FasterRCNN

FasterRCNN is a public object detection model that is supported by NVIDIA TAO. FasterRCNN in TAO supports below tasks:

• dataset_convert

• train

• evaluate

• inference

• prune

• export

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

            

            
tao model faster_rcnn <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 in the following sections.

The dataset structure of FasterRCNN 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 FasterRCNN training, use this command:

            

            
tao model faster_rcnn 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.

The experiments specification (spec file for short) defines all the necessary parameters required to in the entire workflow of a FasterRCNN model, from training to export. Below is a sample of the FasterRCNN spec file. 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 proto message. The top level structure of the spec file is summarized in the table below. From the table, we can see the spec file has 9 components: random_seed, verbose, enc_key, dataset_config, augmentation_config, model_config, training_config, inference_config and evaluation_config.

Here’s a sample of the FasterRCNN spec file:

            

            
random_seed: 42
enc_key: 'nvidia_tlt'
verbose: True
model_config {
input_image_config {
image_type: RGB
image_channel_order: 'bgr'
size_height_width {
height: 384
width: 1248
}
    image_channel_mean {
        key: 'b'
        value: 103.939
}
    image_channel_mean {
        key: 'g'
        value: 116.779
}
    image_channel_mean {
        key: 'r'
        value: 123.68
}
image_scaling_factor: 1.0
max_objects_num_per_image: 100
}
arch: "resnet:18"
anchor_box_config {
scale: 64.0
scale: 128.0
scale: 256.0
ratio: 1.0
ratio: 0.5
ratio: 2.0
}
freeze_bn: True
freeze_blocks: 0
freeze_blocks: 1
roi_mini_batch: 256
rpn_stride: 16
use_bias: False
roi_pooling_config {
pool_size: 7
pool_size_2x: False
}
all_projections: True
use_pooling:False
}
dataset_config {
  data_sources: {
    tfrecords_path: "/workspace/tao-experiments/tfrecords/kitti_trainval/kitti_trainval*"
    image_directory_path: "/workspace/tao-experiments/data/training"
  }
image_extension: 'png'
target_class_mapping {
key: 'car'
value: 'car'
}
target_class_mapping {
key: 'van'
value: 'car'
}
target_class_mapping {
key: 'pedestrian'
value: 'person'
}
target_class_mapping {
key: 'person_sitting'
value: 'person'
}
target_class_mapping {
key: 'cyclist'
value: 'cyclist'
}
validation_fold: 0
}
augmentation_config {
preprocessing {
output_image_width: 1248
output_image_height: 384
output_image_channel: 3
min_bbox_width: 1.0
min_bbox_height: 1.0
}
spatial_augmentation {
hflip_probability: 0.5
vflip_probability: 0.0
zoom_min: 1.0
zoom_max: 1.0
translate_max_x: 0
translate_max_y: 0
}
color_augmentation {
hue_rotation_max: 0.0
saturation_shift_max: 0.0
contrast_scale_max: 0.0
contrast_center: 0.5
}
}
training_config {
enable_augmentation: True
enable_qat: False
batch_size_per_gpu: 8
num_epochs: 12
retrain_pruned_model: "/workspace/tao-experiments/data/faster_rcnn/model_1_pruned.tlt"
rpn_min_overlap: 0.3
rpn_max_overlap: 0.7
classifier_min_overlap: 0.0
classifier_max_overlap: 0.5
gt_as_roi: False
std_scaling: 1.0
classifier_regr_std {
key: 'x'
value: 10.0
}
classifier_regr_std {
key: 'y'
value: 10.0
}
classifier_regr_std {
key: 'w'
value: 5.0
}
classifier_regr_std {
key: 'h'
value: 5.0
}

rpn_mini_batch: 256
rpn_pre_nms_top_N: 12000
rpn_nms_max_boxes: 2000
rpn_nms_overlap_threshold: 0.7

regularizer {
type: L2
weight: 1e-4
}

optimizer {
sgd {
lr: 0.02
momentum: 0.9
decay: 0.0
nesterov: False
}
}

learning_rate {
soft_start {
base_lr: 0.02
start_lr: 0.002
soft_start: 0.1
annealing_points: 0.8
annealing_points: 0.9
annealing_divider: 10.0
}
}

