YOLOv4

YOLOv4 is an object detection model that is included in the TAO Toolkit. YOLOv4 supports the following tasks:

• dataset_convert

• kmeans

• train

• evaluate

• inference

• prune

• export

These tasks can be invoked from the TAO Toolkit Launcher using the following convention on the command line:

            

            
tao yolo_v4 <sub_task> <args_per_subtask>
        

where args_per_subtask are the command line arguments required for a given subtask. Each subtask is explained in detail below.

The dataset structure of YOLOv4 is identical to that of DetectNet_v2. The only difference is the command line used to generate the TFRecords from KITTI text labels. To generate TFRecords for YOLOv4 training, use this command:

            

            
tao yolo_v4 dataset_convert [-h] -d <dataset_spec>
                                     -o <output_tfrecords_file>
                                     [--gpu_index <gpu_index>]
        

Required Arguments

• -d, --dataset_spec: path to the dataset spec file.

• -o, --output_filename: path to the output TFRecords file.

Optional Arguments

• --gpu_index: The GPU index to run this command on. We can specify the GPU index used to run this command if the machine has multiple GPUs installed. Note that this command can only run on a single GPU.

Below is a sample for the YOLOv4 spec file. It has 6 major components: yolov4_config, training_config, eval_config, nms_config, augmentation_config, and dataset_config. The format of the spec file is a protobuf text (prototxt) message, and each of its fields can be either a basic data type or a nested message. The top-level structure of the spec file is summarized in the table below.

            

            
random_seed: 42
yolov4_config {
  big_anchor_shape: "[(114.94, 60.67), (159.06, 114.59), (297.59, 176.38)]"
  mid_anchor_shape: "[(42.99, 31.91), (79.57, 31.75), (56.80, 56.93)]"
  small_anchor_shape: "[(15.60, 13.88), (30.25, 20.25), (20.67, 49.63)]"
  box_matching_iou: 0.25
  matching_neutral_box_iou: 0.5
  arch: "resnet"
  nlayers: 18
  loss_loc_weight: 1.0
  loss_neg_obj_weights: 1.0
  loss_class_weights: 1.0
  label_smoothing: 0.0
  big_grid_xy_extend: 0.05
  mid_grid_xy_extend: 0.1
  small_grid_xy_extend: 0.2
  freeze_bn: false
  freeze_blocks: 0
  force_relu: false
}
training_config {
  batch_size_per_gpu: 8
  num_epochs: 80
  enable_qat: false
  checkpoint_interval: 10
  learning_rate {
    soft_start_cosine_annealing_schedule {
      min_learning_rate: 1e-7
      max_learning_rate: 1e-4
      soft_start: 0.3
    }
  }
  regularizer {
    type: L1
    weight: 3e-5
  }
  optimizer {
    adam {
      epsilon: 1e-7
      beta1: 0.9
      beta2: 0.999
      amsgrad: false
    }
  }
  pretrain_model_path: "EXPERIMENT_DIR/pretrained_resnet18/tlt_pretrained_object_detection_vresnet18/resnet_18.hdf5"
}
eval_config {
  average_precision_mode: SAMPLE
  batch_size: 8
  matching_iou_threshold: 0.5
}
nms_config {
  confidence_threshold: 0.001
  clustering_iou_threshold: 0.5
  top_k: 200
}
augmentation_config {
  hue: 0.1
  saturation: 1.5
  exposure:1.5
  vertical_flip:0
  horizontal_flip: 0.5
  jitter: 0.3
  output_width: 1248
  output_height: 384
  output_channel: 3
  randomize_input_shape_period: 100
  mosaic_prob: 0.5
  mosaic_min_ratio:0.2
  image_mean {
    key: 'b'
    value: 103.9
  }
  image_mean {
    key: 'g'
    value: 116.8
  }
  image_mean {
    key: 'r'
    value: 123.7
  }
}
dataset_config {
  data_sources: {
      tfrecords_path: "/workspace/tao-experiments/data/training/tfrecords/<tfrecords pattern>"
      image_directory_path: "/workspace/tao-experiments/data/training"
  }
  include_difficult_in_training: true
  image_extension: "png"
  target_class_mapping {
      key: "car"
      value: "car"
  }
  target_class_mapping {
      key: "pedestrian"
      value: "pedestrian"
  }
  target_class_mapping {
      key: "cyclist"
      value: "cyclist"
  }
  target_class_mapping {
      key: "van"
      value: "car"
  }
  target_class_mapping {
      key: "person_sitting"
      value: "pedestrian"
  }
  validation_data_sources: {
      tfrecords_path: "/workspace/tao-experiments/data/val/tfrecords/<tfrecords pattern>"
      image_directory_path: "/workspace/tao-experiments/data/val"
  }
}
        

Training Config

The training configuration (training_config) defines the parameters needed for training, evaluation, and inference. Details are summarized in the table below.

Visualization during training

Visualization during training is configured by the visualizer parameter. The parameters of it are described in the table below.

Visualization during training supports 3 types of visualizations, namely: scalar, image and histogram. These types of visualization all leverage the TensorBoard tool. Each type will have a tab in TensorBoard GUI interface. With the scalar tab, it can visualize scalars like loss, learning rate and validation mAP over time(training step). With the image tab, it can visualize augmented images during training, with bounding boxes drawn on the them. With the histogram tab, it can visualize histograms of each layer’s weights and bias of the model being trained.

If the parameter enabled is set to True, then all above visualizations will be enabled. Otherwise, all visualization will be disabled.

