MaskRCNN
--------

.. _maskrcnn:

MaskRCNN supports the following tasks:

* train
* evaluate
* inference
* export

These tasks may be invoked from the TLT launcher using the following convention on the command line:

.. code::

   tlt mask_rcnn <sub_task> <args_per_subtask>

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


Creating a Configuration File
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. _creating_a_configuration_file_maskrcnn:

Below is a sample MaskRCNN spec file. It has three major components: top level experiment
configs, :code:`data_config`, and :code:`maskrcnn_config`, explained below in detail. The format of
the spec file is a protobuf text (prototxt) message and each of its fields can be either a
basic data type or a nested message. The top level structure of the spec file is summarized in
the table below.

Here's a sample of the MaskRCNN spec file:

.. code::

    seed: 123
    use_amp: False
    warmup_steps: 0
    checkpoint: "/workspace/tlt-experiments/maskrcnn/pretrained_resnet50/tlt_instance_segmentation_vresnet50/resnet50.hdf5"
    learning_rate_steps: "[60000, 80000, 100000]"
    learning_rate_decay_levels: "[0.1, 0.02, 0.002]"
    total_steps: 120000
    train_batch_size: 2
    eval_batch_size: 4
    num_steps_per_eval: 10000
    momentum: 0.9
    l2_weight_decay: 0.0001
    warmup_learning_rate: 0.0001
    init_learning_rate: 0.02
     
    data_config{
            image_size: "(832, 1344)"
            augment_input_data: True
            eval_samples: 500
            training_file_pattern: "/workspace/tlt-experiments/data/train*.tfrecord"
            validation_file_pattern: "/workspace/tlt-experiments/data/val*.tfrecord"
            val_json_file: "/workspace/tlt-experiments/data/annotations/instances_val2017.json"
     
            # dataset specific parameters
            num_classes: 91
            skip_crowd_during_training: True
    }
     
    maskrcnn_config {
            nlayers: 50
            arch: "resnet"
            freeze_bn: True
            freeze_blocks: "[0,1]"
            gt_mask_size: 112
            
            # Region Proposal Network
            rpn_positive_overlap: 0.7
            rpn_negative_overlap: 0.3
            rpn_batch_size_per_im: 256
            rpn_fg_fraction: 0.5
            rpn_min_size: 0.
     
            # Proposal layer.
            batch_size_per_im: 512
            fg_fraction: 0.25
            fg_thresh: 0.5
            bg_thresh_hi: 0.5
            bg_thresh_lo: 0.
     
            # Faster-RCNN heads.
            fast_rcnn_mlp_head_dim: 1024
            bbox_reg_weights: "(10., 10., 5., 5.)"
     
            # Mask-RCNN heads.
            include_mask: True
            mrcnn_resolution: 28
     
            # training
            train_rpn_pre_nms_topn: 2000
            train_rpn_post_nms_topn: 1000
            train_rpn_nms_threshold: 0.7
     
            # evaluation
            test_detections_per_image: 100
            test_nms: 0.5
            test_rpn_pre_nms_topn: 1000
            test_rpn_post_nms_topn: 1000
            test_rpn_nms_thresh: 0.7
     
            # model architecture
            min_level: 2
            max_level: 6
            num_scales: 1
            aspect_ratios: "[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]"
            anchor_scale: 8
     
            # localization loss
            rpn_box_loss_weight: 1.0
            fast_rcnn_box_loss_weight: 1.0
            mrcnn_weight_loss_mask: 1.0
    }

+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| **Field**                  | **Description**                                                                | **Data Type and Constraints** | **Recommended/Typical Value** |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| seed                       | The random seed for the experiment                                             | Unsigned int                  | 123                           |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| warmup_steps               | The steps taken for learning rate to ramp up to the init_learning_rate         | Unsigned int                  | --                            |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| warmup_learning_rate       | The initial learning rate during the warmup phase                              | float                         | --                            |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| learning_rate_steps        | A list of steps at which the learning rate decays by the factor specified      | string                        | --                            |
|                            | in learning_rate_decay_levels                                                  |                               |                               |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| learning_rate_decay_levels | A list of decay factors. The length should match the length of                 | string                        | --                            |
|                            | learning_rate_steps.                                                           |                               |                               |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| total_steps                | The total number of training iterations                                        | Unsigned int                  | --                            |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| train_batch_size           | The batch size during training                                                 | Unsigned int                  | 4                             |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| eval_batch_size            | The batch size during validation or evaluation                                 | Unsigned int                  | 8                             |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| num_steps_per_eval         | Save a checkpoint and run evaluation every N steps.                            | Unsigned int                  | --                            |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| momentum                   | Momentum of the SGD optimizer                                                  | float                         | 0.9                           |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| l2_weight_decay            | L2 weight decay                                                                | float                         | 0.0001                        |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| use_amp                    | Specifies whether to use Automatic Mixed Precision training                    | boolean                       | False                         |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| checkpoint                 | The path to a pretrained model                                                 | string                        | --                            |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| maskrcnn_config            | The architecture of the model                                                  | message                       | --                            |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| data_config                | The input data configuration                                                   | message                       | --                            |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+
| skip_checkpoint_variables  | If specified, the weights of the layers with matching regular expressions will | string                        | --                            |
|                            | not be loaded. This is especially helpful for transfer learning.               |                               |                               |
+----------------------------+--------------------------------------------------------------------------------+-------------------------------+-------------------------------+

.. Note:: When using :code:`skip_checkpoint_variables`, you can first find the model structure in the
          training log (Part of the MaskRCNN+ResNet50 model structure is shown below). If, for example,
          you want to retrain all prediction heads, you can set :code:`skip_checkpoint_variables` to
          “head”. TLT uses the Python re library to check whether “head” matches any layer name or
          :code:`re.search($skip_checkpoint_variables, $layer_name)`.

