# EfficientDet

With EfficientDet, 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 convention from command line:

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tao efficientdet <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.

## Data Input for EfficientDet

EfficientDet expects directories of images for training or validation and annotation JSON files in COCO format. See the Data Annotation Format page for more information about the data format for EfficientDet.

## Pre-processing the Dataset

The raw image data and the corresponding annotation file need to be converted to TFRecords before training and evaluation. The dataset_convert tool helps to achieve seamless conversion while providing insight on potential issues in an annotation file. The following sections detail how to use dataset_convert.

### Sample Usage of the Dataset Converter Tool

The dataset_convert tool is described below:

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tao efficientdet dataset-convert [-h] -i <image_directory>
-a <annotation_json_file>
-o <tfrecords_output_directory>
[-t <tag>]
[-s <num_shards>]
[--include_mask]


You can use the following arguments:

• -i, --image_dir: The path to the directory where raw images are stored

• -a, --annotations_file: The annotation JSON file

• -o, --output_dir: The output directory where TFRecords are saved

• -t, --tag: The tag for the converted TFRecords (e.g. “train”). The tag defaults to the name of the annotation file.

• -s, --num_shards: The number of shards for the converted TFRecords. The default value is 256.

• --include_mask: Whether to include segmentation groundtruth during conversion. The default value is False.

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

Note

A log file named <tag>_warnings.json will be generated in the output_dir if the bounding box of an object is out of bounds with respect to the image frame or if an object mask is out of bounds with respect to its bounding box. The log file records the image_id that has problematic object IDs. For example, {"200365": {"box": [918], "mask": []} means the bounding box of object 918 is out of bounds in image 200365.

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

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tao efficientdet dataset_convert -i /path/to/image_dir
-a /path/to/train.json
-o /path/to/output_dir


## Creating a Configuration File

Below is a sample for the EfficientDet spec file. It has 5 major components: model_config, training_config, eval_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:

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training_config {
train_batch_size: 16
iterations_per_loop: 10
checkpoint_period: 10
num_examples_per_epoch: 14700
num_epochs: 300
model_name: 'efficientdet-d0'
profile_skip_steps: 100
tf_random_seed: 42
lr_warmup_epoch: 5
lr_warmup_init: 0.00005
learning_rate: 0.1
amp: True
moving_average_decay: 0.9999
l2_weight_decay: 0.00004
l1_weight_decay: 0.0
checkpoint: "/path/to/your/pretrained_model"
# pruned_model_path: "/path/to/your/pruned/model"
}
dataset_config {
num_classes: 91
image_size: "512,512"
training_file_pattern: "/path/to/coco/train-*"
validation_file_pattern: "/path/to/coco/val-*"
validation_json_file: "/path/to/coco/annotations/instances_val2017.json"
}
eval_config {
eval_batch_size: 16
eval_epoch_cycle: 10
eval_after_training: True
eval_samples: 5000
min_score_thresh: 0.4
max_detections_per_image: 100
}
model_config {
model_name: 'efficientdet-d0'
min_level: 3
max_level: 7
num_scales: 3
}
augmentation_config {
rand_hflip: True
random_crop_min_scale: 0.1
random_crop_max_scale: 2.0
}


### Training Config

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

 Field Description Data Type and Constraints Recommended/Typical Value train_batch_size The batch size for each GPU, so the effective batch size is batch_size_per_gpu * num_gpus. Unsigned int, positive 16 num_epochs The number of epochs to train the network Unsigned int, positive 300 num_examples_per _epoch Total number of images in the training set divided by the number of GPUs Unsigned int, positive – checkpoint The path to the pretrained model, if any String – pruned_model_path The path to a TAO pruned model for re-training, if any String – checkpoint_period The number of training epochs that should run per model checkpoint/validation Unsigned int, positive 10 amp Whether to use mixed precision training Boolean – moving_average_decay Moving average decay Float 0.9999 l2_weight_decay L2 weight decay Float – l1_weight_decay L1 weight decay Float – lr_warmup_epoch The number of warmup epochs in the learning rate schedule Unsigned int, positive – lr_warmup_init The initial learning rate in the warmup period Float – learning_rate The maximum learning rate Float – tf_random_seed The random seed Unsigned int, positive 42 clip_gradients_norm Clip gradients by the norm value Float 5 skip_checkpoint _variables If specified, the weights of the layers with matching regular expressions will not be loaded. This is especially helpful for transfer learning. string “-predict*”

### Evaluation Config

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

 Field Description Data Type and Constraints Recommended/Typical Value eval_epoch_cycle The number of training epochs that should run per validation Unsigned int, positive 10 max_detections_per_image The maximum number of detections to visualize Unsigned int, positive 100 min_score_thresh The minimum confidence of the predicted box that can be considered a match Float 0.5 eval_batch_size The batch size for each GPU, so the effective batch size is batch_size_per_gpu * num_gpus Unsigned int, positive 16 eval_samples The number of samples for evaluation Unsigned int –

### Dataset Config

The data configuration (data_config) specifies the input data source and format. This is used for training, evaluation, and inference. A detailed description is summarized in the table below.