The parameter num_images is used to limit the maximum number of images to be visualized on the image tab in TensorBoard.

During the training, visualization can be done anywhere that can access the TensorBoard log directory. Usually the TAO Toolkit containers will map volumes to host machine, so TensorBoard can be called on host machine. The command tensorboard --logdir=/path/to/logs can be used to open the TensorBoard visualization GUI in web browser. Make sure tensorboard is installed before running this command. One can run pip3 install tensorboard to install it if it is not installed in the environment. The /path/to/logs argument is the result directory for training, with the suffix /logs appended.

Early Stopping

The parameters for early stopping are described in the table below.

Evaluation Config

The evaluation configuration (eval_config) defines the parameters needed for the evaluation either during training or standalone evaluation. Details are summarized in the table below.

NMS Config

The NMS configuration (nms_config) defines the parameters needed for the NMS postprocessing. NMS config applies to the NMS layer of the model in training, validation, evaluation, inference, and export. Details are summarized in the table below.

Augmentation Config

The augmentation configuration (augmentation_config) defines the parameters needed for online data augmentation. Details are summarized in the table below.

Dataset Config

YOLOv4 supports two data formats: the sequence format (images folder and raw labels folder with KITTI format) and the tfrecords format (images folder and TFRecords). From our experience, if mosaic augmentation is disabled (mosaic_prob=0), training with TFRecords format is faster. If mosaic augmentation is enabled (mosaic_prob>0), training with sequence format is faster. The train and evaluate command will determine the data format based on your dataset_config.

The YOLOv4 dataloader assumes the training/validation split is already done and the data is prepared in KITTI format: images and labels are in two separate folders, where each image in the image folder has a .txt label file with the same filename in the label folder, and the label file content follows KITTI format. The COCO data format is supported but only through TFRecords. Prepare the TFRecords using dataset_convert.

Sequence format

The following is an example dataset_config element if you want to use sequence format:

            

            
dataset_config {
   data_sources: {
       label_directory_path: "/workspace/tao-experiments/data/training/label_2"
       image_directory_path: "/workspace/tao-experiments/data/training/image_2"
   }
   data_sources: {
       label_directory_path: "/workspace/tao-experiments/data/training/label_3"
       image_directory_path: "/workspace/tao-experiments/data/training/image_3"
   }
   include_difficult_in_training: true
   target_class_mapping {
       key: "car"
       value: "car"
   }
   target_class_mapping {
       key: "pedestrian"
       value: "pedestrian"
   }
   target_class_mapping {
       key: "cyclist"
       value: "cyclist"
   }
   target_class_mapping {
       key: "van"
       value: "car"
   }
   target_class_mapping {
       key: "person_sitting"
       value: "pedestrian"
   }
   validation_data_sources: {
       label_directory_path: "/workspace/tao-experiments/data/val/label_1"
       image_directory_path: "/workspace/tao-experiments/data/val/image_1"
   }
}
        

The parameters in dataset_config are defined as follows:

• data_sources: The path to datasets to train on. If you have multiple data sources for training, you may use multiple data_sources. This field contains 2 parameters:

  • label_directory_path: The path to the data source label folder.

  • image_directory_path: The path to the data source image folder.

• include_difficult_in_training: A flag specifying whether to include difficult boxes in training. If set to false, difficult boxes will be ignored. Difficult boxes are those with non-zero occlusion levels in KITTI labels.

• target_class_mapping: This parameter maps the class names in the labels to the target class to be trained in the network. An element is defined for every source class to target class mapping. This field was included with the intention of grouping similar class objects under one umbrella. For example, “car”, “van”, “heavy_truck”, etc. may be grouped under “automobile”. The “key” field is the value of the class name in the tfrecords file, and the “value” field corresponds to the value that the network is expected to learn.

• validation_data_sources: Captures the path to datasets to validate on. If you have multiple data sources for validation, you may use multiple validation_data_sources. Like data_sources, this field contains two parameters.

TFRecords format

TFRecords format requires tfrecords for all labels. This requires running of tao yolo_v4 dataset-convert command. The command has same functionality and argument requirements as that of detectnet_v2 and for details of how to generate tfrecords, check Pre-processing the Dataset in detectnet_v2.

The following is an example dataset_config element if you want to use tfrecords format that was converted from KITTI data format. Here, we assume your tfrecords are all generated under a folder called tfrecords, which is under same parent folder with images and labels:

            

            
dataset_config {
   data_sources: {
       tfrecords_path: "/workspace/tao-experiments/data/training/tfrecords/<tfrecords pattern>"
       image_directory_path: "/workspace/tao-experiments/data/training"
   }
   include_difficult_in_training: true
   image_extension: "png"
   target_class_mapping {
       key: "car"
       value: "car"
   }
   target_class_mapping {
       key: "pedestrian"
       value: "pedestrian"
   }
   target_class_mapping {
       key: "cyclist"
       value: "cyclist"
   }
   target_class_mapping {
       key: "van"
       value: "car"
   }
   target_class_mapping {
       key: "person_sitting"
       value: "pedestrian"
   }
   validation_data_sources: {
       tfrecords_path: "/workspace/tao-experiments/data/val/tfrecords/<tfrecords pattern>"
       image_directory_path: "/workspace/tao-experiments/data/val"
   }
}
        

The parameters in dataset_config are defined as follows:

• data_sources: The path to datasets to train on. If you have multiple data sources for training, you may use multiple data_sources. This field contains 2 parameters:

  • tfrecords_path: The path to the data source tfrecords.

  • image_directory_path: The path to the root directory containing the image folder.

