Image Classification

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

Here is an example of a specification file for model classification:

            

            
model_config {
  # Model Architecture can be chosen from:
  # ['resnet', 'vgg', 'googlenet', 'alexnet']
  arch: "resnet"
  # for resnet --> n_layers can be [10, 18, 50]
  # for vgg --> n_layers can be [16, 19]
  n_layers: 101
  use_batch_norm: True
  use_bias: False
  all_projections: False
  use_pooling: True
  use_imagenet_head: True
  resize_interpolation_method: BICUBIC
  # if you want to use the pretrained model,
  # image size should be "3,224,224"
  # otherwise, it can be "3, X, Y", where X,Y >= 16
  input_image_size: "3,224,224"
}
train_config {
  train_dataset_path: "/path/to/your/train/data"
  val_dataset_path: "/path/to/your/val/data"
  pretrained_model_path: "/path/to/your/pretrained/model"
  # Only ['sgd', 'adam'] are supported for optimizer
  optimizer {
      sgd {
      lr: 0.01
      decay: 0.0
      momentum: 0.9
      nesterov: False
      }
  }
  batch_size_per_gpu: 50
  n_epochs: 150
  # Number of CPU cores for loading data
  n_workers: 16
  # regularizer
  reg_config {
      # regularizer type can be "L1", "L2" or "None".
      type: "L2"
      # if the type is not "None",
      # scope can be either "Conv2D" or "Dense" or both.
      scope: "Conv2D,Dense"
      # 0 < weight decay < 1
      weight_decay: 0.000015
  }
  # learning_rate
  lr_config {
      cosine {
      learning_rate: 0.04
      soft_start: 0.0
      }
  }
  enable_random_crop: True
  enable_center_crop: True
  enable_color_augmentation: True
  mixup_alpha: 0.2
  label_smoothing: 0.1
  preprocess_mode: "caffe"
  image_mean {
    key: 'b'
    value: 103.9
  }
  image_mean {
    key: 'g'
    value: 116.8
  }
  image_mean {
    key: 'r'
    value: 123.7
  }
}
        

The classification experiment specification consists of three main components:

• model_config

• eval_config

• train_config

Model Config

The table below describes the configurable parameters in the model_config.

BatchNormalization Parameters

The parameter batch_norm_config defines parameters for BatchNormalization layers in the model (momentum and epsilon).

Activation functions

The parameter activation defines the parameters for activation functions in the model.

Eval Config

The table below defines the configurable parameters for evaluating a classification model.

Training Config

This section defines the configurable parameters for the classification model trainer.

Learning Rate Scheduler

The parameter lr_config defines the parameters for learning rate scheduler The learning rate scheduler can be either step, soft_anneal or cosine.

Step

The parameter step defines the step learning rate scheduler.

Soft Annealing

The parameter soft_anneal defines the soft annealing learning rate scheduler.

Cosine

The parameter cosine defines the cosine learning rate scheduler.

Use the tlt classification train command to tune a pre-trained model:

            

            
tlt classification train [-h] -e <spec file>
                              -k <encoding key>
                              -r <result directory>
                              [--gpus <num GPUs>]
                              [--num_processes <number_of_processes>]
                              [--gpu_index <gpu_index>]
                              [--use_amp]
                              [--log_file <log_file_path>]
        

Required Arguments

• -r, --results_dir: Path to a folder where the experiment outputs should be written.

• -k, --key: User specific encoding key to save or load a .tlt model.

• -e, --experiment_spec_file: Path to the experiment spec file.

Optional Arguments

• --gpus: Number of GPUs to use and processes to launch for training. The default value is 1.

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

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

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

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

• -h, --help: Print the help message.

Input Requirement

• Input size: 3 * H * W (W, H >= 32)

• Input format: JPG, JPEG, PNG

Sample Usage

Here’s an example of using the tlt classification train command:

            

            
tlt classification train -e /workspace/tlt_drive/spec/spec.cfg -r /workspace/output -k $YOUR_KEY
        

Model parallelism

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

            

            
model_parallelism: 0.3
model_parallelism: 0.7
        

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

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

After the model has been trained, using the experiment config file, and by following the steps to train a model, the next step is to evaluate this model on a test set to measure the accuracy of the model. The TLT toolkit includes the tlt classification evaluate command to do this.

The classification app computes evaluation loss, Top-k accuracy, precision, and recall as metrics.

When training is complete, the model is stored in the output directory of your choice in $OUTPUT_DIR. Evaluate a model using the tlt classification evaluate command:

            

            
tlt classification evaluate [-h] -e <experiment_spec_file>
                                 -k <key>
                                 [--gpu_index <gpu_index>]
                                 [--log_file <log_file>]
        

Required Arguments

• -e, --experiment_spec_file: Path to the experiment spec file.

