueError: meta_type must be one of the supported types.
"""
if meta_type not in self.supported_meta_types:
raise ValueError(
f"meta_type must be one of {self.supported_meta_types}. Found {meta_type}"
)
meta_type_param = "metaType"
meta_value_param = "metaValue"
data_row_id_param = "dataRowId"
query_str = """mutation CreateAssetMetadataPyApi(
$%s: AttachmentType!, $%s: String!, $%s: ID!) {
createAssetMetadata(data: {
metaType: $%s metaValue: $%s dataRowId: $%s}) {%s}} """ % (
meta_type_param, meta_value_param, data_row_id_param,
meta_type_param, meta_value_param, data_row_id_param,
query.results_query_part(Entity.AssetMetadata))
res = self.client.execute(
query_str, {
meta_type_param: meta_type,
meta_value_param: meta_value,
data_row_id_param: self.uid
})
return Entity.AssetMetadata(self.client, res["createAssetMetadata"])
class ProjectMember(DbObject):
user = Relationship.ToOne("User", cache=True)
role = Relationship.ToOne("Role", cache=True)
class Project(DbObject, Updateable, Deletable):
""" A Project is a container that includes a labeling frontend, an ontology,
datasets and labels.
Attributes:
name (str)
description (str)
updated_at (datetime)
created_at (datetime)
setup_complete (datetime)
last_activity_time (datetime)
auto_audit_number_of_labels (int)
auto_audit_percentage (float)
datasets (Relationship): `ToMany` relationship to Dataset
created_by (Relationship): `ToOne` relationship to User
organization (Relationship): `ToOne` relationship to Organization
reviews (Relationship): `ToMany` relationship to Review
labeling_frontend (Relationship): `ToOne` relationship to LabelingFrontend
labeling_frontend_options (Relationship): `ToMany` relationship to LabelingFrontendOptions
labeling_parameter_overrides (Relationship): `ToMany` relationship to LabelingParameterOverride
webhooks (Relationship): `ToMany` relationship to Webhook
benchmarks (Relationship): `ToMany` relationship to Benchmark
active_prediction_model (Relationship): `ToOne` relationship to PredictionModel
predictions (Relationship): `ToMany` relationship to Prediction
ontology (Relationship): `ToOne` relationship to Ontology
"""
name = Field.String("name")
description = Field.String("description")
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
setup_complete = Field.DateTime("setup_complete")
last_activity_time = Field.DateTime("last_activity_time")
auto_audit_number_of_labels = Field.Int("auto_audit_number_of_labels")
auto_audit_percentage = Field.Float("auto_audit_percentage")
# Relationships
datasets = Relationship.ToMany("Dataset", True)
created_by = Relationship.ToOne("User", False, "created_by")
organization = Relationship.ToOne("Organization", False)
reviews = Relationship.ToMany("Review", True)
labeling_frontend = Relationship.ToOne("LabelingFrontend")
labeling_frontend_options = Relationship.ToMany(
"LabelingFrontendOptions", False, "labeling_frontend_options")
labeling_parameter_overrides = Relationship.ToMany(
"LabelingParameterOverride", False, "labeling_parameter_overrides")
webhooks = Relationship.ToMany("Webhook", False)
benchmarks = Relationship.ToMany("Benchmark", False)
active_prediction_model = Relationship.ToOne("PredictionModel", False,
"active_prediction_model")
predictions = Relationship.ToMany("Prediction", False)
ontology = Relationship.ToOne("Ontology", True)
def members(self):
""" Fetch all current members for this project
Returns:
A `PaginatedCollection of `ProjectMember`s
"""
id_param = "projectId"
query_str = """query ProjectMemberOverviewPyApi($%s: ID!) {
project(where: {id : $%s}) { id members(skip: %%d first: %%d){ id user { %s } role { id name } }
}
}""" % (id_param, id_param, query.results_query_part(Entity.User))
return PaginatedCollection(self.client, query_str,
{id_param: str(self.uid)},
["project", "members"], ProjectMember)
def create_label(self, **kwargs):
""" Creates a label on a Legacy Editor project. Not supported in the new Editor.
Args:
**kwargs: Label attributes. At minimum, the label `DataRow`.
"""
# Copy-paste of Client._create code so we can inject
# a connection to Type. Type objects are on their way to being
# deprecated and we don't want the Py client lib user to know
# about them. At the same time they're connected to a Label at
# label creation in a non-standard way (connect via name).
logger.warning(
"`create_label` is deprecated and is not compatible with the new editor."
)
Label = Entity.Label
kwargs[Label.project] = self
kwargs[Label.seconds_to_label] = kwargs.get(
Label.seconds_to_label.name, 0.0)
data = {
Label.attribute(attr) if isinstance(attr, str) else attr:
value.uid if isinstance(value, DbObject) else value
for attr, value in kwargs.items()
}
query_str, params = query.create(Label, data)
# Inject connection to Type
query_str = query_str.replace(
"data: {", "data: {type: {connect: {name: \"Any\"}} ")
res = self.client.execute(query_str, params)
return Label(self.client, res["createLabel"])
def labels(self, datasets=None, order_by=None):
""" Custom relationship expansion method to support limited filtering.
Args:
datasets (iterable of Dataset): Optional collection of Datasets
whose Labels are sought. If not provided, all Labels in
this Project are returned.
order_by (None or (Field, Field.Order)): Ordering clause.
"""
Label = Entity.Label
if datasets is not None:
where = " where:{dataRow: {dataset: {id_in: [%s]}}}" % ", ".join(
'"%s"' % dataset.uid for dataset in datasets)
else:
where = ""
if order_by is not None:
query.check_order_by_clause(Label, order_by)
order_by_str = "orderBy: %s_%s" % (order_by[0].graphql_name,
order_by[1].name.upper())
else:
order_by_str = ""
id_param = "projectId"
query_str = """query GetProjectLabelsPyApi($%s: ID!)