lambda_rpn_regr: 1.0
lambda_rpn_class: 1.0
lambda_cls_regr: 1.0
lambda_cls_class: 1.0
}
inference_config {
images_dir: '/workspace/tao-experiments/data/testing/image_2'
model: '/workspace/tao-experiments/data/faster_rcnn/frcnn_kitti_resnet18_retrain.epoch12.tlt'
batch_size: 1
detection_image_output_dir: '/workspace/tao-experiments/data/faster_rcnn/inference_results_imgs_retrain'
labels_dump_dir: '/workspace/tao-experiments/data/faster_rcnn/inference_dump_labels_retrain'
rpn_pre_nms_top_N: 6000
rpn_nms_max_boxes: 300
rpn_nms_overlap_threshold: 0.7
object_confidence_thres: 0.0001
bbox_visualize_threshold: 0.6
classifier_nms_max_boxes: 100
classifier_nms_overlap_threshold: 0.3
}
evaluation_config {
model: '/workspace/tao-experiments/data/faster_rcnn/frcnn_kitti_resnet18_retrain.epoch12.tlt'
batch_size: 1
validation_period_during_training: 1
rpn_pre_nms_top_N: 6000
rpn_nms_max_boxes: 300
rpn_nms_overlap_threshold: 0.7
classifier_nms_max_boxes: 100
classifier_nms_overlap_threshold: 0.3
object_confidence_thres: 0.0001
use_voc07_11point_metric:False
gt_matching_iou_threshold: 0.5
}
        

Dataset

The dataset_config defines the dataset of a FasterRCNN experiments (including training dataset and validation dataset). The definition of FasterRCNN dataset is identical to that of DetectNet_v2. Check the DetectNet_v2 dataset_config documentation for the details of this parameter.

Data augmentation

The augmentation_config defines the data augmentation during the training of a FasterRCNN model. The definition of FasterRCNN data augmentation is identical to that of DetectNet_v2. Check the DetectNet_v2 augmentation_config documentation for the details of this parameter.

Model architecture

The model_config defines the FasterRCNN model architecture. In this parameter, we can choose the backbone of the FasterRCNN model, enabling BatchNormalization layers or not, whether or not to freeze the BatchNormalization layers during training, and whether or not to freeze some blocks in the model during training. With this parameter, we can define a specialized FasterRCNN model architecture from the general FasterRCNN application, according to the use cases. Detailed description of this parameter is summarized in the table below.

Each of the above proto message parameters will be described in detail below.

Input image configurations

The input_image_config defines the supported format of images by FasterRCNN model. We can customize the input image size, the per-channel mean values and scaling factor for image preprocessing. We can also specify the image type (RGB or grayscale) for our training/validation dataset, and the order of the channel if we are going to use RGB images during training. This is described in the table below in detail.

Anchor boxes

The parameter anchor_box_config defines the anchor box sizes and aspect ratios in the FasterRCNN model. There are two sub-parameters for it: scale and ratio. Each of them is a list of floats as below.

ROIPooling(CropAndResize)

The roi_pooling_config parameter defines the parameters required in ROIPooling(CropAndResize) layer in the model. Described in the table below.

Activation function

The parameter activation defines the type and parameter for the activation function in a FasterRCNN model. This parameter is only valid for EfficientNet.

Training configurations

The proto message training_config defines all the necessary parameters required for a FasterRCNN training experiment. Each parameter is described in the table below.

The description for proto messages are summarized further below.

Regularizer

Optimizer

Three types of optimizers are supported by FasterRCNN: Adam, SGD and RMSProp. Only one of them should be specified in spec file. No matter which one is chosen, it will be wrapped in a optimizer proto. For example:

            

            
optimizer {
adam {
lr: 0.00001
beta_1: 0.9
beta_2: 0.999
decay: 0.0
}
}
        

The Adam optimizer parameters are summarized in the table below.

The SGD optimizer parameters are summarized in the table below.

The RMSProp optimizer parameters are summarized in the table below.

Learning rate scheduler

The parameter learning_rate defines the learning rate scheduler in a FasterRCNN training. Two types of learning rate schedulers are supported in FasterRCNN: soft_start and step. NO matter which one is chosen, it will be wrapped in a learning_rate proto message. For example:

            

            
learning_rate {
step {
base_lr: 0.00001
gamma: 1.0
step_size: 30
}
}
        

The parameters of soft_start scheduler is described in the table below.

