Training ShapeMask on Cloud TPU

This document demonstrates how to run the ShapeMask model using Cloud TPU with the COCO dataset.

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 the following billable components of Google Cloud:

• Compute Engine
• Cloud TPU
• 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.

Before you begin

Before starting this tutorial, check that your Google Cloud project is correctly set up.

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.

   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

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

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

   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

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

This walkthrough uses billable components of Google Cloud. Check the Cloud TPU pricing page to estimate your costs. Be sure to clean up resources you create when you've finished with them to avoid unnecessary charges.

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

Set up your resources

This section provides information on setting up Cloud Storage, VM, and Cloud TPU resources for this tutorial.

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 Google Cloud CLI 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. 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 instance.

   Note: This tutorial uses TensorFlow 1.15.5 which is based on the Debian 10 image and defaults to Python 3.6. Earlier versions of TensorFlow 1.15.x were based on Debian 9 and default to Python 3.5.

   $ gcloud compute tpus execution-groups create --vm-only \
    --name=shapemask-tutorial \
    --zone=us-central1-a \
    --disk-size=300 \
    --machine-type=n1-standard-16 \
    --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 ctpu installs on the VM.

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

   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 shapemask-tutorial --zone=us-central1-a
   

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

8. Create an environment variable to store your Cloud Storage bucket location.

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

9. Create an environment variable for the data directory.

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

10. Clone the tpu repository.

    (vm)$ git clone -b shapemask https://github.com/tensorflow/tpu/
    

11. Install the packages needed to pre-process the data.

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

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.

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

   (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}
   

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

Set up and start the Cloud TPU

1. Launch a Cloud TPU resource using the gcloud command.

   (vm)$ gcloud compute tpus execution-groups create \
    --tpu-only \
    --accelerator-type=v3-8  \
    --name=shapemask-tutorial \
    --zone=us-central1-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.

   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.

3. Add an environment variable for your Cloud TPU's name.

   (vm)$ export TPU_NAME=shapemask-tutorial
   

Run the training and evaluation script

1. Create the following environment variables:

   (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/shapemask_exp
   (vm)$ export RESNET_CHECKPOINT=gs://cloud-tpu-checkpoints/shapemask/retinanet/resnet101-checkpoint-2018-02-24
   (vm)$ export TRAIN_FILE_PATTERN=${DATA_DIR}/train-*
   (vm)$ export EVAL_FILE_PATTERN=${DATA_DIR}/val-*
   (vm)$ export VAL_JSON_FILE=${DATA_DIR}/instances_val2017.json
   (vm)$ export SHAPE_PRIOR_PATH=gs://cloud-tpu-checkpoints/shapemask/kmeans_class_priors_91x20x32x32.npy
   (vm)$ export PYTHONPATH=${PYTHONPATH}:$HOME/tpu/models
   

2. Run the following script to perform training.

   Note: The script takes approximately 25 minutes to run.

   (vm)$ python3 ~/tpu/models/official/detection/main.py \
   --model=shapemask \
   --use_tpu=True \
   --tpu=${TPU_NAME} \
   --num_cores=8 \
   --model_dir=${MODEL_DIR} \
   --mode="train" \
   --eval_after_training=False \
   --params_override="{train: {iterations_per_loop: 1000, train_batch_size: 64, total_steps: 1000, learning_rate: {total_steps: 1000, warmup_learning_rate: 0.0067, warmup_steps: 500, init_learning_rate: 0.08, learning_rate_levels: [0.008, 0.0008], learning_rate_steps: [30000, 40000]}, checkpoint: { path: ${RESNET_CHECKPOINT}, prefix: resnet101/ }, train_file_pattern: ${TRAIN_FILE_PATTERN} }, resnet: {resnet_depth: 101}, eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN}, eval_samples: 5000 }, shapemask_head: {use_category_for_mask: true, shape_prior_path: ${SHAPE_PRIOR_PATH}}, shapemask_parser: {output_size: [1024, 1024]}, }"
   

   Command flag descriptions

   model

   The model to train.

   use_tpu

   Set to true to train on a Cloud TPU.

   tpu_name

   The name of the Cloud TPU to use for training.

   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.

   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.

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

Scaling your model with Cloud TPU Pods

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

• v2-32
• v3-32

When working with Cloud TPU Pods, you first train the model using a Pod, then use a single Cloud TPU device to evaluate the model.

Training with Cloud TPU Pods

If you have already deleted your Compute Engine instance, create a new one following the steps in Set up your resources.

