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.

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 Cloud Console, on the project selector page, select or create a 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 Google Cloud project. Learn how to confirm 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 a 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 Cloud TPU.
```
gcloud config set project ${PROJECT_ID}
```

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 instance.
```
$ ctpu up --zone=us-central1-a \
 --vm-only \
 --disk-size-gb=300 \
 --machine-type=n1-standard-16 \
 --tf-version=2.3 \
 --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.
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 shapemask-tutorial --zone=us-central1-a
```
As you continue these instructions, run each command that begins with (vm)$ in your VM session window.
Create an environment variable to store your Cloud Storage bucket location.
```
(vm)$ export STORAGE_BUCKET=gs://bucket-name
```
Create an environment variable for the data directory.
```
(vm)$ export DATA_DIR=${STORAGE_BUCKET}/coco
```

Clone the tpu repository.

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

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

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.
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

Launch a Cloud TPU resource.

Run the following command to create your Cloud TPU.

(vm)$ ctpu up --tpu-only \
 --tf-version=2.3 \
 --tpu-size=v3-8 \
 --name=shapemask-tutorial

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=shapemask-tutorial
```

Set up and start training the Cloud TPU

Run the following command to create your Cloud TPU.

(vm)$ ctpu up --tpu-only \
  --tpu-size=v3-8 \
  --zone=us-central1-a \
  --name=shapemask-tutorial \
  --tf-version=2.3

Parameter	Description
`tpu-size`	Specifies the size of the Cloud TPU. This tutorial uses a v3-8 TPU size for the single device training and evaluation.
`zone`	The zone where you plan to create your Cloud TPU. This should be the same zone you used for the Compute Engine VM. For example, `us-central1-a`.
`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=shapemask-tutorial
```

Run the training and evaluation

The following script runs a sample training that trains for just 100 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=${DATA_DIR}/instances_val2017.json
(vm)$ export SHAPE_PRIOR_PATH=gs://cloud-tpu-checkpoints/shapemask/kmeans_class_priors_91x20x32x32.npy
(vm)$ export MODEL_DIR=${STORAGE_BUCKET}/shapemask

Run the following command to train the ShapeMask model:

(vm)$ python3 /usr/share/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu=${TPU_NAME} \
--model_dir=${MODEL_DIR} \
--mode=train \
--model=shapemask \
--params_override="{train: {total_steps: 100, learning_rate: {init_learning_rate: 0.08, learning_rate_levels: [0.008, 0.0008], learning_rate_steps: [15000, 20000], }, 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}, shapemask_head: {use_category_for_mask: true, shape_prior_path: ${SHAPE_PRIOR_PATH}}, shapemask_parser: {output_size: [1024, 1024]}}"

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 a TPU of the same size and TensorFlow version.
`RESNET_CHECKPOINT`	Specifies a pretrained checkpoint. ShapeMask 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} \
--mode=eval \
--model=shapemask \
--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 a 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=us-central1-a --name=shapemask-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 --tpu-only \
  --tpu-size=v3-32  \
  --zone=us-central1-a \
  --name=shapemask-tutorial \
  --tf-version=2.3 
```

Update the TPU_NAME and MODEL_DIR environment variables.

(vm)$ export TPU_NAME=shapemask-tutorial
(vm)$ export MODEL_DIR=${STORAGE_BUCKET}/shapemask-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=shapemask \
--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}} shapemask_head: {use_category_for_mask: true, shape_prior_path: ${SHAPE_PRIOR_PATH}} }"

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 a TPU of the same size and TensorFlow version.
`RESNET_CHECKPOINT`	Specifies a pretrained checkpoint. ShapeMask 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 --tpu-only \
 --zone=us-central1-a \
 --name=shapemask-tutorial

Launch a new TPU device to run evaluation.

(vm)$ ctpu up --tpu-only \
  --tpu-size=v3-8 \
  --zone=us-central1-a \
  --tf-version=2.3 \
  --name=shapemask-tutorial

Update the TPU_NAME environment variable.

(vm)$ export TPU_NAME=shapemask-tutorial

Start the evaluation.

(vm)$ python3 /usr/share/models/official/vision/detection/main.py \
--strategy_type=tpu \
--tpu=shapemask-tutorial \
--model_dir=${MODEL_DIR} \
--mode=eval \
--model=shapemask \
--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 Platform account for the resources used in this tutorial:

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 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=us-central1-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.
Run the following command to verify the Compute Engine VM and Cloud TPU have been shut down:
```
$ ctpu status --zone=us-central1-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 "us-central1-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.
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 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.