The parameters of step scheduler is described 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 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 path to the directory used to save the .tlt model, with the suffix /logs appended.

Early Stopping

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

Inference configurations

The parameter inference_config defines all the parameters required for running inference against a FasterRCNN model.

TensorRT based inference

The parameter trt_inference defines all the parameters for TensorRT based inference. When specified, Inference will use TensorRT engine instead of the .tlt model. The TensorRT engine is assumed to be generated by the tao-converter tool. All the parameters are summarized in the table below.

Evaluation configurations

The parameter evaluation_config defines all the required parameters for running evaluation against a FasterRCNN model. This parameter is very similar to inference_config.

TensorRT based evaluation

In the above table, the definition of trt_evaluation is the same as trt_inference parameter described before.

Evaluation IoU Range

The gt_matching_iou_threshold_range parameter is described in table below.

To run training of a FasterRCNN model, use this command:

            

            
tao model faster_rcnn train [-h] -e <experiment_spec> -r <results_dir>
                           [-k <enc_key>]
                           [--gpus <num_gpus>]
                           [--num_processes <number_of_processes>]
                           [--gpu_index <gpu_index>]
                           [--use_amp]
                           [--log_file <log_file_path>]
        

Required Arguments

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

• -r, --results_dir: Output directory of the training experiment.

Optional Arguments

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

• -k, --enc_key: TAO encoding key, can override the one in the spec file.

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

• --num_processes, -np: Number of processes to be spawned for training. It defaults to be -1(equal to --gpus, for the use case of data parallelism). In the case of model parallelism, this argument should be explicitly set to 1 or more, depending on the actual scenario. Setting --gpus to be larger than 1 and --num_processes to 1 corresponding to the model parallelism use case; while setting both --gpus and num_processes to be larger than 1 corresponding to the case of enabling both model parallelism and data parallelism. For example, --gpus=4 and --num_processes=2 means 2 horovod processes will be spawned and each of them will occupy 2 GPUs for model parallelism.

• --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.

Input Requirement

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

• Image format: JPG, JPEG, PNG

• Label format: KITTI detection

Sample Usage

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

            

            
tao model faster_rcnn train --gpu_index 0 -e <experiment_spec> -r <results_dir>
        

Using a Pretrained Model

Usually, using a pretrained model (weights) file for the initial training of FasterRCNN helps get better accuracy. NVIDIA recommends using the pretrained weights provided in NVIDIA GPU Cloud (NGC). FasterRCNN loads the pretrained weights by name. That is, layer by layer, if TAO finds a layer whose name and weights (bias) shape in the pretrained weights file matches a layer in the TAO model, it will load that layer’s weights (and bias, if any) into the model. If some layer in the TAO cannot find a matching layer in the pretrained weights, then TAO will skip that layer and will use random initialization for that layer instead. An exception is that if TAO finds a matching layer in the pretrained weights (and bias, if any) but the shape of the pretrained weights (or bias, if any) in that layer does not match the shape of weights (bias) for the corresponding layer in a TAO model, it will also skip that layer.

For some layers that have no weights (bias), nothing will be done for it(hence will be skipped). So, in total, there are three possible statuses to indicate how a layer’s pretrained weights loading is going on:

• "Yes" means a layer has weights (bias) and is loaded from the pretrained weights file successfully for initialization.

• "No" means a layer has weights (bias) but due to mismatched weights (bias) shape(or probably something else), the weights (bias) cannot be loaded successfully and will use random initialization instead.

• "None" means a layer has no weights (bias) at all and will not load any weights. In the FasterRCNN training log, there is a table that shows the pretrained weights loading status for each layer in the model.

To use a pretrained model in FasterRCNN training, set the pretrained_weights path to point to a pretrained .tlt model (generated with the same encryption key as the FasterRCNN training), a Keras .hdf5 model or a Keras .h5 weights.

Re-training a pruned model

A FasterRCNN model can be retrained one or more times. The typical use case is retraining for a pruned model. To retrain an existing FasterRCNN model, set the retrain_pruned_model path to point to an existing FasterRCNN model.