{project (where: {id: $%s})
{labels (skip: %%d first: %%d %s %s) {%s}}}""" % (
id_param, id_param, where, order_by_str,
query.results_query_part(Label))
return PaginatedCollection(self.client, query_str,
{id_param: self.uid}, ["project", "labels"],
Label)
def export_labels(self, timeout_seconds=60):
""" Calls the server-side Label exporting that generates a JSON
payload, and returns the URL to that payload.
Will only generate a new URL at a max frequency of 30 min.
Args:
timeout_seconds (float): Max waiting time, in seconds.
Returns:
URL of the data file with this Project's labels. If the server didn't
generate during the `timeout_seconds` period, None is returned.
"""
sleep_time = 2
id_param = "projectId"
query_str = """mutation GetLabelExportUrlPyApi($%s: ID!)
{exportLabels(data:{projectId: $%s }) {downloadUrl createdAt shouldPoll} }
""" % (id_param, id_param)
while True:
res = self.client.execute(query_str, {id_param: self.uid})
res = res["exportLabels"]
if not res["shouldPoll"]:
return res["downloadUrl"]
timeout_seconds -= sleep_time
if timeout_seconds <= 0:
return None
logger.debug("Project '%s' label export, waiting for server...",
self.uid)
time.sleep(sleep_time)
def export_issues(self, status=None):
""" Calls the server-side Issues exporting that
returns the URL to that payload.
Args:
status (string): valid values: Open, Resolved
Returns:
URL of the data file with this Project's issues.
"""
id_param = "projectId"
status_param = "status"
query_str = """query GetProjectIssuesExportPyApi($%s: ID!, $%s: IssueStatus) {
project(where: { id: $%s }) {
issueExportUrl(where: { status: $%s })
}
}""" % (id_param, status_param, id_param, status_param)
valid_statuses = {None, "Open", "Resolved"}
if status not in valid_statuses:
raise ValueError("status must be in {}. Found {}".format(
valid_statuses, status))
res = self.client.execute(query_str, {
id_param: self.uid,
status_param: status
})
res = res['project']
logger.debug("Project '%s' issues export, link generated", self.uid)
return res.get('issueExportUrl')
def upsert_instructions(self, instructions_file: str):
"""
* Uploads instructions to the UI. Running more than once will replace the instructions
Args:
instructions_file (str): Path to a local file.
* Must be either a pdf, text, or html file.
Raises:
ValueError:
* project must be setup
* instructions file must end with one of ".text", ".txt", ".pdf", ".html"
"""
if self.setup_complete is None:
raise ValueError(
"Cannot attach instructions to a project that has not been set up."
)
frontend = self.labeling_frontend()
frontendId = frontend.uid
if frontend.name != "Editor":
logger.warning(
f"This function has only been tested to work with the Editor front end. Found %s",
frontend.name)
supported_instruction_formats = (".text", ".txt", ".pdf", ".html")
if not instructions_file.endswith(supported_instruction_formats):
raise ValueError(
f"instructions_file must end with one of {supported_instruction_formats}. Found {instructions_file}"
)
lfo = list(self.labeling_frontend_options())[-1]
instructions_url = self.client.upload_file(instructions_file)
customization_options = json.loads(lfo.customization_options)
customization_options['projectInstructions'] = instructions_url
option_id = lfo.uid
self.client.execute(
"""mutation UpdateFrontendWithExistingOptionsPyApi (
$frontendId: ID!,
$optionsId: ID!,
$name: String!,
$description: String!,
$customizationOptions: String!
) {
updateLabelingFrontend(
where: {id: $frontendId},
data: {name: $name, description: $description}
) {id}
updateLabelingFrontendOptions(
where: {id: $optionsId},
data: {customizationOptions: $customizationOptions}
) {id}
}""", {
"frontendId": frontendId,
"optionsId": option_id,
"name": frontend.name,
"description": "Video, image, and text annotation",
"customizationOptions": json.dumps(customization_options)
})
def labeler_performance(self):
""" Returns the labeler performances for this Project.
Returns:
A PaginatedCollection of LabelerPerformance objects.
"""
id_param = "projectId"
query_str = """query LabelerPerformancePyApi($%s: ID!) {
project(where: {id: $%s}) {
labelerPerformance(skip: %%d first: %%d) {
count user {%s} secondsPerLabel totalTimeLabeling consensus
averageBenchmarkAgreement lastActivityTime}
}}""" % (id_param, id_param, query.results_query_part(Entity.User))
def create_labeler_performance(client, result):
result["user"] = Entity.User(client, result["user"])
# python isoformat doesn't accept Z as utc timezone
result["lastActivityTime"] = datetime.fromisoformat(
result["lastActivityTime"].replace('Z', '+00:00'))
return LabelerPerformance(**{
utils.snake_case(key): value
for key, value in result.items()
})
return PaginatedCollection(self.client, query_str,
{id_param: self.uid},
["project", "labelerPerformance"],
create_labeler_performance)
def review_metrics(self, net_score):
""" Returns this Project's review metrics.
Args:
net_score (None or Review.NetScore): Indicates desired metric.
Returns:
int, aggregation count of reviews for given `net_score`.
"""
if net_score not in (None, ) + tuple(Entity.Review.NetScore):
raise InvalidQueryError(
"Review metrics net score must be either None "
"or one of Review.NetScore values")
id_param = "projectId"
net_score_literal = "None" if net_score is None else net_score.name
query_str = """query ProjectReviewMetricsPyApi($%s: ID!){
project(where: {id:$%s})
{reviewMetrics {labelAggregate(netScore: %s) {count}}}
}""" % (id_param, id_param, net_score_literal)
res = self.client.execute(query_str, {id_param: self.uid})
return res["project"]["reviewMetrics"]["labelAggregate"]["count"]
def setup(self, labeling_frontend, labeling_frontend_options):
""" Finalizes the Project setup.
