训练 Edge 可导出模型

创建自定义模型的方法，是使用准备好的数据集对其进行训练。AutoML API 使用数据集中的条目来训练、测试模型并评估其性能。您可以查看结果、根据需要调整训练数据集，并使用改进后的数据集训练新的模型。

训练模型可能需要几个小时才能完成。借助 AutoML API，您可以检查训练的状态。

每次开始训练时，AutoML Vision 都会创建新模型，因此您的项目可能包含大量模型。您可以获取项目中模型的列表并删除不再需要的模型。或者，您也可以使用 Cloud AutoML Vision 界面来列出和删除通过 AutoML API 创建且不再需要的模型。

模型基于 Google 最先进的研究。您的模型将以 TF Lite 软件包的形式提供。如需详细了解如何使用 TensorFlow Lite SDK 集成 TensorFlow Lite 模型，请参阅下列针对 iOS 和 Android 的链接。

训练 Edge 模型

如果您拥有一个包含一组带标签的固定训练项的数据集，您就可以创建并训练自定义 Edge 模型了。

TensorFlow Serving 和 TF Lite 模型

在训练 Edge 模型时，您可以在 modelType 字段中指定三个不同的值，具体取决于您的模型需求：

  • mobile-low-latency-1 表示低延时，
  • mobile-versatile-1 表示通用目的，
  • mobile-high-accuracy-1 表示较高预测质量。

模型类型也将显示在 API 请求响应中。

网页界面

  1. 打开 AutoML Vision Object Detection UI。

    数据集页面显示当前项目的可用数据集。

    列出数据集页面
  2. 选择要用于训练自定义模型的数据集。

  3. 准备好数据集后，选择训练标签页和训练新模型按钮。

    此操作会打开包含训练选项的训练新模型侧边窗口。

  4. 在定义模型训练部分中，更改模型名称（或使用默认值）并选择 Edge 作为模型类型。选择训练 Edge 模型后，选择继续。 训练 Edge 模型单选按钮图片

  5. 在接下来的模型优化选项部分中，选择所需的优化条件：较高准确率 (Higher accuracy)、最佳权衡 (Best tradeoff) 或较快预测速度 (Faster prediction)。选择优化规范后，再选择继续。

    最佳权衡单选按钮图片

  6. 在接下来的设置节点时预算部分中，使用建议的节点时预算或指定其他值。

    默认情况下，对于大多数数据集来说，24 节点时足以训练模型。此建议值是使模型完全收敛的估算值。但是，您可以选择其他数值。图像分类的最小节点时为 8。对于 Object Detection，此最小值为 20。

    设置节点预算部分

  7. 选择开始训练以开始训练模型。

训练模型可能需要几个小时才能完成。模型训练成功后，您用于 Google Cloud Platform 项目的电子邮件地址会收到一封邮件。

REST

在使用任何请求数据之前，请先进行以下替换：

  • project-id：您的 GCP 项目 ID。
  • dataset-id：您的数据集的 ID。此 ID 是数据集名称的最后一个元素。例如：
    • 数据集名称：projects/project-id/locations/location-id/datasets/3104518874390609379
    • 数据集 ID：3104518874390609379
  • display-name：您选择的字符串显示名。

字段特定注意事项：

  • imageObjectDetectionModelMetadata.trainBudgetMilliNodeHours - 创建此模型的训练预算，以毫节点时表示（此字段中的值 1000 表示 1 个节点时）。实际的 trainCostMilliNodeHours 将等于或小于此值。如果进一步的模型训练不再提供任何改进，则训练将停止，而不使用全部预算，并且 stopReason 将为 MODEL_CONVERGED。

    注意：node_hour = actual_hour * number_of_nodes_involved。

    对于模型类型 mobile-low-latency-1、mobile-versatile-1 或 mobile-high-accuracy-1，训练预算必须介于 1000 到 100000 毫节点时之间（包括 1000 和 100000 毫节点时）。默认值为 24000，代表实际用时一天。

HTTP 方法和网址： 

POST https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/models

请求 JSON 正文： 

{
  "displayName": "DISPLAY_NAME",
  "datasetId": "DATASET_ID",
  "imageObjectDetectionModelMetadata": {
    "modelType": "mobile-low-latency-1",
    "trainBudgetMilliNodeHours": "24000"
  }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中，然后执行以下命令： 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: project-id" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/models"

