PoseClassificationNet

PoseClassificationNet takes a sequence of skeletons (body poses) as network input and predicts the actions of one or more persons in those frames. The model supported in the current version is based on the spatial-temporal graph convolutional network (ST-GCN), which is the most commonly used baseline for skeleton-based action recognition due to its simplicity and computational efficiency. Unlike pixel-based action recognition, ST-GCN is able to exploit the local pattern and correlation from a spatial-temporal graph of human skeletons. This model can be used to train graph convolutional networks (GCNs) for other purposes through transfer learning. Newer architectures with state-of-the-art performance will be released in the future. TAO Toolkit provides the network backbone for 3D poses.

PoseClassificationNet requires a sequence of skeletons (body poses) for input. The coordinates need to be normalized. For example, 3D joints are produced relative to the root keypoint (i.e. pelvis) and normalized by the focal length (1200.0 for 1080P). The entrypoint for dataset conversion generates an array of spatio-temporal sequences based on the output JSON metadata from the deepstream-bodypose-3d app.

The input data for training or inference are formatted as a NumPy array in five dimensions (N, C, T, V, M):

• N: The number of sequences

• C: The number of input channels, which is set to 3 in the NGC model

• T: The maximum sequence length in frames, which is 300 (10 seconds for 30 FPS) in the NGC model

• V: The number of joint points, set to 34 for the NVIDIA format

• M: The number of persons. The pre-trained model assumes a single object, but it can also support multiple people

The output of model inference is an array of N elements that gives the predicted action class for each sequence.

The labels used for training or evaluation are stored as a pickle file that consists of a list of two lists, including N elements each. The first list contains N strings of sample names. The second list contains the labeled action class ID of each sequence. The following is an example:

            

            
[["xl6vmD0XBS0.json", "OkLnSMGCWSw.json", "IBopZFDKfYk.json", "HpoFylcrYT4.json", "mlAtn_zi0bY.json", ...], [235, 388, 326, 306, 105, ...]]
        

The graph to model skeletons is defined by two configuration parameters:

• graph_layout (string): Must be one the following candidates:

  • nvidia consists of 34 joints. For more information, please refer to AR SDK Programming Guide.

  • openpose consists of 18 joints. For more information, please refer to OpenPose.

  • human3.6m consists of 17 joints. For more information, please refer to Human3.6M.

  • ntu-rgb+d consists of 25 joints. For more information, please refer to NTU RGB+D.

  • ntu_edge consists of 24 joints. For more information, please refer to NTU RGB+D.

  • coco consists of 17 joints. For more information, please refer to COCO.

• graph_strategy (string): Must be one of the following candidates (for more information, refer to the “Partition Strategies” section in this paper):

  • uniform: Uniform Labeling

  • distance: Distance Partitioning

  • spatial: Spatial Configuration

The spec file for PoseClassificationNet includes model, dataset, and train parameters. Here is an example spec for training a 3D-pose-based model on the NVIDIA dataset. It contains six classes: “sitting_down”, “getting_up”, “sitting”, “standing”, “walking”, “jumping”:

            

            
model:
  model_type: ST-GCN
  pretrained_model_path: "/path/to/pretrained_model.pth"
  input_channels: 3
  dropout: 0.5
  graph_layout: "nvidia"
  graph_strategy: "spatial"
  edge_importance_weighting: True
dataset:
  train_dataset:
    data_path: "/path/to/train_data.npy"
    label_path: "/path/to/train_label.pkl"
  val_dataset:
    data_path: "/path/to/val_data.npy"
    label_path: "/path/to/val_label.pkl"
  num_classes: 6
  label_map:
    sitting_down: 0
    getting_up: 1
    sitting: 2
    standing: 3
    walking: 4
    jumping: 5
  batch_size: 16
  num_workers: 1
train:
  optim:
    lr: 0.1
    momentum: 0.9
    nesterov: True
    weight_decay: 0.0001
    lr_scheduler: "MultiStep"
    lr_steps:
    - 10
    - 60
    lr_decay: 0.1
  num_epochs: 70
  checkpoint_interval: 5
        

model

The model parameter provides options to change the PoseClassificationNet architecture.