• image_extension: Image extensions of images contained in the image folder. Note, to use tfrecords format, all images must have same extensions and currently we support jpg and png

• validation_data_sources: Captures the path to datasets to validate on. This field contains two parameters same as data_sources. If you have multiple data sources for validation, you may use multiple validation_data_sources.

All other fields are same as those in sequence format dataset_config.

Class Weighting Config

YOLOV4 supports class-level weighting on the loss function during training. The following is an example of the class weighting configuration (class_weighting_config) to set weights for two classes.

            

            
class_weighting_config {
  class_weighting{
      key: "person"
      value: 1.0
  }
  class_weighting{
      key: "bus"
      value: 5.0
  }
}
        

The parameters in class_weighting_config are defined as follows:

• class_weighting: This parameter maps the class name to the corresponding class weight on the loss function. The class weight value should be greater than 0. If a class weight is not explictly set in the config, a default value of 1.0 will be assigned implictly.

YOLO4 Config

The YOLOv4 configuration (yolov4_config) defines the parameters needed for building the YOLOv4 model. Details are summarized in the table below.

The anchor shape should match most ground truth boxes in the dataset to help the network learn bounding boxes. The YOLOv4 paper proposes using the kmeans algorithm to get the anchor shapes, and the tao yolo_v4 kmeans command is implemented in the TAO algorithm. You should use the output as your anchor shape in the yolov4_config spec file.

            

            
tao yolo_v4 kmeans [-h] -l <label_folders>
                        -i <image_folders>
                        -x <network base input width>
                        -y <network base input height>
                        [-n <num_clusters>]
                        [--max_steps <kmeans max steps>]
                        [--min_x <ignore boxes with width less than this value>]
                        [--min_y <ignore boxes with height less than this value>]
        

Required Arguments

• -l: Paths to the training label folders. Multiple folder paths should be separated with spaces.

• -i: Paths to corresponding training image folders. Folder counts and orders must match label folders.

• -x: The base network input width. This should be the output_width in the augmentation config part of your spec file.

• -y: The base network input height. This should be the output_height in the augmentation config part of your spec file.

Optional Arguments

• -n: The number of shape clusters. This defines how many shape centers the command will output. The default is 9 (3 per group and 3 groups).

• --max_steps: The max number of steps the kmeans algorithm should run. If the algorithm does not converge at this step, a suboptimal result will be returned. The default value is 10000.

• --min_x: Ignore ground truth boxes with width less than this value in a reshaped image (images are first reshaped to network base shape as -x, -y).

• --min_y: Ignore ground truth boxes with height less than this value in a reshaped image (images are first reshaped to network base shape as -x, -y).

• -h, --help: Show this help message and exit.

Train the YOLOv4 model using this command:

            

            
tao yolo_v4 train [-h] -e <experiment_spec>
                       -r <output_dir>
                       -k <key>
                       [--gpus <num_gpus>]
                       [--gpu_index <gpu_index>]
                       [--use_amp]
                       [--log_file <log_file_path>]
        

Required Arguments

• -r, --results_dir: The path to the folder where the experiment output is written.

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

• -e, --experiment_spec_file: The experiment specification file to set up the evaluation experiment. This should be the same as the training-specification file.

Optional Arguments

• --gpus: The number of GPUs to use for training in a multi-GPU scenario (default: 1).

• --gpu_index: The GPU indices used to run the training. You can specify the indices of GPUs to use for training when the machine has multiple GPUs installed.

• --use_amp: A flag to enable AMP training.

• --log_file: THe path to the log file. The default path is stdout.

• -h, --help: Show this help message and exit.

Input Requirement

• Input size: C * W * H (where C = 1 or 3, W >= 128, H >= 128, W, H are multiples of 32)

• Image format: JPG, JPEG, PNG

• Label format: KITTI detection

Sample Usage

Here’s an example of using the train command on a YOLOv4 model:

            

            
tao yolo_v4 train --gpus 2 -e /path/to/spec.txt -r /path/to/result -k $KEY
        

To run evaluation on a YOLOv4 model, use this command:

            

            
tao yolo_v4 evaluate [-h] -e <experiment_spec_file>
                          -m <model_file>
                          -k <key>
                          [--gpu_index <gpu_index>]
                          [--log_file <log_file_path>]
        

Required Arguments

• -e, --experiment_spec_file: The experiment spec file to set up the evaluation experiment. This should be the same as the training-specification file.

• -m, --model: The path to the model file to use for evaluation. The model can be either a .tlt model file or TensorRT engine.

• -k, --key: The key to load the model (not needed if the model is a TensorRT engine).

Optional Arguments

• -h, --help: Show this help message and exit.

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

• --log_file: The path to the log file. The default path is stdout.

The inference tool for YOLOv4 networks, which may be used to visualize bboxes or generate frame-by-frame KITTI format labels on a single image or a directory of images. An example of the command for this tool is shown here:

            

            
tao yolo_v4 inference [-h] -i <input directory>
                           -o <output annotated image directory>
                           -e <experiment spec file>
                           -m <model file>
                           -k <key>
                           [-l <output label directory>]
                           [-t <visualization threshold>]
                           [--gpu_index <gpu_index>]
                           [--log_file <log_file_path>]
        

Required Arguments

• -m, --model: The path to the trained model (TAO model) or TensorRT engine.

• -i, --in_image_dir: The directory of input images for inference.

• -o, --out_image_dir: The directory path to output annotated images.

• -k, --key: The key to load model (not needed if model is a TensorRT engine).