Args:
labeling_frontend (LabelingFrontend): Which UI to use to label the
data.
labeling_frontend_options (dict or str): Labeling frontend options,
a.k.a. project ontology. If given a `dict` it will be converted
to `str` using `json.dumps`.
"""
organization = self.client.get_organization()
if not isinstance(labeling_frontend_options, str):
labeling_frontend_options = json.dumps(labeling_frontend_options)
self.labeling_frontend.connect(labeling_frontend)
LFO = Entity.LabelingFrontendOptions
labeling_frontend_options = self.client._create(
LFO, {
LFO.project: self,
LFO.labeling_frontend: labeling_frontend,
LFO.customization_options: labeling_frontend_options,
LFO.organization: organization
})
timestamp = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
self.update(setup_complete=timestamp)
def validate_labeling_parameter_overrides(self, data):
for idx, row in enumerate(data):
if len(row) != 3:
raise TypeError(
f"Data must be a list of tuples containing a DataRow, priority (int), num_labels (int). Found {len(row)} items. Index: {idx}"
)
data_row, priority, num_labels = row
if not isinstance(data_row, DataRow):
raise TypeError(
f"data_row should be be of type DataRow. Found {type(data_row)}. Index: {idx}"
)
for name, value in [["Priority", priority],
["Number of labels", num_labels]]:
if not isinstance(value, int):
raise TypeError(
f"{name} must be an int. Found {type(value)} for data_row {data_row}. Index: {idx}"
)
if value < 1:
raise ValueError(
f"{name} must be greater than 0 for data_row {data_row}. Index: {idx}"
)
def set_labeling_parameter_overrides(self, data):
""" Adds labeling parameter overrides to this project.
See information on priority here:
https://docs.labelbox.com/en/configure-editor/queue-system#reservation-system
>>> project.set_labeling_parameter_overrides([
>>> (data_row_1, 2, 3), (data_row_2, 1, 4)])
Args:
data (iterable): An iterable of tuples. Each tuple must contain
(DataRow, priority<int>, number_of_labels<int>) for the new override.
Priority:
* Data will be labeled in priority order.
- A lower number priority is labeled first.
- Minimum priority is 1.
* Priority is not the queue position.
- The position is determined by the relative priority.
- E.g. [(data_row_1, 5,1), (data_row_2, 2,1), (data_row_3, 10,1)]
will be assigned in the following order: [data_row_2, data_row_1, data_row_3]
* Datarows with parameter overrides will appear before datarows without overrides.
* The priority only effects items in the queue.
- Assigning a priority will not automatically add the item back into the queue.
Number of labels:
* The number of times a data row should be labeled.
- Creates duplicate data rows in a project (one for each number of labels).
* New duplicated data rows will be added to the queue.
- Already labeled duplicates will not be sent back to the queue.
* The queue will never assign the same datarow to a single labeler more than once.
- If the number of labels is greater than the number of labelers working on a project then
the extra items will remain in the queue (this can be fixed by removing the override at any time).
* Setting this to 1 will result in the default behavior (no duplicates).
Returns:
bool, indicates if the operation was a success.
"""
self.validate_labeling_parameter_overrides(data)
data_str = ",\n".join(
"{dataRow: {id: \"%s\"}, priority: %d, numLabels: %d }" %
(data_row.uid, priority, num_labels)
for data_row, priority, num_labels in data)
id_param = "projectId"
query_str = """mutation SetLabelingParameterOverridesPyApi($%s: ID!){
project(where: { id: $%s }) {setLabelingParameterOverrides
(data: [%s]) {success}}} """ % (id_param, id_param, data_str)
res = self.client.execute(query_str, {id_param: self.uid})
return res["project"]["setLabelingParameterOverrides"]["success"]
def unset_labeling_parameter_overrides(self, data_rows):
""" Removes labeling parameter overrides to this project.
* This will remove unlabeled duplicates in the queue.
Args:
data_rows (iterable): An iterable of DataRows.
Returns:
bool, indicates if the operation was a success.
"""
id_param = "projectId"
query_str = """mutation UnsetLabelingParameterOverridesPyApi($%s: ID!){
project(where: { id: $%s}) {
unsetLabelingParameterOverrides(data: [%s]) { success }}}""" % (
id_param, id_param, ",\n".join("{dataRowId: \"%s\"}" % row.uid
for row in data_rows))
res = self.client.execute(query_str, {id_param: self.uid})
return res["project"]["unsetLabelingParameterOverrides"]["success"]
def upsert_review_queue(self, quota_factor):
""" Sets the the proportion of total assets in a project to review.
More information can be found here:
https://docs.labelbox.com/en/quality-assurance/review-labels#configure-review-percentage
Args:
quota_factor (float): Which part (percentage) of the queue
to reinitiate. Between 0 and 1.
"""
if not 0. < quota_factor < 1.:
raise ValueError("Quota factor must be in the range of [0,1]")
id_param = "projectId"
quota_param = "quotaFactor"
query_str = """mutation UpsertReviewQueuePyApi($%s: ID!, $%s: Float!){
upsertReviewQueue(where:{project: {id: $%s}}
data:{quotaFactor: $%s}) {id}}""" % (
id_param, quota_param, id_param, quota_param)
res = self.client.execute(query_str, {
id_param: self.uid,
quota_param: quota_factor
})
def extend_reservations(self, queue_type):
""" Extends all the current reservations for the current user on the given
queue type.
Args:
queue_type (str): Either "LabelingQueue" or "ReviewQueue"
Returns:
int, the number of reservations that were extended.
