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 model 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.

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

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:

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.

If you have masks saved in COCO JSON format, you can use the UNet dataset converter to convert these masks to UNet PNG mask images. The following sections detail how to use dataset_convert.

Sample Usage of the COCO to UNet format Dataset Converter Tool

The dataset_convert tool is described below:

            

            
tao model unet dataset-convert [-h] -f <path_to_coco_json_file>
                              -r <results dir to save mask images>
                              [-n <number of images>]
        

You can use the following arguments.

Required Arguments

• -f, --coco_file: The path to the directory where raw images are stored

• -r, --results_dir: The path to the results directory where the PNG mask images will be saved

Optional Arguments

• -n, --num_files: Number of first ‘n’ images to be converted to mask images from the COCO JSON file. If not provided, all the images in the COCO JSON file are converted.

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

  Note

  A log file named skipped_annotations_log.json will be generated in the results_dir if the tightest bounding box of the segmentation mask is out of bounds with respect to the image frame. The log file records the image_id and annotation_id values associated with the problematic segmentation annotations. Annotations that are missing segmentation and images that are missing annotation fields are also recorded in the .json file. For example, the following log line means the segmentation with id 562121 is out of bounds in image 226111.

  Copy

  Copied!

              
  
              
  {"error": "The segmentation map is out of bounds or faulty.", "image_id": 226111, "annotation_id": 562121 }
          

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

            

            
tao model unet dataset_convert -f /path/to/coco_json_file
                         -r /path/to/results_dir/images
                         -n 8
        

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 to 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:

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 model 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

Sample Usage

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

            

            
tao model 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
        

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 model 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.

Using the Prune Command

Here’s an example of using the prune task:

            

            
tao model 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
        

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.

Execute evaluate on a UNet model as follows:

            

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

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.

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.

Exporting the UNet Model

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

            

            
tao model unet export [-h] -m </path/to the .tlt model file generated by tao model 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.

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.

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

Sample Usage for the Export Subtask

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

            

            
tao model 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
        

For deployment, refer to :ref:TAO Deploy documentation <unet_with_tao_deploy>

Refer to the Integrating a UNet Model page for more information about deploying a UNet model to DeepStream.