configuring_the_client.md

Configuring the Client Samples

• DetectNet_v2
  • Configuring the DetectNet_v2 model entry in the model repository
  • Configuring the Post-processor
• Classification
  • Configuring the Classification model entry in the model repository
• LPRNet
  • Configuring the LPRNet model entry in the model repository
  • Configuring the LPRNet model Post-processor
• YOLOv3
  • Configuring the YOLOv3 model entry in the model repository
  • Configuring the YOLOv3 model Post-processor
• Peoplesegnet
  • Configuring the Peoplesegnet model entry in the model repository
  • Configuring the Peoplesegnet model Post-processor
• Retinanet
  • Configuring the Retinanet model entry in the model repository
  • Configuring the Retinanet model Post-processor
• Multitask_classification
  • Configuring the Multitask_classification model entry in the model repository
  • Configuring the Multitask_classification model Post-processor
• Pose_classification
  • Configuring the Pose_classification model entry in the model repository
  • Configuring the Pose_classification model Post-processor
  • Configuring the Pose_classification data converter
• Re_identification
  • Configuring the Re_identification model entry in the model repository
  • Configuring the Re_identification model Post-processor

The inference client samples provided in this provide several parameters that the user can configure. This section elaborates about those parameters in more detail.

DetectNet_v2

The DetectNet_v2 inference sample has 2 components that can be configured

1. Model Repository
2. Post Processor

Configuring the DetectNet_v2 model entry in the model repository

The model repository is the location on the Triton Server, where the model served from. Triton expects the models in the model repository to be follow the layout defined here.

A sample model repository for a DetectNet_v2 PeopleNet model would have the following contents.

model_repository_root/
    peoplenet_tao/
        config.pbtxt
        1/
            model.plan

The config.pbtxt file, describes the model configuration for the model. A sample model configuration file for the PeopleNet model would look like this.

name: "peoplenet_tao"
platform: "tensorrt_plan"
max_batch_size: 16
input [
    {
        name: "input_1"
        data_type: TYPE_FP32
        format: FORMAT_NCHW
        dims: [ 3, 544, 960 ]
    }
]
output [
    {
        name: "output_bbox/BiasAdd"
        data_type: TYPE_FP32
        dims: [ 12, 34, 60 ]
    },
    {
        name: "output_cov/Sigmoid"
        data_type: TYPE_FP32
        dims: [ 3, 34, 60 ]
    }
]
dynamic_batching { }

The following table explains the parameters in the config.pbtxt

Parameter Name	Description	Type	Supported Values	Sample Values
name	The user readable name of the served model	string		peoplenet_tao
platform	The backend used to parse and run the model	string	tensorrt_plan	tensorrt_plan
max_batch_size	The maximum batch size used to create the TensorRT engine. This should be the same as the max_batch_size parameter of the tao-converter	int		16
input	Configuration elements for the input nodes	list of protos/node
output	Configuration elements for the output nodes	list of protos/node
dynamic_batching	Configuration element to enable dynamic batching using Triton	proto element

The input and output elements in the config.pbtxt provide the configurable parameters for the input and output nodes of the model that is being served. As seen in the sample, a detectnet_v2 model has 1 input node ("input_1") and 2 outputs nodes, namely:

• output_bbox/BiasAdd
• output_cov/Sigmoid

All the parameters defined the input and output elements remain the same for any DetectNet_v2 model trained using TAO Toolkit, except for dims. You may derive the dimensions of the input and output nodes as follows:

1. For input_1, the parameter dims is the input dimensions of the model in C, H, W order (where C = Channels, H = height, W = Width). This parameter should be indentical to the dimension mentioned to -d option of the tao-converter.

2. For output_cov/Sigmoid, the parameter dims is the output dimension of the coverage blob in C, H, W order. The value for the dimensions can be calculated as C = num_classes, H = ceil(input_h/model_stride) , W = ceil(input_w/model_stride)

3. For output_bbox/BiasAdd, the parameter dims is the output dimension of the coverage blob in C, H, W order. The value for the dimensions can be calculated as C = num_classes * 4, H = ceil(input_h/model_stride) , W = ceil(input_w/model_stride)

   Note:

   model_stride=16 for all combinations of backbones with DetectNet_v2, except for a Detectnet_v2 model with efficientnet_b0 where the model_stride=32. For a complete list of backbone supported refer to the TAO Toolkit documentation.