"""
if queue_type not in ("LabelingQueue", "ReviewQueue"):
raise InvalidQueryError("Unsupported queue type: %s" % queue_type)
id_param = "projectId"
query_str = """mutation ExtendReservationsPyApi($%s: ID!){
extendReservations(projectId:$%s queueType:%s)}""" % (
id_param, id_param, queue_type)
res = self.client.execute(query_str, {id_param: self.uid})
return res["extendReservations"]
def create_prediction_model(self, name, version):
""" Creates a PredictionModel connected to a Legacy Editor Project.
Args:
name (str): The new PredictionModel's name.
version (int): The new PredictionModel's version.
Returns:
A newly created PredictionModel.
"""
logger.warning(
"`create_prediction_model` is deprecated and is not compatible with the new editor."
)
PM = Entity.PredictionModel
model = self.client._create(PM, {
PM.name.name: name,
PM.version.name: version
})
self.active_prediction_model.connect(model)
return model
def create_prediction(self, label, data_row, prediction_model=None):
""" Creates a Prediction within a Legacy Editor Project. Not supported
in the new Editor.
Args:
label (str): The `label` field of the new Prediction.
data_row (DataRow): The DataRow for which the Prediction is created.
prediction_model (PredictionModel or None): The PredictionModel
within which the new Prediction is created. If None then this
Project's active_prediction_model is used.
Return:
A newly created Prediction.
Raises:
labelbox.excepions.InvalidQueryError: if given `prediction_model`
is None and this Project's active_prediction_model is also
None.
"""
logger.warning(
"`create_prediction` is deprecated and is not compatible with the new editor."
)
if prediction_model is None:
prediction_model = self.active_prediction_model()
if prediction_model is None:
raise InvalidQueryError(
"Project '%s' has no active prediction model" % self.name)
label_param = "label"
model_param = "prediction_model_id"
project_param = "project_id"
data_row_param = "data_row_id"
Prediction = Entity.Prediction
query_str = """mutation CreatePredictionPyApi(
$%s: String!, $%s: ID!, $%s: ID!, $%s: ID!) {createPrediction(
data: {label: $%s, predictionModelId: $%s, projectId: $%s,
dataRowId: $%s})
{%s}}""" % (label_param, model_param, project_param,
data_row_param, label_param, model_param,
project_param, data_row_param,
query.results_query_part(Prediction))
params = {
label_param: label,
model_param: prediction_model.uid,
data_row_param: data_row.uid,
project_param: self.uid
}
res = self.client.execute(query_str, params)
return Prediction(self.client, res["createPrediction"])
def enable_model_assisted_labeling(self, toggle: bool = True) -> bool:
""" Turns model assisted labeling either on or off based on input
Args:
toggle (bool): True or False boolean
Returns:
True if toggled on or False if toggled off
"""
project_param = "project_id"
show_param = "show"
query_str = """mutation toggle_model_assisted_labelingPyApi($%s: ID!, $%s: Boolean!) {
project(where: {id: $%s }) {
showPredictionsToLabelers(show: $%s) {
id, showingPredictionsToLabelers
}
}
}""" % (project_param, show_param, project_param, show_param)
params = {project_param: self.uid, show_param: toggle}
res = self.client.execute(query_str, params)
return res["project"]["showPredictionsToLabelers"][
"showingPredictionsToLabelers"]
def upload_annotations(
self,
name: str,
annotations: Union[str, Path, Iterable[Dict]],
validate: bool = True) -> 'BulkImportRequest': # type: ignore
""" Uploads annotations to a new Editor project.
Args:
name (str): name of the BulkImportRequest job
annotations (str or Path or Iterable):
url that is publicly accessible by Labelbox containing an
ndjson file
OR local path to an ndjson file
OR iterable of annotation rows
validate (bool):
Whether or not to validate the payload before uploading.
Returns:
BulkImportRequest
"""
if isinstance(annotations, str) or isinstance(annotations, Path):
def _is_url_valid(url: Union[str, Path]) -> bool:
""" Verifies that the given string is a valid url.
Args:
url: string to be checked
Returns:
True if the given url is valid otherwise False
"""
if isinstance(url, Path):
return False
parsed = urlparse(url)
return bool(parsed.scheme) and bool(parsed.netloc)
if _is_url_valid(annotations):
return BulkImportRequest.create_from_url(client=self.client,
project_id=self.uid,
name=name,
url=str(annotations),
validate=validate)
else:
path = Path(annotations)
if not path.exists():
raise FileNotFoundError(
f'{annotations} is not a valid url nor existing local file'
)
return BulkImportRequest.create_from_local_file(
client=self.client,
project_id=self.uid,
name=name,
file=path,
validate_file=validate,
)
elif isinstance(annotations, Iterable):
return BulkImportRequest.create_from_objects(
client=self.client,
project_id=self.uid,
name=name,
predictions=annotations, # type: ignore
validate=validate)
else:
raise ValueError(
f'Invalid annotations given of type: {type(annotations)}')
class Dataset(DbObject, Updateable, Deletable):
""" A Dataset is a collection of DataRows.
Attributes:
name (str)
description (str)
updated_at (datetime)
created_at (datetime)
projects (Relationship): `ToMany` relationship to Project
data_rows (Relationship): `ToMany` relationship to DataRow
created_by (Relationship): `ToOne` relationship to User
organization (Relationship): `ToOne` relationship to Organization
"""
name = Field.String("name")
description = Field.String("description")
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
# Relationships
projects = Relationship.ToMany("Project", True)
data_rows = Relationship.ToMany("DataRow", False)
created_by = Relationship.ToOne("User", False, "created_by")
organization = Relationship.ToOne("Organization", False)
def create_data_row(self, **kwargs):
""" Creates a single DataRow belonging to this dataset.