• -e, --config_path: Path to an experiment spec file for training.

Optional Arguments

• -t, --draw_conf_thres: Threshold for drawing a bbox. default: 0.3.

• -h, --help: Show this help message and exit.

• -l, --out_label_dir: The directory to output KITTI labels.

• --gpu_index: The GPU index used to run the inference. We 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.

• --log_file: The path to the log file. The default path is stdout.

Pruning removes parameters from the model to reduce the model size without compromising the integrity of the model itself using the tao yolo_v4 prune command.

The tao yolo_v4 prune command includes these parameters:

            

            
tao yolo_v4 prune [-h] -m <pretrained_model>
                       -o <output_file>
                       -k <key>
                       [-n <normalizer>]
                       [-eq <equalization_criterion>]
                       [-pg <pruning_granularity>]
                       [-pth <pruning threshold>]
                       [-nf <min_num_filters>]
                       [-el [<excluded_list>]
        

Required Arguments

• -m, --model: The path to pretrained YOLOv4 model

• -o, --output_file: The path to the output checkpoints

• -k, --key: The key to load a .tlt model

Optional Arguments

• -h, --help: Show this help message and exit.

• -n, –normalizer: Use max to normalize by dividing each norm by the maximum norm within a layer; use L2 to normalize by dividing by the L2 norm of the vector comprising all kernel norms. The default value is max.

• -eq, --equalization_criterion: Criteria to equalize the stats of inputs to an element-wise op layer or depth-wise convolutional layer. This parameter is useful for ResNets and MoblieNets. Options are arithmetic_mean, geometric_mean, union, and intersection. The default value is union.

• -pg, -pruning_granularity: The number of filters to remove at a time (default: 8)

• -pth: The threshold to compare a normalized norm against (default: 0.1)

• -nf, --min_num_filters: The minimum number of filters to keep per layer (default: 16)

• -el, --excluded_layers: A list of excluded_layers. Examples: -i item1 item2 (default: [])

After pruning, the model needs to be retrained. See Re-training the Pruned Model for more details.

Using the Prune Command

Here’s an example of using the tao yolo_v4 prune command:

            

            
tao yolo_v4 prune -m /workspace/output/weights/resnet_003.tlt \
                  -o /workspace/output/weights/resnet_003_pruned.tlt \
                  -eq union \
                  -pth 0.7 -k $KEY
        

Once the model has been pruned, there might be a slight decrease in accuracy because some previously useful weights may have been removed. To regain accuracy, we recommend retraining the pruned model over the same dataset. To do this, use the tao yolo_v4 train command as documented in Training the model, with an updated spec file that points to the newly pruned model as the pruned_model_path.

We recommend turning off the regularizer in the training_config for detectnet to recover the accuracy when retraining a pruned model. You may do this by setting the regularizer type to NO_REG as mentioned in the Training Config section. All other parameters in the spec file can be carried over from the previous training.

Exporting the model decouples the training process from inference and allows conversion to TensorRT engines outside the TAO environment. TensorRT engines are specific to each hardware configuration and should be generated for each unique inference environment. The exported model may be used universally across training and deployment hardware. The exported model format is referred to as .etlt. Like .tlt, the .etlt model format is also an encrypted model format with the same key of the .tlt model that it is exported from. This key is required when deploying this model.

INT8 Mode Overview

TensorRT engines can be generated in INT8 mode to improve performance, but require a calibration cache at engine creation-time. If tao yolo_v4 export is run with the --data_type flag set to int8, the calibration cache is generated using a calibration tensor file. Pre-generating the calibration information and caching it removes the need for calibrating the model on the inference machine. Moving the calibration cache is usually much more convenient than moving the calibration tensorfile since it is a much smaller file and can be moved with the exported model. Using the calibration cache also speeds up engine creation, as building the cache can take several minutes depending on the size of the Tensorfile and the model itself.

The export tool can generate an INT8 calibration cache by ingesting training data using either of these options:

• Option 1: Using the training data loader to load the training images for INT8 calibration. This option is now the recommended approach to support multiple image directories by leveraging the training dataset loader. This also ensures two important aspects of data during calibration:

  • Data pre-processing in the INT8 calibration step is the same as in the training process.

  • The data batches are sampled randomly across the entire training dataset, thereby improving the accuracy of the INT8 model.

• Option 2: Pointing the tool to a directory of images that you want to use to calibrate the model. For this option, make sure to create a sub-sampled directory of random images that best represent your training dataset.

FP16/FP32 Model

The calibration.bin is only required if you need to run inference at INT8 precision. For FP16/FP32 based inference, the export step is much simpler. All that is required is to provide a .tlt model from the training/retraining step to be converted into .etlt.

Exporting the Model

Here’s an example of the command line arguments of the tao yolo_v4 export command:

            

            
tao yolo_v4 export [-h]
                    -m <path to the .tlt model file generated by tao train>
                    -k <key>
                    [-o <path to output file>]
                    [--cal_data_file <path to tensor file>]
                    [--cal_image_dir <path to the directory images to calibrate the model]
                    [--cal_cache_file <path to output calibration file>]
                    [--data_type <Data type for the TensorRT backend during export>]
                    [--batches <Number of batches to calibrate over>]
                    [--max_batch_size <maximum trt batch size>]
                    [--max_workspace_size <maximum workspace size]
                    [--batch_size <batch size to TensorRT engine>]
                    [--experiment_spec <path to experiment spec file>]
                    [--engine_file <path to the TensorRT engine file>]
                    [--gen_ds_config]
                    [--verbose]
                    [--strict_type_constraints]
                    [--force_ptq]
                    [--gpu_index <gpu_index>]
                    [--log_file <log_file_path>]
        

Required Arguments

• -m, --model: The path to the .tlt model file to be exported.

