Tutorial: Run inference on an object table by using a classification model

This tutorial shows you how to create an object table based on the imagesfrom a public dataset,and then run inference on that object table using theResNet 50 model.

The ResNet 50 model

The ResNet 50 model analyzes image files and outputs a batch of vectorsrepresenting the likelihood that an image belongs the corresponding class(logits). For more information, see theUsage section on themodel's TensorFlow Hub page.

The ResNet 50 model input takes a tensor ofDType =float32 in the shape[-1, 224, 224, 3]. The output is an array oftensors oftf.float32 in the shape[-1, 1024].

Required permissions

• To create the dataset, you need thebigquery.datasets.create permission.

• To create the connection resource, you need the following permissions:

  • bigquery.connections.create
  • bigquery.connections.get

• To grant permissions to the connection's service account, you need thefollowing permission:

  • resourcemanager.projects.setIamPolicy

• To create the object table, you need the following permissions:

  • bigquery.tables.create
  • bigquery.tables.update
  • bigquery.connections.delegate

• To create the bucket, you need thestorage.buckets.create permission.

• To upload the model to Cloud Storage, you need thestorage.objects.create andstorage.objects.get permissions.

• To load the model into BigQuery ML, you need the followingpermissions:

  • bigquery.jobs.create
  • bigquery.models.create
  • bigquery.models.getData
  • bigquery.models.updateData

• To run inference, you need the following permissions:

  • bigquery.tables.getData on the object table
  • bigquery.models.getData on the model
  • bigquery.jobs.create

Costs

In this document, you use the following billable components of Google Cloud:

• BigQuery: You incur storage costs for the object table you create in BigQuery.
• BigQuery ML: You incur costs for the model you create and the inference you perform in BigQuery ML.
• Cloud Storage: You incur costs for the objects you store in Cloud Storage.

To generate a cost estimate based on your projected usage, use thepricing calculator.

For more information on BigQuery storage pricing, seeStorage pricing in the BigQuerydocumentation.

For more information on BigQuery ML pricing, seeBigQuery ML pricing inthe BigQuery documentation.

For more information on Cloud Storage pricing, see theCloud Storage pricing page.

Before you begin

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.create permission.Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the BigQuery and BigQuery Connection API APIs.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enable permission.Learn how to grant roles.

Enable the APIs

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.create permission.Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the BigQuery and BigQuery Connection API APIs.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enable permission.Learn how to grant roles.

Enable the APIs

Create a reservation

To use animported modelwith an object table, you mustcreate a reservationthat uses the BigQueryEnterprise or Enterprise Plus edition,and thencreate a reservation assignmentthat uses theQUERY job type.

Create a dataset

Create a dataset namedresnet_inference_test:

Create a connection

Create a connection namedlake-connection:

Create a Cloud Storage bucket

Create a Cloud Storage bucket tocontain the model files.

Grant permissions to the connection's service account

gcloudstoragebucketsadd-iam-policy-bindinggs://BUCKET_NAME\--member=serviceAccount:MEMBER\--role=roles/storage.objectViewer

bqmk--table \--external_table_definition='gs://cloud-samples-data/vision/*.jpg@us.lake-connection' \--object_metadata=SIMPLE \resnet_inference_test.vision_images

Upload the model to Cloud Storage

Get the model files and make them available in Cloud Storage:

1. Downloadthe ResNet 50 model to your local machine. This gives you asaved_model.pb file and avariables folder for the model.
2. Upload thesaved_model.pb file andthevariables folder to the bucket you previously created.

Load the model into BigQuery ML

1. Go to theBigQuery page.

   Go to BigQuery

2. In theEditor pane, run the following SQL statement:

   CREATEMODEL`resnet_inference_test.resnet`OPTIONS(model_type='TENSORFLOW',model_path='gs://BUCKET_NAME/*');

   ReplaceBUCKET_NAME with the name of the bucketyou previously created.

Inspect the model

Inspect the uploaded model to see what its input and output fields are:

1. Go to theBigQuery page.

   Go to BigQuery

2. In the left pane, clickexploreExplorer:

   

3. In theExplorer pane, expand your project, clickDatasets, and thenclick theresnet_inference_test dataset.

4. Go to theModels tab.

5. Click theresnet model.

6. In the model pane that opens, click theSchema tab.

7. Look at theLabels section. This identifies the fields that are outputby the model. In this case, the field name value isactivation_49.

8. Look at theFeatures section. This identifies the fields that mustbe input into the model. You reference them in theSELECT statementfor theML.DECODE_IMAGE function. In this case, the field name value isinput_1.

Run inference

Run inference on thevision_images object table using theresnet model:

1. Go to theBigQuery page.

   Go to BigQuery

2. In theEditor pane, run the following SQL statement:

   SELECT*FROMML.PREDICT(MODEL`resnet_inference_test.resnet`,(SELECTuri,ML.RESIZE_IMAGE(ML.DECODE_IMAGE(data),224,224,FALSE)ASinput_1FROMresnet_inference_test.vision_images));

   The results should look similar to the following:

   -------------------------------------------------------------------------------------------------------------------------------------| activation_49           | uri                                                                                           | input_1 |—------------------------------------------------------------------------------------------------------------------------------------| 1.0254175464297077e-07  | gs://cloud-samples-data/vision/automl_classification/flowers/daisy/21652746_cc379e0eea_m.jpg  | 0.0     |—------------------------------------------------------------------------------------------------------------------------------------| 2.1671139620593749e-06  |                                                                                               | 0.0     |—-------------------------------------| 8.346052027263795e-08   |                                                                                               | 0.0     |—-------------------------------------| 1.159310958342985e-08   |                                                                                               | 0.0     |—------------------------------------------------------------------------------------------------------------------------------------

Clean up

Caution: Deleting a project has the following effects:
• Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as anappspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

1. In the Google Cloud console, go to theManage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then clickDelete.
3. In the dialog, type the project ID, and then clickShut down to delete the project.