.. code::

    [MaskRCNN] INFO    : ================ TRAINABLE VARIABLES ==================
    [MaskRCNN] INFO    : [#0001] conv1/kernel:0                                               => (7, 7, 3, 64)
    [MaskRCNN] INFO    : [#0002] bn_conv1/gamma:0                                             => (64,)
    [MaskRCNN] INFO    : [#0003] bn_conv1/beta:0                                              => (64,)
    [MaskRCNN] INFO    : [#0004] block_1a_conv_1/kernel:0                                     => (1, 1, 64, 64)
    [MaskRCNN] INFO    : [#0005] block_1a_bn_1/gamma:0                                        => (64,)
    [MaskRCNN] INFO    : [#0006] block_1a_bn_1/beta:0                                         => (64,)
    [MaskRCNN] INFO    : [#0007] block_1a_conv_2/kernel:0                                     => (3, 3, 64, 64)
    [MaskRCNN] INFO    : [#0008] block_1a_bn_2/gamma:0                                        => (64,)
    [MaskRCNN] INFO    : [#0009] block_1a_bn_2/beta:0                                         => (64,)
    [MaskRCNN] INFO    : [#0010] block_1a_conv_3/kernel:0                                     => (1, 1, 64, 256)
    [MaskRCNN] INFO    : [#0011] block_1a_bn_3/gamma:0                                        => (256,)
    [MaskRCNN] INFO    : [#0012] block_1a_bn_3/beta:0                                         => (256,)
    [MaskRCNN] INFO    : [#0110] block_3d_bn_3/gamma:0                                        => (1024,)
    [MaskRCNN] INFO    : [#0111] block_3d_bn_3/beta:0                                         => (1024,)
    [MaskRCNN] INFO    : [#0112] block_3e_conv_1/kernel:0                                     => (1, 1, 1024, [MaskRCNN] INFO    : [#0144] block_4b_bn_1/beta:0                                         => (512,)
                                    …     …       …    …                                                                       ...
    [MaskRCNN] INFO    : [#0174] fpn/post_hoc_d5/kernel:0                                     => (3, 3, 256, 256)
    [MaskRCNN] INFO    : [#0175] fpn/post_hoc_d5/bias:0                                       => (256,)
    [MaskRCNN] INFO    : [#0176] rpn_head/rpn/kernel:0                                        => (3, 3, 256, 256)
    [MaskRCNN] INFO    : [#0177] rpn_head/rpn/bias:0                                          => (256,)
    [MaskRCNN] INFO    : [#0178] rpn_head/rpn-class/kernel:0                                  => (1, 1, 256, 3)
    [MaskRCNN] INFO    : [#0179] rpn_head/rpn-class/bias:0                                    => (3,)
    [MaskRCNN] INFO    : [#0180] rpn_head/rpn-box/kernel:0                                    => (1, 1, 256, 12)
    [MaskRCNN] INFO    : [#0181] rpn_head/rpn-box/bias:0                                      => (12,)
    [MaskRCNN] INFO    : [#0182] box_head/fc6/kernel:0                                        => (12544, 1024)
    [MaskRCNN] INFO    : [#0183] box_head/fc6/bias:0                                          => (1024,)
    [MaskRCNN] INFO    : [#0184] box_head/fc7/kernel:0                                        => (1024, 1024)
    [MaskRCNN] INFO    : [#0185] box_head/fc7/bias:0                                          => (1024,)
    [MaskRCNN] INFO    : [#0186] box_head/class-predict/kernel:0                              => (1024, 91)
    [MaskRCNN] INFO    : [#0187] box_head/class-predict/bias:0                                => (91,)
    [MaskRCNN] INFO    : [#0188] box_head/box-predict/kernel:0                                => (1024, 364)
    [MaskRCNN] INFO    : [#0189] box_head/box-predict/bias:0                                  => (364,)
    [MaskRCNN] INFO    : [#0190] mask_head/mask-conv-l0/kernel:0                              => (3, 3, 256, 256)
    [MaskRCNN] INFO    : [#0191] mask_head/mask-conv-l0/bias:0                                => (256,)
    [MaskRCNN] INFO    : [#0192] mask_head/mask-conv-l1/kernel:0                              => (3, 3, 256, 256)
    [MaskRCNN] INFO    : [#0193] mask_head/mask-conv-l1/bias:0                                => (256,)
    [MaskRCNN] INFO    : [#0194] mask_head/mask-conv-l2/kernel:0                              => (3, 3, 256, 256)
    [MaskRCNN] INFO    : [#0195] mask_head/mask-conv-l2/bias:0                                => (256,)
    [MaskRCNN] INFO    : [#0196] mask_head/mask-conv-l3/kernel:0                              => (3, 3, 256, 256)
    [MaskRCNN] INFO    : [#0197] mask_head/mask-conv-l3/bias:0                                => (256,)
    [MaskRCNN] INFO    : [#0198] mask_head/conv5-mask/kernel:0                                => (2, 2, 256, 256)
    [MaskRCNN] INFO    : [#0199] mask_head/conv5-mask/bias:0                                  => (256,)
    [MaskRCNN] INFO    : [#0200] mask_head/mask_fcn_logits/kernel:0                           => (1, 1, 256, 91)
    [MaskRCNN] INFO    : [#0201] mask_head/mask_fcn_logits/bias:0                             => (91,)


MaskRCNN Config
***************

The MaskRCNN configuration (:code:`maskrcnn_config`) defines the model structure. This model
is used for training, evaluation, and inference. A detailed description is included in the
table below. Currently, MaskRCNN only supports ResNet10/18/34/50/101 as its backbone.

