def test_batch_predict(self):
# Setup Expected Response
expected_response = {}
expected_response = prediction_service_pb2.BatchPredictResult(
**expected_response
)
operation = operations_pb2.Operation(
name="operations/test_batch_predict", done=True
)
operation.response.Pack(expected_response)
# Mock the API response
channel = ChannelStub(responses=[operation])
patch = mock.patch("google.api_core.grpc_helpers.create_channel")
with patch as create_channel:
create_channel.return_value = channel
client = automl_v1beta1.PredictionServiceClient()
# Setup Request
name = client.model_path("[PROJECT]", "[LOCATION]", "[MODEL]")
input_config = {}
output_config = {}
response = client.batch_predict(name, input_config, output_config)
result = response.result()
assert expected_response == result
assert len(channel.requests) == 1
expected_request = prediction_service_pb2.BatchPredictRequest(
name=name, input_config=input_config, output_config=output_config
)
actual_request = channel.requests[0][1]
assert expected_request == actual_request
def batch_predict(
self,
name,
input_config,
output_config,
params=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None,
):
"""
Perform a batch prediction. Unlike the online ``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`` method. Once the operation is
done, ``BatchPredictResult`` is returned in the ``response`` field.
Available for following ML problems:
- Image Classification
- Image Object Detection
- Video Classification
- Video Object Tracking \* Text Extraction
- Tables
Example:
>>> from google.cloud import automl_v1beta1
>>>
>>> client = automl_v1beta1.PredictionServiceClient()
>>>
>>> name = client.model_path('[PROJECT]', '[LOCATION]', '[MODEL]')
>>>
>>> # TODO: Initialize `input_config`:
>>> input_config = {}
>>>
>>> # TODO: Initialize `output_config`:
>>> output_config = {}
>>>
>>> response = client.batch_predict(name, input_config, output_config)
>>>
>>> def callback(operation_future):
... # Handle result.
... result = operation_future.result()
>>>
>>> response.add_done_callback(callback)
>>>
>>> # Handle metadata.
>>> metadata = response.metadata()
Args:
name (str): Name of the model requested to serve the batch prediction.
input_config (Union[dict, ~google.cloud.automl_v1beta1.types.BatchPredictInputConfig]): Required. The input configuration for batch prediction.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.automl_v1beta1.types.BatchPredictInputConfig`
output_config (Union[dict, ~google.cloud.automl_v1beta1.types.BatchPredictOutputConfig]): Required. The Configuration specifying where output predictions should
be written.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.automl_v1beta1.types.BatchPredictOutputConfig`
params (dict[str -> str]): Additional domain-specific parameters for the predictions, any string
must be up to 25000 characters long.
- For Text 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.
- For Image 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.
- For Image 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) No more than this number of
bounding boxes will be produced per image. Default is 100, the
requested value may be limited by server.
- For Video 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. 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. The default is "false".
``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. 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. The default
is "false".
- For Video 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) No more than
this number of bounding boxes will be returned per frame. Default is
100, the requested value may be limited by server.
``min_bounding_box_size`` - (float) Only bounding boxes with shortest
edge at least that long as a relative value of video frame size will
be returned. Value in 0 to 1 range. Default is 0.
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will
be retried using a default configuration.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.automl_v1beta1.types._OperationFuture` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if "batch_predict" not in self._inner_api_calls:
self._inner_api_calls[
"batch_predict"] = google.api_core.gapic_v1.method.wrap_method(
self.transport.batch_predict,
default_retry=self._method_configs["BatchPredict"].retry,
default_timeout=self._method_configs["BatchPredict"].
timeout,
client_info=self._client_info,
)
request = prediction_service_pb2.BatchPredictRequest(
name=name,
input_config=input_config,
output_config=output_config,
params=params,
)
if metadata is None:
metadata = []
metadata = list(metadata)
try:
routing_header = [("name", name)]
except AttributeError:
pass
else:
routing_metadata = google.api_core.gapic_v1.routing_header.to_grpc_metadata(
routing_header)
metadata.append(routing_metadata)
operation = self._inner_api_calls["batch_predict"](request,
retry=retry,
timeout=timeout,
metadata=metadata)
return google.api_core.operation.from_gapic(
operation,
self.transport._operations_client,
prediction_service_pb2.BatchPredictResult,
metadata_type=proto_operations_pb2.OperationMetadata,
)