Training RetinaNet on Cloud TPU (TF 2.x)

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

Note: Training and evaluation require TensorFlow 2.1 or a later version.

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.

Warning: This tutorial uses a third-party dataset. Google provides no representation, warranty, or other guarantees about the validity, or any other aspects of this dataset.

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.

Important: Set up your Compute Engine VM, your Cloud TPU node and your Cloud Storage bucket in the same region/zone to reduce network latency and network costs.

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 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:

   [e-mail]
   

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

   Note: In the following command, replace bucket-name with the name you want to assign to your bucket.

   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 you set up in the previous step. If you want finer-grain permissions, review the access level permissions.

6. Launch a Compute Engine VM 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=2.4.1
   

   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 ctpu installs on the VM.

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

7. When prompted, press y to create your Cloud TPU resources.

   Note: The first time you run gcloud compute tpus execution-groups on a project it takes about 5 minutes to perform startup tasks such as SSH key propagation and API turnup.

   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.

   Note: If you are not connected to the Compute Engine instance, you can connect by running the following command:

   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.

8. 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)$ sudo pip3 install --user -r /usr/share/models/official/requirements.txt
   

Prepare the COCO dataset

The COCO dataset will be stored on your Cloud Storage, so set a storage bucket variable specifying the name of the bucket you created:

(vm)$ export STORAGE_BUCKET=gs://bucket-name

(vm)$ export DATA_DIR=${STORAGE_BUCKET}/coco

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. The COCO download and conversion script takes approximately 1 hour to complete.

Copy the data to your Cloud Storage bucket

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. Launch a Cloud TPU resource using the gcloud command.

   (vm)$ gcloud compute tpus execution-groups create \
    --tpu-only \
    --accelerator-type=v3-8  \
    --name=retinanet-tutorial \
    --zone=europe-west4-a \
    --tf-version=2.4.1
   

   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.

   accelerator-type

   The type of the Cloud TPU to create.

   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 ctpu 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. Note: If you are not connected to the Compute Engine instance, you can connect by running the following command:

   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.

3. Create an environment variable for the TPU name.

   (vm)$ export TPU_NAME=retinanet-tutorial
   

4. Add the top-level /models folder to the Python path with the command:

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

   Note: If you want to monitor the model's output and performance, follow the guide to setting up TensorBoard.

Single Cloud TPU device training

The following training scripts were run on a Cloud TPU v3-8. It will take more time, but you can also run them on a Cloud TPU v2-8.

This sample script below trains for only 10 steps and takes less than 5 minutes to run on a v3-8 TPU Node. To train to convergence takes about 22,500 steps and approximately 1 1/2 hours on a Cloud TPU v3-8 TPU.

1. Set up the following environment variables:

   (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/retinanet-train
   (vm)$ 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/models/official/vision/detection/main.py \
        --strategy_type=tpu \
        --tpu=${TPU_NAME} \
        --model_dir=${MODEL_DIR} \
        --mode="train" \
        --params_override="{ type: retinanet, train: { total_steps: 10, 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

   strategy_type

   To train the RetinaNet model on a TPU, you must set the distribution_strategy to tpu.

   tpu

   The name of the Cloud TPU. This is set using the TPU_NAME environment variable.

   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

   Set this to train to train the model or eval to evaluate the model.

   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.

While the model is training, you can see the progress by viewing the log output. Output similar to the following shows the training is progressing normally:

Train Step: 10/10  / loss = {
  'total_loss': 2.4581615924835205,
  'cls_loss': 1.4098565578460693,
  'box_loss': 0.012001709081232548,
  'model_loss': 2.0099422931671143,
  'l2_regularization_loss': 0.44821977615356445,
  'learning_rate': 0.008165999
}
/ training metric = {
  'total_loss': 2.4581615924835205,
  'cls_loss': 1.4098565578460693,
  'box_loss': 0.012001709081232548,
  'model_loss': 2.0099422931671143,
  'l2_regularization_loss': 0.44821977615356445,
 'learning_rate': 0.008165999
}

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/models/official/vision/detection/main.py \
         --strategy_type=tpu \
         --tpu=${TPU_NAME} \
         --model_dir=${MODEL_DIR} \
         --checkpoint_path=${MODEL_DIR} \
         --mode=eval_once \
         --params_override="{ type: retinanet, eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN}, eval_samples: ${EVAL_SAMPLES} } }"
   

   Command flag descriptions

   strategy_type

   The distribution strategy to use. Either tpu or multi_worker_gpu.

   tpu

   The name of the Cloud TPU. This is set using the TPU_NAME environment variable.

   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.

   At the end of the evaluation, you will see messages similar to the following on the console:

   Accumulating evaluation results...
   DONE (t=7.66s).
    Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
    Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000
    Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
    Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
    Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
    Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
    Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000
    Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000
    Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
    Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
    Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
    Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
   

   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 v2-32 Pod slice:

Caution: Scaling to larger Pod slices are experimental with this model.

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=2.4.1 \
     --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 the Cloud TPU name variable. This will either be a name you set with the --name parameter or the default, your username:

   (vm)$ export TPU_NAME=retinanet-tutorial
   

4. Set up the following environment variable:

   (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/retinanet-pod
   

5. Run the Pod training script on a v2-32 TPU node

   The following sample training script was run on a Cloud TPU v2-32 Pod. It trains for only 10 steps and takes less than 5 minutes to run. To train to convergence requires 2109 steps and takes approximately 50 minutes on a v2-32 TPU Pod.

   (vm)$  python3 /usr/share/models/official/vision/detection/main.py \
       --strategy_type=tpu \
       --tpu=${TPU_NAME} \
       --model_dir=${MODEL_DIR} \
       --mode="train" \
       --params_override="{ type: retinanet, train: { total_steps: 10, batch_size: 256, 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

   strategy_type

   The distribution strategy to use. Either tpu or multi_worker_gpu.

   tpu

   Specifies the name of the Cloud TPU. This is set using the TPU_NAME environment variable.

   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.

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 VM:

   (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
   

Note: For free storage limits and other pricing information, see the Cloud Storage pricing guide.

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 backbone network (for example, ResNet-101 instead of ResNet-50). A larger input image and a more powerful backbone 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 more work, you can also swap in an alternative backbone 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.