+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| **Field**                 | **Description**                                                     | **Data Type and Constraints**                    | **Recommended/Typical Value**      |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| nlayers                   | The number of layers in ResNet arch                                 | message                                          | 50                                 |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| arch                      | The backbone feature extractor name                                 | string                                           | resnet                             |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| freeze_bn                 | Whether to freeze all BatchNorm layers in the backbone              | boolean                                          | False                              |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| freeze_blocks             | A list of conv blocks in the backbone to freeze                     | string                                           | --                                 |
|                           |                                                                     |                                                  |                                    |
|                           |                                                                     | ResNet: For the ResNet series, the block IDs     |                                    |
|                           |                                                                     | valid for freezing are any subset of             |                                    |
|                           |                                                                     | [0, 1, 2, 3] (inclusive)                         |                                    |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| gt_mask_size              | The groundtruth mask size                                           | Unsigned int                                     | 112                                |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| rpn_positive_overlap      | The lower-bound threshold to assign positive labels for anchors     | float                                            | 0.7                                |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| rpn_negative_overlap      | The upper-bound threshold to assign negative labels for anchors     | float                                            | 0.3                                |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| rpn_batch_size_per_im     | The number of sampled anchors per image in RPN                      | Unsigned int                                     | 256                                |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| rpn_fg_fraction           | The desired fraction of positive anchors in a batch                 | Unsigned int                                     | 0.5                                |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| rpn_min_size              | The minimum proposal height and width                               |                                                  | 0                                  |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| batch_size_per_im         | The RoI minibatch size per image                                    | Unsigned int                                     | 512                                |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| fg_fraction               | The target fraction of RoI minibatch that is labeled as             | float                                            | 0.25                               |
|                           | foreground                                                          |                                                  |                                    |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| fast_rcnn_mlp_head_dim    | The Fast-RCNN classification head dimension                         | Unsigned int                                     | 1024                               |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| bbox_reg_weights          | The bounding-box regularization weights                             | string                                           | “(10, 10, 5, 5)”                   |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| include_mask              | Specifies whether to include a mask head                            | boolean                                          | True                               |
|                           |                                                                     |                                                  |                                    |
|                           |                                                                     |                                                  | (currently only True is supported) |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| mrcnn_resolution          | The mask-head resolution                                            | Unsigned int                                     | 28                                 |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| train_rpn_pre_nms_topn    | The number of top-scoring RPN proposals to keep before applying     | Unsigned int                                     | 2000                               |
|                           | NMS (per FPN level) during training                                 |                                                  |                                    |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| train_rpn_post_nms_topn   | The number of top-scoring RPN proposals to keep after applying NMS  | Unsigned int                                     | 1000                               |
|                           | (total number produced) during training                             |                                                  |                                    |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| train_rpn_nms_threshold   | The NMS IOU threshold in RPN during training                        | float                                            | 0.7                                |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| test_detections_per_image | The number of bounding box candidates after NMS                     | Unsigned int                                     | 100                                |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| test_nms                  | The NMS IOU threshold during test                                   | float                                            | 0.5                                |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| test_rpn_pre_nms_topn     | The number of top-scoring RPN proposals to keep before applying NMS | Unsigned int                                     | 1000                               |
|                           | (per FPN level) during test                                         |                                                  |                                    |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| test_rpn_post_nms_topn    | The number of top scoring RPN proposals to keep after applying NMS  | Unsigned int                                     | 1000                               |
|                           | (total number produced) during test                                 |                                                  |                                    |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| test_rpn_nms_threshold    | The NMS IOU threshold in RPN during test                            | float                                            | 0.7                                |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| min_level                 | The minimum level of the output feature pyramid                     | Unsigned int                                     | 2                                  |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| max_level                 | The maximum level of the output feature pyramid                     | Unsigned int                                     | 6                                  |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| num_scales                | The number of anchor octave scales on each pyramid level (e.g. if   | Unsigned int                                     | 1                                  |
|                           | set to 3, the anchor scales are [2^0, 2^(1/3), 2^(2/3)])            |                                                  |                                    |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| aspect_ratios             | A list of tuples representing the aspect ratios of anchors on each  | string                                           | "[(1.0, 1.0),                      |
|                           | pyramid level                                                       |                                                  | (1.4, 0.7),                        |
|                           |                                                                     |                                                  | (0.7, 1.4)]"                       |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| anchor_scale              | Scale of the base-anchor size to the feature-pyramid stride         | Unsigned int                                     | 8                                  |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| rpn_box_loss_weight       | The weight for adjusting RPN box loss in the total loss             | float                                            | 1.0                                |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| fast_rcnn_box_loss_weight | The weight for adjusting FastRCNN box regression loss in the total  | float                                            | 1.0                                |
|                           | loss                                                                |                                                  |                                    |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+
| mrcnn_weight_loss_mask    | The weight for adjusting mask loss in the total loss                | float                                            | 1.0                                |
+---------------------------+---------------------------------------------------------------------+--------------------------------------------------+------------------------------------+

.. Note:: The :code:`min_level`, :code:`max_level`, :code:`num_scales`, :code:`aspect_ratios`,
          and :code:`anchor_scale` are used to determine anchor generation for MaskRCNN.
          :code:`anchor_scale` is the base anchor scale, while :code:`min_level` and 
          :code:`max_level` set the range of the scales on different feature maps. For example,
          the actual anchor scale for the feature map at :code:`min_level` will be `anchor_scale *
          2^min_level` and the actual anchor scale for the feature map at :code:`max_level` will be
          `anchor_scale * 2^max_level`. And it will generate anchors of different
          :code:`aspect_ratios` based on the actual anchor scale.


Data Config
***********

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

+----------------------------+------------------------------------------------------------------+-------------------------------+-------------------------------+
| **Field**                  | **Description**                                                  | **Data Type and Constraints** | **Recommended/Typical Value** |
+----------------------------+------------------------------------------------------------------+-------------------------------+-------------------------------+
| image_size                 | The image dimension as a tuple within quote marks. “(height,     | string                        | “(832, 1344)”                 |
|                            | width)” indicates the dimension of the resized and padded input. |                               |                               |
+----------------------------+------------------------------------------------------------------+-------------------------------+-------------------------------+
| augment_input_data         | Specifies whether to augment the data                            | boolean                       | True                          |
+----------------------------+------------------------------------------------------------------+-------------------------------+-------------------------------+
| eval_samples               | The number of samples for evaluation                             | Unsigned int                  | --                            |
+----------------------------+------------------------------------------------------------------+-------------------------------+-------------------------------+
| training_file_pattern      | The TFRecord path for training                                   | string                        | --                            |
+----------------------------+------------------------------------------------------------------+-------------------------------+-------------------------------+
| validation_file_pattern    | The TFRecord path for validation                                 | string                        | --                            |
+----------------------------+------------------------------------------------------------------+-------------------------------+-------------------------------+
| val_json_file              | The annotation file path for validation                          | string                        | --                            |
+----------------------------+------------------------------------------------------------------+-------------------------------+-------------------------------+
| num_classes                | The number of classes                                            | Unsigned int                  | --                            |
+----------------------------+------------------------------------------------------------------+-------------------------------+-------------------------------+
| skip_crowd_during_training | Whether to skip crowd during training                            | boolean                       | True                          |
+----------------------------+------------------------------------------------------------------+-------------------------------+-------------------------------+

