Heart Rate Estimation

See the Data Annotation Format page for more information about the data format for Heart Rate Estimation.

HeartRateNet dataset conversion also requires an experiment spec to generate the TFRecords. A detailed description is included in the table below.

To generate TFRecords, use the following command:

            

            
tao heartratenet dataset_convert -e <experiment_spec_file>
        

Required Arguments

• -e, --experiment_spec_file: The path to the dataset spec

Sample Usage

Here is an example of using dataset_convert to generate TFRecords:

            

            
tao heartratenet dataset_convert -e /workspace/examples/heartratenet/specs/heartratenet_data_generation.yaml
        

The configuration file for HeartRateNet has four key components for training and evaluating the models. The key parts of the configuration files are given below.

• dataloader: Configures the dataset.

• model: Configures the model.

• loss: Configures the loss.

• optimizer: Configures the optimizer.

            

            
__class_name__: HeartRateNetTrainer
checkpoint_dir: "/workspace/tao-experiments/heartratenet/model/"
results_dir: "/workspace/tao-experiments/heartratenet/"
random_seed: 32
log_every_n_secs: 20
checkpoint_n_epoch: 1
num_epoch: 20
summary_every_n_steps: 1
infrequency_summary_every_n_steps: 0
last_step: 1
evaluation_window: 10
low_freq_cutoff: 0.67
high_freq_cutoff: 4.0
fps: 20.0
model_type: HRNet_release
dataloader:
    __class_name__: HeartRateNetDataloader
    image_info:
        num_channels: 3
        image_height: 72
        image_width: 72
        data_format: channels_first
    dataset_info:
        tfrecords_directory_path: /workspace/tao-experiments/heartratenet/data
        tfrecord_folder_name: ''
        tfrecord_train_file_name: train.tfrecord
        tfrecord_test_file_name: test.tfrecord
        tfrecord_validation_file_name: validation.tfrecord
    model_info:
        model_type: HRNet_release
model:
    __class_name__: HeartRateNet
    model_parameters:
        input_size: 72
        data_format: channels_first
        conv_dropout_rate: 0.0
        fully_connected_dropout_rate: 0.0
        use_batch_norm: False
        model_type: HRNet_release
        frozen_blocks: 0
loss:
    __class_name__: HeartRateNetLoss
    loss_function_name: MSE
optimizer:
    __class_name__: AdaDeltaOptimizer
    rho: 0.95
    epsilon: 1.0e-7
    learning_rate_schedule:
        __class_name__: ConstantLearningRateSchedule
        learning_rate: 1.0
        

Dataloader

The dataloader configuration defines how the data will be loaded.

Model

The model configuration allows you to customize your HeartRateNet model.

Loss

The loss configuration allows you to pick between different losses.

Optimizer

The optimizer configuration allows you to configure the optimizer.

Train the HeartRateNet model using the train command:

            

            
tao heartratenet train -e <experiment_spec>
                   -k <key>
                   -r <results_dir>
                   -ll <log_level>
        

Required Arguments

• -r, --results_dir: The folder path where training logs are saved

• -k, --key: The encryption key to decrypt the model

• -e, --experiment_spec_file: The experiment specification file

Optional Arguments

• -ll, --log_level: The log level to use.

Sample Usage

Here’s an example of using the train command on a HeartRateNet model:

            

            
tao heartratenet train -e /workspace/examples/heartratenet/specs/<config_file>.yaml
                   -k 'test'
                   -r /workspace/tao-experiments/gesturenet/
        

Use the evaluate command to run evaluation for a HeartRateNet model:

            

            
tao heartratenet evaluate -e <experiment_spec_file>
                    -m <model_folder_path>
                    -k <key>
                    -r <results_dir>
        

Required Arguments

• -e, --experiment_spec_file: The experiment spec file to set up evaluation experiment

• -m, --model_folder_path: The model folder path where the models are saved

• -k, --key: The encryption key used to train the model

• -r, --results_dir: The directory to save the evaluation results

Optional Arguments

• -ll, --log_level: The log level to use.

To run inference for a HeartRateNet model, use this command:

            

            
tao heartratenet inference -m <model_full_path>
                       --subject_infer_dir <subject_infer_dir>
                       --subject <subject>
                       -f <video fps>
                       -r <results_dir>
                       -c <channels_format>
                       -ll <log_level>
                       -k <key>
        

Required Arguments

• -m, --model_full_path: Path to the model to use.

• --subject_infer_dir: Path to the subject inference directory.

• -f, --fps: FPS for input video

• -k, --key: Encryption key that was used to train the model.

• --subject: Subject to run inference on.

• -r, --results_dir: Directory to save the results.

Optional Arguments

• -i, --input_size: Size of input crop to network

• -lf, --low_freq: Low band for band pass filter

• -hf, --high_freq: High band for band pass filter

• -c, --channels_format: Channel format of data input.

• -ll, --log_level: Log level to use.

HeartRateNet includes the heartratenet export command to export the trained model to TAO format. To generate an encoded TAO model, use the following commands:

            

            
tao heartratenet export -m <model_filename>
                    -k <key>
                    -o <output_file>
                    --log_level <log_level>
        

Required Arguments

• -m, --model_filename: The absolute path to the desired model file

• -k, --key: The encryption key to use

• -o, --out_file: The absolute path for the desired exported model

Optional Arguments

• -t, --export_type: The export type to use

• -ll, --log_level: The log level to use

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

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