SSD

With SSD, 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:

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tao model ssd <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 ssd 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:

Parameter

Datatype

Default

Description

Supported Values

root_directory_path string The path to the dataset root directory
image_dir_name string The relative path to the directory containing images from the path in root_directory_path.
label_dir_name string The relative path to the directory containing labels from the path in root_directory_path.
partition_mode string The method employed when partitioning the data to multiple folds. Two methods are supported:
  • Random partitioning: The data is divided in to 2 folds, train and val. This mode requires that the val_split parameter be set.
  • Sequence-wise partitioning: The data is divided into n partitions (defined by the num_partitions parameter) based on the number of sequences available.
  • random
  • sequence
num_partitions int 2 (if partition_mode is random) The number of partitions to use to split the data (N folds). This field is ignored when the partition model is set to random, as by default only two partitions are generated: val and train. In sequence mode, the data is split into n-folds. The number of partitions is ideally fewer than the total number of sequences in the kitti_sequence_to_frames file. n=2 for random partition n< number of sequences in the kitti_sequence_to_frames_file
image_extension str .png The extension of the images in the image_dir_name parameter. .png .jpg .jpeg
val_split float 20 The percentage of data to be separated for validation. This only works under “random” partition mode. This partition is available in fold 0 of the TFrecords generated. Set the validation fold to 0 in the dataset_config. 0-100
kitti_sequence_to_frames_file str The name of the KITTI sequence to frame mapping file. This file must be present within the dataset root as mentioned in the root_directory_path.
num_shards int 10 The number of shards per fold. 1-20

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

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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:

Parameter

Datatype

Default

Description

Supported Values

root_directory_path string The path to the dataset root directory
image_dir_names string (repated) The relative path to the directory containing images from the path in root_directory_path for each partition.
annotation_files string (repated) The relative path to the directory containing JSON file from the path in root_directory_path for each partition.
num_partitions int 2 The number of partitions in the data. The number of partition must match the length of the list for image_dir_names and annotation_files. By default, two partitions are generated: val and train. n==len(annotation_files)
num_shards int (repeated) [10] The number of shards per partitions. If only one value is provided, same number of shards is applied in all partitions

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.

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coco_config { root_directory_path: "/workspace/tao-experiments/data/coco" image_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:

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tao model ssd 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:

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


Below is a sample of the SSD spec file. It has six major components: ssd_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.

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random_seed: 42 ssd_config { aspect_ratios: "[[1.0, 2.0, 0.5], [1.0, 2.0, 0.5, 3.0, 1.0/3.0], [1.0, 2.0, 0.5, 3.0, 1.0/3.0], [1.0, 2.0, 0.5, 3.0, 1.0/3.0], [1.0, 2.0, 0.5], [1.0, 2.0, 0.5]]" scales: "[0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05]" two_boxes_for_ar1: true clip_boxes: false variances: "[0.1, 0.1, 0.2, 0.2]" arch: "resnet" nlayers: 18 freeze_bn: false freeze_blocks: 0 } training_config { batch_size_per_gpu: 16 num_epochs: 80 enable_qat: false learning_rate { soft_start_annealing_schedule { min_learning_rate: 5e-5 max_learning_rate: 2e-2 soft_start: 0.15 annealing: 0.8 } } regularizer { type: L1 weight: 3e-5 } } eval_config { validation_period_during_training: 10 average_precision_mode: SAMPLE batch_size: 16 matching_iou_threshold: 0.5 } nms_config { confidence_threshold: 0.01 clustering_iou_threshold: 0.6 top_k: 200 } augmentation_config { output_width: 300 output_height: 300 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: "/path/to/train/tfrecord" # option 2 # label_directory_path: "/path/to/train/labels" # image_directory_path: "/path/to/train/images" } 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: "/path/to/val/labels" image_directory_path: "/path/to/val/images" } }

The top level structure of the spec file is summarized in the sections below.