Training the Model
^^^^^^^^^^^^^^^^^^

Train the MaskRCNN model using this command:

.. code::

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

Required Arguments
******************

* :code:`-d, --model_dir`: The path to the folder where the experiment output is written.
* :code:`-k, --key`: The encryption key to decrypt the model.
* :code:`-e, --experiment_spec_file`: The experiment specification file to set up the evaluation.
  experiment. This should be the same as the training specification file.

Optional Arguments
******************

* :code:`--gpus num_gpus`: The number of GPUs to use and processes to launch for training. The
  default value is 1.
* :code:`--gpu_index`: The index of the (descrete) GPU for exporting the model if the machine
  has multiple GPUs installed. Note that export can only run on a single GPU.
* :code:`--log_file`: The path to the log file. The default path is :code:`stdout`.
* :code:`-h, --help`: Show this help message and exit.

Sample Usage
************

Here's an example of using the :code:`train` command on a MaskRCNN model:

.. code::

    tlt mask_rcnn train --gpus 2 -e /path/to/spec.txt -d /path/to/result -k $KEY

Evaluating the Model
^^^^^^^^^^^^^^^^^^^^

To run evaluation for a MaskRCNN model, use this command:

.. code::

    tlt mask_rcnn evaluate [-h] -e <experiment_spec_file>
                                -m <model_file>
                                -k <key>
                                [--gpu_index <gpu_index>]
                                [--log_file <log_file_path>]

Required Arguments
******************

* :code:`-e, --experiment_spec_file`: The experiment spec file to set up the evaluation experiment.
  This should be the same as the training spec file.
* :code:`-m, --model`: The path to the model file to use for evaluation
* :code:`-k, --key`: The key to load the model. This argument is not required if :code:`-m`
  is followed by a TensorRT engine.


Optional Arguments
******************

* :code:`--gpu_index`: The index of the (descrete) GPU for exporting the model if the machine
  has multiple GPUs installed. Note that export can only run on a single GPU.
* :code:`--log_file`: The path to the log file. The default path is :code:`stdout`.
* :code:`-h, --help`: Show this help message and exit.

Running Inference on the Model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The :code:`inference` tool for MaskRCNN networks can be used to visualize bboxes or generate
frame-by-frame COCO-format labels on a directory of images. Here's an example of using this tool:

.. code::

    tlt mask_rcnn inference [-h] -i <input directory> 
                                 -o <output annotated image directory> 
                                 -e <experiment spec file> 
                                 -m <model file> 
                                 -k <key>
                                 [-l <label file>]
                                 [-b <batch size>]
                                 [-t <bbox confidence threshold>]
                                 [-mt <mask confidence threshold>]
                                 [--include_mask]
                                 [--gpu_index <gpu_index>]
                                 [--log_file <log_file_path>]

Required Arguments
******************

* :code:`-m, --model_path`: The path to the trained MaskRCNN model (either a :code:`.tlt` model or a
  converted TensorRT engine).
* :code:`-i, --image_dir`: The directory of input images for inference.
* :code:`-e, --config_path`: The path to an experiment spec file for training.
* :code:`-o, --out_image_path`: The directory path to output annotated images.
* :code:`-k, --key`: The key to load a :code:`.tlt` model (not needed if TensorRT engine is used).

Optional Arguments
******************

* :code:`-b, --batch_size`: The batch size to run inference (only needed with a :code:`.tlt` model)
* :code:`-t, --threshold`: The threshold for drawing a bbox (default: 0.3)
* :code:`-mt, --mask_threshold`: The threshold for drawing a mask. This argument is only required
  with the TensorRT engine (default: 0.4).
* :code:`-l, --out_label_path`: The label .txt file containing groundtruth class labels. This
  argument is only supported with the TensorRT engine.
* :code:`--include_mask`: Specifies whether to draw masks on the annotated output.
* :code:`--gpu_index`: The index of the (discrete) GPU for exporting the model if the machine
  has multiple GPUs installed. Note that export can only run on a single GPU.
* :code:`--log_file`: The path to the log file. The default path is :code:`stdout`.
* :code:`-h, --help`: Show this help message and exit.

Exporting the Model
-------------------

.. _exporting_the_model_mask_rcnn:

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 :code:`.etlt`. Like the :code:`.tlt` model format,
:code:`.etlt` is an encrypted model format, and it uses the same key as the :code:`.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 :code:`tlt export` is run with the :code:`--data_type` flag set to :code:`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 the INT8 calibration cache by ingesting training data using one of
these options:

* **Option 1**: Use the training data loader to load the training images for INT8 calibration.
  This option is now the recommended approach to support multiple image directories by leveraging
  the training dataset loader. This also ensures two important aspects of data during calibration:

  * Data pre-processing in the INT8 calibration step is the same as in the training process.

  * The data batches are sampled randomly across the entire training dataset, thereby improving
    the accuracy of the INT8 model.

* **Option 2**: Point the tool to a directory of images that you want to use to calibrate
  the model. For this option, make sure to create a sub-sampled directory of random images that
  best represent your training dataset.

FP16/FP32 Model
^^^^^^^^^^^^^^^

The :code:`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 you need to do is provide
a :code:`.tlt` model from the training/retraining step to be converted into :code:`.etlt` format.

