Training Mask RCNN on Cloud TPU (TF 2.x)

Overview

This tutorial demonstrates how to run the Mask RCNN model using Cloud TPU with the COCO dataset.

Mask RCNN is a deep neural network designed to address object detection and image segmentation, one of the more difficult computer vision challenges.

The Mask RCNN model generates bounding boxes and segmentation masks for each instance of an object in the image. The model is based on the Feature Pyramid Network (FPN) and a ResNet50 backbone.

This tutorial uses Tensorflow Keras APIs to train the model. The Keras API is a high-level TensorFlow API and is the recommended way to build and run a machine learning model on Cloud TPU. The API simplifies the model development process by hiding most of the low-level implementation, which makes it easier to switch between TPU and other platforms such as GPU or CPU.

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

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

Sign in to your Google Account.
If you don't already have one, sign up for a new account.
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 the project selector page
Make sure that billing is enabled for your Cloud project. Learn how to confirm that billing is enabled for your 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.

Open a Cloud Shell window.

Open Cloud Shell
Create an environment variable for your project's ID.
```
export PROJECT_ID=project-id
```
Configure gcloud command-line tool to use the project where you want to create the 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.

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

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 ctpu up 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.

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.
Launch a Compute Engine VM using the ctpu up command.
```
$ ctpu up --project=${PROJECT_ID} \
 --zone=europe-west4-a \
 --vm-only \
 --disk-size-gb=300 \
 --machine-type=n1-standard-8 \
 --name=mask-rcnn-tutorial \
 --tf-version=2.4.1
```
Command flag descriptions

project

Your GCP project ID

zone

The zone where you plan to create your Cloud TPU.

vm-only

Creates the VM without creating a Cloud TPU. By default the ctpu up command creates a VM and a Cloud TPU.

disk-size-gb

The size of the hard disk in GB of the VM created by the ctpu up command.

machine-type

The machine type of the Compute Engine VM to create.

name

The name of the Cloud TPU to create.

tf-version

The version of Tensorflow ctpu installs on the VM.
The configuration you specified appears. Enter y to approve or n to cancel.
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 mask-rcnn-tutorial --zone=europe-west4-a
```
As you continue these instructions, run each command that begins with (vm)$ in your VM session window.

Install extra packages

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

(vm)$ pip3 install --user -r /usr/share/models/official/requirements.txt

Prepare the data

Add an environment variable for your storage bucket. Replace bucket-name with your bucket name.
```
(vm)$ export STORAGE_BUCKET=gs://bucket-name
```
Add an environment variable for the data directory.
```
(vm)$ export DATA_DIR=${STORAGE_BUCKET}/coco
```

Add an environment variable for the model directory.

(vm)$ export MODEL_DIR=${STORAGE_BUCKET}/mask-rcnn

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. If your Cloud Shell times out, rerun the script using the nohup command which allows you to reconnect to the VM and see any output from the script.
```
(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.
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 and start training the Cloud TPU

Run the following command to create your Cloud TPU.
```
(vm)$ ctpu up --project=${PROJECT_ID} \
  --tpu-only \
  --tpu-size=v3-8 \
  --zone=europe-west4-a \
  --name=mask-rcnn-tutorial \
  --tf-version=2.4.1
```
Command flag descriptions

project

Your GCP project ID

tpu-only

Create a Cloud TPU only. By default the ctpu up command creates a VM and a Cloud TPU.

tpu-size

The type of the Cloud TPU to create.

zone

The zone where you plan to create your Cloud TPU.

name

The name of the Cloud TPU to create.

tf-version

The version of Tensorflow ctpu installs on the VM.
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.
Add an environment variable for your Cloud TPU's name.
```
(vm)$ export TPU_NAME=mask-rcnn-tutorial
```

Run the training and evaluation

The following script runs a sample training that trains for just 10 steps and takes approxiately 6 mimutes to complete on a v3-8 TPU. To train to convergence takes about 22,500 steps and approximately 6 hours on a v3-8 TPU.