• -k, --key: The key used to save the .tlt model file.

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

Optional Arguments

• -h, --help: Show this help message and exit.

• -o, --output_file: The path to save the exported model to. The default path is ./<input_file>.etlt.

• --data_type: The desired engine data type. The options are {fp32, fp16, int8} The default value is fp32. A calibration cache is generated in INT8 mode. If using INT8 mode, the following INT8 arguments are required.

• --gen_ds_config: A Boolean flag indicating whether to generate the template DeepStream related configuration (“nvinfer_config.txt”) as well as a label file (“labels.txt”) in the same directory as the output_file. Note that the config file is NOT a complete configuration file and requires the user to update the sample config files in DeepStream with the parameters generated.

• -s, --strict_type_constraints: A Boolean flag indicating whether to apply the TensorRT strict type constraints when building the TensorRT engine.

• --gpu_index: The index of the (discrete) GPU for exporting the model if the machine has multiple GPUs installed. Note that export can only run on a single GPU.

• --log_file: The path to the log file. The default path is stdout.

INT8 Export Mode Required Arguments

• --cal_data_file: The tensorfile generated for calibrating the engine. This can also be an output file if used with --cal_image_dir.

• --cal_image_dir: A directory of images to use for calibration.

INT8 Export Optional Arguments

• --cal_cache_file: The path to save the calibration cache file to. The default value is ./cal.bin.

• --batches: The number of batches to use for calibration and inference testing. The default value is 10.

• --batch_size: The batch size to use for calibration. The default value is 8.

• --max_batch_size: The maximum batch size of the TensorRT engine. The default value is 16.

• --max_workspace_size: The maximum workspace size of TensorRT engine. The default value is 1073741824 = 1<<30

• --engine_file: The path to the serialized TensorRT engine file. Note that this file is hardware specific, and cannot be generalized across GPUs. It is useful to quickly test your model accuracy using TensorRT on the host. As TensorRT engine file is hardware specific, you cannot use this engine file for deployment unless the deployment GPU is identical to the training GPU.

• --force_ptq: A boolean flag to force post training quantization on the exported etlt model.

Sample usage

The following is a sample command to export a YOLOv4 model in INT8 mode:

            

            
tao yolo_v4 export -m /workspace/yolov4_resnet18_epoch_100.tlt  \
                         -o /workspace/yolov4_resnet18_epoch_100_int8.etlt \
                         -e /workspace/yolov4_retrain_resnet18_kitti.txt \
                         -k $KEY \
                         --cal_image_dir /workspace/data/training/image_2 \
                         --data_type int8 \
                         --batch_size 8 \
                         --batches 10 \
                         --cal_cache_file /export/cal.bin  \
                         --cal_data_file /export/cal.tensorfile
        

The deep learning and computer vision models that you’ve trained can be deployed on edge devices, such as a Jetson Xavier or Jetson Nano, a discrete GPU, or in the cloud with NVIDIA GPUs. TAO Toolkit has been designed to integrate with DeepStream SDK, so models trained with TAO Toolkit will work out of the box with DeepStream SDK.

DeepStream SDK is a streaming analytic toolkit to accelerate building AI-based video analytic applications. This section will describe how to deploy your trained model to DeepStream SDK.

To deploy a model trained by TAO Toolkit to DeepStream we have two options:

• Option 1: Integrate the .etlt model directly in the DeepStream app. The model file is generated by export.

• Option 2: Generate a device specific optimized TensorRT engine using tao-converter. The generated TensorRT engine file can also be ingested by DeepStream.

Machine-specific optimizations are done as part of the engine creation process, so a distinct engine should be generated for each environment and hardware configuration. If the TensorRT or CUDA libraries of the inference environment are updated (including minor version updates), or if a new model is generated, new engines need to be generated. Running an engine that was generated with a different version of TensorRT and CUDA is not supported and will cause unknown behavior that affects inference speed, accuracy, and stability, or it may fail to run altogether.

Option 1 is very straightforward. The .etlt file and calibration cache are directly used by DeepStream. DeepStream will automatically generate the TensorRT engine file and then run inference. TensorRT engine generation can take some time depending on size of the model and type of hardware. Engine generation can be done ahead of time with Option 2. With option 2, the tao-converter is used to convert the .etlt file to TensorRT; this file is then provided directly to DeepStream.

See the Exporting the Model section for more details on how to export a TAO model.

TensorRT Open Source Software (OSS)

The TensorRT OSS build is required for YOLOv4 models. This is required because several TensorRT plugins that are required by these models are only available in TensorRT open source repo and not in the general TensorRT release. Specifically, for YOLOv4, we need the batchTilePlugin and batchedNMSPlugin.

If the deployment platform is x86 with an NVIDIA GPU, follow the TensorRT OSS on x86 instructions; if your deployment is on an NVIDIA Jetson platform, follow the TensorRT OSS on Jetson (ARM64) instructions.