Exporting the MaskRCNN Model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Here's an example of the command line arguments of the :code:`tlt mask_rcnn export` command:

.. code::

    tlt mask_rcnn export [-h] -m <path to the .tlt model file generated by tlt train>
                              -k <key>
                              [-o <path to output file>]
                              [--cal_data_file <path to tensor file>]
                              [--cal_image_dir <path to the directory images to calibrate the model]
                              [--cal_cache_file <path to output calibration file>]
                              [--data_type <Data type for the TensorRT backend during export>]
                              [--batches <Number of batches to calibrate over>]
                              [--max_batch_size <maximum trt batch size>]
                              [--max_workspace_size <maximum workspace size]
                              [--batch_size <batch size to TensorRT engine>]
                              [--experiment_spec <path to experiment spec file>]
                              [--engine_file <path to the TensorRT engine file>]
                              [--verbose <Verbosity of the logger>]    
                              [--force_ptq <Flag to force PTQ>]
                              [--strict_type_constraints <Flag to apply strict type constraints>]
                              [--gpu_index <gpu_index>]
                              [--log_file <log_file_path>]

Required Arguments
******************

* :code:`-m, --model`: The path to the :code:`.tlt` model file to be exported using :code:`export`.
* :code:`-k, --key`: The key used to save the :code:`.tlt` model file.
* :code:`-e, --experiment_spec`: The path to the spec file.

Optional Arguments
******************

* :code:`-o, --output_file`: The path to save the exported model to. The default path
  is :code:`./<input_file>.etlt`.
* :code:`--data_type`: The desired engine data type. The options are :code:`fp32`, :code:`fp16`, and
  :code:`int8`. The default value is :code:`fp32`. A calibration cache will be generated in INT8
  mode. If using INT8, the following INT8 arguments are required.
* :code:`-s, --strict_type_constraints`: A Boolean flag indicating whether to apply the
  TensorRT strict type constraints when building the TensorRT engine.
* :code:`--gpu_index`: The index of the (descrete) GPU for exporting the model if the machine
  has multiple GPUs installed. Note that export can only run on a single GPU.
* :code:`--log_file`: The path to the log file. The default path is :code:`stdout`.
* :code:`-h, --help`: Show this help message and exit.

INT8 Export Mode Required Arguments
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

* :code:`--cal_data_file`: The tensorfile generated for calibrating the engine. This can also be
  an output file if used with :code:`--cal_image_dir`.
* :code:`--cal_image_dir`: A directory of images to use for calibration.

.. Note:: The :code:`--cal_image_dir` parameter applies the necessary preprocessing
          to generate a tensorfile at the path mentioned in the :code:`--cal_data_file`
          parameter, which is in turn used for calibration. The number of batches in the
          tensorfile generated is obtained from the value set to the :code:`--batches` parameter,
          and the :code:`batch_size` is obtained from the value set to the :code:`--batch_size`
          parameter. Ensure that the directory mentioned in :code:`--cal_image_dir` has at least
          :code:`batch_size * batches` number of images in it. The valid image extensions are .jpg,
          .jpeg, and .png. In this case, the :code:`input_dimensions` of the calibration tensors
          are derived from the input layer of the :code:`.tlt` model.

INT8 Export Optional Arguments
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

* :code:`--cal_cache_file`: The path to save the calibration cache file to. The default value is
  :code:`./cal.bin`.
* :code:`--batches`: The number of batches to use for calibration and inference testing. The default
  value is 10.
* :code:`--batch_size`: The batch size to use for calibration. The default value is 8.
* :code:`--max_batch_size`: The maximum batch size of the TensorRT engine. The default value is 16.
* :code:`--max_workspace_size`: Maximum workspace size of the TensorRT engine. The default value is
  `1073741824 = 1<<30`
* :code:`--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 the TensorRT engine file is hardware specific, you cannot
  use this engine file for deployment unless the deployment GPU is identical to the training GPU.
* :code:`--force_ptq`: A Boolean flag to force post training quantization on the exported etlt
  model

.. Note:: MaskRCNN does not support QAT.

Sample usage
^^^^^^^^^^^^

Here's a sample command to export a MaskRCNN model in INT8 mode:

.. code::

  tlt mask_rcnn export -m /ws/model.step-25000.tlt \
                       -k nvidia_tlt \
                       -e /ws/maskrcnn_train_resnet50.txt \
                       --batch_size 1 \
                       --data_type int8 \
                       --cal_image_dir /raw-data/val2017 \
                       --batches 10 \
                       --cal_cache_file /export/maskrcnn.cal \
                       --cal_data_file /export/maskrcnn.tensorfile

Deploying to DeepStream
-----------------------

.. _deploying_to_deepstream:

The deep learning and computer vision models that you trained can be deployed on edge devices--such
as a Jetson Xavier, Jetson Nano, or 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 it.

.. _Deepstream SDK: https://developer.nvidia.com/deepstream-sdk

DeepStream SDK is a streaming analytic toolkit to accelerate building AI-based video
analytic applications. This section describes how to deploy a TLT MaskRCNN model to DeepStream SDK.

To deploy a model trained by TLT to DeepStream, you have two options:

* **Option 1**: Integrate the :code:`.etlt` model directly in the DeepStream
  app. The model file is generated by export.
* **Option 2**: Generate a device-specific optimized TensorRT engine using :code:`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 if there are minor version updates or
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 :code:`.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. Generation can be done ahead of time with Option 2.
With option 2, use :code:`tlt-converter` to convert the :code:`.etlt` file to TensorRT engine format
and then provide the engine file directly to DeepStream.

For MaskRCNN, there are some steps that need to be completed before the
models will work with DeepStream. These steps have detailed instructions in the
following sections:

* **Step 1**: Build TensorRT open source software (OSS). This is required because several
  TensorRT plugins that are required by these models are only available in the TensorRT open
  source repo and not in the general TensorRT release. For more information and instructions,
  see the **TensorRT Open Source Software** section.
* **Step 2**: Build custom parsers for DeepStream. The parsers are required to convert the
  raw tensor data from the inference to (x,y) location of bounding boxes around the detected
  object.

