UNET

UNet is a semantic segmentation model that supports the following tasks:

  • train

  • prune

  • evaluate

  • inference

  • export

These tasks may be invoked from the TAO Toolkit Launcher by following this convention from the command line:

tao unet <sub_task> <args_per_subtask>

where args_per_subtask are the command line arguments required for a given subtask. Each of these subtasks is explained in detail below.

Data Input for Semantic Segmentation

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

Creating a Configuration File

To perform training, evaluation, pruning, and inference for Unet, you will need to configure several components, each with their own parameters. The train, evaluate, prune, and inference tasks for a UNet experiment share the same configuration file.

The specification file for Unet training configures these components for the training pipeline:

  • Model

  • Trainer

  • Dataset

Model Config

The segmentation model can be configured using the model_config option in the spec file.

The following is a sample model config to instantiate a resnet18 model with blocks 0 and 1 frozen and all shortcuts set to projection layers:

# Sample model config for to instantiate a resnet18 model freeze blocks 0, 1
# with all shortcuts having projection layers.

model_config {
  num_layers: 18
  all_projections: true
  arch: "resnet"
  freeze_blocks: 0
  freeze_blocks: 1
  use_batch_norm: true
  initializer: HE_UNIFORM
  training_precision {
    backend_floatx: FLOAT32
  }
  model_input_height: 320
  model_input_width: 320
  model_input_channels: 3
}

The following table describes the model_config parameters:

Parameter

Datatype

Default

Description

Supported Values

all_projections

bool

False

For templates with shortcut connections, this parameter defines whether or not all shortcuts should be instantiated with 1x1 projection layers, irrespective of whether there is a change in stride across the input and output.

True/False (only to be used in resnet templates)

arch

string

resnet

The architecture of the backbone feature extractor to be used for training

resnet, vgg, vanilla_unet, efficientnet_b0, vanilla_dynamic

num_layers

int

18

The depth of the feature extractor for scalable templates

  • resnets: 10, 18, 34, 50, 101

  • vgg: 16, 19

use_pooling

Boolean

False

A Boolean value that determines whether to use strided convolutions or MaxPooling while downsampling. When True, MaxPooling is used to downsample; however, for an object detection network, we recommend setting this to False and using strided convolutions.

False/True

use_batch_norm

Boolean

False

A Boolean value that determines whether to use batch normalization layers or not

True/False

training precision

Proto Dictionary

Contains a nested parameter that sets the precision of the back-end training framework

backend_floatx: FLOAT32

load_graph

Boolean

False

For a pruned model, set this parameter as True. Pruning modifies the original graph, hence both the pruned model graph and the weights need to be imported.

True/False

freeze_blocks

float (repeated)

This parameter defines which blocks may be frozen from the instantiated feature extractor template, and is different for different feature extractor templates.

  • ResNet series: For the ResNet series, the block ID’s valid for freezing is any subset of [0, 1, 2, 3](inclusive)

  • VGG series: For the VGG series, the block ID’s valid for freezing is any subset of [1, 2, 3, 4, 5](inclusive)

freeze_bn

Boolean

False

You can choose to freeze the Batch Normalization layers in the model during training.

True/False

initializer

enum

GLOROT_UNIFORM

Initialization of convolutional layers. Supported initializations are He Uniform, He Normal, and Glorot uniform.

HE_UNIFORM, HE_NORMAL, GLOROT_UNIFORM

model_input_height

int

The model input height dimension of the model, which should be a multiple of 16.

>100

model_input_width

int

The model input width dimension of the model, which should be a multiple of 16.

>100

model_input_channels

int

The model-input channels dimension of the model, which should be set to 3 for a Resnet/VGG backbone. It can be set to 1 or 3 for vanilla_unet based on the image input channel dimensions. If the input image channel is 1 and the model-input channels is set to 3 for standard UNet, the input grayscale image is converted to RGB.

1/3

Note

The vanilla_unet model was originally proposed in this paper: U-Net: Convolutional Networks for Biomedical Image Segmentation. This model is recommended for the Binary Segmentation use case. The input dimesions for standard UNet is fixed at 572 x 572.