TensorRT OSS on x86

Building TensorRT OSS on x86:

1. Install Cmake (>=3.13).

   Note

   TensorRT OSS requires cmake >= v3.13, so install cmake 3.13 if your cmake version is lower than 3.13c

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   sudo apt remove --purge --auto-remove cmake
   wget https://github.com/Kitware/CMake/releases/download/v3.13.5/cmake-3.13.5.tar.gz
   tar xvf cmake-3.13.5.tar.gz
   cd cmake-3.13.5/
   ./configure
   make -j$(nproc)
   sudo make install
   sudo ln -s /usr/local/bin/cmake /usr/bin/cmake
           

   
   

2. Get GPU architecture. The GPU_ARCHS value can be retrieved by the deviceQuery CUDA sample:

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   cd /usr/local/cuda/samples/1_Utilities/deviceQuery
   sudo make
   ./deviceQuery
           

   If the /usr/local/cuda/samples doesn’t exist in your system, you could download deviceQuery.cpp from this GitHub repo. Compile and run deviceQuery.

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   nvcc deviceQuery.cpp -o deviceQuery
   ./deviceQuery
           

   This command will output something like this, which indicates the GPU_ARCHS is 75 based on CUDA Capability major/minor version.

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   Detected 2 CUDA Capable device(s)
   
   Device 0: "Tesla T4"
     CUDA Driver Version / Runtime Version          10.2 / 10.2
     CUDA Capability Major/Minor version number:    7.5
           

3. Build TensorRT OSS:

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   git clone -b 21.08 https://github.com/nvidia/TensorRT
   cd TensorRT/
   git submodule update --init --recursive
   export TRT_SOURCE=`pwd`
   cd $TRT_SOURCE
   mkdir -p build && cd build
           

   Note

   Make sure your GPU_ARCHS from step 2 is in TensorRT OSS CMakeLists.txt. If GPU_ARCHS is not in TensorRT OSS CMakeLists.txt, add -DGPU_ARCHS=<VER> as below, where <VER> represents GPU_ARCHS from step 2.

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   /usr/local/bin/cmake .. -DGPU_ARCHS=xy  -DTRT_LIB_DIR=/usr/lib/x86_64-linux-gnu/ -DCMAKE_C_COMPILER=/usr/bin/gcc -DTRT_BIN_DIR=`pwd`/out
   make nvinfer_plugin -j$(nproc)
           

   After building ends successfully, libnvinfer_plugin.so* will be generated under \`pwd\`/out/.

4. Replace the original libnvinfer_plugin.so*:

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   sudo mv /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.8.x.y ${HOME}/libnvinfer_plugin.so.8.x.y.bak   // backup original libnvinfer_plugin.so.x.y
   sudo cp $TRT_SOURCE/`pwd`/out/libnvinfer_plugin.so.8.m.n  /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.8.x.y
   sudo ldconfig
           

TensorRT OSS on Jetson (ARM64)

1. Install Cmake (>=3.13)

   Note

   TensorRT OSS requires cmake >= v3.13, while the default cmake on Jetson/Ubuntu 18.04 is cmake 3.10.2.

   Upgrade TensorRT OSS using:

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   sudo apt remove --purge --auto-remove cmake
   wget https://github.com/Kitware/CMake/releases/download/v3.13.5/cmake-3.13.5.tar.gz
   tar xvf cmake-3.13.5.tar.gz
   cd cmake-3.13.5/
   ./configure
   make -j$(nproc)
   sudo make install
   sudo ln -s /usr/local/bin/cmake /usr/bin/cmake
           

2. Get GPU architecture based on your platform. The GPU_ARCHS for different Jetson platform are given in the following table.

   Jetson Platform	GPU_ARCHS
   Nano/Tx1	53
   Tx2	62
   AGX Xavier/Xavier NX	72

3. Build TensorRT OSS:

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   git clone -b 21.03 https://github.com/nvidia/TensorRT
   cd TensorRT/
   git submodule update --init --recursive
   export TRT_SOURCE=`pwd`
   cd $TRT_SOURCE
   mkdir -p build && cd build
           

   Note

   The -DGPU_ARCHS=72 below is for Xavier or NX, for other Jetson platform, change 72 referring to GPU_ARCHS from step 2.

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   /usr/local/bin/cmake .. -DGPU_ARCHS=72  -DTRT_LIB_DIR=/usr/lib/aarch64-linux-gnu/ -DCMAKE_C_COMPILER=/usr/bin/gcc -DTRT_BIN_DIR=`pwd`/out
   make nvinfer_plugin -j$(nproc)
           

   After building ends successfully, libnvinfer_plugin.so* will be generated under ‘pwd’/out/.

4. Replace "libnvinfer_plugin.so*" with the newly generated.

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   sudo mv /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.8.x.y ${HOME}/libnvinfer_plugin.so.8.x.y.bak   // backup original libnvinfer_plugin.so.x.y
   sudo cp `pwd`/out/libnvinfer_plugin.so.8.m.n  /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.8.x.y
   sudo ldconfig
           

Generating an Engine Using tao-converter

The tao-converter tool is provided with the TAO Toolkit to facilitate the deployment of TAO trained models on TensorRT and/or Deepstream. This section elaborates on how to generate a TensorRT engine using tao-converter.

For deployment platforms with an x86-based CPU and discrete GPUs, the tao-converter is distributed within the TAO docker. Therefore, we suggest using the docker to generate the engine. However, this requires that the user adhere to the same minor version of TensorRT as distributed with the docker. The TAO docker includes TensorRT version 8.0.

Instructions for x86

For an x86 platform with discrete GPUs, the default TAO package includes the tao-converter built for TensorRT 8.2.5.1 with CUDA 11.4 and CUDNN 8.2. However, for any other version of CUDA and TensorRT, please refer to the overview section for download. Once the tao-converter is downloaded, follow the instructions below to generate a TensorRT engine.

