Using the Configuration File

The Vitis™ AI Library provides a way to read model parameters by reading the configuration file. It facilitates uniform configuration management of model parameters. The configuration file is located in /usr/share/vitis_ai_library/models/[model_name]/[model_name].prototxt.

model
{
  name: "yolov3_voc"
  kernel {
     name: "yolov3_voc"
     mean: 0.0
     mean: 0.0
     mean: 0.0
     scale: 0.00390625
     scale: 0.00390625
     scale: 0.00390625
  }
  model_type : YOLOv3
  yolo_v3_param {
    …
  }
  is_tf: false
}

Table 1. Compiling Model and Kernel Parameters
Model/Kernel	Parameter Type	Description
model	name	This name should be same as the ${MODEL_NAME}.
model	model_type	This type should depend on which type of model you used.
kernel	name	This name should be filled as the result of your DNNC compile. Sometimes, its name may have an extra postfix “_0”, here need fill the name with such postfix. (For example: inception_v1_0)
	mean	Normally there are three lines, each of them corresponding to the mean-value of “BRG” which are pre-defined in the model.
	scale	Normally there are three lines. Each of them is corresponds to the RGB-normalized scale. If the model had no scale in training stage, here should fill with one.
	is_tf	Bool type, if your model is trained by tensorflow, please add this and set with “true”. It could be blank in the prototxt or set as “false” when the model is caffe.

yolo_v3_param

  model_type : YOLOv3
  yolo_v3_param {
    num_classes: 20
    anchorCnt: 3
    conf_threshold: 0.3
    nms_threshold: 0.45
    biases: 10
    biases: 13
    biases: 16
    biases: 30
    biases: 33
    biases: 23
    biases: 30
    biases: 61
    biases: 62
    biases: 45
    biases: 59
    biases: 119
    biases: 116
    biases: 90
    biases: 156
    biases: 198
    biases: 373
    biases: 326
    test_mAP: false
  }

Below are the YOLOv3 model’s parameters. You can modify them as your model requires.

Table 2. YOLOv3 Model Parameters
Parameter Type	Description
num_classes	The actual number of the model’s detection categories
anchorCnt	The number of this model’s anchor
conf_threshold	The threshold of the boxes’ confidence, which could be modified to fit your practical application
nms_threshold	The threshold of NMS
biases	These parameters are same as the model’s. Each bias need writes in a sperate line. (Biases amount) = anchorCnt * (output-node amount) * 2. set correct lines in the prototxt.
test_mAP	If your model was trained with letterbox and you want to test its mAP, set this as “true”. Normally it is “false” for executing much faster.

SSD_param

model_type : SSD
ssd_param :
{
    num_classes : 4
    nms_threshold : 0.4
    conf_threshold : 0.0
    conf_threshold : 0.6
    conf_threshold : 0.4
    conf_threshold : 0.3
    keep_top_k : 200
    top_k : 400
    prior_box_param {
    layer_width : 60,
    layer_height: 45,
    variances: 0.1
    variances: 0.1
    variances: 0.2
    variances: 0.2
    min_sizes: 21.0
    max_sizes: 45.0
    aspect_ratios: 2.0
    offset: 0.5
    step_width: 8.0
    step_height: 8.0
    flip: true
    clip: false
    }
}

Below are the SSD parameters. The parameters of SSD-model include all kinds of threshold and PriorBox requirements. You can reference your SSD deploy.prototxt to fill them.

Table 3. SSD Model Parameters
Parameter Type	Description
num_classes	The actual number of the model’s detection categories
anchorCnt	The number of this model’s anchor
conf_threshold	The threshold of the boxes’ confidence. Each category could have a different threshold, but its amount must be equal to num_classes.
nms_threshold	The threshold of NMS
biases	These parameters are same as the model’s. Each bias need writes in a separate line. (Biases amount) = anchorCnt * (output-node amount) * 2. Set correct lines in the prototxt.
test_mAP	If your model was trained with letterbox and you want to test its mAP, set this as “true”. Normally it is “false” for executing much faster
keep_top_k	Each category of detection objects’ top K boxes
top_k	All the detection object’s top K boxes, except the background (the first category)
prior_box_param	There is more than one PriorBox, which could be found in the original model (deploy.prototxt) for corresponding each different scale. These PriorBoxes should oppose each other. (see the following table for Prior Box Parameters)

Table 4. PriorBox Parameters
Parameter Type	Description
layer_width/layer_height	The input width/height of this layer. Such numbers could be computed from the net structure.
ariances	These numbers are used for boxes regression, just only to fill them as original model. There should be four variances.
min_sizes/max_size	Filled as the “deploy.prototxt”, but each number should be written in a separate line.
aspect_ratios	The ratio’s number (each one should be written in a separate line). Default has 1.0 as its first ratio. If you set a new number here, there will be two ratios created when the opposite is true. One is a filled number; another is its reciprocal. For example, this parameter has only one set element, “ratios: 2.0”. The ratio vector has three numbers: 1.0, 2.0. 0.5
offset	Normally, the PriorBox is created by each central point of the feature map, so that offset is 0.5.
step_width/step_height	Copy from the original file. If there are no such numbers there, you can use the following formula to compute them: step_width = img_width ÷ layer_width step_height = img_height ÷ layer_height
offset	Normally, PriorBox is created by each central point of the feature map, so that the offset is 0.5.
flip	Control whether rotate the PriorBox and change the ratio of length/width.
clip	Set as false. If true, it will let the detection boxes’ coordinates keep at [0, 1].

Example Code

The following is the example code.

Mat img = cv::imread(argv[1]);
auto yolo = vitis::ai::YOLOv3::create("yolov3_voc",   true);
auto results   = yolo->run(img);
for(auto   &box : results.bboxes){
    int label = box.label;
    float xmin = box.x * img.cols + 1;
    float ymin = box.y * img.rows + 1;
    float xmax = xmin + box.width * img.cols;
    float ymax = ymin + box.height * img.rows;
    if(xmin < 0.) xmin = 1.;
    if(ymin < 0.) ymin = 1.;
    if(xmax > img.cols) xmax = img.cols;
    if(ymax > img.rows) ymax = img.rows;
    float confidence = box.score;
    cout << "RESULT: "   << label <<   "\t" << xmin << "\t"   << ymin << "\t"
            << xmax <<   "\t"   << ymax << "\t" << confidence   << "\n";
    rectangle(img, Point(xmin, ymin), Point(xmax, ymax), Scalar(0, 255, 0), 1, 1, 0);
}
imshow("", img);
waitKey(0);

You should use the “create” to create the YOLOv3 object.

static std::unique_ptr<YOLOv3> create(const std::string& model_name, bool need_mean_scale_process = true);

Note: The model_name is same as the prototxt’s. For more details about the example, refer to ~/Vitis-AI/Vitis-AI-Library/yolov3/test/test_yolov3.cpp.

Using the Configuration File - 1.1 English

Vitis AI Library User Guide (UG1354)

yolo_v3_param

SSD_param

Example Code