TensorRT Open Source Software (OSS)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

For MaskRCNN, we need the :code:`generateDetectionPlugin`, `multilevelCropAndResizePlugin`, `resizeNearestPlugin` and :code:`multilevelProposeROI` plugins from the 
TensorRT OSS build.

If the deployment platform is x86 with an NVIDIA GPU, follow the **TensorRT OSS on x86**
instructions. On the other hand, if your deployment is on NVIDIA Jetson platform, follow the
**TensorRT OSS on Jetson (ARM64)** instructions.

TensorRT OSS on x86
*******************

.. _tensorrt_oss_on_x86:

Building TensorRT OSS on x86:

1. Install Cmake (>=3.13).

    .. Note:: TensorRT OSS requires cmake version v3.13 or greater, so install cmake 3.13
       if your cmake version is lower than 3.13c

    .. code::

        sudo apt remove --purge --auto-remove cmake
        wget https://github.com/Kitware/CMake/releases/download/v3.13.5/cmake-3.13.5.tar.gz
        tar xvf cmake-3.13.5.tar.gz
        cd cmake-3.13.5/
        ./configure
        make -j$(nproc)
        sudo make install
        sudo ln -s /usr/local/bin/cmake /usr/bin/cmake

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

   .. code::

      cd /usr/local/cuda/samples/1_Utilities/deviceQuery
      sudo make
      ./deviceQuery

   If the :code:`/usr/local/cuda/samples` doesn’t exist in your system, you could download
   :code:`deviceQuery.cpp` from this repo. Compile and run :code:`deviceQuery`:

   .. code::

       nvcc deviceQuery.cpp -o deviceQuery
       ./deviceQuery

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

   .. code::

       Detected 2 CUDA Capable device(s)

       Device 0: "Tesla T4"
         CUDA Driver Version / Runtime Version          10.2 / 10.2
         CUDA Capability Major/Minor version number:    7.5

3. Build TensorRT OSS:

   .. code::

       git clone -b release/7.0 https://github.com/nvidia/TensorRT
       cd TensorRT/
       git submodule update --init --recursive
       export TRT_SOURCE=`pwd`
       cd $TRT_SOURCE
       mkdir -p build && cd build

   .. Note::

      Make sure your :code:`GPU_ARCHS` from step 2 is in TensorRT OSS :code:`CMakeLists.txt`. If
      :code:`GPU_ARCHS` is not in TensorRT OSS :code:`CMakeLists.txt`, add :code:`-DGPU_ARCHS=<VER>`
      as below, where :code:`<VER>` represents the :code:`GPU_ARCHS` from step 2.

   .. code::

       /usr/local/bin/cmake .. -DGPU_ARCHS=xy  -DTRT_LIB_DIR=/usr/lib/aarch64-linux-gnu/ -DCMAKE_C_COMPILER=/usr/bin/gcc -DTRT_BIN_DIR=`pwd`/out
       make nvinfer_plugin -j$(nproc)

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

4. Replace the original :code:`libnvinfer_plugin.so*`:

   .. code::

      sudo mv /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.7.x.y ${HOME}/libnvinfer_plugin.so.7.x.y.bak   // backup original libnvinfer_plugin.so.x.y
      sudo cp $TRT_SOURCE/`pwd`/out/libnvinfer_plugin.so.7.m.n  /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.7.x.y
      sudo ldconfig

TensorRT OSS on Jetson (ARM64)
******************************

.. _tensorrt_oss_on_jetson_arm64:

1. Install Cmake (>=3.13)

   .. Note::

      TensorRT OSS requires cmake version v3.13 or greater, while the default version of cmake on
      Jetson/Ubuntu 18.04 is cmake 3.10.2.

   Upgrade TensorRT OSS as follows:

   .. code::

      sudo apt remove --purge --auto-remove cmake
      wget https://github.com/Kitware/CMake/releases/download/v3.13.5/cmake-3.13.5.tar.gz
      tar xvf cmake-3.13.5.tar.gz
      cd cmake-3.13.5/
      ./configure
      make -j$(nproc)
      sudo make install
      sudo ln -s /usr/local/bin/cmake /usr/bin/cmake

2. Get the GPU architecture for your platform. The :code:`GPU_ARCHS` for different Jetson
   platforms are given in the following table:

   +----------------------+---------------+
   | **Jetson Platform**  | **GPU_ARCHS** |
   +----------------------+---------------+
   | Nano/Tx1             | 53            |
   +----------------------+---------------+
   | Tx2                  | 62            |
   +----------------------+---------------+
   | AGX Xavier/Xavier NX | 72            |
   +----------------------+---------------+

3. Build TensorRT OSS:

   .. code::

      git clone -b release/7.0 https://github.com/nvidia/TensorRT
      cd TensorRT/
      git submodule update --init --recursive
      export TRT_SOURCE=`pwd`
      cd $TRT_SOURCE
      mkdir -p build && cd build

   .. Note:: The :code:`-DGPU_ARCHS=72` below is for Jetson Xavier or NX; for other Jetson
             platforms, change :code:`72` to the :code:`GPU_ARCHS` number shown in step 2.

   .. code::

      /usr/local/bin/cmake .. -DGPU_ARCHS=72  -DTRT_LIB_DIR=/usr/lib/aarch64-linux-gnu/ -DCMAKE_C_COMPILER=/usr/bin/gcc -DTRT_BIN_DIR=`pwd`/out
      make nvinfer_plugin -j$(nproc)

   After building completes successfully, :code:`libnvinfer_plugin.so*` will be generated under
   :code:`‘pwd’/out/.`

4. Replace :code:`"libnvinfer_plugin.so*"` with the newly generated version:

   .. code::

      sudo mv /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.7.x.y ${HOME}/libnvinfer_plugin.so.7.x.y.bak   // backup original libnvinfer_plugin.so.x.y
      sudo cp `pwd`/out/libnvinfer_plugin.so.7.m.n  /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.7.x.y
      sudo ldconfig

Generating an Engine Using tlt-converter
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. _generating_an_engine_using_tlt-converter:

This is part of option 2 from the DeepStream deployment table above. The :code:`tlt-converter` tool
is provided with the Transfer Learning Toolkit to facilitate the deployment of TLT trained
models on TensorRT and/or Deepstream. For deployment platforms with an x86-based CPU and discrete
GPUs, the :code:`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 7.1. To
use the engine with a different minor version of TensorRT, copy the converter from
:code:`/opt/nvidia/tools/tlt-converter` to the target machine and follow the instructions for x86
to run it and generate a TensorRT engine.