1. Unzip the zip file on the target machine.

2. Install the OpenSSL package using the command:

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   sudo apt-get install libssl-dev
           

3. Export the following environment variables:

            

            
$ export TRT_LIB_PATH=”/usr/lib/x86_64-linux-gnu”
$ export TRT_INC_PATH=”/usr/include/x86_64-linux-gnu”
        

4. Run the tao-converter using the sample command below and generate the engine.

5. Instructions to build TensorRT OSS on Jetson can be found in the TensorRT OSS on x86 section above or in this GitHub repo.

Instructions for Jetson

For the Jetson platform, the tao-converter is available to download in the NVIDIA developer zone. You may choose the version you wish to download as listed in the overview section. Once the tao-converter is downloaded, please follow the instructions below to generate a TensorRT engine.

1. Unzip the zip file on the target machine.

2. Install the OpenSSL package using the command:

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   sudo apt-get install libssl-dev
           

3. Export the following environment variables:

            

            
$ export TRT_LIB_PATH=”/usr/lib/aarch64-linux-gnu”
$ export TRT_INC_PATH=”/usr/include/aarch64-linux-gnu”
        

1. For Jetson devices, TensorRT comes pre-installed with Jetpack. If you are using older JetPack, upgrade to JetPack-5.0DP.

2. Instructions to build TensorRT OSS on Jetson can be found in the TensorRT OSS on Jetson (ARM64) section above or in this GitHub repo.

3. Run the tao-converter using the sample command below and generate the engine.

Using the tao-converter

            

            
tao-converter [-h] -k <encryption_key>
              -d <input_dimensions>
              -o <comma separated output nodes>
              [-c <path to calibration cache file>]
              [-e <path to output engine>]
              [-b <calibration batch size>]
              [-m <maximum batch size of the TRT engine>]
              [-t <engine datatype>]
              [-w <maximum workspace size of the TRT Engine>]
              [-i <input dimension ordering>]
              [-p <optimization_profiles>]
              [-s]
              [-u <DLA_core>]
              input_file
        

Required Arguments

• input_file: The path to the .etlt model exported using tao yolo_v4 export.

• -k: The key used to encode the .tlt model when training.

• -d: A comma-separated list of input dimensions that should match the dimensions used for tao yolo_v4 export.

• -o: A comma-separated list of output blob names that should match the output configuration used for tao yolo_v4 export. For YOLOv4, set this argument to BatchedNMS.

• -p: Optimization profiles for .etlt models with dynamic shape. Use a comma-separated list of optimization profile shapes in the format <input_name>,<min_shape>,<opt_shape>,<max_shape>, where each shape has the format: <n>x<c>x<h>x<w>. The input name for YOLOv4 is Input

Optional Arguments

• -e: The path to save the engine to. The default path is ./saved.engine.

• -t: The desired engine data type. The options are {fp32, fp16, int8}. The default value is fp32. A calibration cache is generated in INT8 mode.

• -w: The maximum workspace size for the TensorRT engine. The default value is 1073741824(1<<30).

• -i: The input dimension ordering; all other TAO commands use NCHW. The options are {nchw, nhwc, nc}.The default value is nchw, so you can omit this argument for YOLOv4.

• -s: TensorRT strict type constraints. A Boolean to apply TensorRT strict type constraints when building the TensorRT engine.

• -u: Specifies the DLA core index when building the TensorRT engine on Jetson devices (only needed if using DLA core).

INT8 Mode Arguments

• -c: The path to the calibration cache file (only used in INT8 mode). The default value is ./cal.bin.

• -b: The batch size used during the export step for INT8-calibration cache generation (default: 8).

• -m: The maximum batch size for the TensorRT engine (default: 16). If you encounter an out-of-memory issue, decrease the batch size accordingly. This parameter is not required for .etlt models generated with dynamic shape (which is only possible for new models introduced since version 3.0).

Sample Output Log

Here is a sample log for exporting a YOLOv4 model:

            

            
tao-converter -k $KEY  \
              -p Input,1x3x384x1248,8x3x384x1248,16x3x384x1248 \
                    -e /export/trt.fp16.engine \
                    -t fp16 \
                    /ws/yolov4_resnet18_epoch_100.etlt
        

Integrating the model with DeepStream

To integrate a model trained by TAO Toolkit with DeepStream, you should generate a device-specific optimized TensorRT engine using tao-converter. The generated TensorRT engine file can then be ingested by DeepStream. DeepStream can also generate TensorRT engine on-the-fly for YOLOv4 if only etlt models are provided.

For YOLOv4, you will need to build the TensorRT open-source plugins and custom bounding-box parser. The instructions to build TensorRT open-source plugins are provided in the TensorRT Open Source Software (OSS) section above. The instructions to build a custom bounding-box parser are provided in the Prerequisites for YOLOv4 Model section below, and the required code can be found in this GitHub repo.

To integrate the models with DeepStream, you need the following:

1. Download and install DeepStream SDK. The installation instructions for DeepStream are provided in the DeepStream Development Guide.

2. An exported .etlt model file and optional calibration cache for INT8 precision.

3. TensorRT 7+ OSS Plugins .

4. A labels.txt file containing the labels for classes in the order in which the networks produces outputs.

5. A sample config_infer_*.txt file to configure the nvinfer element in DeepStream. The nvinfer element handles everything related to TensorRT optimization and engine creation in DeepStream.

DeepStream SDK ships with an end-to-end reference application which is fully configurable. Users can configure input sources, inference model, and output sinks. The app requires a primary object detection model, followed by an optional secondary classification model. The reference application is installed as deepstream-app. The graphic below shows the architecture of the reference application.