PowerShell

将请求正文保存在名为 request.json 的文件中，然后执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/models" | Select-Object -Expand Content

您应该会看到类似如下所示的输出。可以使用操作 ID（本例中为 IOD2106290444865378475）来获取任务的状态。如需查看示例，请参阅处理长时间运行的操作。

{
  "name": "projects/PROJECT_ID/locations/us-central1/operations/IOD2106290444865378475",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-07-29T17:16:34.476787Z",
    "updateTime": "2019-07-29T17:16:34.476787Z",
    "createModelDetails": {}
  }
}

Go

在试用此示例之前，请按照客户端库页面中与此编程语言对应的设置说明执行操作。

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	"cloud.google.com/go/automl/apiv1/automlpb"
)

// visionObjectDetectionCreateModel creates a model for image object detection.
func visionObjectDetectionCreateModel(w io.Writer, projectID string, location string, datasetID string, modelName string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// datasetID := "IOD123456789..."
	// modelName := "model_display_name"

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %w", err)
	}
	defer client.Close()

	req := &automlpb.CreateModelRequest{
		Parent: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		Model: &automlpb.Model{
			DisplayName: modelName,
			DatasetId:   datasetID,
			ModelMetadata: &automlpb.Model_ImageObjectDetectionModelMetadata{
				ImageObjectDetectionModelMetadata: &automlpb.ImageObjectDetectionModelMetadata{},
			},
		},
	}

	op, err := client.CreateModel(ctx, req)
	if err != nil {
		return fmt.Errorf("CreateModel: %w", err)
	}
	fmt.Fprintf(w, "Processing operation name: %q\n", op.Name())
	fmt.Fprintf(w, "Training started...\n")

	return nil
}

Java

在试用此示例之前，请按照客户端库页面中与此编程语言对应的设置说明执行操作。

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.ImageObjectDetectionModelMetadata;
import com.google.cloud.automl.v1.LocationName;
import com.google.cloud.automl.v1.Model;
import com.google.cloud.automl.v1.OperationMetadata;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class VisionObjectDetectionCreateModel {

  static void createModel() throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String datasetId = "YOUR_DATASET_ID";
    String displayName = "YOUR_DATASET_NAME";
    createModel(projectId, datasetId, displayName);
  }

  // Create a model
  static void createModel(String projectId, String datasetId, String displayName)
      throws IOException, ExecutionException, InterruptedException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
      // A resource that represents Google Cloud Platform location.
      LocationName projectLocation = LocationName.of(projectId, "us-central1");
      // Set model metadata.
      ImageObjectDetectionModelMetadata metadata =
          ImageObjectDetectionModelMetadata.newBuilder().build();
      Model model =
          Model.newBuilder()
              .setDisplayName(displayName)
              .setDatasetId(datasetId)
              .setImageObjectDetectionModelMetadata(metadata)
              .build();

      // Create a model with the model metadata in the region.
      OperationFuture<Model, OperationMetadata> future =
          client.createModelAsync(projectLocation, model);
      // OperationFuture.get() will block until the model is created, which may take several hours.
      // You can use OperationFuture.getInitialFuture to get a future representing the initial
      // response to the request, which contains information while the operation is in progress.
      System.out.format("Training operation name: %s\n", future.getInitialFuture().get().getName());
      System.out.println("Training started...");
    }
  }
}

Node.js

在试用此示例之前，请按照客户端库页面中与此编程语言对应的设置说明执行操作。

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const dataset_id = 'YOUR_DATASET_ID';
// const displayName = 'YOUR_DISPLAY_NAME';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function createModel() {
  // Construct request
  const request = {
    parent: client.locationPath(projectId, location),
    model: {
      displayName: displayName,
      datasetId: datasetId,
      imageObjectDetectionModelMetadata: {},
    },
  };

  // Don't wait for the LRO
  const [operation] = await client.createModel(request);
  console.log(`Training started... ${operation}`);
  console.log(`Training operation name: ${operation.name}`);
}

createModel();

Python

在试用此示例之前，请按照客户端库页面中与此编程语言对应的设置说明执行操作。

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# dataset_id = "YOUR_DATASET_ID"
# display_name = "your_models_display_name"

client = automl.AutoMlClient()

# A resource that represents Google Cloud Platform location.
project_location = f"projects/{project_id}/locations/us-central1"
# Leave model unset to use the default base model provided by Google
# train_budget_milli_node_hours: The actual train_cost will be equal or
# less than this value.
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#imageobjectdetectionmodelmetadata
metadata = automl.ImageObjectDetectionModelMetadata(
    train_budget_milli_node_hours=24000
)
model = automl.Model(
    display_name=display_name,
    dataset_id=dataset_id,
    image_object_detection_model_metadata=metadata,
)

# Create a model with the model metadata in the region.
response = client.create_model(parent=project_location, model=model)

print(f"Training operation name: {response.operation.name}")
print("Training started...")