>>> dataset.create_data_row(row_data="http://my_site.com/photos/img_01.jpg")
Args:
**kwargs: Key-value arguments containing new `DataRow` data. At a minimum,
must contain `row_data`.
Raises:
InvalidQueryError: If `DataRow.row_data` field value is not provided
in `kwargs`.
InvalidAttributeError: in case the DB object type does not contain
any of the field names given in `kwargs`.
"""
DataRow = Entity.DataRow
if DataRow.row_data.name not in kwargs:
raise InvalidQueryError(
"DataRow.row_data missing when creating DataRow.")
# If row data is a local file path, upload it to server.
row_data = kwargs[DataRow.row_data.name]
if os.path.exists(row_data):
kwargs[DataRow.row_data.name] = self.client.upload_file(row_data)
kwargs[DataRow.dataset.name] = self
return self.client._create(DataRow, kwargs)
def create_data_rows(self, items):
""" Creates multiple DataRow objects based on the given `items`.
Each element in `items` can be either a `str` or a `dict`. If
it is a `str`, then it is interpreted as a local file path. The file
is uploaded to Labelbox and a DataRow referencing it is created.
If an item is a `dict`, then it could support one of the two following structures
1. For static imagery, video, and text it should map `DataRow` fields (or their names) to values.
At the minimum an `item` passed as a `dict` must contain a `DataRow.row_data` key and value.
2. For tiled imagery the dict must match the import structure specified in the link below
https://docs.labelbox.com/data-model/en/index-en#tiled-imagery-import
>>> dataset.create_data_rows([
>>> {DataRow.row_data:"http://my_site.com/photos/img_01.jpg"},
>>> "path/to/file2.jpg",
>>> {"tileLayerUrl" : "http://", ...}
>>> ])
For an example showing how to upload tiled data_rows see the following notebook:
https://github.com/Labelbox/labelbox-python/blob/ms/develop/model_assisted_labeling/tiled_imagery_mal.ipynb
Args:
items (iterable of (dict or str)): See above for details.
Returns:
Task representing the data import on the server side. The Task
can be used for inspecting task progress and waiting until it's done.
Raises:
InvalidQueryError: If the `items` parameter does not conform to
the specification above or if the server did not accept the
DataRow creation request (unknown reason).
ResourceNotFoundError: If unable to retrieve the Task for the
import process. This could imply that the import failed.
InvalidAttributeError: If there are fields in `items` not valid for
a DataRow.
"""
file_upload_thread_count = 20
DataRow = Entity.DataRow
def upload_if_necessary(item):
if isinstance(item, str):
item_url = self.client.upload_file(item)
# Convert item from str into a dict so it gets processed
# like all other dicts.
item = {DataRow.row_data: item_url, DataRow.external_id: item}
return item
with ThreadPool(file_upload_thread_count) as thread_pool:
items = thread_pool.map(upload_if_necessary, items)
def convert_item(item):
# Don't make any changes to tms data
if "tileLayerUrl" in item:
return item
# Convert string names to fields.
item = {
key if isinstance(key, Field) else DataRow.field(key): value
for key, value in item.items()
}
if DataRow.row_data not in item:
raise InvalidQueryError(
"DataRow.row_data missing when creating DataRow.")
invalid_keys = set(item) - set(DataRow.fields())
if invalid_keys:
raise InvalidAttributeError(DataRow, invalid_keys)
# Item is valid, convert it to a dict {graphql_field_name: value}
# Need to change the name of DataRow.row_data to "data"
return {
"data" if key == DataRow.row_data else key.graphql_name: value
for key, value in item.items()
}
# Prepare and upload the desciptor file
items = [convert_item(item) for item in items]
data = json.dumps(items)
descriptor_url = self.client.upload_data(data)
# Create data source
dataset_param = "datasetId"
url_param = "jsonUrl"
query_str = """mutation AppendRowsToDatasetPyApi($%s: ID!, $%s: String!){
appendRowsToDataset(data:{datasetId: $%s, jsonFileUrl: $%s}
){ taskId accepted } } """ % (dataset_param, url_param,
dataset_param, url_param)
res = self.client.execute(query_str, {
dataset_param: self.uid,
url_param: descriptor_url
})
res = res["appendRowsToDataset"]
if not res["accepted"]:
raise InvalidQueryError(
"Server did not accept DataRow creation request")
# Fetch and return the task.
task_id = res["taskId"]
user = self.client.get_user()
task = list(user.created_tasks(where=Entity.Task.uid == task_id))
# Cache user in a private variable as the relationship can't be
# resolved due to server-side limitations (see Task.created_by)
# for more info.
if len(task) != 1:
raise ResourceNotFoundError(Entity.Task, task_id)
task = task[0]
task._user = user
return task
def data_rows_for_external_id(self, external_id, limit=10):
""" Convenience method for getting a single `DataRow` belonging to this
`Dataset` that has the given `external_id`.
Args:
external_id (str): External ID of the sought `DataRow`.
limit (int): The maximum number of data rows to return for the given external_id
Returns:
A single `DataRow` with the given ID.
Raises:
labelbox.exceptions.ResourceNotFoundError: If there is no `DataRow`
in this `DataSet` with the given external ID, or if there are
multiple `DataRows` for it.
"""
DataRow = Entity.DataRow
where = DataRow.external_id == external_id
data_rows = self.data_rows(where=where)
# Get at most `limit` data_rows.
data_rows = list(islice(data_rows, limit))
if not len(data_rows):
raise ResourceNotFoundError(DataRow, where)
return data_rows
def data_row_for_external_id(self, external_id):
""" Convenience method for getting a single `DataRow` belonging to this
`Dataset` that has the given `external_id`.
Args:
external_id (str): External ID of the sought `DataRow`.
Returns:
A single `DataRow` with the given ID.
