As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

Source code for google.cloud.automl_v1.types.image

# -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import proto  # type: ignore

from google.cloud.automl_v1.types import classification


__protobuf__ = proto.module(
    package="google.cloud.automl.v1",
    manifest={
        "ImageClassificationDatasetMetadata",
        "ImageObjectDetectionDatasetMetadata",
        "ImageClassificationModelMetadata",
        "ImageObjectDetectionModelMetadata",
        "ImageClassificationModelDeploymentMetadata",
        "ImageObjectDetectionModelDeploymentMetadata",
    },
)


[docs]class ImageClassificationDatasetMetadata(proto.Message): r"""Dataset metadata that is specific to image classification. Attributes: classification_type (google.cloud.automl_v1.types.ClassificationType): Required. Type of the classification problem. """ classification_type = proto.Field( proto.ENUM, number=1, enum=classification.ClassificationType, )
[docs]class ImageObjectDetectionDatasetMetadata(proto.Message): r"""Dataset metadata specific to image object detection. """
[docs]class ImageClassificationModelMetadata(proto.Message): r"""Model metadata for image classification. Attributes: base_model_id (str): Optional. The ID of the ``base`` model. If it is specified, the new model will be created based on the ``base`` model. Otherwise, the new model will be created from scratch. The ``base`` model must be in the same ``project`` and ``location`` as the new model to create, and have the same ``model_type``. train_budget_milli_node_hours (int): The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual ``train_cost`` will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using full budget and the stop_reason will be ``MODEL_CONVERGED``. Note, node_hour = actual_hour \* number_of_nodes_invovled. For model type ``cloud``\ (default), the train budget must be between 8,000 and 800,000 milli node hours, inclusive. The default value is 192, 000 which represents one day in wall time. For model type ``mobile-low-latency-1``, ``mobile-versatile-1``, ``mobile-high-accuracy-1``, ``mobile-core-ml-low-latency-1``, ``mobile-core-ml-versatile-1``, ``mobile-core-ml-high-accuracy-1``, the train budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24, 000 which represents one day in wall time. train_cost_milli_node_hours (int): Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget. stop_reason (str): Output only. The reason that this create model operation stopped, e.g. ``BUDGET_REACHED``, ``MODEL_CONVERGED``. model_type (str): Optional. Type of the model. The available values are: - ``cloud`` - Model to be used via prediction calls to AutoML API. This is the default value. - ``mobile-low-latency-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models. - ``mobile-versatile-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. - ``mobile-high-accuracy-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models. - ``mobile-core-ml-low-latency-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards. Expected to have low latency, but may have lower prediction quality than other models. - ``mobile-core-ml-versatile-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards. - ``mobile-core-ml-high-accuracy-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models. node_qps (float): Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed. node_count (int): Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the node_qps field. """ base_model_id = proto.Field(proto.STRING, number=1,) train_budget_milli_node_hours = proto.Field(proto.INT64, number=16,) train_cost_milli_node_hours = proto.Field(proto.INT64, number=17,) stop_reason = proto.Field(proto.STRING, number=5,) model_type = proto.Field(proto.STRING, number=7,) node_qps = proto.Field(proto.DOUBLE, number=13,) node_count = proto.Field(proto.INT64, number=14,)
[docs]class ImageObjectDetectionModelMetadata(proto.Message): r"""Model metadata specific to image object detection. Attributes: model_type (str): Optional. Type of the model. The available values are: - ``cloud-high-accuracy-1`` - (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models. - ``cloud-low-latency-1`` - A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models. - ``mobile-low-latency-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models. - ``mobile-versatile-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. - ``mobile-high-accuracy-1`` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models. node_count (int): Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the qps_per_node field. node_qps (float): Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed. stop_reason (str): Output only. The reason that this create model operation stopped, e.g. ``BUDGET_REACHED``, ``MODEL_CONVERGED``. train_budget_milli_node_hours (int): The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual ``train_cost`` will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using full budget and the stop_reason will be ``MODEL_CONVERGED``. Note, node_hour = actual_hour \* number_of_nodes_invovled. For model type ``cloud-high-accuracy-1``\ (default) and ``cloud-low-latency-1``, the train budget must be between 20,000 and 900,000 milli node hours, inclusive. The default value is 216, 000 which represents one day in wall time. For model type ``mobile-low-latency-1``, ``mobile-versatile-1``, ``mobile-high-accuracy-1``, ``mobile-core-ml-low-latency-1``, ``mobile-core-ml-versatile-1``, ``mobile-core-ml-high-accuracy-1``, the train budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24, 000 which represents one day in wall time. train_cost_milli_node_hours (int): Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget. """ model_type = proto.Field(proto.STRING, number=1,) node_count = proto.Field(proto.INT64, number=3,) node_qps = proto.Field(proto.DOUBLE, number=4,) stop_reason = proto.Field(proto.STRING, number=5,) train_budget_milli_node_hours = proto.Field(proto.INT64, number=6,) train_cost_milli_node_hours = proto.Field(proto.INT64, number=7,)
[docs]class ImageClassificationModelDeploymentMetadata(proto.Message): r"""Model deployment metadata specific to Image Classification. Attributes: node_count (int): Input only. The number of nodes to deploy the model on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the model's [node_qps][google.cloud.automl.v1.ImageClassificationModelMetadata.node_qps]. Must be between 1 and 100, inclusive on both ends. """ node_count = proto.Field(proto.INT64, number=1,)
[docs]class ImageObjectDetectionModelDeploymentMetadata(proto.Message): r"""Model deployment metadata specific to Image Object Detection. Attributes: node_count (int): Input only. The number of nodes to deploy the model on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the model's [qps_per_node][google.cloud.automl.v1.ImageObjectDetectionModelMetadata.qps_per_node]. Must be between 1 and 100, inclusive on both ends. """ node_count = proto.Field(proto.INT64, number=1,)
__all__ = tuple(sorted(__protobuf__.manifest))