 Field Description Data Type and Constraints Recommended/Typical Value image_size The image dimension as a tuple within quote marks. “(height, width)” indicates the dimension of the resized and padded input. String “(512, 512)” training_file_pattern The TFRecord path for training String – validation_file_pattern The TFRecord path for validation String – val_json_file The annotation file path for validation String – num_classes The number of classes. If there are N categories in the annotation, num_classes should be N+1 (background class) Unsigned int – max_instances_per_image The maximum number of object instances to parse (default: 100) Unsigned int 100 skip_crowd_during_training Specifies whether to skip crowd during training Boolean True

### Model Config

The model configuration (model_config) specifies the model structure. A detailed description is summarized in the table below.

 Field Description Data Type and Constraints Recommended/Typical Value model_name EfficientDet model name string “efficientdet_d0” min_level The minimum level of the output feature pyramid Unsigned int 3 (only 3 is supported) max_level The maximum level of the output feature pyramid Unsigned int 7 (only 7 is supported) num_scales The number of anchor octave scales on each pyramid level (e.g. if set to 3, the anchor scales are [2^0, 2^(1/3), 2^(2/3)]) Unsigned int 3 max_instances_per_image The maximum number of object instances to parse (default: 100) Unsigned int 100 aspect_ratios A list of tuples representing the aspect ratios of anchors on each pyramid level string “[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]” anchor_scale Scale of the base-anchor size to the feature-pyramid stride Unsigned int 4

### Augmentation Config

The augmentation_config parameter defines image augmentation after preprocessing.

 Field Description Data Type and Constraints Recommended/Typical Value rand_hflip Whether to perform random horizontal flip Boolean – random_crop_min_scale The minimum scale of RandomCrop augmentation. Default: 0.1 Float 0.1 random_crop_max_scale The maximum scale of RandomCrop augmentation. Default: 2.0 Float 2.0

## Training the Model

Train the EfficientDet model using this command:

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tao efficientdet train [-h] -e <experiment_spec>
-d <output_dir>
-k <key>
[--gpus <num_gpus>]
[--gpu_index <gpu_index>]
[--log_file <log_file_path>]


### Required Arguments

• -d, --model_dir: The path to the folder where the experiment output is written

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

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

### Optional Arguments

• --gpus: The number of GPUs to be used for training in a multi-GPU scenario. The default value is 1.

• --gpu_index: The indices of the GPUs to use for training. This argument can be used when the machine has multiple GPUs installed.

• --log_file: The path to the log file. The default value is stdout.

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

### Input Requirement

• Input size: C * W * H (where C = 1 or 3, W >= 128, H >= 128; W, H are multiples of 32)

• Image format: JPG

• Label format: COCO detection

### Sample Usage

Here’s an example of the train command:

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## Running Inference with an EfficientDet Model

The inference tool for EfficientDet models can be used to visualize bboxes and generate frame-by- frame KITTI format labels on a directory of images.

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tao efficientdet inference [-h] -i <input directory>
-o <output annotated image directory>
-e <experiment spec file>
-m <model file>
-k <key>
[-l <output label directory>]
[--gpu_index <gpu_index>]
[--log_file <log_file_path>]


### Required Arguments

• -m, --model_path: The path to the pretrained model (supports both the TAO model and TensorRT engine)

• -i, --in_image_path: The directory of input images for inference

• -o, --out_image_path: The directory path to output annotated images

• -k, --key: The key to load a TAO model (it’s not required if a TensorRT engine is used)

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

### Optional Arguments

• -l, --out_label_path: The directory to output KITTI labels

• --label_map: The path to a text file of training labels

• --gpu_index: The index of the GPU to run inference on. This argument can be used when the machine has multiple GPUs installed. Note that inference can only run on a single GPU.

• --log_file: The path to the log file. The default value is stdout.

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

### Sample Usage

Here’s an example of using the inference command:

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## Re-training the Pruned Model

Once the model has been pruned, there might be a slight decrease in accuracy because some previously useful weights may have been removed. To regain the accuracy, we recommend that you retrain this pruned model over the same dataset. To do this, use the tao efficientdet 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.

