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Mask R-CNN for TensorFlow

This directory provides a script to train a Mask R-CNN model to achieve state-of-the-art accuracy, and is tested and maintained by Habana. To obtain model performance data, refer to the Habana Model Performance Data page.

For more information on training deep learning models using Gaudi, refer to developer.habana.ai.

Table of Contents

Model Overview

Mask R-CNN is a convolution-based neural network for the task of object instance segmentation. The paper describing the model can be found here. This model is an optimized version of the implementation in NVIDIA's Mask R-CNN for Gaudi.

Model Changes

  • Support for Habana device was added
  • Horovod support

Model Architecture

Mask R-CNN builds on top of Faster R-CNN adding an additional mask head for the task of image segmentation.

The architecture consists of the following:

  • ResNet-50 backbone with Feature Pyramid Network (FPN)
  • Region proposal network (RPN) head
  • RoI Align
  • Bounding and classification box head
  • Mask head

Setup

Please follow the instructions provided in the Gaudi Installation Guide to set up the environment including the $PYTHON environment variable. To achieve the best performance, please follow the methods outlined in the Optimizing Training Platform guide.
The guides will walk you through the process of setting up your system to run the model on Gaudi.

Clone Habana Model-References

In the docker container, clone this repository and switch to the branch that matches your SynapseAI version. You can run the hl-smi utility to determine the SynapseAI version.

git clone -b [SynapseAI version] https://github.com/HabanaAI/Model-References

Go to the Mask R-CNN directory:

cd Model-References/TensorFlow/computer_vision/maskrcnn

Note: If the repository is not in the PYTHONPATH, make sure to update by running the below:

export PYTHONPATH=$PYTHONPATH:root/Model-References

Install Model Requirements

  1. In the docker container, go to the Mask R-CNN directory:
cd /root/Model-References/TensorFlow/computer_vision/maskrcnn
  1. Install the required packages using pip:
$PYTHON -m pip install -r requirements.txt

Setting up COCO 2017 Dataset

This repository provides scripts to download and pre-process the COCO 2017 dataset. If you already have the data then you do not need to run the following script. Proceed to Download the pre-trained weights.

The following script saves TFRecords files to the data directory, /data/tensorflow/coco2017/tf_records.

cd dataset
bash download_and_preprocess_coco.sh /data/tensorflow/coco2017/tf_records

By default, the data directory is organized in the following structure:

<data_directory>
    raw-data/
        train2017/
        train2017.zip
        test2017/
        test2017.zip
        val2017/
        val2017.zip
        annotations_trainval2017.zip
        image_info_test2017.zip
    annotations/
        instances_train2017.json
        person_keypoints_train2017.json
        captions_train2017.json
        instances_val2017.json
        person_keypoints_val2017.json
        image_info_test2017.json
        image_info_test-dev2017.json
        captions_val2017.json
    train-*.tfrecord
    val-*.tfrecord

Download Pre-trained Weights

This directory also provides scripts to download the pre-trained weights of ResNet-50 backbone. The script creates a new directory with the name weights in the current directory and downloads the pre-trained weights in it.

./download_and_process_pretrained_weights.sh

Ensure that the weights folder created has a resnet folder in it. Inside the resnet folder there should be three folders for checkpoints and weights: extracted_from_maskrcnn, resnet-nhwc-2018-02-07 and resnet-nhwc-2018-10-14. Before moving to the next step, ensure resnet-nhwc-2018-02-07 is not empty.

Training Examples

Single Card and Multi-Card Training Examples

Run training on 1 HPU:

  • Using mask_rcnn_main.py with all default parameters:

    $PYTHON mask_rcnn_main.py

  • Using mask_rcnn_main.py with exemplary parameters:

    TF_BF16_CONVERSION=full $PYTHON mask_rcnn_main.py --mode=train --checkpoint="weights/resnet/resnet-nhwc-2018-02-07/model.ckpt-112603" --init_learning_rate=0.005 --learning_rate_steps=240000,320000 --model_dir="results" --total_steps=360000 --training_file_pattern="/data/tensorflow/coco2017/tf_records/train-*.tfrecord" --validation_file_pattern="/data/tensorflow/coco2017/tf_records/val-*.tfrecord" --val_json_file="/data/tensorflow/coco2017/tf_records/annotations/instances_val2017.json"

  • For Gaudi2, training batch size can be increased for better performance:

    TF_BF16_CONVERSION=full $PYTHON mask_rcnn_main.py --mode=train --checkpoint="weights/resnet/resnet-nhwc-2018-02-07/model.ckpt-112603" --init_learning_rate=0.015 --train_batch_size=12 --learning_rate_steps=80000,106667 --model_dir="results" --total_steps=120000 --training_file_pattern="/data/tensorflow/coco2017/tf_records/train-*.tfrecord" --validation_file_pattern="/data/tensorflow/coco2017/tf_records/val-*.tfrecord" --val_json_file="/data/tensorflow/coco2017/tf_records/annotations/instances_val2017.json"

You can train the model in mixed precision by setting the TF_BF16_CONVERSION=full environment variable. This approach is similar to Automatic Mixed Precision of TensorFlow, which can reduce memory requirements and speed up training. For more details on the mixed precision training JSON recipe files, refer to the TensorFlow Mixed Precision Training on Gaudi documentation.

Run training on 8 HPUs:

NOTE: mpirun map-by PE attribute value may vary on your setup. For the recommended calculation, refer to the instructions detailed in mpirun Configuration.