列出操作状态

使用以下代码示例列出项目的操作并过滤结果。

REST

在使用任何请求数据之前，请先进行以下替换：

  • project-id：您的 GCP 项目 ID。

HTTP 方法和网址： 

GET https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/operations

如需发送请求，请选择以下方式之一：

curl

执行以下命令：

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: project-id" \
     "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/operations"

PowerShell

执行以下命令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/operations" | Select-Object -Expand Content

您看到的输出会因您请求的操作而异。

您还可以使用选择查询参数（operationId、done 和 worksOn）过滤返回的操作。例如，如需返回已完成运行的操作列表，请修改网址：

GET https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/operations?filter="done=true"

Go

在试用此示例之前，请按照 API 与参考文档 > 客户端库页面上与此编程语言对应的设置说明进行操作。

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	longrunning "cloud.google.com/go/longrunning/autogen/longrunningpb"
	"google.golang.org/api/iterator"
)

// listOperationStatus lists existing operations' status.
func listOperationStatus(w io.Writer, projectID string, location string) error {
	// projectID := "my-project-id"
	// location := "us-central1"

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %w", err)
	}
	defer client.Close()

	req := &longrunning.ListOperationsRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
	}

	it := client.LROClient.ListOperations(ctx, req)

	// Iterate over all results
	for {
		op, err := it.Next()
		if err == iterator.Done {
			break
		}
		if err != nil {
			return fmt.Errorf("ListOperations.Next: %w", err)
		}

		fmt.Fprintf(w, "Name: %v\n", op.GetName())
		fmt.Fprintf(w, "Operation details:\n")
		fmt.Fprintf(w, "%v", op)
	}

	return nil
}

Java

在试用此示例之前，请按照 API 与参考文档 > 客户端库页面上与此编程语言对应的设置说明进行操作。

import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.LocationName;
import com.google.longrunning.ListOperationsRequest;
import com.google.longrunning.Operation;
import java.io.IOException;

class ListOperationStatus {

  static void listOperationStatus() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    listOperationStatus(projectId);
  }

  // Get the status of an operation
  static void listOperationStatus(String projectId) throws IOException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
      // A resource that represents Google Cloud Platform location.
      LocationName projectLocation = LocationName.of(projectId, "us-central1");

      // Create list operations request.
      ListOperationsRequest listrequest =
          ListOperationsRequest.newBuilder().setName(projectLocation.toString()).build();

      // List all the operations names available in the region by applying filter.
      for (Operation operation :
          client.getOperationsClient().listOperations(listrequest).iterateAll()) {
        System.out.println("Operation details:");
        System.out.format("\tName: %s\n", operation.getName());
        System.out.format("\tMetadata Type Url: %s\n", operation.getMetadata().getTypeUrl());
        System.out.format("\tDone: %s\n", operation.getDone());
        if (operation.hasResponse()) {
          System.out.format("\tResponse Type Url: %s\n", operation.getResponse().getTypeUrl());
        }
        if (operation.hasError()) {
          System.out.println("\tResponse:");
          System.out.format("\t\tError code: %s\n", operation.getError().getCode());
          System.out.format("\t\tError message: %s\n\n", operation.getError().getMessage());
        }
      }
    }
  }
}

Node.js

在试用此示例之前，请按照 API 与参考文档 > 客户端库页面上与此编程语言对应的设置说明进行操作。

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function listOperationStatus() {
  // Construct request
  const request = {
    name: client.locationPath(projectId, location),
    filter: `worksOn=projects/${projectId}/locations/${location}/models/*`,
  };

  const [response] = await client.operationsClient.listOperations(request);

  console.log('List of operation status:');
  for (const operation of response) {
    console.log(`Name: ${operation.name}`);
    console.log('Operation details:');
    console.log(`${operation}`);
  }
}

listOperationStatus();

