Training RetinaNet on Cloud TPU

This document describes an implementation of the RetinaNet object detection model. The code is available on GitHub.

The instructions below assume you are already familiar with running a model on Cloud TPU. If you are new to Cloud TPU, you can refer to the Quickstart for a basic introduction.

If you plan to train on a TPU Pod slice, review Training on TPU Pods to understand parameter changes required for Pod slices.

Objectives

  • Create a Cloud Storage bucket to hold your dataset and model output
  • Prepare the COCO dataset
  • Set up a Compute Engine VM and Cloud TPU node for training and evaluation
  • Run training and evaluation on a single Cloud TPU or a Cloud TPU Pod

Costs

This tutorial uses billable components of Google Cloud, including:

  • Compute Engine
  • Cloud TPU
  • Cloud Storage

Use the pricing calculator to generate a cost estimate based on your projected usage. New Google Cloud users might be eligible for a free trial.

Before you begin

This section provides information on setting up Cloud Storage bucket and a Compute Engine VM.

  1. Open a Cloud Shell window.

    Open Cloud Shell

  2. Create a variable for your project's ID.

    export PROJECT_ID=project-id
    
  3. Configure the gcloud command-line tool to use the project where you want to create Cloud TPU.

    gcloud config set project ${PROJECT_ID}
    

    The first time you run this command in a new Cloud Shell VM, an Authorize Cloud Shell page is displayed. Click Authorize at the bottom of the page to allow gcloud to make GCP API calls with your credentials.

  4. Create a Service Account for the Cloud TPU project.

    gcloud beta services identity create --service tpu.googleapis.com --project $PROJECT_ID
    

    The command returns a Cloud TPU Service Account with following format:

    service-PROJECT_NUMBER@cloud-tpu.iam.gserviceaccount.com
    

  5. Create a Cloud Storage bucket using the following command:

    gsutil mb -p ${PROJECT_ID} -c standard -l europe-west4 -b on gs://bucket-name
    

    This Cloud Storage bucket stores the data you use to train your model and the training results. The gcloud compute tpus execution-groups tool used in this tutorial sets up default permissions for the Cloud TPU Service Account. If you want finer-grain permissions, review the access level permissions.

    The bucket location must be in the same region as your virtual machine (VM) and your TPU node. VMs and TPU nodes are located in specific zones, which are subdivisions within a region.

  6. Launch a Compute Engine VM and Cloud TPU using the gcloud command.

    $ gcloud compute tpus execution-groups create \
     --vm-only \
     --name=retinanet-tutorial \
     --zone=europe-west4-a \
     --disk-size=300 \
     --machine-type=n1-standard-8 \
     --tf-version=1.15.5
    

    Command flag descriptions

    vm-only
    Create a VM only. By default the gcloud compute tpus execution-groups command creates a VM and a Cloud TPU.
    name
    The name of the Cloud TPU to create.
    zone
    The zone where you plan to create your Cloud TPU.
    disk-size
    The size of the hard disk in GB of the VM created by the gcloud compute tpus execution-groups command.
    machine-type
    The machine type of the Compute Engine VM to create.
    tf-version
    The version of Tensorflow gcloud compute tpus execution-groups installs on the VM.

    For more information on the gcloud command, see the gcloud Reference.

  7. The configuration you specified appears. Enter y to approve or n to cancel.

    When the gcloud compute tpus execution-groups command has finished executing, verify that your shell prompt has changed from username@projectname to username@vm-name. This change shows that you are now logged into your Compute Engine VM.

    gcloud compute ssh retinanet-tutorial --zone=europe-west4-a
    

    As you continue these instructions, run each command that begins with (vm)$ in your Compute Engine instance.

    When the gcloud command launches a Compute Engine virtual machine (VM), it automatically places the RetinaNet model files from TensorFlow branch in the /usr/share/tpu/models/official/detection/ directory.

