DSSD
With DSSD, 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 dssd <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 expect data in KITTI format for training and evaluation.
See the Data Annotation Format page for more information about the KITTI data format.
The dssd 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 parameterscoco_config
: A nested prototxt configuration with multiple input parametersimage_directory_path
: The path to the dataset root. Theimage_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:
|
|
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.
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.
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 dssd 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 dssd dataset_convert -d /path/to/spec.txt
-o /path/to/tfrecords/train
Below is a sample for the DSSD spec file. It has six major components: dssd_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
dssd_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"
}
}
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 |
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: DSSD does not support loading a pruned non-QAT model and retraining |
|
learning_rate |
Only soft_start_annealing_schedule with these nested parameters is supported:
1. min_learning_rate: The minimum learning during the entire experiment |
Message type. |
– |
|
regularizer |
This parameter configures the regularizer to be used while training and contains
1. type: The type of regularizer to use. NVIDIA supports NO_REG, L1, and L2 |
Message type. |
L1 (Note: NVIDIA suggests using the L1 regularizer when training a network before |
|
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 |
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. |
|
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 |
|
patience |
The number of checks of |
int |
|
min_delta |
The delta of the minimum value of |
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 |
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 avaible 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.
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 |
– |
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:
When using raw KITTI labels and images, set:
|
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 |
data_sources
and validation_data_sources
are both repeated fields.
Multiple datasets can be added to sources.
DSSD Config
The DSSD configuration (dssd_config
) defines the parameters needed for building the DSSD
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 length of the outer list must be equivalent to the number of feature layers used for anchor box generation, and the i-th layer will have anchor boxes with aspect ratios defined in aspect_ratios[i]. 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], [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 |
This setting is only relevant for layers that have 1.0 as the aspect ratio. If two_boxes_for_ar1 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 this parameter is 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 |
If both appear in the config, scales will be generated evenly by splitting the space between min_scale and max_scale. |
float |
– |
variances |
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. |
– |
|
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 |
– |
pred_num_channels |
This setting controls the number of channels of the convolutional layers in the DSSD prediction module. Setting this value to 0 will disable the DSSD prediction module. Supported values for this setting are 0, 256, 512 and 1024. A larger value gives a larger network and usually means the network is harder to train. |
Unsigned int |
512 |
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)
|
– |
Train the DSSD model using this command:
tao dssd 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 tostdout
.-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 a DSSD model:
tao dssd train --gpus 2 -e /path/to/dssd_spec.txt -r /path/to/result -k $KEY
Use following command to run evaluation for a DSSD model:
tao dssd 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 orTensorRT
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 isstdout
.
Here is a sample command to evaluate a DSSD model:
tao dssd evaluate -m /path/to/trained_tlt_dssd_model -k <model_key> -e /path/to/dssd_spec.txt
The inference
tool for DSSD 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:
tao dssd 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 isstdout
.
Here is a sample of using inference with the DSSD model:
tao dssd inference -i /path/to/input/images_dir -o /path/to/output/dir -m /path/to/trained_tlt_dssd_model -k <model_key> -e /path/to/dssd_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:
tao dssd 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 arearithmetic_mean
,geometric_mean
,union
, andintersection
. (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 tostdout
.
Here’s an example of using the prune
command:
tao dssd 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 dssd 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 DSSD 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.
DSSD 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.
Exporting command
Use the following command to export a DSSD model:
tao dssd export [-h] -m <path to the .tlt model file generated by tao train>
-k <key>
-e <path to experiment spec file>]
[-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>]
[--strict_type_constraints]
[--force_ptq]
[--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 usingexport
.-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
.--data_type
: The desired engine data type. The options are “fp32”, “fp16”, “int8”. The default value is “fp32”. If using int8, the following INT8 arguments are required.-s, --strict_type_constraints
: A Boolean flag to indicate whether or not to apply the TensorRTstrict_type_constraints
when building the TensorRT engine. Note this is only for applying the strict type of INT8 mode.--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 theoutput_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.
