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 name.

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

    gcloud config set project ${PROJECT_NAME}
    
  4. Create a Cloud Storage bucket using the following command:

    gsutil mb -p ${PROJECT_NAME} -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.

  5. Launch a Compute Engine VM using the ctpu up command.

    ctpu up --zone=europe-west4-a \
     --vm-only \
     --disk-size-gb=300 \
     --machine-type=n1-standard-8 \
     --tf-version=1.15.2 \
     --name=retinanet-tutorial
    
  6. The configuration you specified appears. Enter y to approve or n to cancel.

  7. When the ctpu up 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 VM session window.

    When the ctpu 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)$ ctpu up --tpu-only \
      --tf-version=1.15.2 \
      --name=retinanet-tutorial
    
    Parameter Description
    --tpu-only Create a Cloud TPU only, do not create a VM.
    --tf-version The version of Tensorflow `ctpu` installs on the VM.
    --name The name of the Cloud TPU.
  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-artifacts/resnet/resnet-nhwc-2018-10-14/model.ckpt-112602
    (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 } }"
    
    Parameter Description
    --use_tpu Train the model on a single Cloud TPU.
    --tpu Specifies the name of the Cloud TPU. This is set by specifying the environment variable (TPU_NAME).
    --num_cores Specfies the number of cores on the Cloud TPU.
    --model_dir Specifies the directory where checkpoints and summaries are stored during model training. If the folder is missing, the program creates one. When using a Cloud TPU, the model_dir must be a Cloud Storage path (`gs://...`). You can reuse an existing folder to load current checkpoint data and to store additional checkpoints as long as the previous checkpoints were created using TPU of the same size and TensorFlow version.
    --mode Specifies the mode in which to run the model. Valid values are: 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} } }"
    
    Parameter Description
    --use_tpu Evaluate the model on a single Cloud TPU.
    --tpu Specifies the name of the Cloud TPU. This is set by specifying the environment variable (TPU_NAME).
    --num_cores Specifies the number of cores on the Cloud TPU.
    model_dir Specifies the directory where checkpoints and summaries are stored during model training. If the folder is missing, the program creates one. When using a Cloud TPU, the model_dir must be a Cloud Storage path (`gs://...`). You can reuse an existing folder to load current checkpoint data and to store additional checkpoints as long as the previous checkpoints were created using TPU of the same size and TensorFlow version.
    --mode Specifies the mode in which to run the model. Valid values are: train and eval.

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)$ ctpu delete --tpu-only --zone=europe-west4-a --name=retinanet-tutorial
  2. Run the ctpu up command, using the tpu-size parameter to specify the Pod slice you want to use. For example, the following command uses a v3-32 Pod slice.

    (vm)$ ctpu up --tpu-only \
      --tpu-size=v3-32 \
      --zone=europe-west4-a \
      --tf-version=1.15.2 \
      --name=retinanet-tutorial-pod
     
  3. 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.

  4. Set up the following environment variables:

    (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/retinanet-model-pod
    (vm)$ export TPU_NAME=retinanet-tutorial-pod
    
  5. 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 }} }"
    
    Parameter Description
    --use_tpu Train the model on a Cloud TPU pod.
    --tpu Specifies the name of the Cloud TPU. This is set by specifying the environment variable (TPU_NAME).
    --num_cores Specfies the number of cores on the Cloud TPU.
    --model_dir Specifies the directory where checkpoints and summaries are stored during model training. If the folder is missing, the program creates one. When using a Cloud TPU, the model_dir must be a Cloud Storage path (`gs://...`). You can reuse an existing folder to load current checkpoint data and to store additional checkpoints as long as the previous checkpoints were created using TPU of the same size and TensorFlow version.
    --mode Specifies the mode in which to run the model.
    --eval_after_training Set to True to evaluate the model after training,
    --params_override Override model parameters with specified values.

Cleaning up

To avoid incurring charges to your Google Cloud Platform account for the resources used in this tutorial:

  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, run ctpu delete with the --zone flag you used when you set up the Cloud TPU to delete your Compute Engine VM and your Cloud TPU:

    $ ctpu delete --zone=europe-west4-a --name=retinanet-tutorial
    
  3. Run the following command to verify the Compute Engine VM and Cloud TPU have been shut down:

    $ ctpu status --zone=europe-west4-a
    

    The deletion might take several minutes. A response like the one below indicates there are no more allocated instances:

    2018/04/28 16:16:23 WARNING: Setting zone to "europe-west4-a"
    No instances currently exist.
     Compute Engine VM:     --
     Cloud TPU:             --
    
  4. Run gsutil as shown, replacing bucket-name with the name of the Cloud Storage bucket you created for this tutorial:

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

What's next

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 more 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.