  8. Use the export command to set these environment variables.

    (vm)$ export STORAGE_BUCKET=gs://bucket-name
    
    (vm)$ export TPU_NAME=retinanet-tutorial
    (vm)$ export DATA_DIR=${STORAGE_BUCKET}/coco
    
  9. Install extra packages

    The RetinaNet training application requires several extra packages. Install them now:

    (vm)$ sudo apt-get install -y python3-tk
    
    (vm)$ pip3 install --user Cython matplotlib opencv-python-headless pyyaml Pillow
    
    (vm)$ pip3 install --user 'git+https://github.com/cocodataset/cocoapi#egg=pycocotools&subdirectory=PythonAPI'
    
    (vm)$ pip3 install --user -U gast==0.2.2
    

Prepare the COCO dataset

  1. Run the download_and_preprocess_coco.sh script to convert the COCO dataset into a set of TFRecords (*.tfrecord) that the training application expects.

    (vm)$ sudo bash /usr/share/tpu/tools/datasets/download_and_preprocess_coco.sh ./data/dir/coco
    

    This installs the required libraries and then runs the preprocessing script. It outputs a number of *.tfrecord files in your local data directory.

  2. After you convert the data into TFRecords, copy them from local storage to your Cloud Storage bucket using the gsutil command. You must also copy the annotation files. These files help validate the model's performance:

    (vm)$ gsutil -m cp ./data/dir/coco/*.tfrecord ${DATA_DIR}
    
    (vm)$ gsutil cp ./data/dir/coco/raw-data/annotations/*.json ${DATA_DIR}
    

Set up the training environment

  1. Run the following command to create your Cloud TPU.

    (vm)$ gcloud compute tpus execution-groups create \
     --tpu-only \
     --name=retinanet-tutorial \
     --zone=europe-west4-a \
     --tf-version=1.15.5
    

    Command flag descriptions

    tpu-only
    Creates the Cloud TPU without creating a VM. By default the gcloud compute tpus execution-groups command creates a VM and a Cloud TPU.
    name
    The name of the Cloud TPU to create.
    zone
    The zone where you plan to create your Cloud TPU.
    tf-version
    The version of Tensorflow gcloud compute tpus execution-groups installs on the VM.
  2. The configuration you specified appears. Enter y to approve or n to cancel.

    You will see a message: Operation success; not ssh-ing to Compute Engine VM due to --tpu-only flag. Since you previously completed SSH key propagation, you can ignore this message.

  3. Update the keepalive values for your VM connection.

    This tutorial requires a long-lived connection to the Compute Engine instance. To ensure you aren't disconnected from the instance, run the following command:

    (vm)$ sudo /sbin/sysctl \
      -w net.ipv4.tcp_keepalive_time=120 \
      net.ipv4.tcp_keepalive_intvl=120 \
      net.ipv4.tcp_keepalive_probes=5
    
  4. You are now ready to run the model on the preprocessed COCO data. First, add the top-level /models folder to the Python path with the command:

    (vm)$ export PYTHONPATH=${PYTHONPATH}:/usr/share/tpu/models
    

Training and evaluation require TensorFlow 1.13 or a later version.

Single Cloud TPU device training

  1. Set up the following environment variables:

    (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/retinanet-model-train
    (vm)$ export RESNET_CHECKPOINT=gs://cloud-tpu-checkpoints/retinanet/resnet50-checkpoint-2018-02-07
    (vm)$ export TRAIN_FILE_PATTERN=${DATA_DIR}/train-*
    (vm)$ export EVAL_FILE_PATTERN=${DATA_DIR}/val-*
    (vm)$ export VAL_JSON_FILE=${DATA_DIR}/instances_val2017.json
    
  2. Run the training script:

    (vm)$ python3 /usr/share/tpu/models/official/detection/main.py \
    --use_tpu=True \
    --tpu=${TPU_NAME} \
    --num_cores=8 \
    --model_dir=${MODEL_DIR} \
    --mode="train" \
    --eval_after_training=True \
    --params_override="{ type: retinanet, train: { checkpoint: { path: ${RESNET_CHECKPOINT}, prefix: resnet50/ }, train_file_pattern: ${TRAIN_FILE_PATTERN} }, eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN}, eval_samples: 5000 } }"
    

    Command flag descriptions

    use_tpu
    Train the model on a single Cloud TPU.
    tpu
    The name of the Cloud TPU. This is set using the TPU_NAME environment variable.
    num_cores
    The number of Cloud TPU cores to use when training.
    model_dir
    The Cloud Storage bucket where checkpoints and summaries are stored during training. You can use an existing folder to load previously generated checkpoints created on a TPU of the same size and TensorFlow version.
    mode
    One of train, eval, or train_and_eval.