INT8 Export Mode Required Arguments
--cal_data_file
: The tensorfile generated fromtlt-int8-tensorfile
for calibrating the engine. This can also be an output file if used with--cal_image_dir
.--cal_image_dir
: The directory of images to use for calibration
The --cal_image_dir
parameter applies the necessary preprocessing
to generate a tensorfile at the path mentioned in the --cal_data_file
parameter, which is in turn used for calibration. The number of generated batches in the
tensorfile is obtained from the value set to the --batches
parameter,
and the batch_size
is obtained from the value set to the --batch_size
parameter. Ensure that the directory mentioned in --cal_image_dir
has at least
batch_size * batches
number of images in it. The valid image extensions are
.jpg
, .jpeg
, and .png
. In this case, the input_dimensions
of the calibration tensors are derived from the input layer of the .tlt
model.
INT8 Export Optional Arguments
--cal_cache_file
: The path to save the calibration cache file to. The default value is./cal.bin
.--batches
: The number of batches to use for calibration and inference testing. The default value is 10.--batch_size
: The batch size to use for calibration. The default value is 8.--max_batch_size
: The maximum batch size of the TensorRT engine. The default value is 16.--max_workspace_size
: The maximum workspace size of the TensorRT engine. The default value is 1073741824 = 1<<30.--engine_file
: The path to the serialized TensorRT engine file. Note that this file is hardware specific and cannot be generalized across GPUs. Use this argument to quickly test your model accuracy using TensorRT on the host. As the TensorRT engine file is hardware specific, you cannot use this engine file for deployment unless the deployment GPU is identical to the training GPU.--force_ptq
: A Boolean flag to force post-training quantization on the exported.etlt
model.
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_cache_file
argument. However, the current version of
QAT doesn’t natively support DLA int8 deployment on Jetson. To deploy
this model on Jetson with DLA int8
, use the --force_ptq
flag to use
TensorRT post-training quantization to generate the calibration cache file.
Exporting a Model
Here’s a sample command using the --cal_image_dir
option:
tao dssd export -m $USER_EXPERIMENT_DIR/data/dssd/dssd_kitti_retrain_epoch12.tlt \
-o $USER_EXPERIMENT_DIR/data/dssd/dssd_kitti_retrain.int8.etlt \
-e $SPECS_DIR/dssd_kitti_retrain_spec.txt \
--key $KEY \
--cal_image_dir $USER_EXPERIMENT_DIR/data/KITTI/val/image_2 \
--data_type int8 \
--batch_size 8 \
--batches 10 \
--cal_data_file $USER_EXPERIMENT_DIR/data/dssd/cal.tensorfile \
--cal_cache_file $USER_EXPERIMENT_DIR/data/dssd/cal.bin \
--engine_file $USER_EXPERIMENT_DIR/data/dssd/detection.trt
The deep learning and computer vision models that you’ve trained can be deployed on edge devices, such as a Jetson Xavier or Jetson Nano, a discrete GPU, or in the cloud with NVIDIA GPUs. TAO Toolkit has been designed to integrate with DeepStream SDK, so models trained with TAO Toolkit will work out of the box with DeepStream SDK.
DeepStream SDK is a streaming analytic toolkit to accelerate building AI-based video analytic applications. This section will describe how to deploy your trained model to DeepStream SDK.
To deploy a model trained by TAO Toolkit to DeepStream we have two options:
Option 1: Integrate the
.etlt
model directly in the DeepStream app. The model file is generated by export.Option 2: Generate a device specific optimized TensorRT engine using
tao-converter
. The generated TensorRT engine file can also be ingested by DeepStream.
Machine-specific optimizations are done as part of the engine creation process, so a distinct engine should be generated for each environment and hardware configuration. If the TensorRT or CUDA libraries of the inference environment are updated (including minor version updates), or if a new model is generated, new engines need to be generated. Running an engine that was generated with a different version of TensorRT and CUDA is not supported and will cause unknown behavior that affects inference speed, accuracy, and stability, or it may fail to run altogether.
Option 1 is very straightforward. The .etlt
file and calibration cache are directly
used by DeepStream. DeepStream will automatically generate the TensorRT engine file and then run
inference. TensorRT engine generation can take some time depending on size of the model
and type of hardware. Engine generation can be done ahead of time with Option 2.
With option 2, the tao-converter
is used to convert the .etlt
file to TensorRT; this
file is then provided directly to DeepStream.
See the Exporting the Model section for more details on how to export a TAO model.
TensorRT Open Source Software (OSS)
TensorRT OSS build is required for DSSD models. This is required because several TensorRT
plugins that are required by these models are only available in TensorRT open source repo and not
in the general TensorRT release. Specifically, for DSSD, we need the batchTilePlugin
and
NMSPlugin
.