            

            
model:
  model_type: ST-GCN
  pretrained_model_path: "/path/to/pretrained_model.pth"
  input_channels: 3
  dropout: 0.5
  graph_layout: "nvidia"
  graph_strategy: "spatial"
  edge_importance_weighting: True
        

dataset

The dataset parameter defines the dataset source, training batch size, and augmentation.

            

            
dataset:
  train_dataset:
    data_path: "/path/to/train_data.npy"
    label_path: "/path/to/train_label.pkl"
  val_dataset:
    data_path: "/path/to/val_data.npy"
    label_path: "/path/to/val_label.pkl"
  num_classes: 6
  label_map:
    sitting_down: 0
    getting_up: 1
    sitting: 2
    standing: 3
    walking: 4
    jumping: 5
  batch_size: 16
  num_workers: 1
        

train

The train parameter defines the hyperparameters of the training process.

            

            
train:
  optim:
    lr: 0.1
    momentum: 0.9
    nesterov: True
    weight_decay: 0.0001
    lr_scheduler: "MultiStep"
    lr_steps:
    - 10
    - 60
    lr_decay: 0.1
  num_epochs: 70
  checkpoint_interval: 5
        

optim

The optim parameter defines the config for the SGD optimizer in training, including the learning rate, learning scheduler, and weight decay.

            

            
optim:
  lr: 0.1
  momentum: 0.9
  nesterov: True
  weight_decay: 0.0001
  lr_scheduler: "MultiStep"
  lr_steps:
  - 10
  - 60
  lr_decay: 0.1
        

Use the following command to run PoseClassificationNet training:

            

            
tao model pose_classification train -e <experiment_spec_file>
                              -r <results_dir>
                              -k <key>
                              [train.gpu_ids=<gpu id list>]
        

Required Arguments

• -e, --experiment_spec_file: The path to the experiment spec file.

• -r, --results_dir: The path to a folder where the experiment outputs should be written.

• -k, --key: The user-specific encoding key to save or load a .tlt model.

Optional Arguments

• train.gpu_ids: The GPU indices list for training. If you set more than one GPU ID, multi-GPU training will be triggered automatically.

Here’s an example of using the PoseClassificationNet training command:

            

            
tao model pose_classification train -e $DEFAULT_SPEC -r $RESULTS_DIR -k $KEY
        

The evaluation metric of PoseClassificationNet is the accuracy of action recognition.

Use the following command to run PoseClassificationNet evaluation:

            

            
tao model pose_classification evaluate -e <experiment_spec_file>
                                 -r <results_dir>
                                 -k <key>
                                 evaluate.checkpoint=<model to be evaluated>
                                 evaluate.test_dataset.data_path=<path to test data>
                                 evaluate.test_dataset.label_path=<path to test labels>
                                 [evaluate.gpu_id=<gpu index>]
        

Required Arguments

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

• -r, --results_dir: The path to a folder where the experiment outputs should be written.

• -k, --key: The encoding key for the .tlt model.

• evaluate.checkpoint: The .tlt model.

• evaluate.test_dataset.data_path: The path to the test data.

• evaluate.test_dataset.label_path: The path to the test labels.

Optional Argument

• evaluate.gpu_id: The GPU index used to run the evaluation. You can specify the GPU index used to run evaluation when the machine has multiple GPUs installed. Note that evaluation can only run on a single GPU.

Here’s an example of using the PoseClassificationNet evaluation command:

            

            
tao model pose_classification evaluate -e $DEFAULT_SPEC -r $RESULTS_DIR -k $KEY evaluate.checkpoint=$TRAINED_TLT_MODEL evaluate.test_dataset.data_path=$TEST_DATA evaluate.test_dataset.label_path=$TEST_LABEL
        

Use the following command to run inference on PoseClassificationNet with the .tlt model.

            

            
tao model pose_classification inference -e <experiment_spec>
                                  -r <results_dir>
                                  -k <key>
                                  inference.checkpoint=<inference model>
                                  inference.output_file=<path to output file>
                                  inference.test_dataset.data_path=<path to inference data>
                                  [inference.gpu_id=<gpu index>]
        

The output will be a text file, where each line corresponds to the predicted action class for an input sequence.