Python

在试用此示例之前，请按照 API 与参考文档 > 客户端库页面上与此编程语言对应的设置说明进行操作。

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"

client = automl.AutoMlClient()
# A resource that represents Google Cloud Platform location.
project_location = f"projects/{project_id}/locations/us-central1"
# List all the operations names available in the region.
response = client._transport.operations_client.list_operations(
    name=project_location, filter_="", timeout=5
)

print("List of operations:")
for operation in response:
    print(f"Name: {operation.name}")
    print("Operation details:")
    print(operation)

其他语言

C#： 请按照客户端库页面上的 C# 设置说明操作，然后访问 .NET 版 AutoML Vision Object Detection 参考文档。

PHP： 请按照客户端库页面上的 PHP 设置说明操作，然后访问 PHP 版 AutoML Vision Object Detection 参考文档。

Ruby 版： 请按照客户端库页面上的 Ruby 设置说明操作，然后访问 Ruby 版 AutoML Vision Object Detection 参考文档。

获取操作状态

REST

在使用任何请求数据之前，请先进行以下替换：

  • project-id：您的 GCP 项目 ID。
  • operation-id：您的操作的 ID。此 ID 是操作名称的最后一个元素。例如：
    • 操作名称：projects/project-id/locations/location-id/operations/IOD5281059901324392598
    • 操作 ID：IOD5281059901324392598

HTTP 方法和网址：

GET https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID

如需发送请求，请选择以下方式之一：

curl

执行以下命令：

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: project-id" \
     "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID"

PowerShell

执行以下命令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID" | Select-Object -Expand Content
完成导入操作后，您应该会看到类似如下所示的输出： 
{
  "name": "projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2018-10-29T15:56:29.176485Z",
    "updateTime": "2018-10-29T16:10:41.326614Z",
    "importDataDetails": {}
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.protobuf.Empty"
  }
}

完成创建模型操作后，您应会看到如下输出：

{
  "name": "projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-07-22T18:35:06.881193Z",
    "updateTime": "2019-07-22T19:58:44.972235Z",
    "createModelDetails": {}
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.Model",
    "name": "projects/PROJECT_ID/locations/us-central1/models/MODEL_ID"
  }
}

Go

在试用此示例之前，请按照客户端库页面中与此编程语言对应的设置说明执行操作。

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	"cloud.google.com/go/automl/apiv1/automlpb"
)

// getOperationStatus gets an operation's status.
func getOperationStatus(w io.Writer, projectID string, location string, datasetID string, modelName string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// datasetID := "ICN123456789..."
	// modelName := "model_display_name"

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %w", err)
	}
	defer client.Close()

	req := &automlpb.CreateModelRequest{
		Parent: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		Model: &automlpb.Model{
			DisplayName: modelName,
			DatasetId:   datasetID,
			ModelMetadata: &automlpb.Model_ImageClassificationModelMetadata{
				ImageClassificationModelMetadata: &automlpb.ImageClassificationModelMetadata{
					TrainBudgetMilliNodeHours: 1000, // 1000 milli-node hours are 1 hour
				},
			},
		},
	}

	op, err := client.CreateModel(ctx, req)
	if err != nil {
		return err
	}
	fmt.Fprintf(w, "Name: %v\n", op.Name())

	// Wait for the longrunning operation complete.
	resp, err := op.Wait(ctx)
	if err != nil && !op.Done() {
		fmt.Println("failed to fetch operation status", err)
		return err
	}
	if err != nil && op.Done() {
		fmt.Println("operation completed with error", err)
		return err
	}
	fmt.Fprintf(w, "Response: %v\n", resp)

	return nil
}

Java

在试用此示例之前，请按照客户端库页面中与此编程语言对应的设置说明执行操作。

import com.google.cloud.automl.v1.AutoMlClient;
import com.google.longrunning.Operation;
import java.io.IOException;

class GetOperationStatus {

  static void getOperationStatus() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String operationFullId = "projects/[projectId]/locations/us-central1/operations/[operationId]";
    getOperationStatus(operationFullId);
  }

  // Get the status of an operation
  static void getOperationStatus(String operationFullId) throws IOException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
      // Get the latest state of a long-running operation.
      Operation operation = client.getOperationsClient().getOperation(operationFullId);