Training

This section outlines how to configure the training parameters. The following is an example training_config element:

training_config {
  batch_size: 2
  epochs: 3
  log_summary_steps: 10
  checkpoint_interval: 1
  loss: "cross_dice_sum"
  learning_rate:0.0001
  lr_scheduler {
    cosine_decay {
      alpha : 0.01
      decay_steps: 500
    }
  }
  regularizer {
    type: L2
    weight: 3.00000002618e-09
  }
  optimizer {
    adam {
      epsilon: 9.99999993923e-09
      beta1: 0.899999976158
      beta2: 0.999000012875
    }
  }
}

The following table describes the parameters for training_config.

Parameter

Datatype

Default

Description

Supported Values

batch_size

int

1

The number of images per batch per gpu

>= 1

epochs

int

None

The number of epochs to train the model. One epoch represents one iteration of training through the entire dataset.

> 1

log_summary_steps

int

1

The summary-steps interval at which train details are printed to stdout

1 - steps per epoch

checkpoint_interval

int

1

The number of epochs interval at which the checkpoint is saved

1 - total number of epochs

loss

string

cross_entropy

The loss to be used for segmentation. The supported losses for tasks are as follows:

  • Binary segmentation: Cross entropy, Dice loss, Cross entropy + dice loss (cross_dice_sum)

  • Multi-class segmentation: Cross entropy loss

cross_entropy, cross_dice_sum, dice

learning_rate

float

0.0001

The learning-rate initialization value.

0 - 1

lr_scheduler

lr_scheduler proto config

None (constant learning rate)

The following lr_schedulers are supported:

  • cosine_decay: * alpha (float) (Recommended: 0 - 0.001) * decay_steps (int) (Recommended: >500)

  • exponential_decay * decay_rate (float) * decay_steps (int) (Recommended: >500)

weights_monitor

bool

False

Specifies whether to log tensorboard visuaization of weight ranges.

True/ False

regularizer

regularizer proto config

This parameter configures the type and weight of the regularizer to be used during training. The two parameters include:

  • type: The type of the regularizer being used

  • weight: The floating point weight of the regularizer

The supported values for type are:

  • L2, L1

optimizer

optimizer proto config

This parameter defines which optimizer to use for training, and the parameters to configure it, namely:

  • adam * epsilon (float): Is a very small number to prevent any division by zero in the implementation * beta1 (float) * beta2 (float)

activation

string

softmax

The activation to be used on the last layer. The supported activations for tasks are as follows:

  • softmax: Binary Segmentation, Multi-class segmentation

softmax

Note

Dice loss is currently supported only for binary segmentation. Generic Dice loss for multi-class segmentation is not supported.

Dataset

This section describes how to configure the dataset_config function. You can feed the input images and corresponding masks either as folders or from text files.

The following is an example dataset_config element using folders as inputs:

  dataset_config {
    dataset: "custom"
    augment: True
    resize_padding: True
    resize_method: BILINEAR
    augmentation_config {
      spatial_augmentation {
        hflip_probability : 0.5
        vflip_probability : 0.5
        crop_and_resize_prob : 0.5
      }
      brightness_augmentation {
        delta: 0.2
      }
    }

    input_image_type: "grayscale"
    train_images_path:"/workspace/tao-experiments/data/unet/isbi/images/train"
    train_masks_path:"/workspace/tao-experiments/data/unet/isbi/masks/train"
    val_images_path:"/workspace/tao-experiments/data/unet/isbi/images/val"
    val_masks_path:"/workspace/tao-experiments/data/unet/isbi/masks/val"
    test_images_path:"/workspace/tao-experiments/data/unet/isbi/images/test"
    data_class_config {
      target_classes {
      name: "foreground"
      mapping_class: "foreground"
      label_id: 0
    }
    target_classes {
      name: "background"
      mapping_class: "background"
      label_id: 1
    }
  }
}

Please refer Structured Images and Masks Folders that provides the description of the contents of images and masks paths.