Required Arguments

• -e, --experiment_spec: The experiment spec file to set up inference.

• -r, --results_dir: The path to a folder where the experiment outputs should be written.

• -k, --key: The encoding key for the .tlt model.

• inference.checkpoint: The .tlt model to perform inference with.

• inference.output_file: The path to the output text file.

• inference.test_dataset.data_path: The path to the test data.

Optional Argument

• inference.gpu_id: The GPU index used to run the inference. You can specify the GPU index used to run inference when the machine has multiple GPUs installed. Note that inference can only run on a single GPU.

Here’s an example of using the PoseClassificationNet inference command:

            

            
tao model pose_classification inference -e $DEFAULT_SPEC -r $RESULTS_DIR -k $KEY inference.checkpoint=$TRAINED_TLT_MODEL inference.output_file=$OUTPUT_FILE inference.test_dataset.data_path=$TEST_DATA
        

The expected output for the NVIDIA test data would be as follows:

            

            
sit
sit
sit_down
...
        

Use the following command to export PoseClassificationNet to .onnx format for deployment:

            

            
tao model pose_classification export -e <experiment_spec>
                               -r <results_dir>
                               -k <key>
                               export.checkpoint=<tlt checkpoint to be exported>
                               [export.onnx_file=<path to exported file>]
                               [export.gpu_id=<gpu index>]
        

Required Arguments

• -e, --experiment_spec: The experiment spec file to set up export.

• -r, --results_dir: The path to a folder where the experiment outputs should be written.

• -k, --key: The encoding key for the .tlt model.

• export.checkpoint: The .tlt model to be exported.

Optional Arguments

• export.onnx_file: The path to save the exported model to. The default path is in the same directory as the \*.tlt model.

• export.gpu_id: The index of the GPU used to run export. If the machine has multiple GPUs, you can specify the GPU index used to run export. Note that export can only run on a single GPU.

Here’s an example of using the PoseClassificationNet export command:

            

            
tao model pose_classification export -e $DEFAULT_SPEC -r $RESULTS_DIR -k $KEY export.checkpoint=$TRAINED_TLT_MODEL
        

Use the following command to convert the output JSON metadata from the deepstream-bodypose-3d app and generate spatio-temporal sequences of body poses for inference:

            

            
tao model pose_classification dataset_convert -e <experiment_spec>
                                        -r <results_dir>
                                        -k <key>
                                        dataset_convert.data=<path to deepstream-bodypose-3d output data>
                                        [dataset_convert.pose_type=<pose type>]
                                        [dataset_convert.num_joints=<number of joints>]
                                        [dataset_convert.input_width=<input width>]
                                        [dataset_convert.input_height=<input height>]
                                        [dataset_convert.focal_length=<focal length>]
                                        [dataset_convert.sequence_length_max=<maximum sequence length>]
                                        [dataset_convert.sequence_length_min=<minimum sequence length>]
                                        [dataset_convert.sequence_length=<sequence length for sampling>]
                                        [dataset_convert.sequence_overlap=<sequence overlap for sampling>]
        

Required Arguments

• -e, --experiment_spec: The experiment spec file to set up dataset conversion

• -r, --results_dir: The path to a folder where the experiment outputs should be written

• -k, --key: The encoding key for the .tlt model

• dataset_convert.data: The output JSON data from the deepstream-bodypose-3d app

Optional Arguments

• dataset_convert.pose_type: The pose type can be chosen from 3dbp, 25dbp, 2dbp

• dataset_convert.num_joints: The number of joint points in the graph layout

• dataset_convert.input_width: The width of input images in pixels for normalization

• dataset_convert.input_height: The height of input images in pixels for normalization

• dataset_convert.focal_length: The focal length of the camera for normalization

• dataset_convert.sequence_length_max: The maximum sequence length for defining array shape

• dataset_convert.sequence_length_min: The minimum sequence length for filtering short sequences

• dataset_convert.sequence_length: The general sequence length for sampling

• dataset_convert.sequence_overlap: The overlap between sequences for sampling

Here’s an example of using the PoseClassificationNet dataset_convert command:

            

            
tao model pose_classification dataset_convert -e $DEFAULT_SPEC -r $RESULTS_DIR -k $KEY dataset_convert.data=$3D_BODYPOSE_JSON
        

The expected output would be a sampled array for each individual tracked ID saved under the directory for results.