Raises:
labelbox.exceptions.ResourceNotFoundError: If there is no `DataRow`
in this `DataSet` with the given external ID, or if there are
multiple `DataRows` for it.
"""
data_rows = self.data_rows_for_external_id(external_id=external_id,
limit=2)
if len(data_rows) > 1:
logger.warning(
f"More than one data_row has the provided external_id : `%s`. Use function data_rows_for_external_id to fetch all",
external_id)
return data_rows[0]
class Dataset(DbObject, Updateable, Deletable):
""" A dataset is a collection of DataRows. For example, if you have a CSV with
100 rows, you will have 1 Dataset and 100 DataRows.
"""
name = Field.String("name")
description = Field.String("description")
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
# Relationships
projects = Relationship.ToMany("Project", True)
data_rows = Relationship.ToMany("DataRow", False)
created_by = Relationship.ToOne("User", False, "created_by")
organization = Relationship.ToOne("Organization", False)
def create_data_row(self, **kwargs):
""" Creates a single DataRow belonging to this dataset.
>>> dataset.create_data_row(row_data="http://my_site.com/photos/img_01.jpg")
Kwargs:
Key-value arguments containing new `DataRow` data.
At a minimum `kwargs` must contain `row_data`. The value for
`row_data` is a string. If it is a path to an existing local
file then it is uploaded to Labelbox's server. Otherwise it is
treated as an external URL.
Raises:
InvalidQueryError: If `DataRow.row_data` field value is not provided
in `kwargs`.
InvalidAttributeError: in case the DB object type does not contain
any of the field names given in `kwargs`.
"""
DataRow = Entity.DataRow
if DataRow.row_data.name not in kwargs:
raise InvalidQueryError(
"DataRow.row_data missing when creating DataRow.")
# If row data is a local file path, upload it to server.
row_data = kwargs[DataRow.row_data.name]
if os.path.exists(row_data):
kwargs[DataRow.row_data.name] = self.client.upload_file(row_data)
kwargs[DataRow.dataset.name] = self
return self.client._create(DataRow, kwargs)
def create_data_rows(self, items):
""" Creates multiple DataRow objects based on the given `items`.
Each element in `items` can be either a `str` or a `dict`. If
it is a `str`, then it is interpreted as a local file path. The file
is uploaded to Labelbox and a DataRow referencing it is created.
If an item is a `dict`, then it should map `DataRow` fields (or their
names) to values. At the minimum an `item` passed as a `dict` must
contain a `DataRow.row_data` key and value.
>>> dataset.create_data_rows([
>>> {DataRow.row_data:"http://my_site.com/photos/img_01.jpg"},
>>> "path/to/file2.jpg"
>>> ])
Args:
items (iterable of (dict or str)): See above for details.
Returns:
Task representing the data import on the server side. The Task
can be used for inspecting task progress and waiting until it's done.
Raises:
InvalidQueryError: If the `items` parameter does not conform to
the specification above or if the server did not accept the
DataRow creation request (unknown reason).
ResourceNotFoundError: If unable to retrieve the Task for the
import process. This could imply that the import failed.
InvalidAttributeError: If there are fields in `items` not valid for
a DataRow.
"""
file_upload_thread_count = 20
DataRow = Entity.DataRow
def upload_if_necessary(item):
if isinstance(item, str):
item_url = self.client.upload_file(item)
# Convert item from str into a dict so it gets processed
# like all other dicts.
item = {DataRow.row_data: item_url, DataRow.external_id: item}
return item
with ThreadPool(file_upload_thread_count) as thread_pool:
items = thread_pool.map(upload_if_necessary, items)
def convert_item(item):
# Convert string names to fields.
item = {
key if isinstance(key, Field) else DataRow.field(key): value
for key, value in item.items()
}
if DataRow.row_data not in item:
raise InvalidQueryError(
"DataRow.row_data missing when creating DataRow.")
invalid_keys = set(item) - set(DataRow.fields())
if invalid_keys:
raise InvalidAttributeError(DataRow, invalid_fields)
# Item is valid, convert it to a dict {graphql_field_name: value}
# Need to change the name of DataRow.row_data to "data"
return {
"data" if key == DataRow.row_data else key.graphql_name: value
for key, value in item.items()
}
# Prepare and upload the desciptor file
data = json.dumps([convert_item(item) for item in items])
descriptor_url = self.client.upload_data(data)
# Create data source
dataset_param = "datasetId"
url_param = "jsonUrl"
query_str = """mutation AppendRowsToDatasetPyApi($%s: ID!, $%s: String!){
appendRowsToDataset(data:{datasetId: $%s, jsonFileUrl: $%s}
){ taskId accepted } } """ % (dataset_param, url_param,
dataset_param, url_param)
res = self.client.execute(query_str, {
dataset_param: self.uid,
url_param: descriptor_url
})
res = res["appendRowsToDataset"]
if not res["accepted"]:
raise InvalidQueryError(
"Server did not accept DataRow creation request")
# Fetch and return the task.
task_id = res["taskId"]
user = self.client.get_user()
task = list(user.created_tasks(where=Entity.Task.uid == task_id))
# Cache user in a private variable as the relationship can't be
# resolved due to server-side limitations (see Task.created_by)
# for more info.
if len(task) != 1:
raise ResourceNotFoundError(Entity.Task, task_id)
task = task[0]
task._user = user
return task
def data_row_for_external_id(self, external_id):
""" Convenience method for getting a single `DataRow` belonging to this
`Dataset` that has the given `external_id`.
Args:
external_id (str): External ID of the sought `DataRow`.
Returns:
A single `DataRow` with the given ID.
Raises:
labelbox.exceptions.ResourceNotFoundError: If there is no `DataRow`
in this `DataSet` with the given external ID, or if there are
multiple `DataRows` for it.
