RetinaNet

With RetinaNet, the following tasks are supported:

• dataset_convert

• train

• evaluate

• prune

• inference

• export

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

            

            
tao retinanet <sub_task> <args_per_subtask>
        

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

The object detection apps in TAO Toolkit expect data in KITTI format for training and evaluation.

See the Data Annotation Format page for more information about the KITTI data format.

The RetinaNet dataloader supports the raw KITTI formatted data as well as TFrecords.

To use TFRecords for optimized iteration across the data batches, the the raw input data need to be converted to TFRecords format first. This can be done using the dataset_convert subtask. Currently, the KITTI and COCO formats are supported.

The dataset_convert tool requires a configuration file as input. Details of the configuration file and examples are included in the following sections.

Configuration File for Dataset Converter

The dataset_convert tool provides several configurable parameters. The parameters are encapsulated in a spec file to convert data from the original annotation format to the TFRecords format which the trainer can ingest. KITTI and COCO formats can be configured by using either kitti_config or coco_config respectively. You may use only one of the two in a single spec file. The spec file is a prototxt format file with following global parameters:

• kitti_config: A nested prototxt configuration with multiple input parameters

• coco_config: A nested prototxt configuration with multiple input parameters

• image_directory_path: The path to the dataset root. The image_dir_name is appended to this path to get the input images and must be the same path specified in the experiment spec file.

• target_class_mapping: The prototxt dictionary that maps the class names in the tfrecords to the target class to be trained in the network.

kitti_config

Here are descriptions of the configurable parameters for the kitti_config field:

The sample configuration file shown below converts the 100% KITTI dataset to the training set.

            

            
kitti_config {
  root_directory_path: "/workspace/tao-experiments/data/"
  image_dir_name: "training/image_2"
  label_dir_name: "training/label_2"
  image_extension: ".png"
  partition_mode: "random"
  num_partitions: 2
  val_split: 0
  num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/data/"
  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"
  }
  target_class_mapping {
      key: "truck"
      value: "car"
  }
        

coco_config

Here are descriptions of the configurable parameters for the coco_config field:

The sample configuration file shown below converts the COCO dataset with training and validation data where number of shard is 32 for validation and 256 for training.

            

            
coco_config {
  root_directory_path: "/workspace/tao-experiments/data/coco"
  img_dir_names: ["val2017", "train2017"]
  annotation_files: ["annotations/instances_val2017.json", "annotations/instances_train2017.json"]
  num_partitions: 2
  num_shards: [32, 256]
}
image_directory_path: "/workspace/tao-experiments/data/coco"
        

Sample Usage of the Dataset Converter Tool

The dataset_convert tool is described below:

            

            
tao retinanet dataset-convert [-h] -d DATASET_EXPORT_SPEC
                                   -o OUTPUT_FILENAME
                                   [-v]
        

You can use the following arguments:

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

• -d, --dataset-export-spec: The path to the detection dataset spec containing the config for exporting .tfrecord files

• -o, --output_filename: The output filename

• -v: Enable verbose mode to show debug messages

The following example shows how to use the command with the dataset:

            

            
tao retinanet dataset_convert -d /path/to/spec.txt
                              -o /path/to/tfrecords/train
        

Below is a sample for the RetinaNet spec file. It has 6 major components: retinanet_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
retinanet_config {
  aspect_ratios_global: "[1.0, 2.0, 0.5]"
  scales: "[0.045, 0.09, 0.2, 0.4, 0.55, 0.7]"
  two_boxes_for_ar1: false
  clip_boxes: false
  loss_loc_weight: 0.8
  focal_loss_alpha: 0.25
  focal_loss_gamma: 2.0
  variances: "[0.1, 0.1, 0.2, 0.2]"
  arch: "resnet"
  nlayers: 18
  n_kernels: 1
  n_anchor_levels: 1
  feature_size: 256
  freeze_bn: false
  freeze_blocks: 0
}
training_config {
  enable_qat: False
  batch_size_per_gpu: 24
  num_epochs: 100
  pretrain_model_path: "YOUR_PRETRAINED_MODEL"
  optimizer {
    sgd {
      momentum: 0.9
      nesterov: True
    }
  }
  learning_rate {
  soft_start_annealing_schedule {
    min_learning_rate: 4e-5
    max_learning_rate: 1.5e-2
    soft_start: 0.15
    annealing: 0.5
    }
  }
  regularizer {
    type: L1
    weight: 2e-5
  }
}
eval_config {
  validation_period_during_training: 10
  average_precision_mode: SAMPLE
  batch_size: 24
  matching_iou_threshold: 0.5
}
nms_config {
  confidence_threshold: 0.01
  clustering_iou_threshold: 0.6
  top_k: 200
}
augmentation_config {
    output_width: 384
    output_height: 1248
    output_channel: 3
    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: {
      # option 1
      tfrecords_path: "/workspace/tao-experiments/data/tfrecords/kitti_train*"

      # option 2
      # 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: "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"
      image_directory_path: "/workspace/tao-experiments/data/val/image"
  }
}
        

Training Config

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

Training Visualization Config

Visualization during training is configured with the visualizer parameter, which contains the parameters described below.

If visualization is enabled, the tensorboard log will be produced during training including the graphs for learning rate, training loss, validation loss, validation mAP and validation AP of each class. The augmented images with bboxes will also be produced in the tensorboard.