Training Config

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

Field Description Data Type and Constraints Recommended/Typical Value
batch_size_per_gpu The batch size for each GPU, so the effective batch size is batch_size_per_gpu * num_gpus Unsigned int, positive
num_epochs The number of epochs to train the network Unsigned int, positive

enable_qat

Whether to use quantization-aware training

Boolean

Note: SSD does not support loading a pruned non-QAT model and retraining
it with QAT enabled, or vice versa. For example, to get a pruned QAT model,
perform the initial training with QAT enabled or enable_qat=True.

learning_rate

Only soft_start_annealing_schedule with these nested parameters is supported.

1. min_learning_rate: The minimum learning during the entire experiment
2. max_learning_rate: The maximum learning during the entire experiment
3. soft_start: Time to lapse before warm up ( expressed in percentage of progress
between 0 and 1)
4. annealing: Time to start annealing the learning rate

Message type

regularizer

This parameter configures the regularizer to be used while training and contains the
following nested parameters.

1. type: The type or regularizer to use. NVIDIA supports NO_REG, L1, and L2
2. weight: The floating point value for the regularizer weight

Message type

L1

Note: NVIDIA suggests using the L1 regularizer when training a network
before pruning as L1 regularization helps make the network weights more
prunable.

max_queue_size The number of prefetch batches in data loading Unsigned int, positive
n_workers The number of workers for data loading (set to less than 4 when using tfrecords) Unsigned int, positive
use_multiprocessing Whether to use multiprocessing mode of keras sequence data loader Boolean
visualizer Training visualization config Message type
early_stopping Early stopping config Message type
Note

The learning rate is automatically scaled with the number of GPUs used during training, or the effective learning rate is learning_rate * n_gpu.

Training Visualization Config

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

Parameter Description Data Type and Constraints Recommended/Typical Value
enabled Boolean flag to enable or disable this feature bool.
num_images The maximum number of images to be visualized in TensorBoard. int. 3

If the 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. And 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.

Parameter Description Data Type and Constraints Recommended/Typical Value
monitor The metric to monitor in order to enable early stopping. string loss
patience The number of checks of monitor value before stopping the training. int
min_delta The delta of the minimum value of monitor value below which we regard it as not decreasing. float

Evaluation Config

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

Field Description Data Type and Constraints Recommended/Typical Value
validation_period_during_training The number of training epochs per validation Unsigned int, positive 10

average_precision_mode

The Average Precision (AP) calculation mode can be either SAMPLE or INTEGRATE. SAMPLE
is used as VOC metrics for VOC 2009 or before. INTEGRATE is used for VOC 2010 or after.

ENUM type ( SAMPLE or INTEGRATE)

SAMPLE

matching_iou_threshold The lowest IoU of the predicted box and ground truth box that can be considered a match. Boolean 0.5
visualize_pr_curve Boolean flag to enable or disable visualization of Precision-Recall curve. Boolean

NMS Config

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

Field Description Data Type and Constraints Recommended/Typical Value
confidence_threshold Boxes with a confidence score less than confidence_threshold are discarded before applying NMS. float 0.01
cluster_iou_threshold The IoU threshold below which boxes will go through the NMS process. float 0.6
top_k top_k boxes will be output after the NMS keras layer. If the number of valid boxes is less than k, the returned array will be padded with boxes whose confidence score is 0. Unsigned int 200
infer_nms_score_bits The number of bits to represent the score values in NMS plugin in TensorRT OSS. The valid range is integers in [1, 10]. Setting it to any other values will make it fall back to ordinary NMS. Currently this optimized NMS plugin is only available in FP16 but it should also be selected by INT8 data type as there is no INT8 NMS in TensorRT OSS and hence this fastest implementation in FP16 will be selected. If falling back to ordinary NMS, the actual data type when building the engine will decide the exact precision(FP16 or FP32) to run at. int. In the interval [1, 10]. 0

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.

Note

The details of augmentation methods can be found in setcion 2.2 and 3.6 of the paper.