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

Optional Arguments

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

• --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.

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

If you followed the example in training a classification model, run the evaluation:

            

            
tlt classification evaluate -e classification_spec.cfg -k $YOUR_KEY
        

TLT evaluates for classification and produces the following metrics:

• Loss

• Top-K accuracy

• Precision (P): TP / (TP + FP)

• Recall (R): TP / (TP + FN)

• Confusion Matrix

The tlt classification inference command runs the inference on a specified set of input images. For classification, tlt classification inference provides class label output over the command-line for a single image or a csv file containing the image path and the corresponding labels for multiple images. TensorRT Python inference can also be enabled.

Execute tlt classification inference on a classification model trained on TLT.

            

            
tlt classification inference [-h] -m <model>
                                  -i <image>
                                  -d <image  dir>
                                  -k <key>
                                  -cm <classmap>
                                  -e <experiment_spec_file>
                                  [-b <batch size>]
                                  [--gpu_index <gpu_index>]
                                  [--log_file <log_file>]
        

Here are the arguments of the tlt classification inference tool:

Required arguments

• -m, --model: Path to the pretrained model (TLT model).

• -i, --image: A single image file for inference.

• -d, --image_dir: The directory of input images for inference.

• -k, --key: Key to load model.

• -cm, --class_map: The json file that specifies the class index and label mapping.

• -e, --experiment_spec_file: Path to the experiment spec file.

Optional arguments

• --batch_size: Inference batch size, default: 1

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

• --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.

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

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

The tlt classification prune command includes these parameters:

            

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

Required Arguments

• -m, --model: Path to pretrained model

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

• -k, --key: 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 elementwise op layer, or depth-wise convolutional layer. This parameter is useful for ResNet and MobileNet. Options are arithmetic_mean, geometric_mean, union, and intersection. (default: union)

• -pg, -pruning_granularity: Number of filters to remove at a time (default: 8)

• -pth: Threshold to compare normalized norm against (default: 0.1)

• -nf, --min_num_filters: Minimum number of filters to keep per layer (default: 16)

• -el, --excluded_layers: List of excluded_layers. Examples: -i item1 item2 (default: [])

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

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

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

Using the Prune Command

Here’s an example of using the tlt classification prune command:

            

            
tlt classification prune -m /workspace/output/weights/resnet_003.tlt
                         -o /workspace/output/weights/resnet_003_pruned.tlt
                         -eq union
                         -pth 0.7 -k $KEY
        

After the model has been pruned, there might be a slight decrease in accuracy. This happens because some previously useful weights may have been removed. In order to regain the accuracy, NVIDIA recommends that you retrain this pruned model over the same dataset. To do this, use the tlt classification train command as documented in Training the model, with an updated spec file that points to the newly pruned model as the pretrained model file.

Users are advised to turn off the regularizer in the training_config for classification to recover the accuracy when retraining a pruned model. You may do this by setting the regularizer type to NO_REG. All the other parameters may be retained in the spec file from the previous training.

Exporting the model decouples the training process from inference and allows conversion to TensorRT engines outside the TLT environment. TensorRT engines are specific to each hardware configuration and should be generated for each unique inference environment. The exported model may be used universally across training and deployment hardware. The exported model format is referred to as .etlt. Like .tlt, the .etlt model format is also a encrypted model format with the same key of the .tlt model that it is exported from. This key is required when deploying this model.

INT8 Mode Overview

TensorRT engines can be generated in INT8 mode to improve performance, but require a calibration cache at engine creation-time. The calibration cache is generated using a calibration tensor file, if export is run with the --data_type flag set to int8. Pre-generating the calibration information and caching it removes the need for calibrating the model on the inference device. Using the calibration cache also speeds up engine creation as building the cache can take several minutes to generate depending on the size of the calibration data and the model itself.

The export tool can generate INT8 calibration cache by ingesting calibration dataset generated by the command tlt classification calibration_tensorfile.

Here is an example of the tlt calibration_tensorfile command:

            

            
tlt classification calibration_tensorfile -e <experiment_spec_file>
                                          -o <output_tensorfile>
                                          [-m <num_batches>]
                                          [--use_validation_set]
                                          [-v]
        

Required Arguments

• -o, --output: Path to output tensorfile.

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

Optional Arguments

• -m, --max_batches: Number of batches of calibration data. The batch size is the training batch size as in spec file.

• --use_validation_set: Use validation dataset only if this flag is set.

• -v, --verbose: Set verbosity of the log.

FP16/FP32 Model

The calibration.bin is only required if you need to run inference at INT8 precision. For FP16/FP32 based inference, the export step is much simpler. All that is required is to provide a .tlt model from the training/retraining step to be converted into an .etlt.

