openpilot_comma/selfdrive/modeld/models

Neural networks in openpilot

To view the architecture of the ONNX networks, you can use netron

Supercombo

Supercombo input format (Full size: 393738 x float32)

• image stream
  • Two consecutive images (256 * 512 * 3 in RGB) recorded at 20 Hz : 393216 = 2 * 6 * 128 * 256
    • Each 256 * 512 image is represented in YUV420 with 6 channels : 6 * 128 * 256
      • Channels 0,1,2,3 represent the full-res Y channel and are represented in numpy as Y[::2, ::2], Y[::2, 1::2], Y[1::2, ::2], and Y[1::2, 1::2]
      • Channel 4 represents the half-res U channel
      • Channel 5 represents the half-res V channel
• wide image stream
  • Two consecutive images (256 * 512 * 3 in RGB) recorded at 20 Hz : 393216 = 2 * 6 * 128 * 256
    • Each 256 * 512 image is represented in YUV420 with 6 channels : 6 * 128 * 256
      • Channels 0,1,2,3 represent the full-res Y channel and are represented in numpy as Y[::2, ::2], Y[::2, 1::2], Y[1::2, ::2], and Y[1::2, 1::2]
      • Channel 4 represents the half-res U channel
      • Channel 5 represents the half-res V channel
• desire
  • one-hot encoded vector to command model to execute certain actions, bit only needs to be sent for 1 frame : 8
• traffic convention
  • one-hot encoded vector to tell model whether traffic is right-hand or left-hand traffic : 2
• recurrent state
  • The recurrent state vector that is fed back into the GRU for temporal context : 512

Supercombo output format (Full size: XXX x float32)

Read here for more.

Driver Monitoring Model

• .onnx model can be run with onnx runtimes
• .dlc file is a pre-quantized model and only runs on qualcomm DSPs

input format

• single image (640 * 320 * 3 in RGB):
  • full input size is 6 * 640/2 * 320/2 = 307200
  • represented in YUV420 with 6 channels:
    • Channels 0,1,2,3 represent the full-res Y channel and are represented in numpy as Y[::2, ::2], Y[::2, 1::2], Y[1::2, ::2], and Y[1::2, 1::2]
    • Channel 4 represents the half-res U channel
    • Channel 5 represents the half-res V channel
  • normalized, ranging from -1.0 to 1.0

output format

• 39 x float32 outputs (parsing example)
  • face pose: 12 = 6 + 6
    • face orientation [pitch, yaw, roll] in camera frame: 3
    • face position [dx, dy] relative to image center: 2
    • normalized face size: 1
    • standard deviations for above outputs: 6
  • face visible probability: 1
  • eyes: 20 = (8 + 1) + (8 + 1) + 1 + 1
    • eye position and size, and their standard deviations: 8
    • eye visible probability: 1
    • eye closed probability: 1
  • wearing sunglasses probability: 1
  • poor camera vision probability: 1
  • face partially out-of-frame probability: 1
  • (deprecated) distracted probabilities: 2
  • face covered probability: 1