We recommend turning off the regularizer or reducing the weight decay in the training_config for EfficientDet to recover the accuracy when retraining a pruned model. To do this, set the regularizer type to NO_REG as mentioned in the Training config section. All the other parameters may be retained in the spec file from the previous training.

## Exporting the Model

Exporting the model decouples the training process from deployment 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. The .etlt model format is also an encrypted model format, and it uses the same key as the .tlt model that it is exported from. This key is required when deploying this model.

### INT8 Mode Overview

TensorRT engines can be generated in INT8 mode to improve performance, but require a calibration cache at engine creation-time. The calibration cache is generated using a calibration tensor file if tao efficientdet 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 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 the 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: It merely requires you to convert the .tlt model from the training/retraining step to .etlt.

### Exporting the EfficientDet Model

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

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tao efficientdet export [-h]  -m <path to the .tlt model file>
-e <path to experiment spec file>
-k <key>
[-o <path to output file>]
[--cal_data_file <path to tensor file>]
[--cal_image_dir <path to the directory images to calibrate the model]
[--cal_cache_file <path to output calibration file>]
[--data_type <Data type for the TensorRT backend during export>]
[--batches <Number of batches to calibrate over>]
[--max_batch_size <maximum trt batch size>]
[--max_workspace_size <maximum workspace size]
[--batch_size <batch size to TensorRT engine>]
[--engine_file <path to the TensorRT engine file>]
[--gpu_index <gpu_index>]
[--log_file <log_file_path>]
[--verbose]


#### Required Arguments

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

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

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

• -o, --output_path: The path to save the exported model to

#### Optional Arguments

• --data_type: The desired engine data type, which generates a calibration cache if in INT8 mode. The options are fp32, fp16, and int8. The default value is fp32. If using INT8, the following INT8 arguments are required.

• --gpu_index: The index of (discrete) GPUs used for exporting the model. You can specify the index of the GPU to run export if the machine has multiple GPUs installed. Note that export can only run on a single GPU.

• --log_file: The path to the log file. The default value is stdout.

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

### INT8 Export Mode Required Arguments

• --cal_image_dir: The directory of images to use for calibration

• --cal_cache_file: The path where the calibration cache file should be saved

### INT8 Export Optional Arguments

• --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 16.

• --max_batch_size: The maximum batch size of the TensorRT engine. The default value is 1.

• --max_workspace_size: The maximum workspace size of TensorRT engine (in Gb). The default value is 2.

• --engine_file: The path to the serialized TensorRT engine file. Note that this file is hardware specific and cannot be generalized across GPUs. The engine file is useful for quickly testing 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.

Note

Due to the complexity of EfficientDet models, the export process with TensorRT engine serialization will take some time to finish. For example, it may take several minutes on a V100 and more than a hour on a Xavier.

### Sample usage

Here’s a sample command to export an EfficientDet model in INT8 mode.

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tao efficientdet export -m /path/to/model.step-0.tlt  \
-o /path/to/export/model.step-0.etlt \
-e /ws/spec.txt \
-k $KEY \ --cal_image_dir /ws/data/ \ --data_type int8 \ --batch_size 1 \ --batches 10 \ --cal_cache_file /path/to/export/cal.bin \ --cal_data_file /path/to/export/cal.tensorfile  ## Deploying to DeepStream 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 EfficientDet 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 EfficientDet, 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: 1. Install Cmake (>=3.13). Note TensorRT OSS requires cmake >= v3.13, so install cmake 3.13 if your cmake version is lower than 3.13c Copy Copied!  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


2. Get GPU architecture. The GPU_ARCHS value can be retrieved by the deviceQuery CUDA sample:

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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 download deviceQuery.cpp from this GitHub repo. Compile and run deviceQuery.

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nvcc deviceQuery.cpp -o deviceQuery
./deviceQuery


This command will output something like this, which indicates the GPU_ARCHS is 75 based on CUDA Capability major/minor version.

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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


3. Build TensorRT OSS:

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git clone -b 21.08 https://github.com/nvidia/TensorRT
cd TensorRT/
git submodule update --init --recursive
export TRT_SOURCE=pwd
cd $TRT_SOURCE mkdir -p build && cd build  Note Make sure your GPU_ARCHS from step 2 is in TensorRT OSS CMakeLists.txt. If GPU_ARCHS is not in TensorRT OSS CMakeLists.txt, add -DGPU_ARCHS=<VER> as below, where <VER> represents GPU_ARCHS from step 2. Copy Copied!  /usr/local/bin/cmake .. -DGPU_ARCHS=xy -DTRT_LIB_DIR=/usr/lib/x86_64-linux-gnu/ -DCMAKE_C_COMPILER=/usr/bin/gcc -DTRT_BIN_DIR=pwd/out make nvinfer_plugin -j$(nproc)


After building ends successfully, libnvinfer_plugin.so* will be generated under \pwd\/out/.

