YOLOv3

YOLOv3 is an object detection model that is included in the TAO Toolkit. YOLOv3 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_v3 <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 YOLOv3 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 YOLOv3 training, use this command:

            

            
tao yolo_v3 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 YOLOv3 spec file. It has six major components: yolov3_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
yolov3_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)]"
  matching_neutral_box_iou: 0.7
  arch: "resnet"
  nlayers: 18
  arch_conv_blocks: 2
  loss_loc_weight: 0.8
  loss_neg_obj_weights: 100.0
  loss_class_weights: 1.0
  freeze_bn: false
  force_relu: false
}
training_config {
  batch_size_per_gpu: 8
  num_epochs: 80
  enable_qat: false
  checkpoint_interval: 10
  learning_rate {
  soft_start_annealing_schedule {
    min_learning_rate: 1e-6
    max_learning_rate: 1e-4
    soft_start: 0.1
    annealing: 0.5
    }
  }
  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: 0
}
dataset_config {
  data_sources: {
      tfrecords_path: "/workspace/tao-experiments/data/tfrecords/kitti_trainval/kitti_trainval*"
      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_fold: 0
}
        

Training Config

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

Evaluation Config

The evaluation configuration (eval_config) defines the parameters needed for evaluation either during training or as a standalone procedure. 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

YOLOv3 supports two data formats: the sequence format (images folder and raw labels folder with KITTI format) and the tfrecords format (images folder and TFRecords). Training with TFRecord dataset in most cases are faster than sequence format and hence TFRecord dataset is the recommended format. However, in some cases like small input resolutions(e.g., 416x416), the sequence format is slightly faster TFRecord.

The YOLOv3 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.

Below is an example dataset_config for TFRecord dataset converted from KITTI data format.

            

            
dataset_config {
data_sources: {
    tfrecords_path: "/workspace/tao-experiments/data/tfrecords/kitti_trainval/kitti_trainval*"
    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_fold: 0
}
        

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: Captures the path to datasets to train on. If you have multiple data sources for training, you may use multiple data_sources. For sequence format, this field contains 2 parameters:

  • label_directory_path: Path to the data source label folder

  • image_directory_path: Path to the data source image folder

  For TFRecord format, this field contains 2 parameters:

  • tfrecords_path: Path to the TFRecord files, this can be a pattern to match multiple TFrecord files.

  • image_directory_path: Path to the data source image folder. Make sure this aligns with the path specified in dataset_convert command.

• include_difficult_in_training: Specifies 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.

• image_extension: The suffix(extension) of the image file. For example, png or jpg, etc. This parameter is only useful when using TFRecord dataset.

• 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 is 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 same parameters. This parameter is exclusive with validation_fold.

• validation_fold: When using TFRecord dataset for training, the validation dataset can be a split(fold) in the training dataset. This parameter is exclusive with validation_data_sources.

YOLOv3 Config

The YOLOv3 configuration (yolov3_config) defines the parameters needed for building the YOLOv3 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. You can use the kmeans algorithm to generate the anchor shapes. The algorithm is implemented in TAO Toolkit as the tao yolo_v3 kmeans command. You can use the output of the algorithm as the anchor shape in the yolov3_config spec file.

            

            
tao yolo_v3 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: The paths to the training label folders. Multiple folder paths should be separated with spaces.

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

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

• -y: The base network input height, which should be output_height in the augmentation config section 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, with 3 groups)

• --max_steps: The maximum 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 the reshaped image (images are first reshaped to the network base shape as -x, -y)

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

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

Train the YOLOv3 model using this command:

            

            
tao yolo_v3 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: Path to the folder where the experiment output is written.

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

• -e, --experiment_spec_file: 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 be used for training in a multi-GPU scenario (the default value is 1).

• --gpu_index: The GPU indices used to run training. You can use GPU indices to specify the GPU(s) 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 YOLOv3 model:

            

            
tao yolo_v3 train --gpus 2 -e /path/to/spec.txt -r /path/to/result -k $KEY
        

To run evaluation for a YOLOv3 model use this command:

            

            
tao yolo_v3 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: Experiment spec file to set up the evaluation experiment. This should be the same as the training specification file.

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

• -k, --key: Provide the key to load the model (not needed if 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. 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: Path to the log file. Defaults to stdout.

The inference tool for YOLOv3 networks 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_v3 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: 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: 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: The threshold for drawing a bbox. The default value is 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 inference. You can specify the index of the 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”.

Pruning removes parameters from the model to reduce the model size without compromising the integrity of the model itself using the tao yolo_v3 prune command.

The tao yolo_v3 prune command includes these parameters:

            

            
tao yolo_v3 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: Path to pretrained YOLOv3 model.

• -o, --output_file: Path to output checkpoints.

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

Optional Arguments

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

• -n, –normalizer: max to normalize by dividing each norm by the maximum norm within a layer; L2 to normalize by dividing by the L2 norm of the vector comprising all kernel norms. (default: 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 mobilenets. Options are arithmetic_mean, geometric_mean, union, and intersection. (default: union)

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

• -pth: Threshold to compare normalized norm against (default:0.1)

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

• -el, --excluded_layers: 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_v3 prune command:

            

            
tao yolo_v3 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 be removed. To regain accuracy, NVIDIA recommends that you retrain this pruned model over the same dataset. To do this, use the tao yolo_v3 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. To do this, set the regularizer type to NO_REG as mentioned Training config. All the other parameters may be retained in the spec file 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 the .tlt model format, .etlt is an encrypted model format, and it uses the same key as 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. The calibration cache is generated using a calibration tensor file, if tao yolo_v3 export is run with the --data_type flag set to int8. 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 to generate depending on the size of the Tensorfile and the model itself.

The export tool can generate the INT8 calibration cache by ingesting training data using one 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 you need to do is provide a .tlt model from the training/retraining step to be converted into .etlt format.

Exporting the Model

Here’s an example of the command line arguments of the tao yolo_v3 export command:

            

            
tao yolo_v3 export [-h]
                    -m <path to the .tlt model file generated by tao train>
                    -k <key>
                    [-o <path to output file>]
                    [--cal_json_file <path to calibration json file>]
                    [--experiment_spec <path to experiment spec file>]
                    [--gen_ds_config]
                    [--verbose]
                    [--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.

• --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.

• --gpu_index: The index of (discrete) GPUs used for exporting the model. You can specify the index of the GPU to run export 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”.

QAT Export Mode Required Arguments

• --cal_json_file: The path to the json file containing tensor scale for QAT models. This argument is required if engine for QAT model is being generated.

Sample Usage

Here’s a sample command to export a YOLOv3 model:

            

            
tao yolo_v3 export -m /workspace/yolov3_resnet18_epoch_100.tlt  \
                         -o /workspace/yolov3_resnet18_epoch_100_int8.etlt \
                         -e /workspace/yolov3_retrain_resnet18_kitti.txt \
                         -k $KEY
        

For TensorRT engine generation, validation, and int8 calibration, please refer to TAO Deploy documentation.

For deploying to deep stream, please refer to Deploying to DeepStream for YOLOv3.