Configuring the Post-processor

The DetectNet_v2 model generates raw output tensors which needs to be post-processed to be able generate renderable bounding boxes. The reference implementation of the post-processor is defined in the here.

A sample configuration file to configure the postprocessor module of a PeopleNet DetectNet_v2 look as shown below

linewidth: 4
stride: 16
classwise_clustering_config{
    key: "person"
    value: {
        coverage_threshold: 0.005
        minimum_bounding_box_height: 4
        dbscan_config{
            dbscan_eps: 0.3
            dbscan_min_samples: 0.05
            dbscan_confidence_threshold: 0.9
        }
        bbox_color{
            R: 0
            G: 255
            B: 0
        }
    }
}
classwise_clustering_config{
    key: "bag"
    value: {
        coverage_threshold: 0.005
        minimum_bounding_box_height: 4
        dbscan_config{
            dbscan_eps: 0.3
            dbscan_min_samples: 0.05
            dbscan_confidence_threshold: 0.9
        }
        bbox_color{
            R: 0
            G: 255
            B: 255
        }
    }
}
classwise_clustering_config{
    key: "face"
    value: {
        coverage_threshold: 0.005
        minimum_bounding_box_height: 4
        dbscan_config{
            dbscan_eps: 0.3
            dbscan_min_samples: 0.05
            dbscan_confidence_threshold: 0.2
        }
        bbox_color{
            R: 255
            G: 0
            B: 0
        }
    }
}

The following table explains the configurable elements of the postprocessor plugin.

Parameter Name	Description	Type	Supported Values	Sample Values
linewidth	The width of the bounding box edges	int	>0	2
stride	The ratio of the input shape to output shape of the model. This value is 32 only for the efficientNet_b0 backbone with DetectNet_v2	int	16,32	16
classwise_clustering_config	Dictionary proto element, defining clustering parameters per class	dict	-	-

For each object class that the DetectNet_v2 network generates an output tensor, there is a classwise_clustering_config, element that defines the clustering parameters for this class.

Parameter	Datatype	Default	Description	Supported Values
coverage_threshold	float		The minimum threshold of the coverage tensor output to be considered a valid candidate box for clustering. The four coordinates from the bbox tensor at the corresponding indices are passed for clustering	0.0 - 1.0
minimum_bounding_box_height	int	--	The minimum height in pixels to consider as a valid detection post clustering.	0 - input image height
bbox_color	BboxColor Proto Object	None	RGB channel wise color intensity per box.	R: 0 - 255 G: 0 - 255 B: 0 - 255
dbscan_config	DBSCANConfig Proto Object	None	Proto object to configure the DBSCAN post processor plugin for the networks	-

The table below expands the configurable parameters defined under the dbscan_config element.

Parameter	Datatype	Default	Description	Supported Values
dbscan_eps	float		The maximum distance between two samples for one to be considered in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. The greater the dbscan_eps value, the more boxes are grouped together.	0.0 - 1.0
dbscan_min_samples	float	--	The total weight in a neighborhood for a point to be considered as a core point. This includes the point itself.	0.0 - 1.0
dbscan_confidence_threshold	float	0.1	The confidence threshold used to filter out the clustered bounding box output from DBSCAN.	> 0.0

Note:

A unique key-value entry has to be defined for every class that the DetectNet_v2 model is trained for.
Please refer to the DetectNet_v2 documentation for more information on how to derive the class labels from the training configuration file of the network at export.

The post processor configuration in a protobuf file, who's schema is defined in this file.

Classification

The Classification inference sample has 1 component that can be configured

1. Model Repository

Configuring the Classification model entry in the model repository

The model repository is the location on the Triton Server, where the model served from. Triton expects the models in the model repository to be follow the layout defined here.