If the deployment platform is x86 with NVIDIA GPU, follow instructions for x86; if your deployment is on NVIDIA Jetson platform, follow instructions for Jetson.
TensorRT OSS on x86
Building TensorRT OSS on x86:
Install Cmake (>=3.13).
NoteTensorRT OSS requires cmake >= v3.13, so install cmake 3.13 if your cmake version is lower than 3.13c
sudo apt remove --purge --auto-remove cmake wget https://github.com/Kitware/CMake/releases/download/v3.13.5/cmake-3.13.5.tar.gz tar xvf cmake-3.13.5.tar.gz cd cmake-3.13.5/ ./configure make -j$(nproc) sudo make install sudo ln -s /usr/local/bin/cmake /usr/bin/cmake
Get GPU architecture. The
GPU_ARCHS
value can be retrieved by thedeviceQuery
CUDA sample:cd /usr/local/cuda/samples/1_Utilities/deviceQuery sudo make ./deviceQuery
If the
/usr/local/cuda/samples
doesn’t exist in your system, you could downloaddeviceQuery.cpp
from this GitHub repo. Compile and rundeviceQuery
.nvcc deviceQuery.cpp -o deviceQuery ./deviceQuery
This command will output something like this, which indicates the
GPU_ARCHS
is75
based on CUDA Capability major/minor version.Detected 2 CUDA Capable device(s) Device 0: "Tesla T4" CUDA Driver Version / Runtime Version 10.2 / 10.2 CUDA Capability Major/Minor version number: 7.5
Build TensorRT OSS:
git clone -b 21.08 https://github.com/nvidia/TensorRT cd TensorRT/ git submodule update --init --recursive export TRT_SOURCE=`pwd` cd $TRT_SOURCE mkdir -p build && cd build
NoteMake sure your
GPU_ARCHS
from step 2 is in TensorRT OSSCMakeLists.txt
. If GPU_ARCHS is not in TensorRT OSSCMakeLists.txt
, add-DGPU_ARCHS=<VER>
as below, where<VER>
representsGPU_ARCHS
from step 2./usr/local/bin/cmake .. -DGPU_ARCHS=xy -DTRT_LIB_DIR=/usr/lib/x86_64-linux-gnu/ -DCMAKE_C_COMPILER=/usr/bin/gcc -DTRT_BIN_DIR=`pwd`/out make nvinfer_plugin -j$(nproc)
After building ends successfully,
libnvinfer_plugin.so*
will be generated under\`pwd\`/out/.
Replace the original
libnvinfer_plugin.so*
:sudo mv /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.8.x.y ${HOME}/libnvinfer_plugin.so.8.x.y.bak // backup original libnvinfer_plugin.so.x.y sudo cp $TRT_SOURCE/`pwd`/out/libnvinfer_plugin.so.8.m.n /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.8.x.y sudo ldconfig
TensorRT OSS on Jetson (ARM64)
Install Cmake (>=3.13)
NoteTensorRT OSS requires cmake >= v3.13, while the default cmake on Jetson/Ubuntu 18.04 is cmake 3.10.2.
Upgrade TensorRT OSS using:
sudo apt remove --purge --auto-remove cmake wget https://github.com/Kitware/CMake/releases/download/v3.13.5/cmake-3.13.5.tar.gz tar xvf cmake-3.13.5.tar.gz cd cmake-3.13.5/ ./configure make -j$(nproc) sudo make install sudo ln -s /usr/local/bin/cmake /usr/bin/cmake
Get GPU architecture based on your platform. The
GPU_ARCHS
for different Jetson platform are given in the following table.Jetson Platform
GPU_ARCHS
Nano/Tx1
53
Tx2
62
AGX Xavier/Xavier NX
72
Build TensorRT OSS:
git clone -b 21.03 https://github.com/nvidia/TensorRT cd TensorRT/ git submodule update --init --recursive export TRT_SOURCE=`pwd` cd $TRT_SOURCE mkdir -p build && cd build
NoteThe
-DGPU_ARCHS=72
below is for Xavier or NX, for other Jetson platform, change72
referring toGPU_ARCHS
from step 2./usr/local/bin/cmake .. -DGPU_ARCHS=72 -DTRT_LIB_DIR=/usr/lib/aarch64-linux-gnu/ -DCMAKE_C_COMPILER=/usr/bin/gcc -DTRT_BIN_DIR=`pwd`/out make nvinfer_plugin -j$(nproc)
After building ends successfully,
libnvinfer_plugin.so*
will be generated under‘pwd’/out/.
