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.
Open a Cloud Shell window.
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}
The first time you run this command in a new Cloud Shell VM, an
Authorize Cloud Shell
page is displayed. ClickAuthorize
at the bottom of the page to allowgcloud
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:
gsutil mb -p ${PROJECT_ID} -c standard -l europe-west4 -b on gs://bucket-name
This Cloud Storage bucket stores the data you use to train your model and the training results. The
gcloud compute tpus execution-groups
tool used in this tutorial sets up default permissions for the Cloud TPU Service Account you set up in the previous step. If you want finer-grain permissions, review the access level permissions.Launch a Compute Engine VM using the
gcloud
command.$ gcloud compute tpus execution-groups create \ --vm-only \ --name=retinanet-tutorial \ --zone=europe-west4-a \ --disk-size=300 \ --machine-type=n1-standard-8 \ --tf-version=2.4.1
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.
For more information on the
gcloud
command, see the gcloud Reference.When prompted, press y to create your Cloud TPU resources.
When the
gcloud compute tpus execution-groups
command has finished executing, verify that your shell prompt has changed fromusername@projectname
tousername@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 Compute Engine instance.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)$ sudo pip3 install --user -r /usr/share/models/official/requirements.txt
Prepare the COCO dataset
The COCO dataset will be stored on your Cloud Storage, so set a storage bucket variable specifying the name of the bucket you created:
(vm)$ export STORAGE_BUCKET=gs://bucket-name
(vm)$ export DATA_DIR=${STORAGE_BUCKET}/coco
Run the download_and_preprocess_coco.sh
script to convert
the COCO dataset into a set of TFRecords (*.tfrecord
) that the training
application expects.
(vm)$ 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.
The COCO download and conversion script takes approximately 1 hour to complete.
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 ${DATA_DIR}
Set up the training environment
Launch a Cloud TPU resource using the
gcloud
command.(vm)$ gcloud compute tpus execution-groups create \ --tpu-only \ --accelerator-type=v3-8 \ --name=retinanet-tutorial \ --zone=europe-west4-a \ --tf-version=2.4.1
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.
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. Note: If you are not connected to the Compute Engine instance, you can connect by running the following command:gcloud compute ssh retinanet-tutorial --zone=europe-west4-a
As you continue these instructions, run each command that begins with
(vm)$
in your Compute Engine instance.Create an environment variable for the TPU name.
(vm)$ export TPU_NAME=retinanet-tutorial
Add the top-level
/models
folder to the Python path with the command:(vm)$ export PYTHONPATH="${PYTHONPATH}:/usr/share/models"
Single Cloud TPU device training
The following training scripts were run on a Cloud TPU v3-8. It will take more time, but you can also run them on a Cloud TPU v2-8.
This sample script below trains for only 10 steps and takes less than 5 minutes to run on a v3-8 TPU Node. To train to convergence takes about 22,500 steps and approximately 1 1/2 hours on a Cloud TPU v3-8 TPU.
Set up the following environment variables:
(vm)$ export MODEL_DIR=${STORAGE_BUCKET}/retinanet-train (vm)$ 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=${DATA_DIR}/instances_val2017.json
Run the training script:
(vm)$ python3 /usr/share/models/official/vision/detection/main.py \ --strategy_type=tpu \ --tpu=${TPU_NAME} \ --model_dir=${MODEL_DIR} \ --mode="train" \ --params_override="{ type: retinanet, 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: ${EVAL_FILE_PATTERN}, eval_samples: 5000 } }"
Command flag descriptions
strategy_type
- To train the RetinaNet model on a TPU, you must set the
distribution_strategy
totpu
. tpu
- The name of the Cloud TPU. This is set using the
TPU_NAME
environment variable. model_dir
- The Cloud Storage bucket where checkpoints and summaries are stored during training. You can use an existing folder to load previously generated checkpoints created on a TPU of the same size and TensorFlow version.
mode
- Set this to
train
to train the model oreval
to evaluate the model. params_override
- A JSON string that overrides default script parameters. For more
information on script parameters, see
/usr/share/models/official/vision/detection/main.py
.