1. Delete the Cloud TPU resource you created for training the model on a single device.

   (vm)$ gcloud compute tpus execution-groups delete shapemask-tutorial \
     --zone=us-central1-a \
     --tpu-only

2. Go to your Cloud Storage bucket and delete the checkpoint file.

3. 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=shapemask-tutorial \
     --accelerator-type=v2-32  \
     --zone=us-central1-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.

4. Run the following script to train the model on a Pod.

   With the given command line the training script takes approximately 45 minutes to run. To run to convergence, set total_steps to 22000.

   (vm)$ python3 ~/tpu/models/official/detection/main.py \
   --model shapemask \
   --use_tpu=True \
   --tpu=${TPU_NAME} \
   --num_cores=32 \
   --model_dir=${MODEL_DIR} \
   --mode="train" \
   --eval_after_training=False \
   --params_override="{train: {iterations_per_loop: 1000, train_batch_size: 256, total_steps: 1000, learning_rate: {total_steps: 1000, warmup_learning_rate: 0.0067, warmup_steps: 500, init_learning_rate: 0.08, learning_rate_levels: [0.008, 0.0008], learning_rate_steps: [15000, 20000]}, checkpoint: { path: ${RESNET_CHECKPOINT}, prefix: resnet101/ }, train_file_pattern: ${TRAIN_FILE_PATTERN} }, resnet: {resnet_depth: 101}, eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN}, eval_samples: 5000 }, shapemask_head: {use_category_for_mask: true, shape_prior_path: ${SHAPE_PRIOR_PATH}}, shapemask_parser: {output_size: [1024, 1024]}, }"
   

   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

   One of train, eval, or train_and_eval.

   model

   The model to train.

   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.

5. The model must be evaluated on a single Cloud TPU device. Delete the Cloud TPU Pod device.

   $ gcloud compute tpus execution-groups delete shapemask-tutorial \
     --zone=us-central1-a
   

6. Create a single Cloud TPU resource.

   (vm)$ gcloud compute tpus execution-groups create \
    --tpu-only \
    --accelerator-type=v3-8  \
    --name=shapemask-tutorial \
    --zone=us-central1-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.

   tpu-size

   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.

7. Run the script to perform evaluation.

   Note: The script takes approximately 25 minutes to run.

   (vm)$ python3 ~/tpu/models/official/detection/main.py \
   --model shapemask \
   --use_tpu=True \
   --tpu=${TPU_NAME} \
   --num_cores=8 \
   --model_dir=${MODEL_DIR} \
   --mode="eval" \
   --eval_after_training=False \
   --params_override="{train: {iterations_per_loop: 1000, train_batch_size: 256, total_steps: 1000, learning_rate: {total_steps: 1000, warmup_learning_rate: 0.0067, warmup_steps: 500, init_learning_rate: 0.08, learning_rate_levels: [0.008, 0.0008], learning_rate_steps: [15000,20000]}, checkpoint: { path: ${RESNET_CHECKPOINT}, prefix: resnet101/ }, train_file_pattern: ${TRAIN_FILE_PATTERN} }, resnet: {resnet_depth: 101}, eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN}, eval_samples: 5000 }, shapemask_head: {use_category_for_mask: true, shape_prior_path: ${SHAPE_PRIOR_PATH}}, shapemask_parser: {output_size: [1024, 1024]}, }"

   Command flag descriptions

   model

   The model to train.

   use_tpu

   Set to true to train on a 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.

   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 evaluation script output looks like this:

   Eval result: {
    'AP75': 0.116238795,
    'AP': 0.121657856,
    'mask_ARmax100': 0.29928473,
    'APl': 0.17029367,
    'mask_ARmax1': 0.17677748,
    'ARs': 0.14137766,
    'mask_AP': 0.12017078,
    'ARmax10': 0.29230836,
    'mask_AP50': 0.20920053,
    'ARm': 0.34366703,
    'AP50': 0.22949784,
    'mask_ARl': 0.41743836,
    'mask_ARs': 0.12669834,
    'APs': 0.046222884,
    'mask_APs': 0.041104294,
    'mask_APl': 0.17535995,
    'mask_ARm': 0.34216145,
    'mask_ARmax10': 0.28690106,
    'APm': 0.14354791,
    'ARmax100': 0.3058479,
    'ARmax1': 0.17576972,
    'ARl': 0.41305476,
    'mask_APm': 0.1422335,
    'mask_AP75': 0.12010279
   }
   

Clean 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.

   (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 shapemask-tutorial \
     --zone=us-central1-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=us-central1-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.

   Caution: All training data will be lost when you delete your bucket, so only do this step when you are finished running the tutorial.

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

What's next

The TensorFlow Cloud TPU tutorials generally train the model using a sample dataset. The results of this training are 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. TensorFlow models trained on Cloud TPUs generally 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 try 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 ShapeMask 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.