Instructions for x86
********************

1. Copy :code:`/opt/nvidia/tools/tlt-converter` to the target machine.
2. Install `TensorRT 7.0+`_ for the respective target machine.
3. For MaskRCNN, you need to build `TensorRT Open source software`_ on the machine.
   Instructions to build TensorRT OSS on x86 can be found in :ref:`TensorRT OSS on x86<tensorrt_oss_on_x86>`
   section above or in this `GitHub repo`_.
4. Run :code:`tlt-converter` using the sample command below and generate the engine.

.. _TensorRT 7.0+: https://developer.nvidia.com/tensorrt
.. _TensorRT Open source software: https://github.com/NVIDIA/TensorRT
.. _GitHub repo: https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps

Instructions for Jetson
***********************

For the Jetson platform, the :code:`tlt-converter` is available to download in the `dev zone`_.
Once the :code:`tlt-converter` is downloaded, follow the instructions below to generate a
TensorRT engine.

.. _dev zone: https://developer.nvidia.com/tlt-converter-trt71

1. Unzip :code:`tlt-converter-trt7.1.zip` on the target machine.
2. Install the open ssl package using this command:

   .. code::

      sudo apt-get install libssl-dev

3. Export the following environment variables:

.. code::

   $ 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 an older
   version of JetPack, upgrade to JetPack 4.4.
5. For MaskRCNN, instructions to build TensorRT OSS on Jetson can be found in the :ref:`TensorRT OSS
   on Jetson (ARM64) <tensorrt_oss_on_jetson_arm64>` section above or in this `GitHub repo`_.
6.  Run the :code:`tlt-converter` using the sample command below and generate the engine.

.. Note:: Make sure to follow the output node names as mentioned in
          :ref:`Exporting the Model<exporting_the_model_mask_rcnn>`.

.. _Jetpack: https://developer.nvidia.com/embedded/jetpack

Using the tlt-converter
***********************

.. code::

    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
~~~~~~~~~~~~~~~~~~

* :code:`input_file`: The path to the :code:`.etlt` model exported using :code:`export`.
* :code:`-k`: The key used to encode the :code:`.tlt` model when training.
* :code:`-d`: A comma-separated list of input dimensions that should match the dimensions used for
  :code:`export`. Unlike :code:`export`, this cannot be inferred from calibration data. This
  parameter is not required for new models introduced in TLT 3.0 (e.g., LPRNet, UNet, GazeNet, etc).
* :code:`-o`: A comma-separated list of output blob names that should match the output configuration
  used for :code:`export`. For MaskRCNN: generate_detections, mask_head/mask_fcn_logits/BiasAdd.

Optional Arguments
~~~~~~~~~~~~~~~~~~

* :code:`-e`: The path to save the engine to. The default path is :code:`./saved.engine`.
* :code:`-t`: The desired engine data type. The options are :code:`fp32`, :code:`fp16`, or
  :code:`int8`. Selecting INT8 mode will generate a calibration cache.
* :code:`-w`: The maximum workspace size for the TensorRT engine. The default value is :code:`1073741824(1<<30)`.
* :code:`-i`: The input-dimension ordering. All other TLT commands use NCHW. The options are
  :code:`nchw`, :code:`nhwc`, and :code:`nc`. The default value is :code:`nchw`, so you can omit
  this argument for MaskRCNN.
* :code:`-p`: Optimization profiles for :code:`.etlt` models with dynamic shape. Use a
  comma-separated list of optimization-profile shapes in the format :code:`<input_name>,<min_shape>,<opt_shape>,<max_shape>`,
  where each shape has the format :code:`<n>x<c>x<h>x<w>`. This can be specified multiple times if
  there are  multiple input tensors for the model. This is only useful for new models introduced in
  TLT 3.0. This parameter is not required for models that already exist in TLT 2.0.
* :code:`-s`: A Boolean value specifying wheter to apply TensorRT strict-type constraints
  when building the TensorRT engine.
* :code:`-u`: (only needed if using DLA core) Specify the DLA core index when building the TensorRT
  engine on Jetson devices.

INT8 Mode Arguments
~~~~~~~~~~~~~~~~~~~

* :code:`-c`: The path to the calibration cache file (only used in INT8 mode). The default value is
  :code:`./cal.bin`.
* :code:`-b`: The batch size used during the export step for INT8 calibration cache generation
  (default: :code:`8`)
* :code:`-m`: The maximum batch size for the TensorRT engine. The default value is :code:`16`. If
  out-of-memory issues occur, decrease the batch size accordingly. This parameter is not required
  for :code:`.etlt` models generated with dynamic shape, which is only possible for new models
  introduced in TLT 3.0.

Sample Output Log
~~~~~~~~~~~~~~~~~

Here is a sample log for exporting a MaskRCNN model:

.. code::

    tlt-converter -d 3,576,960 \
                  -k nvidia_tlt \
                  -o generate_detections,mask_head/mask_fcn_logits/BiasAdd \
                  /workspace/tlt-experiments/mask_rcnn/model.step-25000.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.

Integrating the model with DeepStream
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

There are two options for integrating TLT models with DeepStream:

* **Option 1**: Integrate the model (.etlt) with the encrypted key 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
  TensorRT engine file can also be ingested by DeepStream.

For MaskRCNN, you will need to build the TensorRT open source plugins and custom bounding-box
parser. The instructions are provided in the TensorRT OSS section above, and the required code can
be found in this `GitHub repo`_.