The following is an example dataset_config element using text files as inputs:

dataset_config {
  dataset: "custom"
  augment: True
  augmentation_config {
    spatial_augmentation {
      hflip_probability : 0.5
      vflip_probability : 0.5
      crop_and_resize_prob : 0.5
    }
    brightness_augmentation {
      delta: 0.2
    }
  }
  input_image_type: "color"
  train_data_sources: {
    data_source: {
      image_path: "/workspace/images_train/images_source1.txt"
      masks_path: "/workspace/labels_train/labels_source1.txt"
    }
    data_source: {
      image_path: "/workspace/images_train/images_source2.txt"
      masks_path: "/workspace/labels_train/labels_source2.txt"
    }
  }
  val_data_sources: {
    data_source: {
      image_path: "/workspace/images_val/images_source1.txt"
      masks_path: "/workspace/labels_val/labels_source1.txt"
    }
    data_source: {
      image_path: "/workspace/images_val/images_source2.txt"
      masks_path: "/workspace/labels_val/labels_source2.txt"
    }
  }
  test_data_sources: {
    data_source: {
      image_path: "/workspace/images_test/images_source1.txt"
      masks_path: "/workspace/labels_test/labels_source1.txt"
    }
    data_source: {
      image_path: "/workspace/images_test/images_source2.txt"
      masks_path: "/workspace/labels_test/labels_source2.txt"
    }
  }
  data_class_config {
    target_classes {
      name: "foreground"
      mapping_class: "foreground"
      label_id: 0
    }
    target_classes {
      name: "background"
      mapping_class: "background"
      label_id: 1
    }
  }
}

Please refer Image and Mask Text files that provides description of the contents of text files.

The following table describes the parameters used to configure dataset_config:

Parameter

Datatype

Default

Description

Supported Values

dataset

string

custom

The input type dataset used. The currently supported dataset is custom to the user. Open source datasets will be added in the future.

custom

augment

bool

False

If the input should augmented online while training, the following augmentations are done at a probability of 0.5 The augmentation config can modified to change the probability for each type of augmentation.

  • Horizontal flip

  • Vertical flip

  • Random crop and resize

  • Random brightness

true / false

augmentation_config

Proto Message

None

Contains the spatial_augmentation proto and brightness_augmentation proto to configure the probability of corresponding augmentations.

spatial_augmentation

Proto Dictionary

None

  • 0.5

  • 0.5

  • 0.5

Contains the following configurable fields. Set to default value if augment arument is set to True.

  • hflip_probability

  • vflip_probability

  • crop_and_resize_prob

brightness_augmentation

Proto Dictionary

0.2

Configure following parameter: delta: Adjust brightness using delta value.

Non-negative integer

input_image_type

string

color

The input image type to indicate if input image is grayscale or color (RGB)

color/ grayscale

resize_padding

bool

False

Image will be resized with zero padding on all sides to preserve aspect ratio

true / false

resize_method

string

BILINEAR

The image is resized using one of the following methods: * BILINEAR: Bilinear interpolation. If antialias is true, becomes a hat/tent filter function with radius 1 when downsampling. * NEAREST_NEIGHBOR: Nearest neighbour interpolation. * BICUBIC: Cubic interpolant of Keys. * AREA: Anti-aliased resampling with area interpolation

BILINEAR NEAREST_NEIGHBOR BICUBIC AREA

train_images_path

string

None

The input train images path

UNIX path string

train_masks_path

string

None

The input train masks path

UNIX path string

val_images_path

string

None

The input validation images path

UNIX path string

val_masks_path

string

None

The input validation masks path

UNIX path string

test_images_path

string

None

The input test images path

UNIX path string

train_data_sources

Proto Message

None

The input training data_source proto that contain text file for training sequences

val_data_sources

Proto Message

None

The input training data_source proto that contain text file for validation sequences

test_data_sources

Proto Message

None

The input training data_source proto that contain text file for testing sequences

data_source

Proto Dictionary

The repeated field for every text file corresponding to a sequence. The following are the parameters of data_source config:

  • image_path (string): The path to text file containing image paths

  • masks_path (string): The path to text file containing mask paths (Mask path is optional for test_data_sources

data_class_config

Proto Dictionary

None

Proto dictionary that contains information of training classes as part of target_classes proto which is described below.

target_classes

Proto Dictionary

The repeated field for every training class. The following are required parameters for the target_classes config:

  • name (string): The name of the target class

  • mapping_class (string): The name of the mapping class for the target class. For example, “car” can be mapped to “vehicle”. If the class needs to be trained as is, then name and mapping_class should be the same.

  • label_id (int): The pixel that belongs to this target class is assigned this label_id value in the mask image.

Note

The supported image extension formats for training images are “.png”, “.jpg”, “.jpeg”, “.PNG”, “.JPG”, and “.JPEG”.