Field Description Data Type and Constraints Recommended/Typical Value
output_channel Output image channel of augmentation pipeline. integer
output_width The width of preprocessed images and the network input. integer, multiple of 32
output_height The height of preprocessed images and the network input. integer, multiple of 32
random_crop_min_scale Minimum patch scale of RandomCrop augmentation. Default:0.3 float >= 1.0
random_crop_max_scale Maximum patch scale of RandomCrop augmentation. Default:1.0 float >= 1.0
random_crop_min_ar Minimum aspect ratio of RandomCrop augmentation. Default:0.5 float > 0
random_crop_max_ar Maximum aspect ratio of RandomCrop augmentation. Default:2.0 float > 0
zoom_out_min_scale Minimum scale of ZoomOut augmentation. Default:1.0 float >= 1.0
zoom_out_max_scale Maximum scale of ZoomOut augmentation. Default:4.0 float >= 1.0
brightness Brightness delta in color jittering augmentation. Default:32 integer >= 0
contrast Contrast delta factor in color jitter augmentation. Default:0.5 float of [0, 1)
saturation Saturation delta factor in color jitter augmentation. Default:0.5 float of [0, 1)
hue Hue delta in color jittering augmentation. Default:18 integer >= 0
random_flip Probablity of performing random horizontal flip. Default:0.5 float of [0, 1)
image_mean A key/value pair to specify image mean values. If omitted, ImageNet mean will be used for image preprocessing. If set, depending on output_channel, either ‘r/g/b’ or ‘l’ key/value pair must be configured. dict
Note

If set random_crop_min_scale = random_crop_max_scale = 1.0, RandomCrop augmentation will be disabled. Similarly, set zoom_out_min_scale = zoom_out_max_scale = 1, ZoomOut augmentation will be disabled. And all color jitter delta values are set to 0, color jittering augmentation will be disabled.


Dataset Config

The dataset_config parameter defines the path to the training dataset, validation dataset, and target_class_mapping.

Field Description Data Type and Constraints Recommended/Typical Value
data_sources The path to the training dataset. When using tfrecord as dataset ingestion, set:
  • tfrecords_path: The path to tfrecords

When using raw KITTI labels and images, set:

  • label_directory_path: The path to the label directory
  • image_directory_path: The path to the image directory
Message type
include_difficult_in_training Specifies whether to include difficult objects in the label (the Pascal VOC difficult label or KITTI occluded objects) bool true
validation_data_sources The path to the training dataset images and labels Message type
target_class_mapping A mapping of classes in labels to the target classes Message type
Note

data_sources and validation_data_sources are both repeated fields. Multiple datasets can be added to sources.