NOTE: Due to high memory requirements, training on 8 HPUs may require increasing the number of huge pages in your system. The recommended number of huge pages is 200000.

For multi-card training, remember to adjust hyperparameters (typically by multiplying init_learning_rate and dividing learning_rate_steps, num_steps_per_eval and total_steps by a number of workers).

  • 8 HPUs bf16-full over mpirun using mask_rcnn_main.py:

    export TF_BF16_CONVERSION=full
mpirun --allow-run-as-root --bind-to core --map-by socket:PE=6 --np 8 -x TF_BF16_CONVERSION $PYTHON mask_rcnn_main.py --mode=train --training_file_pattern="/data/tensorflow/coco2017/tf_records/train-*.tfrecord" --validation_file_pattern="/data/tensorflow/coco2017/tf_records/val-*.tfrecord" --val_json_file="/data/tensorflow/coco2017/tf_records/annotations/instances_val2017.json" --init_learning_rate=0.04 --learning_rate_steps=30000,40000 --total_steps=45000 --checkpoint="weights/resnet/resnet-nhwc-2018-02-07/model.ckpt-112603" --model_dir="results"

  • For Gaudi2, training batch size can be increased for better performance:

    export TF_BF16_CONVERSION=full;
mpirun --allow-run-as-root --bind-to core --map-by socket:PE=6 --np 8 -x TF_BF16_CONVERSION $PYTHON mask_rcnn_main.py --mode=train --training_file_pattern="/data/tensorflow/coco2017/tf_records/train-*.tfrecord" --validation_file_pattern="/data/tensorflow/coco2017/tf_records/val-*.tfrecord" --val_json_file="/data/tensorflow/coco2017/tf_records/annotations/instances_val2017.json" --init_learning_rate=0.12 --train_batch_size=12 --learning_rate_steps=10000,13334 --total_steps=15000 --checkpoint="weights/resnet/resnet-nhwc-2018-02-07/model.ckpt-112603" --model_dir="results"

  • 8 HPUs, FP32 over mpirun using mask_rcnn_main.py:

    export TF_BF16_CONVERSION=0
mpirun --allow-run-as-root --bind-to core --map-by socket:PE=6 --np 8 -x TF_BF16_CONVERSION $PYTHON mask_rcnn_main.py --mode=train --training_file_pattern="/data/tensorflow/coco2017/tf_records/train-*.tfrecord" --validation_file_pattern="/data/tensorflow/coco2017/tf_records/val-*.tfrecord" --val_json_file="/data/tensorflow/coco2017/tf_records/annotations/instances_val2017.json" --init_learning_rate=0.04 --learning_rate_steps=30000,40000 --num_steps_per_eval=3696 --total_steps=45000 --checkpoint="weights/resnet/resnet-nhwc-2018-02-07/model.ckpt-112603" --model_dir="results"

Parameters

You can modify the training behavior through the various flags in the mask_rcnn_main.py. Flags in the mask_rcnn_main.py script are as follows:

  • mode: Run train, train_and_eval or eval on MS COCO. train_and_eval by default.
  • training_file_pattern: TFRecords file pattern for the training files.
  • validation_file_pattern: TFRecords file pattern for the validation files.
  • val_json_file: Filepath for the validation json file
  • checkpoint: Path to model checkpoint.
  • model_dir: Model directory.
    WARNING: Unexpected errors can occur if data from previous runs remains in the model directory. In particular, the model will not continue training if it has previously executed more total_steps than the current run, and saved data to the model directory.
  • total_steps: The number of steps to use for training should be adjusted according to the train_batch_size flag. Note that for first 100 steps performance is not reported by the script (-1 will be shown).
  • train_batch_size: Batch size for training.
  • noeval_after_training: Disable single evaluation steps after training when train command is used.
  • use_fake_data: Use fake input.
  • pyramid_roi_impl: Implementation to use for PyramidRoiAlign. habana (default), habana_fp32 and gather can be used.
  • eval_samples: Number of eval samples. Number of steps will be divided by eval_batch_size.
  • eval_batch_size: Batch size for evaluation.
  • num_steps_per_eval: Number of steps used for evaluation.
  • deterministic: Enable deterministic behavior.
  • save_summary_steps: Steps between saving summaries to TensorBoard.
  • device: Device type. CPU and HPU can be used.
  • profile: Gather TensorBoard profiling data.
  • init_learning_rate: Initial learning rate.
  • learning_rate_steps: Warmup learning rate decay factor. Expected format: "first_value,second_value".

Supported Configuration

Validated on SynapseAI Version TensorFlow Version(s) Mode
Gaudi 1.14.0 2.15.0 Training
Gaudi2 1.14.0 2.15.0 Training

Changelog

1.7.0

  • Fixed printing evaluation time

1.5.0

  • Added note about Gaudi2.
  • profile now accept steps.
  • Removed demo script.

1.4.0

  • References to custom demo script were replaced by community entry points in README.
  • Default values of some parameters were changed.
  • Added flag to specify maximum number of CPUss to be used.
  • Disabled cache and prefetch when not enough RAM.

1.3.0

  • Set up BF16 conversion pass using a config from habana-tensorflow package instead of recipe json file.
  • Fixed freeze in multi-card scenario when checkpoint already exist.
  • Updated requirements.txt.
  • Changed python or python3 to $PYTHON to execute correct version based on environment setup.

1.2.0

  • Removed workaround for Multiclass NMS.

Known issues

For Gaudi2, default eval_batch_size=4 should be used to ensure proper accuracy.