Training the Model

After preparing input data as per these instructions and setting up a spec file. You are now ready to start training a semantic segmentation network.

The following is the UNet training command:

tao unet train [-h] -k <key>
                    -r <result directory>
                    -e <spec_file>
                    [-m <Pre-trained weights to initialize>]
                    [-n <name of the model>
                    [--gpus <num GPUs>]
                    [--gpu_index <space separate gpu indices>]
                    [--use_amp]

Required Arguments

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

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

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

Optional Arguments

  • -m, --pretrained_model_file: The path to a pre-trained model to initialize. This parameter defaults to None. This parameter is configured to prune model for re-training.

  • -n, --model_name: The name that the final checkpoint will be saved as in the weights directory. The default value is model.tlt.

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

  • --gpu_index: The indices of the GPUs to use for training. The GPU indices are described in the ./deviceQuery CUDA samples.

  • --use_amp: A flag that enables Automatic Mixed Precision mode

  • -h, --help: Prints this help message.

Input Requirement

  • Input size: C * W * H (where C = 3 or 1, W = 572, H = 572 for vanilla unet and W >= 128, H >= 128 and W, H are multiples of 32 for other archs).

  • Image format: JPG, JPEG, PNG, BMP

  • Label format: Image/Mask pair

Note

The images and masks need not be equal to model input size. The images/ masks will be resized to the model input size during training.

Sample Usage

Here is an example of a command for two-GPU training:

tao unet train -e </path/to/spec/file>
               -r </path/to/experiment/output>
               -k <key_to_load_the_model>
               -n <name_string_for_the_model>
               -m <Pre-trained weights to initialize the model>
               --gpus 2

Note

UNet supports resuming training from intermediate checkpoints. If a previously running training experiment is stopped prematurely, you can restart the training from the last checkpoint by simply re-running the UNet training command with the same command-line arguments as before. The trainer for UNet finds the last saved checkpoint in the results directory and resumes the training from there. The interval at which the checkpoints are saved are defined by the checkpoint_interval parameter under the “training_config” for UNet. Do not use a pre-trained weights argument when resuming training.

Note

UNet supports Tensorboard visualization for losses. The tensorboard logs are saved in the output directory in order to visualize them.

Pruning the Model

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

The prune task includes these parameters:

tao unet prune [-h] -m <pretrained_model>
                    -e <spec_file>
                    -o <output_file>
                    -k <key>
                    [-n <normalizer>]
                    [-eq <equalization_criterion>]
                    [-pg <pruning_granularity>]
                    [-pth <pruning threshold>]`
                    [-nf <min_num_filters>]
                    [-el [<excluded_list>]

Required Arguments

  • -m, --pretrained_model: The path to the model to be pruned. Usually, the last epoch model is used.

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

  • -o, --output_file: The path to the pruned model.

  • -k, --key: The key to load a .tlt model.

Optional Arguments

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

  • -n, –normalizer: Specify max to normalize by dividing each norm by the maximum norm within a layer; specify L2 to normalize by dividing by the L2 norm of the vector comprising all kernel norms. The default value is max.

  • -eq, --equalization_criterion: Criteria to equalize the stats of inputs to an element-wise op layer or depth-wise convolutional layer. This parameter is useful for resnets and mobilenets. The options are arithmetic_mean, geometric_mean, union, and intersection (default: union).

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

  • -pth: The threshold to compare the normalized norm against (default :0.1)

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

  • -el, --excluded_layers: A list of excluded_layers (e.g. -i item1 item2) (default: [])

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

Note

Evaluation and inference are not directly supported for pruned models. You must re-train a pruned model before pefroming evaluation and inference.

Using the Prune Command

Here’s an example of using the prune task:

tao unet prune -e </path/to/spec/file>
               -m </path/to/weights to be pruned>
               -o </path/to/pruned weights>
               -eq union
               -pth 0.7
               -k $KEY

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 using the train command, as documented in the Training the model section, with the -m, --pretrained_model argument pointing to the newly pruned model as the pretrained model file.

We recommend setting the regularizer weight to zero in the training_config for UNet to recover the accuracy when retraining a pruned model. All other parameters may be retained in the spec file from the previous training.

To load the pruned model, as well as for re-training, set the load_graph flag under model_config to true.