Replace
"libnvinfer_plugin.so*"
with the newly generated.sudo mv /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.8.x.y ${HOME}/libnvinfer_plugin.so.8.x.y.bak // backup original libnvinfer_plugin.so.x.y sudo cp `pwd`/out/libnvinfer_plugin.so.8.m.n /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.8.x.y sudo ldconfig
Generating an Engine Using tao-converter
The tao-converter
tool is provided with the TAO Toolkit
to facilitate the deployment of TAO trained models on TensorRT and/or Deepstream.
This section elaborates on how to generate a TensorRT engine using tao-converter
.
For deployment platforms with an x86-based CPU and discrete GPUs, the tao-converter
is distributed within the TAO docker. Therefore, we suggest using the docker to generate
the engine. However, this requires that the user adhere to the same minor version of
TensorRT as distributed with the docker. The TAO docker includes TensorRT version 8.0.
Instructions for x86
For an x86 platform with discrete GPUs, the default TAO package includes the tao-converter
built for TensorRT 8.2.5.1 with CUDA 11.4 and CUDNN 8.2. However, for any other version of CUDA and
TensorRT, please refer to the overview section for download. Once the
tao-converter
is downloaded, follow the instructions below to generate a TensorRT engine.
Unzip the zip file on the target machine.
Install the OpenSSL package using the command:
sudo apt-get install libssl-dev
Export the following environment variables:
$ export TRT_LIB_PATH=”/usr/lib/x86_64-linux-gnu”
$ export TRT_INC_PATH=”/usr/include/x86_64-linux-gnu”
Run the
tao-converter
using the sample command below and generate the engine.Instructions to build TensorRT OSS on Jetson can be found in the TensorRT OSS on x86 section above or in this GitHub repo.
Make sure to follow the output node names as mentioned in Exporting the Model
section of the respective model.
Instructions for Jetson
For the Jetson platform, the tao-converter
is available to download in the NVIDIA developer zone. You may choose
the version you wish to download as listed in the overview section.
Once the tao-converter
is downloaded, please follow the instructions below to generate a
TensorRT engine.
Unzip the zip file on the target machine.
Install the OpenSSL package using the command:
sudo apt-get install libssl-dev
Export the following environment variables:
$ export TRT_LIB_PATH=”/usr/lib/aarch64-linux-gnu”
$ export TRT_INC_PATH=”/usr/include/aarch64-linux-gnu”
For Jetson devices, TensorRT comes pre-installed with Jetpack. If you are using older JetPack, upgrade to JetPack-5.0DP.
Instructions to build TensorRT OSS on Jetson can be found in the TensorRT OSS on Jetson (ARM64) section above or in this GitHub repo.
Run the
tao-converter
using the sample command below and generate the engine.
Make sure to follow the output node names as mentioned in Exporting the Model
section of the respective model.
Using the tao-converter
tao-converter [-h] -k <encryption_key>
-d <input_dimensions>
-o <comma separated output nodes>
[-c <path to calibration cache file>]
[-e <path to output engine>]
[-b <calibration batch size>]
[-m <maximum batch size of the TRT engine>]
[-t <engine datatype>]
[-w <maximum workspace size of the TRT Engine>]
[-i <input dimension ordering>]
[-p <optimization_profiles>]
[-s]
[-u <DLA_core>]
input_file
Required Arguments
input_file
: Path to the.etlt
model exported usingtao dssd export
.-k
: The key used to encode the.tlt
model when doing the training.-d
: Comma-separated list of input dimensions that should match the dimensions used fortao dssd export
.-o
: Comma-separated list of output blob names that should match the output configuration used fortao dssd export
. For DSSD, set this argument toNMS
.