"""
DataRow = Entity.DataRow
where = DataRow.external_id == external_id
data_rows = self.data_rows(where=where)
# Get at most two data_rows.
data_rows = [row for row, _ in zip(data_rows, range(2))]
if len(data_rows) != 1:
raise ResourceNotFoundError(DataRow, where)
return data_rows[0]
class Webhook(DbObject, Updateable):
""" Represents a server-side rule for sending notifications to a web-server
whenever one of several predefined actions happens within a context of
a Project or an Organization.
"""
# Status
ACTIVE = "ACTIVE"
INACTIVE = "INACTIVE"
REVOKED = "REVOKED"
# Topic
LABEL_CREATED = "LABEL_CREATED"
LABEL_UPDATED = "LABEL_UPDATED"
LABEL_DELETED = "LABEL_DELETED"
updated_at = Field.DateTime("updated_at")
created_at = Field.DateTime("created_at")
url = Field.String("url")
topics = Field.String("topics")
status = Field.String("status")
@staticmethod
def create(client, topics, url, secret, project):
""" Creates a Webhook.
Args:
client (Client): The Labelbox client used to connect
to the server.
topics (list of str): A list of topics this Webhook should
get notifications for.
url (str): The URL to which notifications should be sent
by the Labelbox server.
secret (str): A secret key used for signing notifications.
project (Project or None): The project for which notifications
should be sent. If None notifications are sent for all
events in your organization.
Returns:
A newly created Webhook.
"""
project_str = "" if project is None \
else ("project:{id:\"%s\"}," % project.uid)
query_str = """mutation CreateWebhookPyApi {
createWebhook(data:{%s topics:{set:[%s]}, url:"%s", secret:"%s" }){%s}
} """ % (project_str, " ".join(topics), url, secret,
query.results_query_part(Entity.Webhook))
return Webhook(client, client.execute(query_str)["createWebhook"])
created_by = Relationship.ToOne("User", False, "created_by")
organization = Relationship.ToOne("Organization")
project = Relationship.ToOne("Project")
def update(self, topics=None, url=None, status=None):
""" Updates this Webhook.
Args:
topics (list of str): The new topics value, optional.
url (str): The new URL value, optional.
status (str): The new status value, optional.
"""
# Webhook has a custom `update` function due to custom types
# in `status` and `topics` fields.
topics_str = "" if topics is None \
else "topics: {set: [%s]}" % " ".join(topics)
url_str = "" if url is None else "url: \"%s\"" % url
status_str = "" if status is None else "status: %s" % status
query_str = """mutation UpdateWebhookPyApi {
updateWebhook(where: {id: "%s"} data:{%s}){%s}} """ % (
self.uid, ", ".join(filter(None,
(topics_str, url_str, status_str))),
query.results_query_part(Entity.Webhook))
self._set_field_values(self.client.execute(query_str)["updateWebhook"])
class BulkImportRequest(DbObject):
"""Represents the import job when importing annotations.
Attributes:
name (str)
state (Enum): FAILED, RUNNING, or FINISHED (Refers to the whole import job)
input_file_url (str): URL to your web-hosted NDJSON file
error_file_url (str): NDJSON that contains error messages for failed annotations
status_file_url (str): NDJSON that contains status for each annotation
created_at (datetime): UTC timestamp for date BulkImportRequest was created
project (Relationship): `ToOne` relationship to Project
created_by (Relationship): `ToOne` relationship to User
"""
name = Field.String("name")
state = Field.Enum(BulkImportRequestState, "state")
input_file_url = Field.String("input_file_url")
error_file_url = Field.String("error_file_url")
status_file_url = Field.String("status_file_url")
created_at = Field.DateTime("created_at")
project = Relationship.ToOne("Project")
created_by = Relationship.ToOne("User", False, "created_by")
@property
def inputs(self) -> List[Dict[str, Any]]:
"""
Inputs for each individual annotation uploaded.
This should match the ndjson annotations that you have uploaded.
Returns:
Uploaded ndjson.
* This information will expire after 24 hours.
"""
return self._fetch_remote_ndjson(self.input_file_url)
@property
def errors(self) -> List[Dict[str, Any]]:
"""
Errors for each individual annotation uploaded. This is a subset of statuses
Returns:
List of dicts containing error messages. Empty list means there were no errors
See `BulkImportRequest.statuses` for more details.
* This information will expire after 24 hours.
"""
self.wait_until_done()
return self._fetch_remote_ndjson(self.error_file_url)
@property
def statuses(self) -> List[Dict[str, Any]]:
"""
Status for each individual annotation uploaded.
Returns:
A status for each annotation if the upload is done running.
See below table for more details
.. list-table::
:widths: 15 150
:header-rows: 1
* - Field
- Description
* - uuid
- Specifies the annotation for the status row.
* - dataRow
- JSON object containing the Labelbox data row ID for the annotation.
* - status
- Indicates SUCCESS or FAILURE.
* - errors
- An array of error messages included when status is FAILURE. Each error has a name, message and optional (key might not exist) additional_info.
* This information will expire after 24 hours.
"""
self.wait_until_done()
return self._fetch_remote_ndjson(self.status_file_url)
@functools.lru_cache()
def _fetch_remote_ndjson(self, url: str) -> List[Dict[str, Any]]:
"""
Fetches the remote ndjson file and caches the results.
Args:
url (str): Can be any url pointing to an ndjson file.
Returns:
ndjson as a list of dicts.
"""
response = requests.get(url)
response.raise_for_status()
return ndjson.loads(response.text)
def refresh(self) -> None:
"""Synchronizes values of all fields with the database.
"""
query_str, params = query.get_single(BulkImportRequest, self.uid)
res = self.client.execute(query_str, params)
res = res[utils.camel_case(BulkImportRequest.type_name())]
self._set_field_values(res)
def wait_until_done(self, sleep_time_seconds: int = 30) -> None:
"""Blocks import job until certain conditions are met.