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. 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_config parameter defines the image size after preprocessing. The augmentation methods in the SSD paper will be performed during training, including random flip, zoom-in, zoom-out and color jittering. And the augmented images will be resized to the output shape defined in augmentation_config. In evaluation process, only the resize will be performed.

Dataset Config

The RetinaNet dataloader assumes data are prepared in KITTI format (images and labels in two separate folders where each image in image folder has a txt label file with same filename in label folder. The label file content follows KITTI format) and training/validation split is already done.

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. This field contains 3 parameters: * label_directory_path: Path to the data source label folder * image_directory_path: Path to the data source image folder * tfrecords_path: Path to the TFRecords

  When using raw KTTTI formatted data as input, only label_directory_path and image_directory_path are required. When using TFRecords as data ingestion, only tfrecords_path is required.

• include_difficult_in_training: Whether to include difficult boxes in training. If set to false, difficult boxes will be ignored. Difficult boxes are those with occlusion level 2 in KITTI labels. (only applicable with raw KITTI formatted data)

• 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. 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

Class Weighting Config

RetinaNet 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 explicitly set in the config, a default value of 1.0 will be assigned implicitly.

RetinaNet Config

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

Focal loss is calculated as follows:

Variances:

Train the RetinaNet model using this command:

            

            
tao retinanet 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 in the training in a multi-GPU scenario (default: 1).

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

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

• --log_file: Path to the log file. Defaults to 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 RetinaNet model:

            

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

To run evaluation for a RetinaNet model use this command:

            

            
tao retinanet 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 (supports both the TAO model and TensorRT engine).

• -k, --key: Provide the key to load the TAO model (it’s not needed if a TensorRT engine is used).

Optional Arguments

• --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.

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

Sample Usage

Here’s an example of using the evaluate command on a RetinaNet model:

            

            
tao retinanet evaluate -e /path/to/spec.txt -m /path/to/model.tlt -k $KEY
        

The inference tool for RetinaNet networks can be used to visualize bboxes, or generate frame by frame KITTI format labels on a directory of images. Two modes are supported, namely TAO model model and TensorRT engine mode. You can execute the TAO model mode using the following command:

            

            
tao retinanet 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 pretrained model (supports both the TAO model and 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 a TAO model (it’s not needed if a TensorRT engine is used).

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

Optional Arguments

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

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

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

• --log_file: Path to the log file. Defaults to stdout.

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

Sample Usage

Here’s an example of using the inference command on a RetinaNet model:

            

            
tao retinanet inference -e /path/to/spec.txt -m /path/to/model.tlt -k $KEY
                        -o /path/to/output_dir -i /path/to/input_dir
        

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

The tao retinanet prune command includes these parameters:

            

            
tao retinanet prune [-h] -m <retinanet model>
                         -o <output_file>
                         -k <key>
                         [-n <normalizer>]
                         [-eq <equalization_criterion>]
                         [-pg <pruning_granularity>]
                         [-pth <pruning threshold>]
                         [-nf <min_num_filters>]
                         [-el [<excluded_list>]
                         [--gpu_index <gpu_index>]
                         [--log_file <log_file_path>]
        

Required Arguments

• -m, --model: Path to a pretrained RetinaNet model.

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

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

Optional Arguments

• -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)

  Note

  NVIDIA recommends changing the threshold to keep the number of parameters in the model to within 10-20% of the original unpruned model.

• -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: [])

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

• --log_file: Path to the log file. Defaults to stdout.

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

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 retinanet prune command:

            

            
tao retinanet 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. This happens because some previously useful weights may have been removed. In order to regain the accuracy, NVIDIA recommends that you retrain this pruned model over the same dataset. To do this, use the tao retinanet train command as documented in Training the model, with an updated spec file that points to the newly pruned model as the pretrained model file.

Users are advised to turn off the regularizer in the training_config for RetinaNet to recover the accuracy when retraining a pruned model. You may do this by setting 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 .tlt, the .etlt model format is also a 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. The calibration cache is generated using a calibration tensor file, if tao retinanet 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 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 an .etlt.

Exporting the RetinaNet Model

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

            

            
tao retinanet export [-h] -m <path to the .tlt model file>
                          --experiment_spec <path to experiment spec file>
                          -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>]
                          [--engine_file <path to the TensorRT engine file>]
                          [--gen_ds_config]
                          [--strict_type_constraints]
                          [--force_ptq]
                          [--gpu_index <gpu_index>]
                          [--log_file <log_file_path>]
                          [--verbose]
        

Required Arguments

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

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

• -e, --experiment_spec: Path to the spec file.

Optional Arguments

• -o, --output_file: Path to save the exported model to. The default 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. We can specify the GPU index to run export if the machine has multiple GPUs installed. Note that export can only run on a single GPU.

• --log_file: Path to the log file. Defaults to stdout.

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

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 RetinaNet model.

            

            
tao retinanet export -m /ws/retinanet_resnet18_epoch_100.tlt  \
                         -o /ws/retinanet_resnet18_epoch_100_int8.etlt \
                         -e /ws/retinanet_retrain_resnet18_kitti.txt \
                         -k $KEY
        

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

Refer to the Integrating a RetinaNet Model page for more information about deploying a RetinaNet model to DeepStream.