Evaluating the Model

Execute evaluate on a UNet model as follows:

tao unet evaluate [-h] -e <experiment_spec>
                       -m <model_file>
                       -o <output folder>
                       -k <key>
                       [--gpu_index]

Required Arguments

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

  • -m, --model_path: The path to the model file to use for evaluation. This could be a .tlt model file or a tensorrt engine generated using the export tool.

  • -o, --output_dir: The output dir where the evaluation metrics are saved as a JSON file. TAO inference is saved to output_dir/results_tlt.json and TRT inference is saved to output_dir/results_trt.json. The results JSON file has the precision, recall, f1-score, and IOU for every class. It also provides the weighted average, macro average and micro average for these metrics. For more information on the averaging metric, see the classification report.

  • -k, -–key: The encryption key to decrypt the model. This argument is only required

    with a .tlt model file.

Optional Arguments

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

  • --gpu_index: The index of the GPU to run evaluation on

If you have followed the example in Training a Unet Model, you may now evaluate the model using the following command:

Sample Usage

Here is an example of a command for evaluating the model:

tao unet evaluate -e </path/to/training/spec/file>
                  -m </path/to/the/model>
                  -o </path/to/evaluation/output>
                  -k <key to load the model>

Note

This command runs evaluation using the images and masks that are provided to val_images_path and val_masks_path or text files provided under the val_data_sources`in :code:`dataset_config.

Using Inference on the Model

The inference task for UNet may be used to visualize segmentation and generate frame-by-frame PNG format labels on a directory of images. An example of the command for this task is shown below:

tao unet inference [-h] -e <experiment_spec>
                        -m <model_file>
                        -o <output folder to save inference images>
                        -k <key>
                        [--gpu_index]

Required Parameters

  • -e, --experiment_spec_file: The path to an inference spec file.

  • -o, --output_dir: The directory to the output annotated images and labels. The annotated images are in vis_overlay_tlt and labels are in mask_labels_tlt. The annotated images are saved in vis_overlay_trt and predicted labels in mask_labels_trt if the TRT engine is used for inference.

  • -k, --enc_key: The key to load the model.

The tool automatically generates segmentation overlayed images in output_dir/vis_overlay_tlt. The labels will be generated in output_dir/mask_labels_tlt. The annotated, segmented images and labels for trt inference are saved in output_dir/vis_overlay_trt and output_dir/mask_labels_trt respectively.

Exporting the Model

The UNet model application in the TAO Toolkit includes an export sub-task to export and prepare a trained UNet model for Deploying to DeepStream. The export sub-task optionally generates the calibration cache for TensorRT INT8 engine calibration.

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

INT8 Mode Overview

TensorRT engines can be generated in INT8 mode to run with lower precision, and thus improve performance. This process requires a cache file that contains scale factors for the tensors to help combat quantization errors, which may arise due to low-precision arithmetic. The calibration cache is generated using a calibration tensorfile when 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. You will need to point the tool to a directory of images to use for calibrating the model. You will also need to create a sub-sampled directory of random images that best represent your training dataset.

../../_images/tao_int8_calibration1.png

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

../../_images/fp16_fp32_export4.png

Exporting the UNet Model

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

tao unet export [-h] -m </path/to the .tlt model file generated by tao train>
                     -k <key>
                     -e </path/to/experiment/spec_file>
                     [-o </path/to/output/file>]
                     [-s <strict_type_constraints>]
                     [--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>]
                     [--gen_ds_config] <Flag to generate ds config and label file>]
                     [--verbose Verbosity of the logger]

Required Arguments

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

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

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

Optional Arguments

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

  • --data_type: The engine data type for generating calibration cache if in INT8 mode. The options are fp32, fp16, and int8. The default value is fp32. If using int8, the int8 argument is required.

  • --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.

  • -s, --strict_type_constraints: A Boolean flag to indicate whether or not to apply the TensorRT strict_type_constraints when building the TensorRT engine. Note this is only for applying the strict type of INT8 mode.

INT8 Export Mode Required Arguments

  • --cal_data_file: The output file used with --cal_image_dir.

