Gaze Estimation - NVIDIA Docs

GazeNet is an NVIDIA developed gaze estimation model which is included in the TAO Toolkit as one of the models supported. With GazeNet the following tasks are supported:

dataset_convert
train
evaluate
inference
export

These tasks may be invoked from the TAO Toolkit Launcher by following the below mentioned convention from command line:

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            tao gazenet <sub_task> <args_per_subtask>

where args_per_subtask are the command line arguments required for a given subtask. Each of these sub-tasks are explained in detail below.

Pre-processing the Dataset

As described in the Data Annotation Format section, the GazeNet app requires a defined JSON format data to be converted to TFRecords. This can be done using the dataset_convert subtask under GazeNet.

The dataset_convert tool takes in a defined json format data and convert it to the TFRecords that the GazeNet model ingests. See the following sections for the sample usage examples.

Sample Usage of the Dataset Converter Tool

The labeling json data format is the accepted dataset format for GazeNet. The labeling json data fromat must be converted to the TFRecord file format for ingestion. The sampe usage for the dataset_convert tool is as mentioned below.

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            tao gazenet dataset_convert [-h] -folder-suffix TFRECORDS_FOLDER_SUFFIX
                                 -norm_folder_name NORM_DATA_FOLDER_NAME
                                 -sets DATASET_NAME
                                 -data_root_path DATASET_ROOT_PATH

You can use these optional arguments:

-h, --help: Show this help message and exit.
-folder-suffix, --ground_truth_experiment_folder_suffix: suffix of folder including generated .tfrecords files.
-norm_folder_name --norm_folder_name: Folder to generate normalized.
-data_root_path, –-data_root_path: root path to the dataset.
-sets --set_ids: name of the dataset.

Here’s an example of using the command with the dataset:

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            tao gazenet dataset_convert -folder-suffix <tfrecord_folder_suffix> -norm_folder_name <norm data folder name>> \
                            -sets sample-set <dataset name> -data_root_path <root path to the dataset>

Output log from executing tao gazenet dataset_convert:

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            Using TensorFlow backend.

Test ['p01-1']
Validation ['p01-0']
Train ['p01-4', 'p01-3', 'p01-2']
Test ['p01-1']
Validation ['p01-0']
Train ['p01-4', 'p01-3', 'p01-2']

Creating an Experiment Specification File

To do training, evaluation, and inference for GazeNet, several components need to be configured, each with their own parameters. The train, evaluate, and inference tasks for a GazeNet experiment share the same configuration file.

The specification file for GazeNet training configures the following components of training pipeline:

Trainer/Evaluator
Model
Loss
Optimizer
Dataloader
Augmentation

Trainer/Ealuator

GazeNet trainer and evaluator share the same configurations.

Here’s a sample example to config GazeNet trainer.

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            __class_name__: GazeNetTrainer
checkpoint_dir: null
checkpoint_n_epoch: 1
dataloader:
  ...
evaluation_metric: rmse
network_inputs: face_and_eyes_and_fgrid
infrequent_summary_every_n_steps: 0
log_every_n_secs: 10
model_selection_metric: logcosh
num_epoch: 2
random_seed: 42
hooks: null
enable_visualization: false
loss:
  ...
model:
  ...
optimizer:
  ...
visualize_bins_2d: 5
visualize_bins_3d: 100
visualize_num_images: 3

The following table describes the parameters used to config the trainer:

Parameter	Datatype	Default	Description	Supported Values
__class_name__	string	GazeNetTrainer	Name for the trainer specification	GazeNetTrainer
`checkpoint_dir`	string	`null`	Path to the checkpoint. If not specified, will save all checkpoints in the output folder	NA
`checkpoint_n_epoch`	int	`1`	Save checkpoint per n number of epochs	1 to num_epoch
`dataloader`	structure	`NA`	Dataloader specification	1 to num_epoch
`evaluation_metric`	string	`rmse`	Metric used during KPI testing	rmse
`network_inputs`	string	`face_and_eyes_and_fgrid`	Input type (only ‘face_and_eyes_and_fgrid’ is supported)	face_and_eyes_and_fgrid
`infrequent_summary_every_n_steps`	int	`0`	Infrequent summary every n epoch	0 to num_epoch
`log_every_n_secs`	int	`10`	Log the training output for every n secs	NA
`model_selection_metric`	string	`logcosh`	Metric used to select final model	logcosh
`num_epoch`	int	`40`	Number of epochs	NA
`random_seed`	int	`42`	Random seed used during the experiments	NA
`enable_visualization`	boolean	`false`	Toggle to enable visualization	false/true
`visualize_bins_2d`	int	`5`	Resolution for 2D data distribution visualization	NA
`visualize_bins_3d`	int	`100`	Resolution for 3D data distribution visualization	NA
`visualize_num_images`	int	`3`	Number of data images to show on Tensorboard	NA

Model

GazeNet can be configured using the model option in the spec file.

Here’s a sample model config to instantiate a GazeNet model with pretrained weights and the number of freeze blocks.

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            model:
  __class_name__: GazeNetBaseModel
  model_parameters:
    dropout_rate: 0.25
    frozen_blocks: 5
    num_outputs: 5
    pretrained_model_path: /workspace/tao-experiments/gazenet/pretrain_models/model.tlt
    regularizer_type: l2
    regularizer_weight: 0.002
    type: GazeNet_public
    use_batch_norm: true

The following table describes the model parameters:

Parameter	Datatype	Default	Description	Supported Values
__class_name__	string	GazeNetBaseModel	Name for the model config	GazeNetBaseModel
`dropout_rate`	float	`0.3`	Probability for drop out	0.0-1.0
`frozen_blocks`	int	`2`	This parameter defines how many blocks that will be frozen during training. If the value for this variable is set to be larger than 0, provide a pretrain model.	0,1,2,3,4,5,6
`num_outputs`	int	`5`	Number of outputs (x, y, z point of regards amd theta, phi gaze vector)	5
`pretrained_model_path`	string	`null`	Path to the pretrain model	NA
`regularization_type`	string	`l2`	Type of the regularization	l1/l2/None
`regularizer_weight`	float	`0.002`	Factor of the regularization	0.0-1.0
`type`	string	`GazeNet_public`	Type of supported GazeNet model. Only “GazeNet_public” is currently supported.	GazeNet_public
`use_batch_norm`	boolean	`true`	Boolean variable to use batch normalization layers or not	true/false

Loss

This section helps you configure the parameters for loss, optimizer, and learning rate scheduler for optimizer.

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            loss:
  __class_name__: GazeLoss
  loss_type: logcosh

The following table describes the loss parameters:

Parameter	Datatype	Default	Description	Supported Values
__class_name__	string	GazeLoss	Name of the loss config	NA
`loss_type`	string	logcosh	Type of the loss function	l1/rmse/cosine/l1_cosine_joint/l2_cosine_joint/logcosh l1: l1 loss rmse: root mean square error l1_cosine_joint: l1 loss for x, y, z point of regards cosine loss for theta and phi l2_cosine_joint: l2 loss for x, y, z point of regards cosine loss for theta and phi logcosh: log-cosh loss

Optimizer

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            optimizer:
  __class_name__: AdamOptimizer
  beta1: 0.9
  beta2: 0.999
  epsilon: 1.0e-08
  learning_rate_schedule:
    __class_name__: SoftstartAnnealingLearningRateSchedule
    annealing: 0.8
    base_learning_rate: 0.003
    last_step: 263000
    min_learning_rate: 5.0e-07
    soft_start: 0.2

The following table describes the optimizer parameters:

Parameter	Datatype	Default	Description	Supported Values
__class_name__	string	AdamOptimizer	Name of the optimizer config	AdamOptimizer/AdadeltaOptimizer/GradientDescentOptimizer
`beta1`	float	`0.9`	The exponential decay rate for the 1st moment estimates	0-1
`beta2`	float	`0.999`	The exponential decay rate for the 2nd moment estimates	0-1
`epsilon`	float	`1.0e-08`	A small constant for numerical stability	NA
`learning_rate_schedule`	structure	`SoftstartAnnealingLearningRateSchedule`	Type of learning rate schedule	SoftstartAnnealingLearningRateSchedule ConstantLearningRateSchedule ExponentialDecayLearningRateSchedule

The following table describes the learning_rate_schedule parameters:

Parameter	Datatype	Default	Description	Supported Values
__class_name__	string	SoftstartAnnealingLearningRateSchedule	Name of the learning rate schedule config	SoftstartAnnealingLearningRateSchedule - This scheduling has soft starting and ending learning rate value ConstantLearningRateSchedule - This scheduling has constant learning rate value ExponentialDecayLearningRateSchedule - This scheduling has learning rate that are decay exponentially
`soft_start`	float	`0.2`	Indicating the fraction of last_step that will be taken before reaching the base_learning rate	0-1
`annealing`	float	`0.8`	Indicating the fraction of last_step after which the learning rate ramps down from base_learning rate	0-1
`base_learning_rate`	float	`0.0002`	Learning rate	0-1
`min_learning_rate`	float	`2.0e-07`	Minimum value the learning rate will be set to	0-1
`last_step`	int	`953801`	Last step the schedule is made for	NA

GazeNet currently supports the soft-start annealing learning rate schedule. The learning rate when plotted as a function of the training progress (0.0, 1.0) results in the following curve.

In this experiment, the soft start was set as 0.2 and annealing as 0.8 with minimum learning rate as 2.0e-07 and a maximum learning rate or base_lr as 0.0002.

Dataloader

The dataloader module provides parameters used for dataset pre-processing, some basic pre-processing, data and dataloader when training. Here is a sample dataloader specification element:

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            dataloader:
  __class_name__: GazeNetDataloaderAugV2
  augmentation_info:
    ...
  batch_size: 128
  dataset_info:
    ...
  eye_scale_factor: 1.8
  face_scale_factor: 1.3
  filter_phases:
    - training
    - testing
    - validation
    - kpi_testing
  filter_info:
    ...
  image_info:
    facegrid:
      channel: 1
      height: 25
      width: 25
    image_face:
      channel: 1
      height: 224
      width: 224
    image_frame:
      channel: 1
      height: 480
      width: 640
    image_left:
      channel: 1
      height: 224
      width: 224
    image_right:
      channel: 1
      height: 224
      width: 224
  input_normalization_type: zero-one
  kpiset_info:
    ...
  learn_delta: false
  use_head_norm: false
  num_outputs: 5
  theta_phi_degrees: false
  use_narrow_eye: true
  add_test_to: null

The following table describes the dataloader specification parameters:

Parameter	Datatype	Default	Description	Supported Values
__class_name__	string	GazeNetDataloaderAugV2	Name of the dataloader specification	GazeNetDataloaderAugV2
`augmentation_info`	structure	`NA`	Augmentation specification	NA
`batch_size`	int	`128`	Batch size	0-1
`dataset_info`	structure	`NA`	dataset specification	0-1
`eye_scale_factor`	float	`1.8`	Scaling factor for eyes (if value is larger than 1, then eye crop is enlarged)	NA
`face_scale_factor`	float	`1.3`	Scaling factor for the face (if value is larger than 1, then face crop is enlarged)	NA
`filter_phases`	structure	`training, testing, validation, kpi_testing`	Phase to apply the filter	training/testing/validation/kpi_testing
`filter_info`	structure	`NA`	Data filter variables and criteria	NA
`image_info`	structure	`NA`	Input image information	facegrid, image_face, image_frame, image_left, image_right
`channel`	int	`NA`	Input normalization type	zero-one
`input_normalization_type`	string	`NA`	Input normalization type	zero-one
`kpiset_info`	structure	`NA`	KPI set information	NA
`learn_delta`	boolean	`false`	Boolean values to enable/disable learning of variable difference	false
`use_head_norm`	boolean	`false`	Data filter variable and criteria	false
`num_outputs`	int	`5`	Number of outputs (x, y, z point of regards amd theta, phi gaze vector)	5
`theta_phi_degrees`	boolean	`false`	Boolean values to enable/disable theta phi learning	false
`use_narrow_eye`	boolean	`true`	Boolean values to enable/disable tight eye input	true/false
`add_test_to`	string	`null`	Testing dataset from dataio can be added to training or validation. By default, will keep testing dataset for KPI usage	null/training/validation