Blocks until the BulkImportRequest.state changes either to
`BulkImportRequestState.FINISHED` or `BulkImportRequestState.FAILED`,
periodically refreshing object's state.
Args:
sleep_time_seconds (str): a time to block between subsequent API calls
"""
while self.state == BulkImportRequestState.RUNNING:
logger.info(f"Sleeping for {sleep_time_seconds} seconds...")
time.sleep(sleep_time_seconds)
self.__exponential_backoff_refresh()
@backoff.on_exception(
backoff.expo,
(labelbox.exceptions.ApiLimitError, labelbox.exceptions.TimeoutError,
labelbox.exceptions.NetworkError),
max_tries=10,
jitter=None)
def __exponential_backoff_refresh(self) -> None:
self.refresh()
@classmethod
def from_name(cls, client, project_id: str,
name: str) -> 'BulkImportRequest':
""" Fetches existing BulkImportRequest.
Args:
client (Client): a Labelbox client
project_id (str): BulkImportRequest's project id
name (str): name of BulkImportRequest
Returns:
BulkImportRequest object
"""
query_str = """query getBulkImportRequestPyApi(
$projectId: ID!, $name: String!) {
bulkImportRequest(where: {
projectId: $projectId,
name: $name
}) {
%s
}
}
""" % query.results_query_part(cls)
params = {"projectId": project_id, "name": name}
response = client.execute(query_str, params=params)
return cls(client, response['bulkImportRequest'])
@classmethod
def create_from_url(cls,
client,
project_id: str,
name: str,
url: str,
validate=True) -> 'BulkImportRequest':
"""
Creates a BulkImportRequest from a publicly accessible URL
to an ndjson file with predictions.
Args:
client (Client): a Labelbox client
project_id (str): id of project for which predictions will be imported
name (str): name of BulkImportRequest
url (str): publicly accessible URL pointing to ndjson file containing predictions
validate (bool): a flag indicating if there should be a validation
if `url` is valid ndjson
Returns:
BulkImportRequest object
"""
if validate:
logger.warn(
"Validation is turned on. The file will be downloaded locally and processed before uploading."
)
res = requests.get(url)
data = ndjson.loads(res.text)
_validate_ndjson(data, client.get_project(project_id))
query_str = """mutation createBulkImportRequestPyApi(
$projectId: ID!, $name: String!, $fileUrl: String!) {
createBulkImportRequest(data: {
projectId: $projectId,
name: $name,
fileUrl: $fileUrl
}) {
%s
}
}
""" % query.results_query_part(cls)
params = {"projectId": project_id, "name": name, "fileUrl": url}
bulk_import_request_response = client.execute(query_str, params=params)
return cls(client,
bulk_import_request_response["createBulkImportRequest"])
@classmethod
def create_from_objects(cls,
client,
project_id: str,
name: str,
predictions: Iterable[Dict],
validate=True) -> 'BulkImportRequest':
"""
Creates a `BulkImportRequest` from an iterable of dictionaries.
Conforms to JSON predictions format, e.g.:
``{
"uuid": "9fd9a92e-2560-4e77-81d4-b2e955800092",
"schemaId": "ckappz7d700gn0zbocmqkwd9i",
"dataRow": {
"id": "ck1s02fqxm8fi0757f0e6qtdc"
},
"bbox": {
"top": 48,
"left": 58,
"height": 865,
"width": 1512
}
}``
Args:
client (Client): a Labelbox client
project_id (str): id of project for which predictions will be imported
name (str): name of BulkImportRequest
predictions (Iterable[dict]): iterable of dictionaries representing predictions
validate (bool): a flag indicating if there should be a validation
if `predictions` is valid ndjson
Returns:
BulkImportRequest object
"""
if validate:
_validate_ndjson(predictions, client.get_project(project_id))
data_str = ndjson.dumps(predictions)
if not data_str:
raise ValueError('annotations cannot be empty')
data = data_str.encode('utf-8')
file_name = _make_file_name(project_id, name)
request_data = _make_request_data(project_id, name, len(data_str),
file_name)
file_data = (file_name, data, NDJSON_MIME_TYPE)
response_data = _send_create_file_command(client,
request_data=request_data,
file_name=file_name,
file_data=file_data)
return cls(client, response_data["createBulkImportRequest"])
@classmethod
def create_from_local_file(cls,
client,
project_id: str,
name: str,
file: Path,
validate_file=True) -> 'BulkImportRequest':
"""
Creates a BulkImportRequest from a local ndjson file with predictions.
Args:
client (Client): a Labelbox client
project_id (str): id of project for which predictions will be imported
name (str): name of BulkImportRequest
file (Path): local ndjson file with predictions
validate_file (bool): a flag indicating if there should be a validation
if `file` is a valid ndjson file
Returns:
BulkImportRequest object
"""
file_name = _make_file_name(project_id, name)
content_length = file.stat().st_size
request_data = _make_request_data(project_id, name, content_length,
file_name)
with file.open('rb') as f:
if validate_file:
reader = ndjson.reader(f)
# ensure that the underlying json load call is valid
# https://github.com/rhgrant10/ndjson/blob/ff2f03c56b21f28f7271b27da35ca4a8bf9a05d0/ndjson/api.py#L53
# by iterating through the file so we only store
# each line in memory rather than the entire file
try:
_validate_ndjson(reader, client.get_project(project_id))
except ValueError:
raise ValueError(f"{file} is not a valid ndjson file")
else:
f.seek(0)
file_data = (file.name, f, NDJSON_MIME_TYPE)
response_data = _send_create_file_command(client, request_data,
file_name, file_data)
return cls(client, response_data["createBulkImportRequest"])