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

Note

If a valid path is provided to the --cal_data_file argument over the command line, the export tool produces an intermediate TensorFile for re-use from random batches of images in the --cal_image_dir directory of images . This tensorfile is used for calibration. If --cal_image_dir is not provided, random input tensors are used for calibration. The number of batches in the generated tensorfile is obtained from the value set to the --batches parameter, and the batch_size is obtained from the value set to the --batch_size parameter. Ensure that the directory mentioned in --cal_image_dir has at least batch_size * batches number of images in it. The valid image extensions are “.jpg”, “.jpeg”, and “.png”. In this case, the input_dimensions of the calibration tensors are derived from the input layer of the .tlt model.

INT8 Export Optional Arguments

  • --cal_cache_file: The path to save the calibration cache file. 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 1.

  • --min_batch_size: The minimum batch size of the TensorRT engine. The default value is 1.

  • --opt_batch_size: The optimum batch size of the TensorRT engine. The default value is 1.

  • --max_workspace_size: The maximum workspace size of the TensorRT engine. The default value is 1073741824 = 1<<30

  • --experiment_spec: The experiment_spec for training/inference/evaluation.

  • --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 allows you to quickly test your model accuracy using TensorRT on the host. Since a TensorRT engine file is hardware specific, you cannot use an engine file for deployment unless the deployment GPU is identical to the training GPU.

Note

UNet does not support QAT.

Sample Usage for the Export Subtask

Here’s a sample command using the --cal_image_dir option for a UNet model.

tao unet export
    -m $USER_EXPERIMENT_DIR/unet/model.tlt
    -o $USER_EXPERIMENT_DIR/unet/model.int8.etlt
    -e $SPECS_DIR/unet_train_spec.txt
    --key $KEY
    --cal_image_dir $USER_EXPERIMENT_DIR/data/isbi/images/val
    --data_type int8
    --batch_size 8
    --batches 10
    --cal_data_file $USER_EXPERIMENT_DIR/export/isbi_cal_data_file.txt
    --cal_cache_file $USER_EXPERIMENT_DIR/export/isbi_cal.bin
    --engine_file $USER_EXPERIMENT_DIR/export/int8.isbi.engine

Deploying to Deepstream

To deploy a UNet model trained by TAO to DeepStream we have to generate a device specific optimized TensorRT engine using tao-converter which can then be ingested by DeepStream. Download the corresponding device specific tao-converter from the TAO converter matrix.

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.

See Exporting the Model for more details on how to export a TAO model.

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 8.0 with CUDA 11.3 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:

    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”
  1. 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.

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:

    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”
  1. For Jetson devices, TensorRT comes pre-installed with Jetpack. If you are using older JetPack, upgrade to JetPack 4.5 or 4.6.

  2. 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

tao-converter [-h] -k <encryption_key>
                   -p <optimization_profiles>
                   [-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>]
                   [-s]
                   [-u <DLA_core>]
                   input_file
Required Arguments
  • input_file: The path to the .etlt model exported using export.

  • -p: Optimization profiles for .etlt models with dynamic shape. Use 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 can be specified multiple times if there are multiple input tensors for the model.

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

Optional Arguments
  • -e: The path to save the engine to. The default path is default: ./saved.engine. Use .engine or .trt as an extension for the engine path.

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

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

  • -i: The input dimension ordering. The default value is nchw. The options are nchw, nhwc, nc. For UNet, we can omit this argument.

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

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

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

  • -o: A comma-separated list of output blob names that should match the output configuration used for export.

INT8 Mode Arguments
  • -c: The path to the calibration cache file for INT8 mode. The default path is ./cal.bin.

  • -b: The batch size used during the export step for INT8 calibration cache generation (default: 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. This parameter is not required for .etlt models generated with dynamic shape (which is only possible for new models introduced in TAO Toolkit 3.21.08).

Sample Output Log

Here is a sample log for exporting a UNet model.

tao-converter -k $KEY
              -c $USER_EXPERIMENT_DIR/export/isbi_cal.bin
              -e $USER_EXPERIMENT_DIR/export/trt.int8.tlt.isbi.engine
              -t int8
              -p input_1,1x1x572x572,4x1x572x572,16x1x572x572
              /workspace/tao-experiments/faster_rcnn/resnet18_pruned.epoch45.etlt
..
[INFO] Some tactics do not have sufficient workspace memory to run. Increasing workspace size may increase performance, please check verbose output.
[INFO] Detected 1 inputs and 2 output network tensors.