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            dataset_info:
  ground_truth_folder_name:
  - Ground_Truth_DataFactory_pipeline
  image_extension: png
  root_path: null
  test_file_name: test.tfrecords
  tfrecord_folder_name:
  - TfRecords_joint_combined
  tfrecords_directory_path:
  - /workspace/tao-experiments/gazenet/data/MPIIFaceGaze/sample-dataset
  tfrecords_set_id:
  - p01-day03
  train_file_name: train.tfrecords
  validate_file_name: validate.tfrecords

The following table describes the dataset_info parameters:

Parameter	Datatype	Default	Description	Supported Values
`ground_truth_folder_name`	string	`NA`	Ground truth folder name	NA
`image_extension`	string	`NA`	Image extension	NA
`root_path`	string	`NA`	Root path	null
`test_file_name`	string	`NA`	File name for test tfrecords	NA
`tfrecord_folder_name`	string	`NA`	Tfrecords folder name	NA
`tfrecords_directory_path`	string	`NA`	Path to Tfrecords directory	NA
`tfrecords_set_id`	string	`NA`	Set ID	NA
`train_file_name`	string	`NA`	File name for train tfrecords	NA
`validate_file_name`	string	`NA`	File name for validate tfrecords	NA

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            filter_info:
  - desired_val_max: 400.0
    desired_val_min: -400.0
    feature_names:
    - label/gaze_cam_x
  - desired_val_max: 400.0
    desired_val_min: -400.0
    feature_names:
    - label/gaze_cam_y
  - desired_val_max: 300.0
    desired_val_min: -300.0
    feature_names:
    - label/gaze_cam_z

The following table describes the filter_info parameters:

Parameter	Datatype	Default	Description	Supported Values
`feature_names`	string	`NA`	Feature name	label/gaze_cam_x, label/gaze_cam_y, label/gaze_cam_z
`desired_val_max`	float	`NA`	Maximum value for the feature	NA
`desired_val_min`	float	`NA`	Minimum value for the feature	NA

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            kpiset_info:
  ground_truth_folder_name_kpi:
  - Ground_Truth_DataFactory_pipeline
  kpi_file_name: test.tfrecords
  kpi_root_path: null
  kpi_tfrecords_directory_path:
  - /workspace/tao-experiments/gazenet/data/MPIIFaceGaze/sample-dataset
  tfrecord_folder_name_kpi:
  - TfRecords_joint_combined
  tfrecords_set_id_kpi:
  - p01-day03

The following table describes the dataset_info parameters:

Parameter	Datatype	Default	Description	Supported Values
`ground_truth_folder_name_kpi`	string	`NA`	Ground truth folder name for KPI dataset	NA
`kpi_file_name`	string	`NA`	File name for KPI tfrecords	NA
`kpi_root_path`	string	`null`	KPI root path	Reserved value, currently only null is supported
`kpi_tfrecords_directory_path`	string	`NA`	Path to KPI Tfrecords directory	NA
`tfrecord_folder_name_kpi`	string	`NA`	KPI tfrecords folder name	NA
`tfrecords_set_id_kpi`	string	`NA`	KPI tfrecords set ID	NA

Augmentation

The augmentation module provides some basic pre-processing and augmentation when training. Here is a sample augmentation element:

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            augmentation_info:
  blur_augmentation:
    blur_probability: 0.0
    kernel_sizes:
    - 1
    - 3
    - 5
    - 7
    - 9
  enable_online_augmentation: true
  gamma_augmentation:
    gamma_max: 1.1
    gamma_min: 0.9
    gamma_probability: 0.1
    gamma_type: uniform
  modulus_color_augmentation:
    contrast_center: 127.5
    contrast_scale_max: 0.0
    hue_rotation_max: 0.0
    saturation_shift_max: 0.0
  modulus_spatial_augmentation:
    hflip_probability: 0.5
    zoom_max: 1.0
    zoom_min: 1.0
  random_shift_bbx_augmentation:
    shift_percent_max: 0.16
    shift_probability: 0.9

The following table describes the augmentation parameters:

Parameter	Datatype	Default	Description	Supported Values
__class_name__	string	GazeNetDataloaderAugV2	Name of the dataloader specification	GazeNetDataloaderAugV2
`augmentation_info`	structure	`NA`	Augmentation specification	NA
`blur_augmentation`	structure	`NA`	Blur augmentation specification	NA
`blur_probability`	float	`0.0`	Probability of imgages to apply blur augmentation	0.0 - 1.0
`kernel_sizes`	int	`1,3,5,7,9`	Kernel size for the blur operation	1, 3, 5, 7, 9
`enable_online_augmentation`	boolean	`true`	Boolean values to enable/disable augmentation	true/false
`gamma_augmentation`	structure	`NA`	Gamma augmentation specification	NA
`gamma_max`	float	`1.1`	Maximum value of gamma variable	1.0 - 1.4
`gamma_min`	float	`0.9`	Minimum value of gamma variable	0.7 - 1.0
`gamma_probability`	float	`0.1`	Probability of data to apply gamma augmentation	uniform
`gamma_type`	string	`uniform`	Type of gamma augmentation	true/false
`modulus_color_augmentation`	structure	`NA`	Color argumentation specification	NA
`contrast_center`	float	`127.5`	Contrast center for color argumentation	0 - 255
`contrast_scale_max`	float	`0.0`	Maximum scale of contrast change	NA
`hue_rotation_max`	float	`0.0`	Maximum hue rotation change	NA
`saturation_shift_max`	float	`0.0`	Maximum saturation shift change	NA
`modulus_spatial_augmentation`	structure	`NA`	Spatial augmentation specification	NA
`hflip_probability`	float	`0.5`	Probability of data to apply horizontal flip	0.0 - 1.0
`zoom_max`	float	`1.0`	Maximum zoom scale	NA
`zoom_min`	float	`1.0`	Minimum zoom scale	NA
`random_shift_bbx_augmentation`	structure	`NA`	Bounding box random ship augmentation	NA
`shift_percent_max`	float	`0.16`	Maximum percent shift of the bounding box	NA
`shift_probability`	float	`0.9`	Probability of data to apply random shift augmentation	0.0 - 1.0

Training the Model

After following the steps to Pre-processing the Dataset to create TFRecords ingestible by the TAO training, and setting up a spec file. You are now ready to start training a gaze estimation network.

GazeNet training command:

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            tao gazenet train [-h] -e <spec_file>
                       -r <result directory>
                       -k <key>

Required Arguments

-r, --results_dir: Path to a folder where experiment outputs should be written.
-k, –key: User specific encoding key to save or load a .tlt model.
-e, --experiment_spec_file: Path to spec file. Absolute path or relative to working directory.

Optional Arguments

-h, --help: To print help message.

Sample Usage

Here is an example of command for gazenet training:

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            tao gazenet train -e <path_to_spec_file>
                  -r <path_to_experiment_output>
                  -k <key_to_load_the_model>

Note

The tao gazenet train tool can support training on images of different resolutions. Face, left eye, and right eye crop is obtained online through dataloader. However, it requires all input images to have the same resolution.

Evaluating the Model

Execute evaluate on a GazeNet model.

