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 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 name.

   export PROJECT_NAME=project-name
   

3. Configure 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:

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

   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 instance.

   $ ctpu up --zone=europe-west4-a \
    --vm-only \
    --disk-size-gb=300 \
    --machine-type=n1-standard-16 \
    --tf-version=1.15 \
    --name=shapemask-tutorial
   

   Note: If you have more than one project, you must specify a project name using --project. If --name is not specified, the TPU and VM names default to your username.

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.

   Note: The first time you run ctpu up on a project it may take several minutes to perform startup tasks such as SSH key propagation and API turnup. Occasionally, during the SSH key propagation, an error message (ERROR: (gcloud.compute.ssh) [/usr/bin/ssh] exited with return code [255]) is returned. If that happens, run the following command to resolve the problem.

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

   As you continue these instructions, run each command that begins with (vm)$ in your VM session window.

Prepare the COCO dataset

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

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

2. Create an environment variable for the data directory.

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

3. Clone the tpu repository.

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

4. 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"
   

5. 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.

6. 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.

Training the models

1. Launch a Cloud TPU resource.

   Run the following command to create your Cloud TPU.

   (vm)$ ctpu up --tpu-only --tf-version=1.15 --name=shapemask-tutorial
   

2. Set up the following environment variables:

   (vm)$ export TPU_NAME=shapemask-tutorial
   (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
   

3. Run the training script:

   (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=True \
   --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]}, }"
   

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=shapemask-tutorial
   

   Important: If you set the TPU resources name when you ran ctpu up, you must specify that name with the --name flag when you run ctpu delete in order to shut down your TPU resources.

3. Run the following command to verify the Compute Engine VM and Cloud TPU have been shut down:

   $ ctpu status --zone=europe-west4-a --name=shapemask-tutorial
   

   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:             --
   

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

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 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 ShapeMask 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.