classification_tengine.pbtxt

# MediaPipe graph that performs object detection with TensorFlow Lite on CPU.
# Used in the examples in
# mediapipe/examples/desktop/object_detection:object_detection_cpu.

# Images on CPU coming into and out of the graph.
input_stream: "input_frame"
output_stream: "classifications"

# Transforms the input image on CPU to a 320x320 image. To scale the image, by
# default it uses the STRETCH scale mode that maps the entire input image to the
# entire transformed image. As a result, image aspect ratio may be changed and
# objects in the image may be deformed (stretched or squeezed), but the object
# detection model used in this graph is agnostic to that deformation.
node {
  calculator: "ImageTransformationCalculator"
  input_stream: "IMAGE:input_frame"
  output_stream: "IMAGE:transformed_input_frame"
  node_options: {
    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
      output_width: 256
      output_height: 256
    }
  }
}

# Converts the transformed input image on CPU into an image tensor stored as a
# TfLiteTensor.
node {
  calculator: "TengineConverterCalculator"
  input_stream: "IMAGE:transformed_input_frame"
  output_stream: "ARRAYS:image_tensor"
  node_options: {
    [type.googleapis.com/mediapipe.TengineConverterCalculatorOptions] {
      tensor_mean: {val1:123.7 val2:116.3 val3:103.5}
      tensor_scale: {val1:0.017124754 val2:0.017507003 val3:0.017429194}
    }
  }
}

# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
# vector of tensors representing, for instance, detection boxes/keypoints and
# scores.resnet18_uint8.tmfile

node {
  calculator: "TengineInferenceCalculator"
  input_stream: "ARRAYS:image_tensor"
  output_stream: "ARRAYS:classification_tensors"
  output_stream: "TENSOR_SHAPE:tensor_shapes"
  output_stream: "QUANT_PARAM:quant_param"
  node_options: {
    [type.googleapis.com/mediapipe.TengineInferenceCalculatorOptions] {
      model_path: "../models/resnet18_uint8.tmfile"
      data_type: "uint8"
      output_num: 1
      max_dim: 4
      tengine_backend: "timvx"
    }
  }
}

# Decodes the detection tensors generated by the TensorFlow Lite model, based on
# the SSD anchors and the specification in the options, into a vector of
# detections. Each detection describes a detected object.
node {
  calculator: "TengineTensorsToClassificationsCalculator"
  input_stream: "ARRAYS:classification_tensors"
  input_stream: "TENSOR_SHAPE:tensor_shapes"
  input_stream: "QUANT_PARAM:quant_param"
  output_stream: "CLASSIFICATION:classifications"
  node_options: {
    [type.googleapis.com/mediapipe.TengineTensorsToClassificationsCalculatorOptions] {
      data_type: "uint8"
    }
  }
}