Optional Arguments
-e
: Path to save the engine to. (default:./saved.engine
)-t
: Desired engine data type, generates calibration cache if in INT8 mode. The default value isfp32
. The options are {fp32
,fp16
,int8
}.-w
: Maximum workspace size for the TensorRT engine. The default value is1073741824(1<<30)
.-i
: Input dimension ordering, all other TAO commands use NCHW. The default value isnchw
. The options are {nchw
,nhwc
,nc
}. For DSSD, we can omit it(defaults tonchw
).-p
: Optimization profiles for.etlt
models with dynamic shape. Comma separated list of optimization profile shapes in the format<input_name>,<min_shape>,<opt_shape>,<max_shape>
, where each shape has the format:<n>x<c>x<h>x<w>
. Can be specified multiple times if there are multiple input tensors for the model. This is only useful for new models introduced in TAO Toolkit 3.21.08. This parameter is not required for models that are already existed in version 2.0.-s
: TensorRT strict type constraints. A Boolean to apply TensorRT strict type constraints when building the TensorRT engine.-u
: Use DLA core. Specifying DLA core index when building the TensorRT engine on Jetson devices.
INT8 Mode Arguments
-c
: Path to calibration cache file, only used in INT8 mode. The default value is./cal.bin
.-b
: Batch size used during the export step for INT8 calibration cache generation. (default:8
).-m
: Maximum batch size for TensorRT engine.(default:16
). If meet with out-of-memory issue, decrease the batch size accordingly. This parameter is not required for.etlt
models generated with dynamic shape. (This is only possible for new models introduced in TAO Toolkit 3.21.08.)
Sample Output Log
Here is a sample log for exporting a DSSD model.
tao-converter -k $KEY \
-d 3,384,1248 \
-o NMS \
-e /export/trt.fp16.engine \
-t fp16 \
-i nchw \
-m 1 \
/ws/dssd_resnet18_epoch_100.etlt
..
[INFO] Some tactics do not have sufficient workspace memory to run. Increasing workspace size may increase performance, please check verbose output.
[INFO] Detected 1 inputs and 2 output network tensors.
Integrating the model to DeepStream
There are 2 options to integrate models from TAO with DeepStream:
Option 1: Integrate the model (.etlt) with the encrypted key directly in the DeepStream app. The model file is generated by
tao dssd export
.Option 2: Generate a device specific optimized TensorRT engine, using tao-converter. The TensorRT engine file can also be ingested by DeepStream.
For DSSD, we will need to build TensorRT Open source plugins and custom bounding box parser. The instructions are provided below in the TensorRT OSS section above and the required code can be found in this GitHub repo.
In order to integrate the models with DeepStream, you need the following:
Download and install DeepStream SDK. The installation instructions for DeepStream are provided in the DeepStream Development Guide.
An exported
.etlt
model file and optional calibration cache for INT8 precision.A
labels.txt
file containing the labels for classes in the order in which the networks produces outputs.A sample
config_infer_*.txt
file to configure the nvinfer element in DeepStream. The nvinfer element handles everything related to TensorRT optimization and engine creation in DeepStream.
DeepStream SDK ships with an end-to-end reference application which is fully configurable. Users
can configure input sources, inference model and output sinks. The app requires a primary object
detection model, followed by an optional secondary classification model. The reference
application is installed as deepstream-app
. The graphic below shows the architecture of the
reference application.
There are typically 2 or more configuration files that are used with this app. In the install
directory, the config files are located in samples/configs/deepstream-app
or
sample/configs/tlt_pretrained_models
. The main config file configures all the high level
parameters in the pipeline above. This would set input source and resolution, number of
inferences, tracker and output sinks. The other supporting config files are for each individual
inference engine. The inference specific config files are used to specify models, inference
resolution, batch size, number of classes and other customization. The main config file will call
all the supporting config files. Here are some config files in
samples/configs/deepstream-app
for your reference.
source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt
: Main config fileconfig_infer_primary.txt
: Supporting config file for primary detector in the pipeline aboveconfig_infer_secondary_*.txt
: Supporting config file for secondary classifier in the pipeline above
The deepstream-app
will only work with the main config file. This file will most likely
remain the same for all models and can be used directly from the DeepStream SDK will little to no
change. User will only have to modify or create config_infer_primary.txt
and
config_infer_secondary_*.txt
.
Integrating an DSSD Model
To run a DSSD model in DeepStream, you need a label file and a DeepStream configuration file. In addition, you need to compile the TensorRT 7+ Open source software and DSSD bounding box parser for DeepStream.
A DeepStream sample with documentation on how to run inference using the trained DSSD models from TAO Toolkit is provided on GitHub here.