4. Replace the original libnvinfer_plugin.so*:

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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)

1. Install Cmake (>=3.13)

Note

TensorRT OSS requires cmake >= v3.13, while the default cmake on Jetson/Ubuntu 18.04 is cmake 3.10.2.

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sudo apt remove --purge --auto-remove cmake
tar xvf cmake-3.13.5.tar.gz
cd cmake-3.13.5/
./configure
make -j$(nproc) sudo make install sudo ln -s /usr/local/bin/cmake /usr/bin/cmake  2. Get GPU architecture based on your platform. The GPU_ARCHS for different Jetson platform are given in the following table.  Jetson Platform GPU_ARCHS Nano/Tx1 53 Tx2 62 AGX Xavier/Xavier NX 72 3. Build TensorRT OSS: Copy Copied!  git clone -b 21.03 https://github.com/nvidia/TensorRT cd TensorRT/ git submodule update --init --recursive export TRT_SOURCE=pwd cd$TRT_SOURCE
mkdir -p build && cd build


Note

The -DGPU_ARCHS=72 below is for Xavier or NX, for other Jetson platform, change 72 referring to GPU_ARCHS from step 2.

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/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$(nproc)  After building ends successfully, libnvinfer_plugin.so* will be generated under ‘pwd’/out/. 4. Replace "libnvinfer_plugin.so*" with the newly generated. Copy Copied!  sudo mv /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.8.x.y${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.

1. Unzip the zip file on the target machine.

2. Install the OpenSSL package using the command:

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sudo apt-get install libssl-dev


3. Export the following environment variables:

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$export TRT_LIB_PATH=”/usr/lib/x86_64-linux-gnu”$ export TRT_INC_PATH=”/usr/include/x86_64-linux-gnu”


1. Run the tao-converter using the sample command below and generate the engine.

2. Instructions to build TensorRT OSS on Jetson can be found in the TensorRT OSS on x86 section above or in this GitHub repo.

Note

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.

1. Unzip the zip file on the target machine.

2. Install the OpenSSL package using the command:

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sudo apt-get install libssl-dev


3. Export the following environment variables:

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$export TRT_LIB_PATH=”/usr/lib/aarch64-linux-gnu”$ export TRT_INC_PATH=”/usr/include/aarch64-linux-gnu”


1. For Jetson devices, TensorRT comes pre-installed with Jetpack. If you are using older JetPack, upgrade to JetPack-5.0DP.

2. Instructions to build TensorRT OSS on Jetson can be found in the TensorRT OSS on Jetson (ARM64) section above or in this GitHub repo.

3. Run the tao-converter using the sample command below and generate the engine.

Note

Make sure to follow the output node names as mentioned in Exporting the Model section of the respective model.

#### Using the tao-converter

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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:The path to the .etlt model exported using tao efficientdet export

• -k: The key used to encode the .tlt model when training

• -d: A comma-separated list of input dimensions that should match the dimensions used for tao efficientdet export

• -o: A comma-separated list of output blob names that should match the output configuration used for tao efficientdet export. For EfficientDet, set this argument to NMS.

##### Optional Arguments
• -e: The path to save the engine to. The default path is ./saved.engine.

• -t: The desired engine data type, which generates calibration cache if in INT8 mode. The default value is fp32. The options are fp32, fp16, and int8.

• -w: The maximum workspace size for the TensorRT engine. The default value is 1073741824(1<<30).

• -i: The input dimension ordering; all other TAO commands use NCHW. The options are nchw, nhwc, nc. For EfficientDet, you can omit this argument since the default value is nchw.

• -p: Optimization profiles for .etlt models with dynamic shape, consisting of a 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>. This argument can be specified multiple times if there are multiple input tensors for the model. This is only useful for new models introduced since version 3.0. This parameter is not required for models that were already in version 2.0.

• -s: TensorRT strict-type constraints. A Boolean to apply TensorRT strict type constraints when building the TensorRT engine.

• -u: Specifies the DLA core index to use when building the TensorRT engine on Jetson devices

##### INT8 Mode Arguments
• -c: The path to the calibration cache file, which is only used in INT8 mode. The default value is ./cal.bin.