SSD config

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

Field Description Data Type and Constraints Recommended/Typical Value
aspect_ratios_global The anchor boxes of aspect ratios defined in aspect_ratios_global will be generated for each feature layer used for prediction. Note that either the aspect_ratios_global or aspect_ratios parameter is required; you don’t need to specify both. string “[1.0, 2.0, 0.5, 3.0, 0.33]”
aspect_ratios The aspect ratio of anchor boxes for different SSD feature layers Note: Either the aspect_ratios_global or aspect_ratios parameter is required; you don’t need to specify both. string “[[1.0,2.0,0.5], [1.0,2.0,0.5], [1.0,2.0,0.5], [1.0,2.0,0.5], [1.0,2.0,0.5], [1.0, 2.0, 0.5, 3.0, 0.33]]”
two_boxes_for_ar1 If this parameter is True, two boxes will be generated with an aspect ratio of 1. One with a scale for this layer and the other with a scale that is the geometric mean of the scale for this layer and the scale for the next layer. Boolean True
clip_boxes If true, all corner anchor boxes will be truncated so they are fully inside the feature images. Boolean False
scales A list of positive floats containing scaling factors per convolutional predictor layer. This list must be one element longer than the number of predictor layers so that, if two_boxes_for_ar1 is true, the second aspect-ratio 1.0 box for the last layer can have a proper scale. Except for the last element in this list, each positive float is the scaling factor for boxes in that layer. For example, if for one layer the scale is 0.1, then the generated anchor box with aspect ratio 1 for that layer (the first aspect-ratio 1 box if two_boxes_for_ar1 is set to True) will have its height and width as 0.1*min(img_h, img_w). min_scale and max_scale are two positive floats. If both of them appear in the config, the program can automatically generate the scales by evenly splitting the space between min_scale and max_scale. string “[0.05, 0.1, 0.25, 0.4, 0.55, 0.7, 0.85]”
min_scale/max_scale variances If both appear in the config, scales will be generated evenly by splitting the space between min_scale and max_scale. A list of 4 positive floats. The four floats, in order, represent variances for box center x, box center y, log box height, and log box width. The box offset for box center (cx, cy) and log box size (height/width) w.r.t. anchor will be divided by their respective variance value. Therefore, larger variances result in less significant differences between two different boxes on encoded offsets. float
steps An optional list inside quotation marks with a length that is the number of feature layers for prediction. The elements should be floats or tuples/lists of two floats. The steps define how many pixels apart the anchor-box center points should be. If the element is a float, both vertical and horizontal margin is the same. Otherwise, the first value is step_vertical and the second value is step_horizontal. If steps are not provided, anchor boxes will be distributed uniformly inside the image. string
offsets An optional list of floats inside quotation marks with length equal to the number of feature layers for prediction. The first anchor box will have a margin of offsets[i]*steps[i] pixels from the left and top borders. If offsets are not provided, 0.5 will be used as default value. string
arch The backbone for feature extraction. Currently, “resnet”, “vgg”, “darknet”, “googlenet”, “mobilenet_v1”, “mobilenet_v2” and “squeezenet” are supported. string resnet
nlayers The number of conv layers in a specific arch. For “resnet”, 10, 18, 34, 50 and 101 are supported. For “vgg”, 16 and 19 are supported. For “darknet”, 19 and 53 are supported. All other networks don’t have this configuration, and users should delete this parameter from the config file. Unsigned int
freeze_bn Whether to freeze all batch normalization layers during training. boolean False
freeze_blocks The list of block IDs to be frozen in the model during training. You can choose to freeze some of the CNN blocks in the model to make the training more stable and/or easier to converge. The definition of a block is heuristic for a specific architecture. For example, by stride or by logical blocks in the model, etc. However, the block ID numbers identify the blocks in the model in a sequential order so you don’t have to know the exact locations of the blocks when you do training. As a general principle, the smaller the block ID, the closer it is to the model input; the larger the block ID, the closer it is to the model output. You can divide the whole model into several blocks and optionally freeze a subset of it. Note that for FasterRCNN, you can only freeze the blocks that are before the ROI pooling layer. Any layer after the ROI pooling layer will not be frozen anyway. For different backbones, the number of blocks and the block ID for each block are different. It deserves some detailed explanations on how to specify the block IDs for each backbone. list(repeated integers)
  • ResNet series. For the ResNet series, the block IDs valid for freezing is any subset of [0, 1, 2, 3] (inclusive)
  • VGG series. For the VGG series, the block IDs valid for freezing is any subset of[1, 2, 3, 4, 5] (inclusive)
  • GoogLeNet. For the GoogLeNet, the block IDs valid for freezing is any subset of[0, 1, 2, 3, 4, 5, 6, 7] (inclusive)
  • MobileNet V1. For the MobileNet V1, the block IDs valid for freezing is any subset of [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] (inclusive)
  • MobileNet V2. For the MobileNet V2, the block IDs valid for freezing is any subset of [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] (inclusive)
  • DarkNet. For the DarkNet 19 and DarkNet 53, the block IDs valid for freezing is any subset of [0, 1, 2, 3, 4, 5] (inclusive)

Train the SSD model using this command:

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tao model ssd train [-h] -e <experiment_spec> -r <output_dir> -k <key> [--gpus <num_gpus>] [--gpu_index <gpu_index>] [--use_amp] [--log_file <log_file>] [-m <resume_model_path>] [--initial_epoch <initial_epoch>]

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 num_gpus: Number of GPUs to use and processes to launch for training. The 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: The path to the log file. Defaults to stdout.