Exporting the Model

Here’s an example of the tlt classification export command:

            

            
tlt classification export [-h] -m <path to the .tlt model file generated by training>
                               -k <key>
                               [-o <path to output file>]
                               [--cal_data_file <path to tensor file>]
                               [--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 for calibration data>]
                               [--strict_type_constraints <Flag to apply strict type constraints>]
                               [--gen_ds_config] <Flag to generate ds config and label file>]
                               [--engine_file <path to the TensorRT engine file>]
                               [--verbose]
                               [--force_ptq]
                               [--gpu_index <gpu_index>]
                               [--log_file <log_file_path>]
        

Required Arguments

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

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

Optional Arguments

• -o, --output_file: Path to save the exported model to. The default is ./<input_file>.etlt.

• --data_type: Desired engine data type, generates calibration cache if in INT8 mode. 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 TensorRT strict type constraints when building the TensorRT engine.

• --gen_ds_config: A Boolean flag indicating whether to generate the template DeepStream related configuration (“nvinfer_config.txt”) as well as a label file (“labels.txt”) in the same directory as the output_file. Note that the config file is NOT a complete configuration file and requires the user to update the sample config files in DeepStream with the parameters generated.

• --gpu_index: The index of (descrete) 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.

• -v, --verbose: Verbose log.

INT8 Export Mode Required Arguments

• --cal_data_file: The input tensorfile for calibration.

INT8 Export Optional Arguments

• --cal_cache_file: Path to save the calibration cache file. The default value is ./cal.bin.

• --batches: Number of batches to use for calibration and inference testing. The default value is 10.

• --batch_size: Batch size to use for calibration. The default value is 8.

• --max_batch_size: Maximum batch size of TensorRT engine. The default value is 16.

• --max_workspace_size: Maximum workspace size of TensorRT engine. The default value is: 1073741824(1<<30).

• --engine_file: Path to the serialized TensorRT engine file. Note that this file is hardware specific and cannot be generalized across GPUs. Useful to quickly test your model accuracy using TensorRT on the host. As TensorRT engine file is hardware specific, you cannot use this engine file for deployment unless the deployment GPU is identical to training GPU.

• --force_ptq: A boolean flag to force post training quantization on the exported etlt model.

Exporting a Model

Here’s a sample command using the data loader for loading calibration data to calibrate a classification model.

            

            
tlt classification export -m /ws/output_retrain/weights/resnet_001.tlt
                          -o /ws/export/final_model.etlt
                          -k $KEY
                          --cal_data_file /ws/export/calibration.tensor
                          --data_type int8
                          --batches 10
                          --cal_cache_file /ws/export/final_model_int8_cache.bin
        

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. TLT has been designed to integrate with DeepStream SDK, so models trained with TLT 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 TLT 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 tlt-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 tlt-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 TLT model.

Generating an Engine Using tlt-converter

The tlt-converter tool is provided with the Transfer Learning Toolkit to facilitate the deployment of TLT trained models on TensorRT and/or Deepstream. This section elaborates on how to generate a TensorRT engine using tlt-converter.

For deployment platforms with an x86-based CPU and discrete GPUs, the tlt-converter is distributed within the TLT 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 TLT docker includes TensorRT version 7.2.

Instructions for x86

For an x86 platform with discrete GPUs, the default TLT package includes the tlt-converter built for TensorRT 7.2 with CUDA 11.1 and CUDNN 8.0. However, for any other version of CUDA and TensorRT, please refer to the overview section for download. Once the tlt-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:

   Copy

   Copied!

               
   
               
   sudo apt-get install libssl-dev
           

3. 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”
        

4. Run the tlt-converter using the sample command below and generate the engine.

Instructions for Jetson

For the Jetson platform, the tlt-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 tlt-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:

   Copy

   Copied!

               
   
               
   sudo apt-get install libssl-dev
           

3. Export the following environment variables:

            

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

4. For Jetson devices, TensorRT 7.1 comes pre-installed with Jetpack. If you are using older JetPack, upgrade to JetPack 4.4 or JetPack 4.5.

5. Run the tlt-converter using the sample command below and generate the engine.

Using the tlt-converter

            

            
tlt-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 using export.

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

• -d: Comma-separated list of input dimensions that should match the dimensions used for tlt classification export. Unlike tlt classification export this cannot be inferred from calibration data. This parameter is not required for new models introduced in TLT v3.0 (for example, LPRNet, UNet, GazeNet, etc).

• -o: Comma-separated list of output blob names that should match the output configuration used for tlt classification export. This parameter is not required for new models introduced in TLT v3.0 (for example, LPRNet, UNet, GazeNet, etc). For classification, set this argument to predictions/Softmax.