A sample model repository for an image classification VehicleTypeNet model would have the following contents.

model_repository_root/
    vehicletypenet_tao/
        config.pbtxt
        labels.txt
        1/
            model.plan

The config.pbtxt file, describes the model configuration for the model. A sample model configuration file for the VehicleTypeNet model would look like this.

name: "vehicletypenet_tao"
platform: "tensorrt_plan"
max_batch_size : 1
input [
    {
        name: "input_1"
        data_type: TYPE_FP32
        format: FORMAT_NCHW
        dims: [ 3, 224, 224 ]
    }
]
output [
    {
        name: "predictions/Softmax"
        data_type: TYPE_FP32
        dims: [6, 1, 1]
        label_filename: "labels.txt"
    }
]
dynamic_batching { }

The following table explains the parameters in the config.pbtxt

Parameter Name	Description	Type	Supported Values	Sample Values
name	The user readable name of the served model	string		peoplenet_tao
platform	The backend used to parse and run the model	string	tensorrt_plan	tensorrt_plan
max_batch_size	The maximum batch size used to create the TensorRT engine. This should be the same as the max_batch_size parameter of the tao-converter	int		16
input	Configuration elements for the input nodes	list of protos/node
output	Configuration elements for the output nodes	list of protos/node
dynamic_batching	Configuration element to enable dynamic batching using Triton	proto element

The input and output elements in the config.pbtxt provide the configurable parameters for the input and output nodes of the model that is being served. As seen in the sample, a classification model has 1 input node input_1 and 1 output node predictions/Softmax

All the parameters defined the input and output elements remain the same for any image classification model trained using TAO Toolkit, except for the dims. You may derive the dimensions of the input and output nodes as follows:

1. For input_1, the parameter dims is the input dimensions of the model in C, H, W order (where C = Channels, H = height, W = Width). This parameter should be indentical to the dimension mentioned to -d option of the tao-converter.

2. For predictions/Softmax, the parameter dims is the output dimension of the coverage blob in C, H, W order. The value for the dimensions can be calculated as C = number of classes, H = 1 , W = 1

LPRNet

The LPRNet inference sample has 2 component that can be configured

1. Model Repository
2. Configuring the LPRNet model Post-processor

Configuring the LPRNet model entry in the model repository

The model repository is the location on the Triton Server, where the model served from. Triton expects the models in the model repository to be follow the layout defined here.

A sample model repository for an LPRnet model would have the following contents.

model_repository_root/
    lprnet_tao/
        config.pbtxt
        dict_us.txt
        1/
            model.plan

The config.pbtxt file, describes the model configuration for the model. A sample model configuration file for the LPRNet model would look like this.

name: "lprnet_tao"
platform: "tensorrt_plan"
max_batch_size : 16
input [
    {
        name: "image_input"
        data_type: TYPE_FP32
        format: FORMAT_NCHW
        dims: [ 3, 48, 96 ]
    }
]
output [
    {
        name: "tf_op_layer_ArgMax"
        data_type: TYPE_INT32
        dims: [ 24 ]
    },
    {
        name: "tf_op_layer_Max"
        data_type: TYPE_FP32
        dims: [ 24 ]
    }

]
dynamic_batching { }

The following table explains the parameters in the config.pbtxt

Parameter Name	Description	Type	Supported Values	Sample Values
name	The user readable name of the served model	string		lprnet_tao
platform	The backend used to parse and run the model	string	tensorrt_plan	tensorrt_plan
max_batch_size	The maximum batch size used to create the TensorRT engine. This should be the same as the max_batch_size parameter of the tao-converter	int		16
input	Configuration elements for the input nodes	list of protos/node
output	Configuration elements for the output nodes	list of protos/node
dynamic_batching	Configuration element to enable dynamic batching using Triton	proto element

The input and output elements in the config.pbtxt provide the configurable parameters for the input and output nodes of the model that is being served. As seen in the sample, a lprnet model has 1 input node image_input and 2 output node tf_op_layer_ArgMax and tf_op_layer_Max. The dims in output node should the 1/4 of the width in the dims of input node. For example, as above, 24 equals to 1/4 of 96.

Configuring the LPRnet model Post-processor

Please generate characters list file under model_repository/lprnet_tao folder. The file name should be characters_list.txt. A sample file for US license plate would look like this

0
1
2
3
4
5
6
7
8
9
A
B
C
D
E
F
G
H
I
J
K
L
M
N
P
Q
R
S
T
U
V
W
X
Y
Z

This characters_list.txt file contains all the characters found in license plate dataset. Each character occupies one line.

YOLOv3

The YOLOv3 inference sample has 2 component that can be configured

1. Model Repository
2. Configuring the YOLOv3 model Post-processor

Configuring the YOLOv3 model entry in the model repository

The model repository is the location on the Triton Server, where the model served from. Triton expects the models in the model repository to be follow the layout defined here.