  • -m, --resume_model_weights: Path to a pre-trained model or model to continue training.

  • --initial_epoch: Epoch number to resume from.

  • --use_multiprocessing: Enable multiprocessing mode in data generator.

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

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 train command on an SSD model:

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


Use the following command to run evaluation for an SSD model:

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tao model ssd evaluate [-h] -m <model> -e <experiment_spec_file> [-k <key>] [--gpu_index <gpu_index>] [--log_file <log_file>]

Required Arguments

  • -m, --model: The .tlt model or TensorRT engine to be evaluated.

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

Optional Arguments

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

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

  • --gpu_index: The index of the GPU to run evaluation (useful 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.

Here is a sample command to evaluate a SSD model:

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tao model ssd evaluate -m /path/to/trained_tlt_ssd_model -k <model_key> -e /path/to/ssd_spec.txt


The inference command for SSD networks can be used to visualize bboxes or generate frame-by-frame KITTI format labels on a directory of images. Here’s an example of using this tool:

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

Required Arguments

  • -m, --model: The path to the pretrained model (TAO model).

  • -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 the load model.

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

Optional Arguments

  • -t, --threshold: The threshold for drawing a bbox and dumping a label file. (default: 0.3).

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

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

  • --gpu_index: The index of the GPU to run inference (useful 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.

Here is a sample of using inference with the SSD model:

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tao model ssd inference -i /path/to/input/images_dir -o /path/to/output/dir -m /path/to/trained_tlt_ssd_model -k <model_key> -e /path/to/ssd_spec.txt


Pruning removes parameters from the model to reduce the model size without compromising the integrity of the model itself.

The prune command includes these parameters:

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tao model ssd 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>] [--gpu_index <gpu_index>] [--log_file <log_file>]

Required Arguments

  • -m, --pretrained_model: The path to the pretrained model.

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

  • -k, --key: The 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: [])

  • --gpu_index: The index of the GPU to run pruning (useful 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. Defaults to stdout.

Here’s an example of using the prune command:

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

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

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. To regain accuracy, NVIDIA recommends that you retrain this pruned model over the same dataset. To do this, use the tao model ssd train command 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 SSD to recover the accuracy when retraining a pruned model. You may do this by setting the regularizer type to NO_REG, as mentioned here. All the other parameters may be retained in the spec file from the previous training.

Note

SSD does not support loading a pruned non-QAT model and retraining it with QAT enabled, or vice versa. For example, to get a pruned QAT model, perform the initial training with QAT enabled or enable_qat=True.

The TAO Toolkit includes the export command to export and prepare TAO models for Deploying to DeepStream. The export command optionally generates the calibration cache for TensorRT INT8 engine calibration.

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. This may be interchangeably referred to as the .trt or .engine file. The same exported TAO model may be used universally across training and deployment hardware. This is referred to as the .etlt file or encrypted TAO file. During model export, the TAO model is encrypted with a private key. This key is required when you deploy this model for inference.

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 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 an INT8 calibration cache by ingesting training data using the following method:

  • 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 model from the train step to export to convert it into an encrypted TAO model.

fp16_fp32_export.png

Exporting command

Use the following command to export an SSD model

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

Required Arguments

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

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

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

Optional Arguments

  • -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. 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.

Note

When exporting a model that was trained with QAT enabled, the tensor scale factors to calibrate the activations are peeled out of the model and serialized to a TensorRT-readable cache file defined by the cal_json_file argument.


Exporting a Model

Here’s a sample command to export SSD model:

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tao model ssd export -m $USER_EXPERIMENT_DIR/data/ssd/ssd_kitti_retrain_epoch12.tlt \ -o $USER_EXPERIMENT_DIR/data/ssd/ssd_kitti_retrain.int8.etlt \ -e $SPECS_DIR/ssd_kitti_retrain_spec.txt \ --key $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 SSD.

© Copyright 2023, NVIDIA.. Last updated on Dec 8, 2023.