Multitask Image Classification

Multitask classification expects a directory of images and two CSVs for training labels and validation labels. The image directory should contain all images for both training and validation (but it can contain additional images). Only images specified in training CSV file will be used during training and same for validation.

The data structure should look like following:

            

            
|--dataset_root:
    |--images
        |--1.jpg
        |--2.jpg
        |--3.jpg
        |--4.jpg
        |--5.jpg
        |--6.jpg
    |--train.csv
    |--val.csv
        

Training and validation CSV files contain the labels for training and validation images. Both CSVs should have same format: the first column of the CSV must be fname, standing for the filename of the image. If you have N tasks, you need additional N columns, each with the task name as column name. For each image (row entry in CSV), there must be one and only one label for each task cell. An example for train.csv with 3 classification tasks (color, type and size) is like following:

Note: currently, multitask image classification only supports RGB training. The trained model will always have 3 input channels. For inferencing on grayscale images, user should load the image as RGB with same values in all channels. This is also how the training script handles grayscale training images.

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

            

            
random_seed: 42
model_config {
  arch: "resnet"
  n_layers: 101
  use_batch_norm: True
  use_bias: False
  all_projections: False
  use_pooling: True
  use_imagenet_head: True
  resize_interpolation_method: BICUBIC
  input_image_size: "3,224,224"
}
training_config {
  batch_size_per_gpu: 16
  checkpoint_interval: 10
  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
  }
  optimizer {
    adam {
      epsilon: 1e-7
      beta1: 0.9
      beta2: 0.999
      amsgrad: false
    }
  }
  pretrain_model_path: "EXPERIMENT_DIR/resnet_101.hdf5"
}
dataset_config {
  image_directory_path: "EXPERIMENT_DIR/data/images"
  train_csv_path: "EXPERIMENT_DIR/data/train.csv"
  val_csv_path: "EXPERIMENT_DIR/data/val.csv"
}
        

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.

Training Config

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

Dataset Config

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

            

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

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.

• --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.

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

            

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

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 multitask_classification evaluate command to do this.

The multitask_classification app computes per-task evaluation loss and accuracy 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 multitask_classification evaluate command:

            

            
tlt multitask_classification evaluate -e <experiment_spec_file>
                                      -k <key>
                                      -m <model>
                                      [--gpu_index <gpu_index>]
                                      [--log_file <log_file>]
                                      [-h]
        

Required Arguments

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

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

• -m, --model: Provide path to the trained model.

Optional Arguments

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

• --gpu_index: The GPU indices used to run the evaluation. 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.

Inferencing models on a labeled dataset can give confusion matrices from which you can see where the model makes mistakes.

TLT offers a command to easily generate confusion matrices for all tasks:

            

            
tlt multitask_classification confmat  -i <img_root>
                                      -l <target_csv>
                                      -k <key>
                                      -m <model>
                                      [--gpu_index <gpu_index>]
                                      [--log_file <log_file>]
                                      [-h]
        

Required Arguments

• -i, --img_root: Path to the image directory.

• -l, --target_csv: Path to the ground truth label CSV file.

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

• -m, --model: Provide path to the trained model.

Optional Arguments

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

• --gpu_index: The GPU indices used to run the confmat. 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.

The tlt multitask_classification inference command runs the inference on a specified image.

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

            

            
tlt multitask_classification inference -m <model> -i <image> -k <key> -cm <classmap> [--gpu_index <gpu_index>] [--log_file <log_file>] [-h]
        

Here are the arguments of the tlt multitask_classification inference tool:

Required arguments

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

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

• -k, --key: Key to load model.

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

Optional arguments

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

• --gpu_index: The GPU indices used to run the inference. 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 multitask_classification prune command.

The tlt multitask_classification prune command includes these parameters:

            

            
tlt multitask_classification prune -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>]
                                   [-h]
        

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 pruning. 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.

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 multitask_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 an encrypted model format with the same key of the .tlt model that it is exported from. This key is required when deploying this model.

Exporting the Model

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

            

            
tlt multitask_classification export
            -m <path to the .tlt model file generated by training>
            -k <key>
            -cm <classmap>
            [-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>]
            [--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.

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

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.

• --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 tensorfile generated for calibrating the engine. This can also be an output file if used with --cal_image_dir.

• --cal_image_dir: A directory of images to use for calibration.

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 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. It is 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 the training GPU.

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

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 tlt-converter -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>]
                  [-h]
                  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 multitask_classification export. Unlike tlt multitask_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 printout when using tlt multitask_classification export. The number of the outputs equals to the number of tasks.

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.

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 multitask_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 Multitask Image Classification Model

See Exporting The Model for more details on how to export a TLT model. After the model has been generated, you can use the DeepStream sample app provided in GitHub repository to integrate the exported model. The GitHub repository also provides a README file for adjustments needed to integrate a custom model you trained on your own dataset.