A sample model repository for an YOLOv3 model would have the following contents.

model_repository_root/
    yolov3_tao/
        config.pbtxt
        1/
            model.plan

The config.pbtxt file, describes the model configuration for the model. A sample model configuration file for the YOLOv3 model would look like this.

name: "yolov3_tao"
platform: "tensorrt_plan"
max_batch_size: 16
input [
  {
    name: "Input"
    data_type: TYPE_FP32
    format: FORMAT_NCHW
    dims: [ 3, 544, 960 ]
  }
]
output [
  {
    name: "BatchedNMS"
    data_type: TYPE_INT32
    dims: [ 1 ]
  },
  {
    name: "BatchedNMS_1"
    data_type: TYPE_FP32
    dims: [ 200, 4 ]
  },
  {
    name: "BatchedNMS_2"
    data_type: TYPE_FP32
    dims: [ 200 ]
  },
  {
    name: "BatchedNMS_3"
    data_type: TYPE_FP32
    dims: [ 200 ]
  }
]
dynamic_batching { }

The following table explains the parameters in the config.pbtxt

Parameter Name	Description	Type	Supported Values	Sample Values
name	The user readable name of the served model	string		yolov3_tao
platform	The backend used to parse and run the model	string	tensorrt_plan	tensorrt_plan
max_batch_size	The maximum batch size used to create the TensorRT engine. This should be the same as the max_batch_size parameter of the tao-converter	int		16
input	Configuration elements for the input nodes	list of protos/node
output	Configuration elements for the output nodes	list of protos/node
dynamic_batching	Configuration element to enable dynamic batching using Triton	proto element

The input and output elements in the config.pbtxt provide the configurable parameters for the input and output nodes of the model that is being served. As seen in the sample, a yolov3 model has 1 input node Input and 4 output node BatchedNMS , BatchedNMS_1 , BatchedNMS_2 and BatchedNMS_3.

Configuring the YOLOv3 model Post-processor

Refer to model_repository/yolov3_tao folder.

Peoplesegnet

The Peoplesegnet inference sample has 2 component that can be configured

1. Model Repository
2. Configuring the Peoplesegnet model Post-processor

Configuring the Peoplesegnet model entry in the model repository

The model repository is the location on the Triton Server, where the model served from. Triton expects the models in the model repository to be follow the layout defined here.

A sample model repository for an Peoplesegnet model would have the following contents.

model_repository_root/
    peoplesegnet_tao/
        config.pbtxt
        1/
            model.plan

The config.pbtxt file, describes the model configuration for the model. A sample model configuration file for the Peoplesegnet model would look like this.

name: "peoplesegnet_tao"
platform: "tensorrt_plan"
max_batch_size: 16
input [
  {
    name: "Input"
    data_type: TYPE_FP32
    format: FORMAT_NCHW
    dims: [ 3, 576, 960 ]
  }
]
output [
  {
    name: "generate_detections"
    data_type: TYPE_FP32
    dims: [ 100, 6 ]
  },
  {
    name: "mask_fcn_logits/BiasAdd"
    data_type: TYPE_FP32
    dims: [ 100, 2, 28, 28 ]
  }
]
dynamic_batching { }

The following table explains the parameters in the config.pbtxt

Parameter Name	Description	Type	Supported Values	Sample Values
name	The user readable name of the served model	string		peoplesegnet_tao
platform	The backend used to parse and run the model	string	tensorrt_plan	tensorrt_plan
max_batch_size	The maximum batch size used to create the TensorRT engine. This should be the same as the max_batch_size parameter of the tao-converter	int		16
input	Configuration elements for the input nodes	list of protos/node
output	Configuration elements for the output nodes	list of protos/node
dynamic_batching	Configuration element to enable dynamic batching using Triton	proto element

The input and output elements in the config.pbtxt provide the configurable parameters for the input and output nodes of the model that is being served. As seen in the sample, a peoplesegnet model has 1 input node Input and 2 output node generate_detections and mask_fcn_logits/BiasAdd.

Configuring the Peoplesegnet model Post-processor

Refer to model_repository/peoplesegnet_tao folder.

Retinanet

The Retinanet inference sample has 2 component that can be configured

1. Model Repository
2. Configuring the Retinanet model Post-processor

Configuring the Retinanet model entry in the model repository

The model repository is the location on the Triton Server, where the model served from. Triton expects the models in the model repository to be follow the layout defined here.