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            tao gazenet evaluate [-h] -type <testing dataset type>
                          -m <model_file>
                          -e <experiment_spec>
                          -k <key>

Required Arguments

-e, --experiment_spec_file: Experiment spec file to set up the evaluation experiment. This should be the same as training spec file.
-m, --model: Path to the model file to use for evaluation. This could be a .tlt model file or a tensorrt engine generated using the export tool.
-k, -–key: Provide the encryption key to decrypt the model. This is a required argument only with a .tlt model file.

Optional Arguments

-h, --help: show this help message and exit.

If you have followed the example in Training the Model, you may now evaluate the model using the following command:

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            tao gazenet evaluate  -type <testing data type>
                      -e <path to training spec file>
                      -m <path to the model>
                      -k <key to load the model>

Note

This command runs evaluation on the testing/KPI dataset.

Use these steps to evaluate on a new test set with ground truth labeled:

Create tfrecords for this test set by following the steps listed in Pre-processing the Dataset section.
Update the dataloader configuration part of the training experiment spec file to update kpiset_info with newly generated tfrecords for the test set. For more information on the dataset config, refer to Creating an Experiment Specification File. The evaluate tool iterates through all the folds in the kpiset_info.

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            kpiset_info:
  ground_truth_folder_name_kpi:
  - Ground_Truth_Folder_Dataset1
  - Ground_Truth_Folder_Dataset2
  kpi_file_name: test.tfrecords
  kpi_root_path: null
  kpi_tfrecords_directory_path:
  - /path_to_kpi_dataset1
  - /path_to_kpi_dataset2
  tfrecord_folder_name_kpi:
  - TfRecords_joint_combined
  - TfRecords_joint_combined
  tfrecords_set_id_kpi:
  - kpi_dataset1
  - kpi_dataset2

The rest of the experiment spec file remains the same as the training spec file.

Run Inference on the Model

The inference task for gazenet may be used to visualize gaze vector. An example of the command for this task is shown below:

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            tao gazenet inference [-h] -e </path/to/inference/spec/file> \
                           -i </path/to/inference/input> \
                           -m <model_file> \
                           -o </path/to/inference/output> \
                           -k <model key>

Required Parameters

-e, --inference_spec: Path to an inference spec file.
-i, --inference_input: The directory of input images or a single image for inference.
-o, --inference_output: The directory to the output images and labels.
-k, --enc_key: Key to load model.

Sample usage for the inference sub-task

Here’s a sample command to run inference for a testing dataset.

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            tao gazenet inference -e $SPECS_DIR/gazenet_tlt_pretrain.yaml \
                      -i $DATA_DOWNLOAD_DIR/inference-set \
                      -m $USER_EXPERIMENT_DIR/experiment_result/exp1/model.tlt \
                      -o $USER_EXPERIMENT_DIR/experiment_result/exp1 \
                      -k $KEY

Exporting the GazeNet Model

Here’s an example of the command line arguments of the export command:

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            tao gazenet export [-h] -m <path to the .tlt model file generated by tao train>
                        -o <path to output file>
                        -t tfonnx
                        -k <key>

Required Arguments

-m, --model_filename: Path to the .tlt model file to be exported using export.
-k, --output_filename: Key used to save the .tlt model file.
-o, --key: Key used to save the .tlt model file.
-t, --export_type: Model type to export to. Only ‘tfonnx’ is support in TAO Toolkit 3.0-21.08.

Sample usage for the export sub-task

Here’s a sample command to export a GazeNet model.

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            tao gazenet export -m $USER_EXPERIMENT_DIR/experiment_result/exp1/model.tlt
                   -o $USER_EXPERIMENT_DIR/experiment_dir_final/gazenet_onnx.etlt
                   -t tfonnx
                   -k $KEY

Deploying to DeepStream 6.0

The pretrained model for GazeNet provided through NGC is available by default with DeepStream 6.0.

For more details, refer to DeepStream TAO Integration for GazeNet.