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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 images from a public dataset, and then run inference on that object table using the ResNet 50 model.

The ResNet 50 model

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

The ResNet 50 model input takes a tensor of DType tf.float32 in the shape [-1, 224, 224, 3]. The output is an array of tensors of tf.float32 in the shape[-1, 1024].

Required permissions

  • To create the dataset, you need the bigquery.datasets.create permission.
  • To create the connection resource, you need the following permissions:

    • bigquery.connections.create
    • bigquery.connections.get
  • 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 the storage.buckets.create permission.

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

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

    • 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

This tutorial uses 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 the pricing calculator. New Google Cloud users might be eligible for a free trial.

For more information on BigQuery storage pricing, see Storage pricing in the BigQuery documentation.

For more information on BigQuery ML pricing, see BigQuery ML pricing in the BigQuery documentation.

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

Before you begin

  1. 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.
  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  3. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

  4. Enable the BigQuery and BigQuery Connection API APIs.

    Enable the APIs

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

    Go to project selector

  6. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

  7. Enable the BigQuery and BigQuery Connection API APIs.

    Enable the APIs

Create a dataset

Create a dataset named resnet_inference_test:

SQL

  1. Go to the BigQuery page.

    Go to BigQuery

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

    CREATE SCHEMA `PROJECT_ID.resnet_inference_test`;
    

    Replace PROJECT_ID with your project ID.

bq

  1. In the Google Cloud console, activate Cloud Shell.

    Activate Cloud Shell

  2. Run the bq mk command to create the dataset:

    bq mk --dataset --location=us PROJECT_ID:resnet_inference_test
    

    Replace PROJECT_ID with your project ID.

Create a connection

Create a connection named lake-connection:

Console

  1. Go to the BigQuery page.

    Go to BigQuery

  2. Click Add data, and then click External data source.

  3. In the Connection type list, select BigLake and remote functions (Cloud Resource).

  4. In the Connection ID field, type lake-connection.

  5. Click Create connection.

  6. In the Connection info pane, copy the value from the Service account id field and save it somewhere. You need this information to grant permissions to the connection's service account.

bq

  1. In Cloud Shell, run the bq mk command to create the connection:

    bq mk --connection --location=us --connection_type=CLOUD_RESOURCE \
    lake-connection
    
  2. Run the bq show command to retrieve information about the connection:

    bq show --connection us.lake-connection
    
  3. From the properties column, copy the value of the serviceAccountId property and save it somewhere. You need this information to grant permissions to the connection's service account.

Create a Cloud Storage bucket

Create a Cloud Storage bucket to contain the model files.

Grant permissions to the connection's service account

Console

  1. Go to the IAM & Admin page.

    Go to IAM & Admin

  2. Click Grant Access.

    The Add principals dialog opens.

  3. In the New principals field, enter the service account ID that you copied earlier.

  4. In the Select a role field, select Cloud Storage, and then select Storage Object Viewer.

  5. Click Save.

gsutil

In Cloud Shell, run the gsutil iam ch command:

gsutil iam ch serviceAccount:MEMBER:objectViewer gs://BUCKET_NAME

Replace MEMBER with the service account ID that you copied earlier. Replace BUCKET_NAME with the name of the bucket you previously created.

For more information, see Add a principal to a bucket-level policy.

Create an object table

Create an object table named vision_images based on the image files in the public gs://cloud-samples-data/vision bucket:

SQL

  1. Go to the BigQuery page.

    Go to BigQuery

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

    CREATE EXTERNAL TABLE resnet_inference_test.vision_images
    WITH CONNECTION `us.lake-connection`
    OPTIONS(
      object_metadata = 'SIMPLE',
      uris = ['gs://cloud-samples-data/vision/*.jpg']
    );
    

bq

In Cloud Shell, run the bq mk command to create the connection:

bq mk --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. Download the ResNet 50 model to your local machine. This gives you a saved_model.pb file and a variables folder for the model.
  2. Upload the saved_model.pb file and the variables folder to the bucket you previously created.

Load the model into BigQuery ML

  1. Go to the BigQuery page.

    Go to BigQuery

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

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

    Replace BUCKET_NAME with the name of the bucket you previously created.

Inspect the model

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

  1. Go to the BigQuery page.

    Go to BigQuery

  2. In the Explorer pane, expand your project, expand the resnet_inference_test dataset, and then expand the Models node.

  3. Click the resnet model.

  4. In the model pane that opens, click the Schema tab.

  5. Look at the Labels section. This identifies the fields that are output by the model. In this case, the field name value is activation_49.

  6. Look at the Features section. This identifies the fields that must be input into the model. You reference them in the SELECT statement for the ML.DECODE_IMAGE function. In this case, the field name value is input_1.

Run inference

Run inference on the vision_images object table using the resnet model:

  1. Go to the BigQuery page.

    Go to BigQuery

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

    SELECT *
    FROM ML.PREDICT(
      MODEL resnet_inference_test.resnet,
      (SELECT uri, ML.DECODE_IMAGE(data) AS input_1
      FROM resnet_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

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

    Go to Manage resources

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