Optional Arguments

• -e: Path to save the engine to. The default value is ./saved.engine.

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

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

• -i: Input dimension ordering, all other TLT commands use NCHW. The default value is nchw. The options are {nchw, nhwc, nc}. For classification, we can omit it (defaults to nchw).

• -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 TLT v3.0. This parameter is not required for models that are already existed in TLT v2.0.

• -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 met 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 TLT v3.0.

Sample Output Log

Here is a sample log for converting a classification model.

            

            
tlt-converter /ws/export/final_model.etlt
              -k $KEY
              -c /ws/export/final_model_int8_cache.bin
              -o predictions/Softmax
              -d 3,224,224
              -i nchw
              -m 64 -t int8
              -e /ws/export/final_model.trt
              -b 64

[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 1 output network tensors.
        

Integrating the model to DeepStream

There are 2 options to integrate models from TLT with DeepStream:

• Option 1: Integrate the model (.etlt) with the encrypted key directly in the DeepStream app. The model file is generated by tlt classification export.

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

In order to integrate the models with DeepStream, you need the following:

1. Download and install DeepStream SDK. The installation instructions for DeepStream are provided in the DeepStream Development Guide.

2. An exported .etlt model file and optional calibration cache for INT8 precision.

3. A labels.txt file containing the labels for classes in the order in which the networks produces outputs.

4. 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 file

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

• config_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. Users will only have to modify or create config_infer_primary.txt and config_infer_secondary_*.txt.

Integrating a Classification Model

See Exporting The Model for more details on how to export a TLT model. After the model has been generated, two extra files are required:

1. Label file

2. DeepStream configuration file

Label File

The label file is a text file, containing the names of the classes that the TLT model is trained to classify against. The order in which the classes are listed must match the order in which the model predicts the output. This order may be deduced from the classmap.json file that is generated by TLT. This file is a simple dictionary containing the ‘class_name’ to ‘index map’. For example, in the sample classification sample notebook file included with the TLT Docker, the classmap.json file generated for Pascal Visual Object Classes (VOC) would look like this:

            

            
{"sheep": 16,"horse": 12,"bicycle": 1, "aeroplane": 0, "cow": 9,
 "sofa": 17, "bus": 5, "dog": 11, "cat": 7, "person": 14, "train": 18,
 "diningtable": 10, "bottle": 4, "car": 6, "pottedplant": 15,
 "tvmonitor": 19, "chair": 8, "bird": 2, "boat": 3, "motorbike": 13}
        

The 0th index corresponds to aeroplane, the 1st index corresponds to bicycle, up to 19, which corresponds to tvmonitor. Here is a sample classification_labels.txt file, arranged in order of index:

            

            
aeroplane;bicycle;bird;boat;bottle;bus;....;tvmonitor
        

DeepStream Configuration File

A typical use case for video analytic is first to do an object detection and then crop the detected object and send it further for classification. This is supported by deepstream-app and the app architecture can be seen above. For example, to classify models of cars on the road, first you will need to detect all the cars in a frame. Once you do detection, you perform classification on the cropped image of the car. In the sample DeepStream app, the classifier is configured as a secondary inference engine after the primary detection. If configured appropriately, deepstream-app will automatically crop the detected image and send the frame to the secondary classifier. The config_infer_secondary_*.txt is used to configure the classification 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>
        

Option 2: Integrate the TensorRT engine file with the DeepStream app.

Step 1: Generate the TensorRT engine using tlt-converter. Detail instructions are provided in the Generating an Engine Using tlt-converter section.

Step 2: After 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 2 approaches. Add the label file generated above using:

            

            
labelfile-path=<Classification labels>
        

For all the options, see the configuration file below. To learn about what all the parameters are used for, refer to DeepStream Development Guide.

            

            
[property]
gpu-id=0
# preprocessing parameters: These are the same for all classification models generated by TLT.
net-scale-factor=1.0
offsets=103.939;116.779;123.68
model-color-format=1
batch-size=30

# Model specific paths. These need to be updated for every classification model.
int8-calib-file=<Path to optional INT8 calibration cache>
labelfile-path=<Path to classification_labels.txt>
tlt-encoded-model=<Path to Classification etlt model>
tlt-model-key=<Key to decrypt model>
infer-dims=c;h;w # where c = number of channels, h = height of the model input, w = width of model input
uff-input-blob-name=input_1
uff-input-order=0
output-blob-names=predictions/Softmax

## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
# process-mode: 2 - inferences on crops from primary detector, 1 - inferences on whole frame
process-mode=2
interval=0
network-type=1 # defines that the model is a classifier.
gie-unique-id=1
classifier-threshold=0.2