• -b: Batch size used during the export step for INT8 calibration cache generation. The default value is 8.

• -m: The maximum batch size for the TensorRT engine. The default value is 16. If you encounter out-of-memory issues, decrease the batch size accordingly.

Note

Due to the complexity of EfficientDet models, the conversion process will take some time to finish. For example, it may take several minutes on a V100 and more than a hour on a Xavier.

##### Sample Output Log

Here is a sample command for exporting an EfficientDet model.

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tao converter -k \$KEY  \
-c /export/model.step-0.cal \
-p Input,1x512x512x3,8x512x512x3,16x512x512x3 \
-e /export/trt.int8.engine \
-t int8 \
-b 8 \
/export/model.step-0.etlt


### Integrating the model to DeepStream

There are two 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 efficientdet export.

• Option 2: Generate a device-specific optimized TensorRT engine using tao-converter. The TensorRT engine file can also be ingested by DeepStream.

For EfficientDet, we will need to build TensorRT Open source plugins and custom bounding box parser. The instructions are provided in the TensorRT OSS section above, and the required code can be found in this GitHub repo.

To integrate the models with DeepStream, you need the following:

• The DeepStream SDK (download page). 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.

The DeepStream SDK ships with an end-to-end reference application that is fully configurable. You 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 two 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 the 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 reference:

• source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt: The main config file

• config_infer_primary.txt: The supporting config file for primary detector in the pipeline above

• config_infer_secondary_*.txt: The 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 with little to no change. You will only need to modify or create config_infer_primary.txt and config_infer_secondary_*.txt.

#### Integrating an EfficientDet Model

To run an EfficientDet model in DeepStream, you need a label file and a DeepStream configuration file. In addition, you need to compile the TensorRT 8+ OSS and EfficientDet bounding box parser for DeepStream.

A DeepStream sample with documentation on how to run inference using the trained EfficientDet models from TAO Toolkit is provided on GitHub here.

##### Prerequisite for EfficientDet Model
1. EfficientDet requires ResizeNearest_TRT and EfficientNMS_TRT. These plugins are available in the TensorRT open source repo. Detailed instructions to build TensorRT OSS can be found in TensorRT Open Source Software (OSS).

2. EfficientDet requires custom bounding-box parsers that are not built-in inside the DeepStream SDK. The source code to build custom bounding-box parsers for EfficientDet is available here. The following instructions can be used to build the bounding-box parser:

1. Install git-lfs (git >= 1.8.2)

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curl -s https://packagecloud.io/install/repositories/github/git-lfs/
script.deb.sh | sudo bash
sudo apt-get install git-lfs
git lfs install


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git clone -b release/tlt3.0 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps


3. Build the custom bounding-box parser:

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// 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

If the COCO annotation file has the following in categories:

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[{'supercategory': 'person', 'id': 1, 'name': 'person'},
{'supercategory': 'car', 'id': 2, 'name': 'car'}]


Then the corresponding maskrcnn_labels.txt file will be as follows:

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BG
person
car


### 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.

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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 the TensorRT engine file with DeepStream app.

1. Generate the TensorRT engine using tao-converter. Detailed instructions are provided in the Generating an engine using tao-converter section above.

2. Once the engine file is generated successfully, modify the following parameters to use this engine with DeepStream.

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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 the [property] section of the primary infer configuration file:

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parse-bbox-func-name=NvDsInferParseCustomEfficientDetTAO
custom-lib-path=<PATH to libnvds_infercustomparser_tlt.so>


Add the label file generated above using the following:

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labelfile-path=<efficientdet 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.

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[property]
gpu-id=0
net-scale-factor=1.0
offsets=0;0;0
model-color-format=0
network-input-order=1
labelfile-path=efficientdet_d0_labels.txt
model-engine-file=./d0_avlp_bs1_int8.engine
int8-calib-file=d0.cal
tlt-encoded-model=d0_avlp.etlt
tlt-model-key=nvidia_tlt
infer-dims=3;512;512
maintain-aspect-ratio=1
uff-input-blob-name=image_arrays:0
batch-size=1
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
num-detected-classes=1
interval=0
gie-unique-id=1
is-classifier=0
#network-type=0
cluster-mode=4
output-blob-names=num_detections;detection_boxes;detection_scores;detection_classes
parse-bbox-func-name=NvDsInferParseCustomEfficientDetTAO
custom-lib-path=nvdsinfer_custombboxparser_efficientdet_tao.so

[class-attrs-all]
pre-cluster-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