      // Display operation details.
      System.out.println("Operation details:");
      System.out.format("\tName: %s\n", operation.getName());
      System.out.format("\tMetadata Type Url: %s\n", operation.getMetadata().getTypeUrl());
      System.out.format("\tDone: %s\n", operation.getDone());
      if (operation.hasResponse()) {
        System.out.format("\tResponse Type Url: %s\n", operation.getResponse().getTypeUrl());
      }
      if (operation.hasError()) {
        System.out.println("\tResponse:");
        System.out.format("\t\tError code: %s\n", operation.getError().getCode());
        System.out.format("\t\tError message: %s\n", operation.getError().getMessage());
      }
    }
  }
}

Node.js

在试用此示例之前，请按照客户端库页面中与此编程语言对应的设置说明执行操作。

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const operationId = 'YOUR_OPERATION_ID';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function getOperationStatus() {
  // Construct request
  const request = {
    name: `projects/${projectId}/locations/${location}/operations/${operationId}`,
  };

  const [response] = await client.operationsClient.getOperation(request);

  console.log(`Name: ${response.name}`);
  console.log('Operation details:');
  console.log(`${response}`);
}

getOperationStatus();

Python

在试用此示例之前，请按照客户端库页面中与此编程语言对应的设置说明执行操作。

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# operation_full_id = \
#     "projects/[projectId]/locations/us-central1/operations/[operationId]"

client = automl.AutoMlClient()
# Get the latest state of a long-running operation.
response = client._transport.operations_client.get_operation(operation_full_id)

print(f"Name: {response.name}")
print("Operation details:")
print(response)

其他语言

C#： 请按照客户端库页面上的 C# 设置说明操作，然后访问 .NET 版 AutoML Vision Object Detection 参考文档。

PHP： 请按照客户端库页面上的 PHP 设置说明操作，然后访问 PHP 版 AutoML Vision Object Detection 参考文档。

Ruby 版： 请按照客户端库页面上的 Ruby 设置说明操作，然后访问 Ruby 版 AutoML Vision Object Detection 参考文档。

取消操作

您可以使用操作 ID 取消导入任务或训练任务。

REST

在试用此示例之前，请按照客户端库页面中与此编程语言对应的设置说明执行操作。

在使用任何请求数据之前，请先进行以下替换：

  • project-id：您的 GCP 项目 ID。
  • operation-id：您的操作的 ID。此 ID 是操作名称的最后一个元素。例如：
    • 操作名称：projects/project-id/locations/location-id/operations/IOD5281059901324392598
    • 操作 ID：IOD5281059901324392598

HTTP 方法和网址：

POST https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID:cancel

如需发送请求，请选择以下方式之一：

curl

执行以下命令：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: project-id" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d "" \
     "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID:cancel"

PowerShell

执行以下命令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -Uri "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID:cancel" | Select-Object -Expand Content
如果请求成功，则将返回空的 JSON 对象： 
{}

获取模型的相关信息

使用以下代码示例获取有关特定的训练后模型的信息。您可以使用此请求返回的信息来修改模式或发送预测请求。

REST

在使用任何请求数据之前，请先进行以下替换：

  • project-id：您的 GCP 项目 ID。
  • model-id：您的模型的 ID（从创建模型时返回的响应中获取）。此 ID 是模型名称的最后一个元素。 例如：
    • 模型名称：projects/project-id/locations/location-id/models/IOD4412217016962778756
    • 模型 ID：IOD4412217016962778756

HTTP 方法和网址： 

GET https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models/model-id

如需发送请求，请选择以下方式之一：

curl

执行以下命令：

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: project-id" \
     "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models/model-id"

PowerShell

执行以下命令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models/model-id" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：



    {
  "name": "projects/project-id/locations/us-central1/models/model-id",
  "displayName": "display-name",
  "datasetId": "dataset-id",
  "createTime": "2019-07-29T17:16:34.476787Z",
  "deploymentState": "UNDEPLOYED",
  "updateTime": "2019-07-29T18:30:13.601461Z",
  "imageObjectDetectionModelMetadata": {
    "modelType": "mobile-low-latency-1",
    "nodeQps": -1,
    "stopReason": "MODEL_CONVERGED",
    "trainBudgetMilliNodeHours": "24000",
    "trainCostMilliNodeHours": "861"
  }
}

Go

在试用此示例之前，请按照客户端库页面中与此编程语言对应的设置说明执行操作。

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	"cloud.google.com/go/automl/apiv1/automlpb"
)

// getModel gets a model.
func getModel(w io.Writer, projectID string, location string, modelID string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// modelID := "TRL123456789..."