A sample model repository for an Retinanet model would have the following contents.

model_repository_root/
    retinanet_tao/
        config.pbtxt
        1/
            model.plan

The config.pbtxt file, describes the model configuration for the model. A sample model configuration file for the Retinanet model would look like this.

name: "retinanet_tao"
platform: "tensorrt_plan"
max_batch_size: 16
input [
  {
    name: "Input"
    data_type: TYPE_FP32
    format: FORMAT_NCHW
    dims: [ 3, 544, 960 ]
  }
]
output [
  {
    name: "NMS"
    data_type: TYPE_FP32
    dims: [ 1, 250, 7 ]
  },
  {
    name: "NMS_1"
    data_type: TYPE_FP32
    dims: [ 1, 1, 1 ]
  }
]
dynamic_batching { }

The following table explains the parameters in the config.pbtxt

Parameter Name	Description	Type	Supported Values	Sample Values
name	The user readable name of the served model	string		retinanet_tao
platform	The backend used to parse and run the model	string	tensorrt_plan	tensorrt_plan
max_batch_size	The maximum batch size used to create the TensorRT engine. This should be the same as the max_batch_size parameter of the tao-converter	int		16
input	Configuration elements for the input nodes	list of protos/node
output	Configuration elements for the output nodes	list of protos/node
dynamic_batching	Configuration element to enable dynamic batching using Triton	proto element

The input and output elements in the config.pbtxt provide the configurable parameters for the input and output nodes of the model that is being served. As seen in the sample, a retinanet model has 1 input node Input and 2 output node NMS and NMS_1.

Configuring the Retinanet model Post-processor

Refer to model_repository/retinanet_tao folder.

Multitask_classification

The Multitask_classification inference sample has 2 component that can be configured

1. Model Repository
2. Configuring the Multitask_classification model Post-processor

Configuring the Multitask_classification model entry in the model repository

The model repository is the location on the Triton Server, where the model served from. Triton expects the models in the model repository to be follow the layout defined here.

A sample model repository for an Multitask_classification model would have the following contents.

model_repository_root/
    multitask_classification_tao/
        config.pbtxt
        1/
            model.plan

The config.pbtxt file, describes the model configuration for the model. A sample model configuration file for the Multitask_classification model would look like this.

name: "multitask_classification_tao"
platform: "tensorrt_plan"
max_batch_size: 16
input [
  {
    name: "input_1"
    data_type: TYPE_FP32
    format: FORMAT_NCHW
    dims: [ 3, 80, 60 ]
  }
]
output [
  {
    name: "season/Softmax"
    data_type: TYPE_FP32
    dims: [ 4, 1, 1 ]
  },
  {
    name: "category/Softmax"
    data_type: TYPE_FP32
    dims: [ 10, 1, 1 ]
  },
  {
    name: "base_color/Softmax"
    data_type: TYPE_FP32
    dims: [ 11, 1, 1 ]
  }
]
dynamic_batching { }

The following table explains the parameters in the config.pbtxt

Parameter Name	Description	Type	Supported Values	Sample Values
name	The user readable name of the served model	string		multitask_classification_tao
platform	The backend used to parse and run the model	string	tensorrt_plan	tensorrt_plan
max_batch_size	The maximum batch size used to create the TensorRT engine. This should be the same as the max_batch_size parameter of the tao-converter	int		16
input	Configuration elements for the input nodes	list of protos/node
output	Configuration elements for the output nodes	list of protos/node
dynamic_batching	Configuration element to enable dynamic batching using Triton	proto element

The input and output elements in the config.pbtxt provide the configurable parameters for the input and output nodes of the model that is being served. As seen in the sample, a Multitask_classification model has 1 input node input_1 and 3 output node season/Softmax , category/Softmax and base_color/Softmax.

Configuring the Multitask_classification model Post-processor

Refer to model_repository/multitask_classification_tao folder.

Pose_classification

The Pose_classification inference sample has 3 components that can be configured

1. Model Repository
2. Configuring the Pose_classification model Post-processor
3. Configuring the Pose_classification data converter

Configuring the Pose_classification model entry in the model repository

The model repository is the location on the Triton Server, where the model served from. Triton expects the models in the model repository to be follow the layout defined here.