.. _GitHub repo: https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps

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

* The DeepStream SDK (download from the `DeepStream SDK Download Page`_). The installation
  instructions for DeepStream are provided in the `DeepStream Development Guide`_.
* An exported :code:`.etlt` model file and optional calibration cache for INT8 precision.
* `TensorRT 7+ OSS Plugins`_ 
* A :code:`labels.txt` file containing the labels for classes in the order in which the networks
  produce outputs. 
* A sample :code:`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 Download Page: https://developer.nvidia.com/deepstream-download
.. _DeepStream Development Guide: https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html
.. _TensorRT 7+ OSS Plugins : https://github.com/NVIDIA/TensorRT/tree/release/7.0

DeepStream SDK ships with an end-to-end reference application that is fully configurable. Users
can configure input sources, the 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 :code:`deepstream-app`. The graphic below shows the architecture of the
reference application:

.. image:: ../../content/arch_ref_appl.png

Typically, two or more configuration files are used with this app. In the install
directory, the config files are located in :code:`samples/configs/deepstream-app` or
:code:`sample/configs/tlt_pretrained_models`. The main config file configures all the high-level
parameters in the pipeline above. This will set the input source and resolution, number of
inferences, tracker, and output sinks. The other supporting config files are for each individual
inference engine. The inference-specific configuration files are used to specify the models,
inference resolution, batch size, number of classes, and other customizations. The main
configuration file will call all the supporting configuration files.

Here are some configuration files in :code:`samples/configs/deepstream-app` for reference:

* :code:`source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt`: The main configuration
  file

* :code:`config_infer_primary.txt`: The supporting configuration file for the primary detector in
  the pipeline above

* :code:`config_infer_secondary_*.txt`: The supporting configuration file for the secondary
  classifier in the pipeline above

The :code:`deepstream-app` will only work with the main configuration file. This file will most
likely remain the same for all models and can be used directly from DeepStream SDK with
little to no change. You will only need to modify or create :code:`config_infer_primary.txt` and
:code:`config_infer_secondary_*.txt`.

Integrating a MaskRCNN Model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Integrating a MaskRCNN model is straightforward in DeepStream as Deepstream 5.0 supports
the instance-segmentation network type out of the box. The configuration file and label file for
the model are provided in the SDK. These files can be used with the provided model as well as
your own trained model. For the provided MaskRCNN model, the config and label file should work
out of the box. For your custom model, minor modifications might be required.

Download Deepstream from the `DeepStream SDK Download Page`_. The installation instructions for
DeepStream are provided in the `DeepStream Development Guide`_. You need to follow the README under
:code:`/opt/nvidia/deepstream/deepstream-5.0/samples/configs/tlt_pretrained_models` to download
the model and int8 calibration file. The config files for the MaskRCNN model are located here:

.. code::

    /opt/nvidia/deepstream/deepstream-5.0/samples/configs/tlt_pretrained_models

:code:`/opt/nvidia/deepstream` is the default DeepStream installation directory. This path will
be different if you are installing in a different directory.


Label File
**********

If the COCO annotation file has the following in “categories”:

.. code::

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

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

.. code::

    BG
    person
    car

Run :code:`deepstream-app` as follows:

.. code::

    deepstream-app -c <deepstream-app config file>

Also, you can use :code:`deepstream-mrcnn-test` to run the MaskRCNN model. See the README under
:code:`$DS_TOP/source/apps/sample_apps/deepstream-mrcnn-test/`. 


DeepStream Configuration File
*****************************

The configuration file is used by deepstream-app (see the
`Deepstream Configuration Guide`_ for more details). You need to enable the :code:`display-mask`
under the :code:`osd` group to see the mask visual view:

.. _Deepstream Configuration Guide: https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html#page/DeepStream_Development_Guide/deepstream_app_config.3.1.html%23

.. code::

    [osd]
    enable=1
    gpu-id=0
    border-width=3
    text-size=15
    text-color=1;1;1;1;
    text-bg-color=0.3;0.3;0.3;1
    font=Serif
    display-mask=1
    display-bbox=0
    display-text=0
    Nvinfer config file

The Nvinfer configuration file is used in the nvinfer plugin; see the `Deepstream plugin manual`_
for more details. The following are key parameters for running the MaskRCNN model:

.. _Deepstream plugin manual: https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html

.. code::

    tlt-model-key=<tlt_encode or TLT Key used during model export>
    tlt-encoded-model=<Path to TLT model>
    parse-bbox-instance-mask-func-name=<post process parser name>
    custom-lib-path=<path to post process parser lib>
    network-type=3 ## 3 is for instance segmentation network
    output-instance-mask=1
    labelfile-path=<Path to label file>
    int8-calib-file=<Path to optional INT8 calibration cache>
    infer-dims=<Inference resolution if different than provided>
    num-detected-classes=<# of classes if different than default>

Here's an example:

.. code::

    [property]
    gpu-id=0
    net-scale-factor=0.017507
    offsets=123.675;116.280;103.53
    model-color-format=0
    tlt-model-key=<tlt_encode or TLT Key used during model export>
    tlt-encoded-model=<Path to TLT model>
    parse-bbox-instance-mask-func-name=<post process parser name>
    custom-lib-path=<path to post process parser lib>
    network-type=3 ## 3 is for instance segmentation network
    labelfile-path=<Path to label file>
    int8-calib-file=<Path to optional INT8 calibration cache>
    infer-dims=<Inference resolution if different than provided>
    num-detected-classes=<# of classes if different than default> 
    uff-input-blob-name=Input
    batch-size=1
    ## 0=FP32, 1=INT8, 2=FP16 mode
    network-mode=2
    interval=0
    gie-unique-id=1
    #no cluster
    ## 0=Group Rectangles, 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering)
    ## MRCNN supports only cluster-mode=4; Clustering is done by the model itself
    cluster-mode=4
    output-instance-mask=1

    [class-attrs-all]
    pre-cluster-threshold=0.8