async def test_batch_predict_field_headers_async():
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(), )
# Any value that is part of the HTTP/1.1 URI should be sent as
# a field header. Set these to a non-empty value.
request = prediction_service.BatchPredictRequest()
request.name = "name/value"
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client._client._transport.batch_predict),
"__call__") as call:
call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
operations_pb2.Operation(name="operations/op"))
await client.batch_predict(request)
# Establish that the underlying gRPC stub method was called.
assert len(call.mock_calls)
_, args, _ = call.mock_calls[0]
assert args[0] == request
# Establish that the field header was sent.
_, _, kw = call.mock_calls[0]
assert (
"x-goog-request-params",
"name=name/value",
) in kw["metadata"]
async def test_batch_predict_async(transport: str = "grpc_asyncio"):
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(),
transport=transport,
)
# Everything is optional in proto3 as far as the runtime is concerned,
# and we are mocking out the actual API, so just send an empty request.
request = prediction_service.BatchPredictRequest()
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client._client._transport.batch_predict),
"__call__") as call:
# Designate an appropriate return value for the call.
call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
operations_pb2.Operation(name="operations/spam"))
response = await client.batch_predict(request)
# Establish that the underlying gRPC stub method was called.
assert len(call.mock_calls)
_, args, _ = call.mock_calls[0]
assert args[0] == request
# Establish that the response is the type that we expect.
assert isinstance(response, future.Future)
async def test_batch_predict_flattened_error_async():
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(), )
# Attempting to call a method with both a request object and flattened
# fields is an error.
with pytest.raises(ValueError):
await client.batch_predict(
prediction_service.BatchPredictRequest(),
name="name_value",
input_config=io.BatchPredictInputConfig(gcs_source=io.GcsSource(
input_uris=["input_uris_value"])),
output_config=io.BatchPredictOutputConfig(
gcs_destination=io.GcsDestination(
output_uri_prefix="output_uri_prefix_value")),
params={"key_value": "value_value"},
)
async def batch_predict(
self,
request: prediction_service.BatchPredictRequest = None,
*,
name: str = None,
input_config: io.BatchPredictInputConfig = None,
output_config: io.BatchPredictOutputConfig = None,
params: Sequence[
prediction_service.BatchPredictRequest.ParamsEntry] = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation_async.AsyncOperation:
r"""Perform a batch prediction. Unlike the online
[Predict][google.cloud.automl.v1.PredictionService.Predict],
batch prediction result won't be immediately available in the
response. Instead, a long running operation object is returned.
User can poll the operation result via
[GetOperation][google.longrunning.Operations.GetOperation]
method. Once the operation is done,
[BatchPredictResult][google.cloud.automl.v1.BatchPredictResult]
is returned in the
[response][google.longrunning.Operation.response] field.
Available for following ML scenarios:
- AutoML Vision Classification
- AutoML Vision Object Detection
- AutoML Video Intelligence Classification
- AutoML Video Intelligence Object Tracking \* AutoML Natural
Language Classification
- AutoML Natural Language Entity Extraction
- AutoML Natural Language Sentiment Analysis
- AutoML Tables
Args:
request (:class:`google.cloud.automl_v1.types.BatchPredictRequest`):
The request object. Request message for
[PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].
name (:class:`str`):
Required. Name of the model requested
to serve the batch prediction.
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
input_config (:class:`google.cloud.automl_v1.types.BatchPredictInputConfig`):
Required. The input configuration for
batch prediction.
This corresponds to the ``input_config`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
output_config (:class:`google.cloud.automl_v1.types.BatchPredictOutputConfig`):
Required. The Configuration
specifying where output predictions
should be written.
This corresponds to the ``output_config`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
params (:class:`Sequence[google.cloud.automl_v1.types.BatchPredictRequest.ParamsEntry]`):
Additional domain-specific parameters for the
predictions, any string must be up to 25000 characters
long.
AutoML Natural Language Classification
``score_threshold`` : (float) A value from 0.0 to 1.0.
When the model makes predictions for a text snippet, it
will only produce results that have at least this
confidence score. The default is 0.5.
AutoML Vision Classification
``score_threshold`` : (float) A value from 0.0 to 1.0.
When the model makes predictions for an image, it will
only produce results that have at least this confidence
score. The default is 0.5.
AutoML Vision Object Detection
``score_threshold`` : (float) When Model detects objects
on the image, it will only produce bounding boxes which
have at least this confidence score. Value in 0 to 1
range, default is 0.5.
``max_bounding_box_count`` : (int64) The maximum number
of bounding boxes returned per image. The default is
100, the number of bounding boxes returned might be
limited by the server. AutoML Video Intelligence
Classification
``score_threshold`` : (float) A value from 0.0 to 1.0.
When the model makes predictions for a video, it will
only produce results that have at least this confidence
score. The default is 0.5.
``segment_classification`` : (boolean) Set to true to
request segment-level classification. AutoML Video
Intelligence returns labels and their confidence scores
for the entire segment of the video that user specified
in the request configuration. The default is true.
``shot_classification`` : (boolean) Set to true to
request shot-level classification. AutoML Video
Intelligence determines the boundaries for each camera
shot in the entire segment of the video that user
specified in the request configuration. AutoML Video
Intelligence then returns labels and their confidence
scores for each detected shot, along with the start and
end time of the shot. The default is false.
WARNING: Model evaluation is not done for this
classification type, the quality of it depends on
training data, but there are no metrics provided to
describe that quality.
``1s_interval_classification`` : (boolean) Set to true
to request classification for a video at one-second
intervals. AutoML Video Intelligence returns labels and
their confidence scores for each second of the entire
segment of the video that user specified in the request
configuration. The default is false.
WARNING: Model evaluation is not done for this
classification type, the quality of it depends on
training data, but there are no metrics provided to
describe that quality.
AutoML Video Intelligence Object Tracking
``score_threshold`` : (float) When Model detects objects
on video frames, it will only produce bounding boxes
which have at least this confidence score. Value in 0 to
1 range, default is 0.5.
``max_bounding_box_count`` : (int64) The maximum number
of bounding boxes returned per image. The default is
100, the number of bounding boxes returned might be
limited by the server.
``min_bounding_box_size`` : (float) Only bounding boxes
with shortest edge at least that long as a relative
value of video frame size are returned. Value in 0 to 1
range. Default is 0.
This corresponds to the ``params`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.api_core.operation_async.AsyncOperation:
An object representing a long-running operation.
The result type for the operation will be :class:`google.cloud.automl_v1.types.BatchPredictResult` Result of the Batch Predict. This message is returned in
[response][google.longrunning.Operation.response] of
the operation returned by the
[PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].
"""
# Create or coerce a protobuf request object.
# Sanity check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name, input_config, output_config, params])
if request is not None and has_flattened_params:
raise ValueError("If the `request` argument is set, then none of "
"the individual field arguments should be set.")
request = prediction_service.BatchPredictRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
if input_config is not None:
request.input_config = input_config
if output_config is not None:
request.output_config = output_config
if params:
request.params.update(params)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = gapic_v1.method_async.wrap_method(
self._client._transport.batch_predict,
default_timeout=60.0,
client_info=DEFAULT_CLIENT_INFO,
)
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (gapic_v1.routing_header.to_grpc_metadata(
(("name", request.name), )), )
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Wrap the response in an operation future.
response = operation_async.from_gapic(
response,
self._client._transport.operations_client,
prediction_service.BatchPredictResult,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response