Resuming an interrupted training

Sometimes a training job can be interrupted due to some reason (e.g., system crash). In these cases, there is no need to redo the training from the start. We can resume the interrupted training from the last checkpoint(saved .tlt model during training). In this case, set the resume_from_model path in spec file to point to the last checkpoint and re-run the training to resume the job.

Input shape: static and dynamic

FasterRCNN training can support both static input shape and dynamic input shape. Static input shape means the input’s width and height are constant numbers like 960 x 544. Static shape is the most commonly used case in practice. To enable static input shape, we should specify it in input_image_config and augmentation_config. We should use size_height_width in input_image_config to specify the input height and width. Again, we should specify the same two numbers in augmentation_config. That is, we specify the output_image_height and output_image_width in augmentation_config.

With static input shape, we can offline resize the images to the target resolution or we can enable automatic resize during training. By setting enable_auto_resize in augmentation_config to True we will enable automatic resize during training. Automatic resize will reduce the effort to manually resize the images each time we want to train the model on a different resolution. But since resize happens during training, it will potentially increase the training time. Users should make this tradeoff between offline resize and automatic(online) resize.

Dynamic input shape means the input’s height and width are not a constant number but rather can change during training for different images. This kind of input shape is originally proposed in the literature(such as in FasterRCNN paper) where we resize the image and keep aspect ratio such that the resultant image’s smaller side is a given number. Besides the limit on smaller side, we also have a limit on the larger side. If we resize and keep aspect ratio but the resultant image’s larger side’s size exceed this limit on larger side, then we will resize and keep aspect ratio such that the larger side’s size is a given number. In that case, the smaller side will be also no more than its limit. FasterRCNN can support this kind of dynamic input shape. To enable this feature, we have to specify size_min in input_image_config and specify output_image_min and output_image_max in augmentation_confg. size_min and output_image_min indicates the limit of the smaller side’s size, while output_image_max indicates the limit on the larger side’s size.

Note that there are some limitations regarding the dynamic shape of FasterRCNN.

• TAO FasterRCNN training/evaluation/inference can only work with batch size 1.

• TAO FasterRCNN export & DeepStream(TensorRT) inference/evaluation does not support dynamic shape for now.

Model parallelism

FasterRCNN supports model parallelism. Model parallelism is a technique that we split the entire model on multiple GPUs and each GPU will hold a part of the model. A model is split by layers. For example, if a model has 100 layers, then we can place the layer 0-49 on GPU 0 and layer 50-99 on GPU 1. Model parallelism will be useful when the model is huge and cannot fit into a single GPU even with batch size 1. Model parallelism is also useful if we want to increase the batch size that is seen by BatchNormalization layers and hence potentially improve the accuracy. This feature can be enabled by setting model_parallelism in training_config. For example,

            

            
model_parallelism: 0.3
model_parallelism: 0.7
        

will enable a 2-GPU model parallelism where the first GPU will hold 30% of the model layers and the second GPU will hold 70% of the model layers. The percentage of model layers can be adjusted with some trial-and-error so all GPUs consumes almost the same GPU memory size and in that case we can use the largest batch size for this model-parallelised training.

Model parallelism can be enabled jointly with data parallelism. For example, in above case we enabled a 2-GPU model parallelism, at the same time we can also enable 4 horovod processes for it. In this case, we have 4 horovod processes for data parallelism and each process will have the model split on 2 GPUs.

To run evaluation for a faster_rcnn model, use this command:

            

            
tao model faster_rcnn evaluate [-h] -e <experiment_spec>
                              [-k <enc_key>]
                              [--gpu_index <gpu_index>]
                              [--log_file <log_file_path>]
                              [-m <model_path>]
        

Required Arguments

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

Optional Arguments

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

• -k, --enc_key：The encoding key, can override the one in the spec file.

• --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.

• -m, --model: Path to the model to run evaluation. The model can be either a .tlt model or a TensorRT engine(any path that does not end with .tlt extension). The model path(if provided in the command line here) will override the evaluation_config.model in the spec file.