While the model is training, you can see the progress by viewing the log output. Output similar to the following shows the training is progressing normally:
Train Step: 10/10 / loss = { 'total_loss': 2.4581615924835205, 'cls_loss': 1.4098565578460693, 'box_loss': 0.012001709081232548, 'model_loss': 2.0099422931671143, 'l2_regularization_loss': 0.44821977615356445, 'learning_rate': 0.008165999 } / training metric = { 'total_loss': 2.4581615924835205, 'cls_loss': 1.4098565578460693, 'box_loss': 0.012001709081232548, 'model_loss': 2.0099422931671143, 'l2_regularization_loss': 0.44821977615356445, 'learning_rate': 0.008165999 }
Single Cloud TPU device evaluation
The following procedure uses the COCO evaluation data. It takes about 10 minutes to run through the evaluation steps.
Set up the following environment variables:
(vm)$ export EVAL_SAMPLES=5000
Run the evaluation script:
(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 \ --params_override="{ type: retinanet, eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN}, eval_samples: ${EVAL_SAMPLES} } }"
Command flag descriptions
strategy_type
- The distribution strategy to use. Either
tpu
ormulti_worker_gpu
. 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
, ortrain_and_eval
. 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 the end of the evaluation, you will see messages similar to the following on the console:
Accumulating evaluation results... DONE (t=7.66s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
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 v2-32 Pod slice:
Delete the Cloud TPU resource you created for training the model on a single device.
(vm)$ gcloud compute tpus execution-groups delete retinanet-tutorial \ --zone=europe-west4-a \ --tpu-only
Run the
gcloud compute tpus execution-groups
command, using theaccelerator-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=retinanet-tutorial \ --accelerator-type=v3-32 \ --zone=europe-west4-a \ --tf-version=2.4.1 \ --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.
Set the Cloud TPU name variable. This will either be a name you set with the
--name
parameter or the default, your username:(vm)$ export TPU_NAME=retinanet-tutorial
Set up the following environment variable:
(vm)$ export MODEL_DIR=${STORAGE_BUCKET}/retinanet-pod
Run the Pod training script on a v2-32 TPU node
The following sample training script was run on a Cloud TPU v2-32 Pod. It trains for only 10 steps and takes less than 5 minutes to run. To train to convergence requires 2109 steps and takes approximately 50 minutes on a v2-32 TPU Pod.
(vm)$ python3 /usr/share/models/official/vision/detection/main.py \ --strategy_type=tpu \ --tpu=${TPU_NAME} \ --model_dir=${MODEL_DIR} \ --mode="train" \ --params_override="{ type: retinanet, train: { total_steps: 10, batch_size: 256, checkpoint: { path: ${RESNET_CHECKPOINT}, prefix: resnet50/ }, train_file_pattern: ${TRAIN_FILE_PATTERN} }, eval: { val_json_file: ${VAL_JSON_FILE}, eval_file_pattern: ${EVAL_FILE_PATTERN}, eval_samples: 5000 } }"
Command flag descriptions
strategy_type
- The distribution strategy to use. Either
tpu
ormulti_worker_gpu
. tpu
- Specifies the name of the Cloud TPU. This is set
using the
TPU_NAME
environment variable. model_dir
- The Cloud Storage bucket where checkpoints and summaries are stored during training. You can use an existing folder to load previously generated checkpoints created on a TPU of the same size and TensorFlow version.
mode
- One of
train
,eval
, ortrain_and_eval
. params_override
- A JSON string that overrides default script parameters. For more
information on script parameters, see
/usr/share/models/official/vision/detection/main.py
.
Cleaning up
To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.
Disconnect from the Compute Engine VM:
(vm)$ exit
Your prompt should now be
username@projectname
, showing you are in the Cloud Shell.In your Cloud Shell, use the following command to delete your Compute Engine VM and Cloud TPU:
$ gcloud compute tpus execution-groups delete retinanet-tutorial \ --zone=europe-west4-a
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=europe-west4-a
You should see an empty list of TPUs like the following:
NAME STATUS
Delete your Cloud Storage bucket using
gsutil
as shown below. Replace bucket-name with the name of your Cloud Storage bucket.$ gsutil rm -r gs://bucket-name
What's next
In this tutorial you have trained the RetinaNet 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.
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 RetinaNet 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.