Single Cloud TPU device evaluation

The following procedure uses the COCO evaluation data. It takes about 10 minutes to run through the evaluation steps.

  1. Set up the following environment variables:

    (vm)$ export EVAL_SAMPLES=5000
    
  2. Run the evaluation script:

      (vm)$ python3 /usr/share/tpu/models/official/detection/main.py \
        --use_tpu=True \
        --tpu=${TPU_NAME} \
        --num_cores=8 \
        --model_dir=${MODEL_DIR} \
        --mode="eval" \
        --params_override="{ type: retinanet, eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN}, eval_samples: ${EVAL_SAMPLES} } }"
    

    Command flag descriptions

    use_tpu
    Set to true to train on a Cloud TPU.
    tpu
    The name of the Cloud TPU to run training or evaluation.
    num_cores
    The number of Cloud TPU cores to use when training.
    model_dir
    The Cloud Storage bucket where checkpoints and summaries are stored during training. You can use an existing folder to load previously generated checkpoints created on a TPU of the same size and TensorFlow version.
    mode
    One of train, eval, or train_and_eval.
    params_override
    A JSON string that overrides default script parameters. For more information on script parameters, see /usr/share/models/official/vision/detection/main.py.

    The evaluation script displays output like the following:

    Eval result: {
      'AP': 0.3371653,
      'ARl': 0.6749888,
      'ARmax100': 0.4848119,
      'APl': 0.48918217,
      'ARmax10': 0.45885247,
      'APs': 0.14764188,
      'ARm': 0.545395,
      'ARs': 0.24094534,
      'AP75': 0.3606217,
      'AP50': 0.51819533,
      'APm': 0.38223606,
      'ARmax1': 0.29476196
    }

    At this point, you can either conclude this tutorial and clean up your GCP resources, or you can further explore running the model on Cloud TPU Pods.

Scaling your model with Cloud TPU Pods

You can get results faster by scaling your model with Cloud TPU Pods. The fully supported RetinaNet model can work with the following Pod slices:

  • v2-32
  • v3-32
  1. Delete the Cloud TPU resource you created for training the model on a single device.

    (vm)$ gcloud compute tpus execution-groups delete retinanet-tutorial \
      --zone=europe-west4-a \
      --tpu-only
  2. Run the gcloud compute tpus execution-groups command, using the accelerator-type parameter to specify the Pod slice you want to use. For example, the following command uses a v3-32 Pod slice.

    (vm)$ gcloud compute tpus execution-groups create --name=retinanet-tutorial \
      --accelerator-type=v3-32  \
      --zone=europe-west4-a \
      --tf-version=1.15.5 \
      --tpu-only
    

    Command flag descriptions

    name
    The name of the Cloud TPU to create.
    accelerator-type
    The type of the Cloud TPU to create.
    zone
    The zone where you plan to create your Cloud TPU.
    tf-version
    The version of Tensorflow gcloud installs on the VM.
    tpu-only
    Create a Cloud TPU only. By default the gcloud command creates a VM and a Cloud TPU.
  3. Set up the following environment variables:

    (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/retinanet-model-pod
    (vm)$ export TPU_NAME=retinanet-tutorial
    