A sample model repository for a Pose_classification model would have the following contents.

model_repository_root/
    pose_classification_tao/
        config.pbtxt
        labels.txt
        1/
            model.plan

The config.pbtxt file, describes the model configuration for the model. A sample model configuration file for the Pose_classification model would look like this.

name: "pose_classification_tao"
platform: "tensorrt_plan"
max_batch_size: 16
input [
  {
    name: "input"
    data_type: TYPE_FP32
    dims: [ 3, 300, 34, 1 ]
  }
]
output [
  {
    name: "fc_pred"
    data_type: TYPE_FP32
    dims: [ 6 ]
    label_filename: "labels.txt"
  }
]
dynamic_batching { }

The following table explains the parameters in the config.pbtxt

Parameter Name	Description	Type	Supported Values	Sample Values
name	The user readable name of the served model	string		pose_classification_tao
platform	The backend used to parse and run the model	string	tensorrt_plan	tensorrt_plan
max_batch_size	The maximum batch size used to create the TensorRT engine. This should be the same as the max_batch_size parameter of the tao-converter	int		16
input	Configuration elements for the input nodes	list of protos/node
output	Configuration elements for the output nodes	list of protos/node
dynamic_batching	Configuration element to enable dynamic batching using Triton	proto element

The input and output elements in the config.pbtxt provide the configurable parameters for the input and output nodes of the model that is being served. As seen in the sample, a Pose_classification model has 1 input node input and 1 output node fc_pred.

Configuring the Pose_classification model Post-processor

Refer to model_repository/pose_classification_tao folder.

Configuring the Pose_classification data converter

When the input is a JSON file generated from the deepstream-bodypose-3d app, it needs to be converted into skeleton sequences to be consumed by the Pose_classification model.

A sample configuration file to configure the dataset converter of Pose Classification looks as shown below

pose_type: "3dbp"
num_joints: 34
frame_width: 1920
frame_height: 1080
focal_length: 1200.0
sequence_length_max: 300
sequence_length_min: 10
sequence_length: 100
sequence_overlap: 0.5

The following table explains the configurable parameters of the dataset converter.

Parameter Name	Description	Type	Supported Values	Sample Values
pose_type	The type of body pose	string	3dbp, 25dbp, or 2dbp	3dbp
num_joints	The total number of joints in the skeleton graph layout	int		34
frame_width	The width of the video frame in pixel	int		1920
frame_height	The height of the video frame in pixel	int		1080
focal_length	The focal length that the video was captured in	float		1200.0
sequence_length_max	The maximum sequence length in frame	int		300
sequence_length_min	The minimum sequence length in frame	int		10
sequence_length	The sequence length for sampling sequences	int		100
sequence_overlap	The overlap between sequences during samping	float		0.5

Re_identification

The Re_identification inference sample has 2 components that can be configured

1. Model Repository
2. Configuring the Re_identification model Post-processor

Configuring the Re_identification model entry in the model repository

The model repository is the location on the Triton Server, where the model served from. Triton expects the models in the model repository to be follow the layout defined here.

A sample model repository for a Re_identification model would have the following contents.

model_repository_root/
    re_identification_tao/
        config.pbtxt
        1/
            model.plan

The config.pbtxt file, describes the model configuration for the model. A sample model configuration file for the Re_identification model would look like this.

name: "re_identification_tao"
platform: "tensorrt_plan"
max_batch_size: 16
input [
  {
    name: "input"
    data_type: TYPE_FP32
    format: FORMAT_NCHW
    dims: [ 3, 256, 128 ]
  }
]
output [
  {
    name: "fc_pred"
    data_type: TYPE_FP32
    dims: [ 256 ]
  }
]
dynamic_batching { }

The following table explains the parameters in the config.pbtxt

Parameter Name	Description	Type	Supported Values	Sample Values
name	The user readable name of the served model	string		re_identification_tao
platform	The backend used to parse and run the model	string	tensorrt_plan	tensorrt_plan
max_batch_size	The maximum batch size used to create the TensorRT engine. This should be the same as the max_batch_size parameter of the tao-converter	int		16
input	Configuration elements for the input nodes	list of protos/node
output	Configuration elements for the output nodes	list of protos/node
dynamic_batching	Configuration element to enable dynamic batching using Triton	proto element

The input and output elements in the config.pbtxt provide the configurable parameters for the input and output nodes of the model that is being served. As seen in the sample, a Re_identification model has 1 input node input and 1 output node fc_pred.

Configuring the Re_identification model Post-processor

Refer to model_repository/re_identification_tao folder.