You can deploy the trained deep learning and computer-vision models on edge devices, such as a Jetson Xavier, Jetson Nano, Tesla, or in the cloud with NVIDIA GPUs. The exported \*.onnx model can be used in the TAO Toolkit Triton Apps.

Running PoseClassificationNet Inference on the Triton Sample

The TAO Toolkit Triton Apps provide an inference sample for Pose Classification. It consumes a TensorRT engine and supports running with either (1) a NumPy array of skeleton series or (2) output JSON metadata from the deepstream-bodypose-3d app.

To use this sample, you need to generate the TensorRT engine from an \*.onnx model using trtexec, which is described in th next section.

Generating TensorRT Engine Using trtexec

For instructions on generating a TensorRT engine using the trtexec command, refer to the trtexec guide for PoseClassificationNet.

Running the Triton Inference Sample

You can generate the TensorRT engine when starting the Triton server using the following command:

            

            
bash scripts/start_server.sh
        

When the server is running, you can get results from a NumPy array of test data with the client using the command mentioned below:

            

            
python tao_client.py <path_to_test_data> \
                    -m pose_classification_tao model \
                    -x 1 \
                    -b 1 \
                    --mode Pose_classification \
                    -i https \
                    -u localhost:8000 \
                    --async \
                    --output_path <path_to_output_directory>
        

            

            
0, 27.6388(2)=sitting, 12.0806(3)=standing, 7.0409(1)=getting_up, -3.4164(0)=sitting_down, -16.4449(4)=walking, -26.9046(5)=jumping
1, 21.5809(2)=sitting, 8.4994(3)=standing, 5.1917(1)=getting_up, -2.3813(0)=sitting_down, -12.4322(4)=walking, -20.4436(5)=jumping
2, 5.6206(0)=sitting_down, 4.7264(4)=walking, -1.0996(5)=jumping, -2.3501(1)=getting_up, -3.2933(3)=standing, -3.5337(2)=sitting
....
        

You can also get inference results from the JSON output of the deepstream-bodypose-3d app using the following command:

            

            
python tao_client.py <path_to_json_file> \
                    --dataset_convert_config ../dataset_convert_specs/dataset_convert_config_pose_classification.yaml \
                    -m pose_classification_tao model \
                    -x 1 \
                    -b 1 \
                    --mode Pose_classification \
                    -i https \
                    -u localhost:8000 \
                    --async \
                    --output_path <path_to_output_directory>
        

Note

The server will perform inference on the input JSON file. The results are also saved as a JSON

file, which follows the same format as the input and adds the predicted "action" to each object at each frame. A sample of the JSON output would be as follows:

The skeleton sequence of each object is broken into segments by a dataset converter (refer to the figure below). The sequence_length and sequence_overlap are configurable in dataset_convert_config_pose_classification.yaml. The output labels are assigned to frames after a certain period of time.

End-to-End Inference Using Triton

A sample for end-to-end inference from video is also provided in the TAO Toolkit Triton Apps. The sample runs deepstream-bodypose-3d to generate metadata of bounding boxes, tracked IDs, and 2D/3D poses that are saved in JSON format. The client implicitly converts the metadata into arrays of skeleton sequences and sends them to the Triton server. The predicted action for each sequence is returned and appended to the JSON metadata at corresponding frames. A video with overlaid metadata is also generated for visualization.

You can start the Triton server using the following command (only the Pose Classification model will be downloaded and converted into a TensorRT engine):

            

            
bash scripts/pose_cls_e2e_inference/start_server.sh
        

Once the Triton server has started, open up another terminal and run the following command to begin body pose estimation using DeepStream and run Pose Classification on the DeepStream output using the Triton server instance that you previously spun up:

            

            
bash scripts/pose_cls_e2e_inference/start_client.sh