Add some required environment variables:

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

Run the following command to train Mask-RCNN model:

(vm)$ python3 /usr/share/models/official/vision/detection/main.py \
   --strategy_type=tpu \
   --tpu=${TPU_NAME} \
   --model_dir=${MODEL_DIR} \
   --mode=train \
   --model=mask_rcnn \
   --params_override="{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} }"

Parameter	Description
`tpu`	Specifies the name of the Cloud TPU. This is set by specifying the environment variable (`TPU_NAME`).
`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.
`RESNET_CHECKPOINT`	Specifies a pretrained checkpoint. Mask-RCNN requires a pre-trained image classification model (like ResNet) as a backbone network. This example uses a pretrained checkpoint created with the ResNet demonstration model. You can instead train your own ResNet model if desired, and specify a checkpoint from your ResNet model directory.

Run the evaluation:

(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 \
   --model=mask_rcnn \
   --params_override="{eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN}, eval_samples: 5000 } }"

Parameter Description

tpu Specifies the name of the Cloud TPU. This is set by specifying the environment variable (TPU_NAME).

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.

From here, 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.

The sample training below runs for just 20 steps and takes approximately 10 minutes to complete on a v3-32 TPU node. To train to convergence takes about 11,250 steps and approximately 2 hours on a v3-32 TPU Pod.

Delete the Cloud TPU resource you created for training the model on a single Cloud TPU device.
```
(vm)$ ctpu delete --tpu-only --zone=europe-west4-a --name=mask-rcnn-tutorial
```

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 --project=${PROJECT_ID} \
  --tpu-only \
  --tpu-size=v3-32  \
  --zone=europe-west4-a \
  --name=mask-rcnn-tutorial \
  --tf-version=2.4.1

Update the TPU_NAME and MODEL_DIR environment variables.

(vm)$ export TPU_NAME=mask-rcnn-tutorial
(vm)$ export MODEL_DIR=${STORAGE_BUCKET}/mask-rcnn-pods

Start 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 \
   --model=mask_rcnn \
   --params_override="{train: { batch_size: 128, iterations_per_loop: 500, total_steps: 20, learning_rate: {'learning_rate_levels': [0.008, 0.0008], 'learning_rate_steps': [10000, 13000] }, 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}} }"

Parameter	Description
`tpu`	Specifies the name of the Cloud TPU. This is set by specifying the environment variable (`TPU_NAME`).
`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.
`RESNET_CHECKPOINT`	Specifies a pretrained checkpoint. Mask-RCNN requires a pre-trained image classification model (like ResNet) as a backbone network. This example uses a pretrained checkpoint created with the ResNet demonstration model. You can instead train your own ResNet model if desired, and specify a checkpoint from your ResNet model directory.

Evaluating the model

In this step, you use a single Cloud TPU node to evaluate the above trained model against the COCO dataset. The evaluation takes about 20 minutes.

Delete the Cloud TPU resource you created to train the model on a Pod.

(vm)$ ctpu delete --project=${PROJECT_ID} \
 --tpu-only \
 --zone=europe-west4-a \
 --name=mask-rcnn-tutorial

Launch a new TPU device to run evaluation.

(vm)$ ctpu up --project=${PROJECT_ID} \
  --tpu-only \
  --tpu-size=v3-8 \
  --zone=europe-west4-a \
  --tf-version=2.4.1 \
  --name=mask-rcnn-tutorial

Update the TPU_NAME environment variable.

(vm)$ export TPU_NAME=mask-rcnn-tutorial

Start the evaluation.

(vm)$ python3 /usr/share/models/official/vision/detection/main.py \
   --strategy_type=tpu \
   --tpu=mask-rcnn-tutorial \
   --model_dir=${MODEL_DIR} \
   --checkpoint_path=${MODEL_DIR} \
   --mode=eval_once \
   --model=mask_rcnn \
   --params_override="{eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN}, eval_samples: 5000 } }"

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.

Clean up the Compute Engine VM instance and Cloud TPU resources.

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.
In your VM or Cloud Shell, run ctpu delete with the --name and --zone flags you used when you set up the Cloud TPU to delete your Cloud TPU:
```
$ ctpu delete --project=${PROJECT_ID} \
  --name=mask-rcnn-tutorial \
  --zone=europe-west4-a
```

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

$ ctpu status --project=${PROJECT_ID} \
  --name=mask-rcnn-tutorial \
  --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:             --

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

In this tutorial you have trained the Mask-RCNN 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.

Learn more about ctpu, including how to install it on a local machine.
Explore the TPU tools in TensorBoard.