Prerequisite for DSSD Model
DSSD requires batchTilePlugin and NMS_TRT. This plugin is available in the TensorRT open source repo, but not in TensorRT 7.0. Detailed instructions to build TensorRT OSS can be found in TensorRT Open Source Software (OSS).
DSSD requires custom bounding box parsers that are not built-in inside the DeepStream SDK. The source code to build custom bounding box parsers for DSSD is available here. The following instructions can be used to build bounding box parser:
Step1: Install git-lfs (git >= 1.8.2)
curl -s https://packagecloud.io/install/repositories/github/git-lfs/
script.deb.sh | sudo bash
sudo apt-get install git-lfs
git lfs install
Step 2: Download Source Code with SSH or HTTPS
git clone -b release/tlt3.0 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps
Step 3: Build
// or Path for DS installation
export CUDA_VER=10.2 // CUDA version, e.g. 10.2
make
This generates libnvds_infercustomparser_tlt.so
in the directory post_processor
.
Label File
The label file is a text file containing the names of the classes that the DSSD model is trained to detect. The order in which the classes are listed here must match the order in which the model predicts the output. During the training, TAO DSSD will specify all class names in lower case and sort them in alphabetical order. For example, if the dataset_config is:
dataset_config {
data_sources: {
label_directory_path: "/workspace/tao-experiments/data/training/label_2"
image_directory_path: "/workspace/tao-experiments/data/training/image_2"
}
target_class_mapping {
key: "car"
value: "car"
}
target_class_mapping {
key: "person"
value: "person"
}
target_class_mapping {
key: "bicycle"
value: "bicycle"
}
validation_data_sources: {
label_directory_path: "/workspace/tao-experiments/data/val/label"
image_directory_path: "/workspace/tao-experiments/data/val/image"
}
}
Then the corresponding dssd_labels.txt file would be:
background
bicycle
car
person
DeepStream Configuration File
The detection model is typically used as a primary inference engine. It can also be used as a
secondary inference engine. To run this model in the sample deepstream-app
, you must modify
the existing config_infer_primary.txt
file to point to this model.
Option 1: Integrate the model (.etlt
) directly in the DeepStream app.
For this option, users will need to add the following parameters in the configuration file.
The int8-calib-file
is only required for INT8 precision.
tlt-encoded-model=<TLT exported .etlt>
tlt-model-key=<Model export key>
int8-calib-file=<Calibration cache file>
The tlt-encoded-model
parameter points to the exported model (.etlt
) from TLT.
The tlt-model-key
is the encryption key used during model export.
Option 2: Integrate TensorRT engine file with DeepStream app.
Step 1: Generate TensorRT engine using tao-converter. Detailed instructions are provided in the Generating an engine using tao-converter section above.
Step 2: Once the engine file is generated successfully, modify the following parameters to use this engine with DeepStream.
model-engine-file=<PATH to generated TensorRT engine>
All other parameters are common between the two approaches. To use the custom bounding box parser instead of the default parsers in DeepStream, modify the following parameters in [property] section of primary infer configuration file:
parse-bbox-func-name=NvDsInferParseCustomNMSTLT
custom-lib-path=<PATH to libnvds_infercustomparser_tlt.so>
Add the label file generated above using:
labelfile-path=<dssd labels>
For all the options, see the sample configuration file below. To learn about what all the parameters are used for, refer to the DeepStream Development Guide.
[property]
gpu-id=0
net-scale-factor=1.0
offsets=103.939;116.779;123.68
model-color-format=1
labelfile-path=<Path to dssd_labels.txt>
tlt-encoded-model=<Path to DSSD etlt model>
tlt-model-key=<Key to decrypt model>
infer-dims=3;384;1248
uff-input-order=0
maintain-aspect-ratio=1
uff-input-blob-name=Input
batch-size=1
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
num-detected-classes=4
interval=0
gie-unique-id=1
is-classifier=0
#network-type=0
output-blob-names=NMS
parse-bbox-func-name=NvDsInferParseCustomNMSTLT
custom-lib-path=<Path to libnvds_infercustomparser_tlt.so>
[class-attrs-all]
threshold=0.3
roi-top-offset=0
roi-bottom-offset=0
detected-min-w=0
detected-min-h=0
detected-max-w=0
detected-max-h=0