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %w", err)
	}
	defer client.Close()

	req := &automlpb.GetModelRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s/models/%s", projectID, location, modelID),
	}

	model, err := client.GetModel(ctx, req)
	if err != nil {
		return fmt.Errorf("GetModel: %w", err)
	}

	// Retrieve deployment state.
	deploymentState := "undeployed"
	if model.GetDeploymentState() == automlpb.Model_DEPLOYED {
		deploymentState = "deployed"
	}

	// Display the model information.
	fmt.Fprintf(w, "Model name: %v\n", model.GetName())
	fmt.Fprintf(w, "Model display name: %v\n", model.GetDisplayName())
	fmt.Fprintf(w, "Model create time:\n")
	fmt.Fprintf(w, "\tseconds: %v\n", model.GetCreateTime().GetSeconds())
	fmt.Fprintf(w, "\tnanos: %v\n", model.GetCreateTime().GetNanos())
	fmt.Fprintf(w, "Model deployment state: %v\n", deploymentState)

	return nil
}

Java

在试用此示例之前，请按照客户端库页面中与此编程语言对应的设置说明执行操作。

import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.Model;
import com.google.cloud.automl.v1.ModelName;
import java.io.IOException;

class GetModel {

  static void getModel() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    getModel(projectId, modelId);
  }

  // Get a model
  static void getModel(String projectId, String modelId) throws IOException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
      // Get the full path of the model.
      ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
      Model model = client.getModel(modelFullId);

      // Display the model information.
      System.out.format("Model name: %s\n", model.getName());
      // To get the model id, you have to parse it out of the `name` field. As models Ids are
      // required for other methods.
      // Name Format: `projects/{project_id}/locations/{location_id}/models/{model_id}`
      String[] names = model.getName().split("/");
      String retrievedModelId = names[names.length - 1];
      System.out.format("Model id: %s\n", retrievedModelId);
      System.out.format("Model display name: %s\n", model.getDisplayName());
      System.out.println("Model create time:");
      System.out.format("\tseconds: %s\n", model.getCreateTime().getSeconds());
      System.out.format("\tnanos: %s\n", model.getCreateTime().getNanos());
      System.out.format("Model deployment state: %s\n", model.getDeploymentState());
    }
  }
}

Node.js

在试用此示例之前，请按照客户端库页面中与此编程语言对应的设置说明执行操作。

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const modelId = 'YOUR_MODEL_ID';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function getModel() {
  // Construct request
  const request = {
    name: client.modelPath(projectId, location, modelId),
  };

  const [response] = await client.getModel(request);

  console.log(`Model name: ${response.name}`);
  console.log(
    `Model id: ${
      response.name.split('/')[response.name.split('/').length - 1]
    }`
  );
  console.log(`Model display name: ${response.displayName}`);
  console.log('Model create time');
  console.log(`\tseconds ${response.createTime.seconds}`);
  console.log(`\tnanos ${response.createTime.nanos / 1e9}`);
  console.log(`Model deployment state: ${response.deploymentState}`);
}

getModel();

Python

在试用此示例之前，请按照客户端库页面中与此编程语言对应的设置说明执行操作。

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# model_id = "YOUR_MODEL_ID"

client = automl.AutoMlClient()
# Get the full path of the model.
model_full_id = client.model_path(project_id, "us-central1", model_id)
model = client.get_model(name=model_full_id)

# Retrieve deployment state.
if model.deployment_state == automl.Model.DeploymentState.DEPLOYED:
    deployment_state = "deployed"
else:
    deployment_state = "undeployed"

# Display the model information.
print(f"Model name: {model.name}")
print("Model id: {}".format(model.name.split("/")[-1]))
print(f"Model display name: {model.display_name}")
print(f"Model create time: {model.create_time}")
print(f"Model deployment state: {deployment_state}")

其他语言

C#： 请按照客户端库页面上的 C# 设置说明操作，然后访问 .NET 版 AutoML Vision Object Detection 参考文档。

PHP： 请按照客户端库页面上的 PHP 设置说明操作，然后访问 PHP 版 AutoML Vision Object Detection 参考文档。

Ruby 版： 请按照客户端库页面上的 Ruby 设置说明操作，然后访问 Ruby 版 AutoML Vision Object Detection 参考文档。