  4. Run the Pod training script on a v3-32 TPU node:

    (vm)$ python3 /usr/share/tpu/models/official/detection/main.py \
    --use_tpu=True \
    --tpu=${TPU_NAME} \
    --num_cores=32 \
    --model_dir=${MODEL_DIR} \
    --mode="train" \
    --eval_after_training=False \
    --params_override="{ type: retinanet, train: { train_batch_size: 1024, total_steps: 2109, learning_rate: { warmup_steps: 820, init_learning_rate: 0.64, learning_rate_levels: [0.064, 0.0064], learning_rate_steps: [1641, 1992] }, checkpoint: { path: ${RESNET_CHECKPOINT}, prefix: resnet50/ }, train_file_pattern: ${TRAIN_FILE_PATTERN} }, resnet: { batch_norm: { batch_norm_momentum: 0.9 }}, fpn: { batch_norm: { batch_norm_momentum: 0.9 }}, retinanet_head: { batch_norm: { batch_norm_momentum: 0.9 }} }"
    

    Command flag descriptions

    use_tpu
    Set to true to train on a Cloud TPU.
    tpu
    The name of the Cloud TPU. If not specified when setting up the Compute Engine VM and Cloud TPU, defaults to your username.
    num_cores
    The number of Cloud TPU cores to use for training.
    model_dir
    The Cloud Storage bucket where checkpoints and summaries are stored during training. You can use an existing folder to load previously generated checkpoints created on a TPU of the same size and TensorFlow version.
    mode
    One of train, eval, train_and_eval, or predict.
    eval_after_training
    Set to true to evaluate the model after training,
    params_override
    A JSON string that overrides default script parameters. For more information on script parameters, see /usr/share/models/official/vision/detection/main.py.

The training script output should have text like the following:

INFO:tensorflow:Loss for final step: 0.96952075.
I1125 21:47:10.729412 140023184553728 estimator.py:371] Loss for final step: 0.96952075.
INFO:tensorflow:training_loop marked as finished
I1125 21:47:10.730288 140023184553728 error_handling.py:101] training_loop marked as finished

Cleaning up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

  1. Disconnect from the Compute Engine instance, if you have not already done so:

    (vm)$ exit
    

    Your prompt should now be username@projectname, showing you are in the Cloud Shell.

  2. In your Cloud Shell, use the following command to delete your Compute Engine VM and Cloud TPU:

    $ gcloud compute tpus execution-groups delete retinanet-tutorial \
      --zone=europe-west4-a
    
  3. Verify the resources have been deleted by running gcloud compute tpus execution-groups list. The deletion might take several minutes. A response like the one below indicates your instances have been successfully deleted.

    $ gcloud compute tpus execution-groups list \
     --zone=europe-west4-a
    

    You should see an empty list of TPUs like the following:

       NAME             STATUS
    
  4. Delete your Cloud Storage bucket using gsutil as shown below. Replace bucket-name with the name of your Cloud Storage bucket.

    $ gsutil rm -r gs://bucket-name
    

What's next

In this tutorial you have trained the RetinaNet model using a sample dataset. The results of this training are (in most cases) not usable for inference. To use a model for inference you can train the data on a publicly available dataset or your own data set. Models trained on Cloud TPUs require datasets to be in TFRecord format.

You can use the dataset conversion tool sample to convert an image classification dataset into TFRecord format. If you are not using an image classification model, you will have to convert your dataset to TFRecord format yourself. For more information, see TFRecord and tf.Example

Hyperparameter tuning

To improve the model's performance with your dataset, you can tune the model's hyperparameters. You can find information about hyperparameters common to all TPU supported models on GitHub. Information about model-specific hyperparameters can be found in the source code for each model. For more information on hyperparameter tuning, see Overview of hyperparameter tuning, Using the Hyperparameter tuning service and Tune hyperparameters.

Inference

Once you have trained your model you can use it for inference (also called prediction). AI Platform is a cloud-based solution for developing, training, and deploying machine learning models. Once a model is deployed, you can use the AI Platform Prediction service.

Train with different image sizes

You can explore using a larger neural network (for example, ResNet-101 instead of ResNet-50). A larger input image and a more powerful neural network will yield a slower but more precise model.

Use a different basis

Alternatively, you can explore pre-training a ResNet model on your own dataset and using it as a basis for your RetinaNet model. With some work, you can also swap in an alternative neural network in place of ResNet. Finally, if you are interested in implementing your own object detection models, this network may be a good basis for further experimentation.