Note

To use the default tao-converter available in the TAO Toolkit package, append tao to the sample usage of the tao_converter as mentioned here.

Once the model and/or TensorRT engine file has been generated, two additional files are required:

  • Label file

  • DS configuration file

Label File

The label file is a text file containing the names of the classes that the UNet model is trained to segment. The order in which the classes are listed here must match the order in which the model predicts the output. This order is derived from the target_class_id_mapping.json file that is saved in the results directory after training. Here is an example of the target_class_id_mapping.json file:

{"0": ["foreground"], "1": ["background"]}

Here is an example of the corresponding unet_labels.txt file. The order in the unet_labels.txt should match the order in the target_class_id_mapping.json keys:

foreground
background

DeepStream Configuration File

The segmentation model is typically used as a primary inference engine. It can also be used as a secondary inference engine. Download ds-tlt from DeepStream tlt apps.

Follow these steps to use TensorRT engine file with the ds-tlt:

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

  1. Once the engine file is generated successfully, do the following to set up ds-tlt with DS 5.1.

  • Set NVDS_VERSION:=5.1 in apps/Makefile and post_processor/Makefile inside deepstream_tlt_apps directory. This repository is downloaded from DeepStream tlt apps.

  • Now, follow the instructions here to install ds-tlt: DS TAO installation.

  • Change the output dimensions for UNet according to your model here: deepstream source code. You need to change MODEL_OUTPUT_WIDTH and MODEL_OUTPUT_HEIGHT in the above source code to your model output dimensions.

    For example, For the Resnet18 - 3 channel model mentioned in this documentation, the lines will be changed to :

    #define MODEL_OUTPUT_WIDTH 320
    #define MODEL_OUTPUT_HEIGHT 320
    

To run this model in the sample ds-tlt, you must modify the existing pgie_unet_tlt_config.txt file here. to point to this model. For all options, see the configuration file below. To learn more about the parameters, refer to the DeepStream Development Guide.

[property]
gpu-id=0
net-scale-factor=0.007843
model-color-format=2
offsets=127.5
labelfile-path=</Path/to/unet_labels.txt>
##Replace following path to your model file
model-engine-file=<Path/to/tensorrt engine generated by tao-converter>
#current DS cannot parse unet etlt model, so you need to
#convert the etlt model to TensoRT engine first use tao-converter
infer-dims=c;h;w # where c = number of channels, h = height of the model input, w = width of model input.
batch-size=1
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
num-detected-classes=2
interval=0
gie-unique-id=1
network-type=2
output-blob-names=softmax_1
segmentation-threshold=0.0
##specify the output tensor order, 0(default value) for CHW and 1 for HWC
segmentation-output-order=1

[class-attrs-all]
roi-top-offset=0
roi-bottom-offset=0
detected-min-w=0
detected-min-h=0
detected-max-w=0
detected-max-h=0

An example of modified config file for resnet18, 3-channel model trained on ISBI dataset is provided below:

[property]
gpu-id=0
net-scale-factor=0.007843

# Since the model input channel is 3, using RGB color format.
model-color-format=0
offsets=127.5;127.5;127.5
labelfile-path=/home/nvidia/deepstream_tlt_apps/configs/unet_tlt/unet_labels.txt
##Replace following path to your model file
model-engine-file=/home/nvidia/deepstream_tlt_apps/models/unet/unet_resnet18_isbi.engine
#current DS cannot parse onnx etlt model, so you need to
#convert the etlt model to TensoRT engine first use tao-converter
infer-dims=3;320;320
batch-size=1
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
num-detected-classes=2
interval=0
gie-unique-id=1
network-type=2
output-blob-names=softmax_1
segmentation-threshold=0.0
##specify the output tensor order, 0(default value) for CHW and 1 for HWC
segmentation-output-order=1

[class-attrs-all]
roi-top-offset=0
roi-bottom-offset=0
detected-min-w=0
detected-min-h=0
detected-max-w=0
detected-max-h=0

Below is the sample ds-tlt command for inference on one image:

ds-tlt configs/unet_tlt/pgie_unet_tlt_config.txt image_isbi_rgb.jpg

Note

png image format is not supported by DS. Inference image needs to be converted to .jpg. Ensure to convert grayscale image to 3 channel image if the model_input_channels is set to 3.