Evaluation Metrics

The PASCAL VOC 2007 vs 2012 metrics

For FasterRCNN, the evaluation will produce 4 metrics for the evaluated model: AP (average precision), precision, recall and RPN_recall for each class in the evaluation dataset. inally, it will also print the mAP (mean average precision) as a single metric number. Two modes are supported for computing the AP, i.e., the PASCAL VOC 2007 and 2012 metrics. This can be configured in the spec file’s evaluation_config.use_voc_11_point_metric parameter. If this parameter is set to True, then AP calculation will use VOC 2007 method, otherwise it will use the VOC 2012 method.

Setting IoU value/range for computing AP/mAP

For matching the detected objects to groundtruth objects, we can define different IoU thresholds. An IoU of 0.5 is used in PASCAL VOC metrics, while in MS COCO a list of IoUs are used to compute the AP. For example, in MS COCO, the mAP@[0.5:0.05:0.95] is the averaged AP at 10 different IoUs, starting from 0.5 and ends with 0.95, with a step size of 0.05. TAO FasterRCNN supports evaluating AP at a list of IoUs and computing the mAP across the range of IoUs. Specifically, setting gt_matching_iou_threshold in evaluation_config will produce the AP/mAP at a single IoU; setting gt_matching_iou_threshold_range for a list (range) of IoUs will produce AP at these IoU values and the mAP. In order to compute PASCAL VOC mAP, we can set the former to 0.5. While in order to compute COCO mAP, we can set the latter to be start: 0.5, step: 0.05 and end: 1.0.

The RPN_recall metric indicates the recall capability of the RPN of the FasterRCNN model. The higher the RPN_recall metric, it means RPN can better detect an object as foreground (but it doesn’t say anything on which class this object belongs to since that is delegated to RCNN). The RPN_recall metric is mainly used for debugging on the accuracy issue of a FasterRCNN model.

The inference tool for FasterRCNN networks can be used to visualize bboxes or generate frame by frame KITTI format labels on a directory of images. You can execute this tool from the command line as shown here:

            

            
tao model faster_rcnn inference [-h] -e <experiment_spec>
                               [-k <enc_key>]
                               [--gpu_index <gpu_index>]
                               [--log_file <log_file_path>]
                               [-m <model_path>]
        

Required Arguments

• -e, --experiment_spec_file: Path to the experiment specification file for FasterRCNN training.

Optional Arguments

• -h, --help: Print help log and exit.

• -k, --enc_key: The encoding key, can override the one in the spec file.

• --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.

• -m, --model: The path to the model to be used for inference. The model can be either a .tlt model or a TensorRT engine(any path that does not end with the .tlt extension). The model path(if provided in the command line here) will override the inference_config.model in the spec file.

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

The tao model faster_rcnn prune command includes these parameters:

            

            
tao model faster_rcnn prune [-h] -m <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 .tlt model to be pruned.

• -o, --output_file: Path to save the pruned .tlt model.

• -k, --ke: Key to load a :code`.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)

  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.

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 model faster_rcnn prune command:

            

            
tao model faster_rcnn prune -m /workspace/output/weights/resnet_003.tlt
                      -o /workspace/output/weights/resnet_003_pruned.tlt
                      -eq union
                      -pth 0.7
                      -k nvidia_tlt
        

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 model faster_rcnn 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(set regularizer type to NO_REG) or use a smaller weight decay in the spec file to recover the accuracy when retraining a pruned model. All the other parameters may be retained in the spec file from the previous training.

For FasterRCNN, it is important to set the retrain_pruned_model path to point to the pruned model.

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.

FasterRCNN export can optionally generate a (partial) DeepStream configuration file and label file. See below.

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 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 device. 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 calibration data 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 Model

Here’s an example of the tao model faster_rcnn export command:

            

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

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 partial 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.

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 FasterRCNN model:

            

            
tao model faster_rcnn export --gpu_index 0
                       -m $USER_EXPERIMENT_DIR/data/faster_rcnn/frcnn_kitti_resnet18_retrain.epoch12.tlt
                       -o $USER_EXPERIMENT_DIR/data/faster_rcnn/frcnn_kitti_resnet18_retrain_int8.etlt
                       -e $SPECS_DIR/default_spec_resnet18_retrain_spec.txt
                       -k nvidia_tlt
        

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

Deploying to DeepStream

For deploying to deepstream, please refer to Deploying to DeepStream for FasterRCNN.