There are typically 2 or more configuration files that are used with this app. In the install directory, the config files are located in samples/configs/deepstream-app or sample/configs/tlt_pretrained_models. The main config file configures all the high level parameters in the pipeline above. This would set input source and resolution, number of inferences, tracker and output sinks. The other supporting config files are for each individual inference engine. The inference specific config files are used to specify models, inference resolution, batch size, number of classes and other customization. The main config file will call all the supporting config files. Here are some config files in samples/configs/deepstream-app for your reference.

• source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt: Main config file

• config_infer_primary.txt: Supporting config file for primary detector in the pipeline above

• config_infer_secondary_*.txt: Supporting config file for secondary classifier in the pipeline above

The deepstream-app will only work with the main config file. This file will most likely remain the same for all models and can be used directly from the DeepStream SDK will little to no change. User will only have to modify or create config_infer_primary.txt and config_infer_secondary_*.txt.

Integrating a YOLOv4 Model

To run a YOLOv4 model in DeepStream, you need a label file and a DeepStream configuration file. In addition, you need to compile the TensorRT 7+ Open source software and YOLOv4 bounding box parser for DeepStream.

A DeepStream sample with documentation on how to run inference using the trained YOLOv4 models from TAO Toolkit is provided on GitHub repo..

Prerequisites for YOLOv4 Model

1. YOLOv4 requires batchTilePlugin, resizeNearestPlugin, and batchedNMSPlugin. These plugins are available in the TensorRT open source repo, but not in TensorRT 7.0. Detailed instructions to build TensorRT OSS can be found in TensorRT Open Source Software (OSS).

2. YOLOv4 requires YOLOv3 custom bounding box parsers that are not built-in inside the DeepStream SDK. The source code to build YOLOv3 custom bounding box parsers is available in GitHub repo. The following instructions can be used to build bounding box parser:

Step1: Install git-lfs (git >= 1.8.2)

            

            
curl -s https://packagecloud.io/install/repositories/github/git-lfs/
script.deb.sh | sudo bash
sudo apt-get install git-lfs
git lfs install
        

Step 2: Download Source Code with SSH or HTTPS

            

            
git clone -b release/tlt3.0 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps
        

Step 3: Build

            

            
// or Path for DS installation
export CUDA_VER=10.2         // CUDA version, e.g. 10.2
make
        

This generates libnvds_infercustomparser_tlt.so in the directory post_processor.

Label File

The label file is a text file containing the names of the classes that the YOLOv4 model is trained to detect. The order in which the classes are listed here must match the order in which the model predicts the output. During the training, TAO YOLOv4 will specify all class names in lower case and sort them in alphabetical order. For example, if the dataset_config is:

            

            
dataset_config {
  data_sources: {
      label_directory_path: "/workspace/tao-experiments/data/training/label_2"
      image_directory_path: "/workspace/tao-experiments/data/training/image_2"
  }
  target_class_mapping {
    key: "car"
    value: "car"
  }
  target_class_mapping {
    key: "person"
    value: "person"
  }
  target_class_mapping {
    key: "bicycle"
    value: "bicycle"
  }
  validation_data_sources: {
      label_directory_path: "/workspace/tao-experiments/data/val/label"
      image_directory_path: "/workspace/tao-experiments/data/val/image"
  }
}
        

Then the corresponding yolov4_labels.txt file would be:

            

            
bicycle
car
person
        

DeepStream Configuration File

The detection model is typically used as a primary inference engine. It can also be used as a secondary inference engine. To run this model in the sample deepstream-app, you must modify the existing config_infer_primary.txt file to point to this model.

Integrate the TensorRT engine file with the DeepStream app

Step 1: Generate TensorRT engine using tao-converter. Detailed instructions are provided in the Generating an engine using tao-converter section above.

Step 2: Once the engine file is generated successfully, modify the following parameters to use this engine with DeepStream.

            

            
model-engine-file=<PATH to generated TensorRT engine>
        

All other parameters are common between the two approaches. To use the custom bounding box parser instead of the default parsers in DeepStream, modify the following parameters in [property] section of primary infer configuration file:

            

            
parse-bbox-func-name=NvDsInferParseCustomBatchedNMSTLT
custom-lib-path=<PATH to libnvds_infercustomparser_tlt.so>
        

Add the label file generated above using:

            

            
labelfile-path=<YOLOv4 labels>
        

For all the options, see the configuration file below. To learn about what all the parameters are used for, refer to the DeepStream Development Guide.

Here’s a sample config file, pgie_yolov4_config.txt:

            

            
[property]
gpu-id=0
net-scale-factor=1.0
offsets=103.939;116.779;123.68
model-color-format=1
labelfile-path=<Path to yolov4_labels.txt>
model-engine-file=<PATH to generated TensorRT engine>
tlt-model-key=<Key to decrypt model>
infer-dims=3;384;1248
maintain-aspect-ratio=1
uff-input-order=0
uff-input-blob-name=Input
batch-size=1
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
num-detected-classes=3
interval=0
gie-unique-id=1
is-classifier=0
#network-type=0
#no cluster
cluster-mode=3
output-blob-names=BatchedNMS
parse-bbox-func-name=NvDsInferParseCustomBatchedNMSTLT
custom-lib-path=<Path to libnvds_infercustomparser_tlt.so>

[class-attrs-all]
pre-cluster-threshold=0.3
roi-top-offset=0
roi-bottom-offset=0
detected-min-w=0
detected-min-h=0
detected-max-w=0
detected-max-h=0