## Neural networks in openpilot
To view the architecture of the ONNX networks, you can use [netron ](https://netron.app/ )
## 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
* **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: 6472 x float32)
* **plan**
* 5 potential desired plan predictions : 4955 = 5 * 991
* predicted mean and standard deviation of the following values at 33 timesteps : 990 = 2 * 33 * 15
* x,y,z position in current frame (meters)
* x,y,z velocity in local frame (meters/s)
* x,y,z acceleration local frame (meters/(s*s))
* roll, pitch , yaw in current frame (radians)
* roll, pitch , yaw rates in local frame (radians/s)
* probability[^1] of this plan hypothesis being the most likely: 1
* **lanelines**
* 4 lanelines (outer left, left, right, and outer right): 528 = 4 * 132
* predicted mean and standard deviation for the following values at 33 x positions : 132 = 2 * 33 * 2
* y position in current frame (meters)
* z position in current frame (meters)
* **laneline probabilties**
* 2 probabilities[^1] that each of the 4 lanelines exists : 8 = 4 * 2
* deprecated probability
* used probability
* **road-edges**
* 2 road-edges (left and right): 264 = 2 * 132
* predicted mean and standard deviation for the following values at 33 x positions : 132 = 2 * 33 * 2
* y position in current frame (meters)
* z position in current frame (meters)
* **leads**
* 2 hypotheses for potential lead cars : 102 = 2 * 51
* predicted mean and stadard deviation for the following values at 0,2,4,6,8,10s : 48 = 2 * 6 * 4
* x position of lead in current frame (meters)
* y position of lead in current frame (meters)
* speed of lead (meters/s)
* acceleration of lead(meters/(s*s))
* probabilities[^1] this hypothesis is the most likely hypothesis at 0s, 2s or 4s from now : 3
* **lead probabilities**
* probability[^1] that there is a lead car at 0s, 2s, 4s from now : 3 = 1 * 3
* **desire state**
* probability[^1] that the model thinks it is executing each of the 8 potential desire actions : 8
* **meta** [^2]
* Various metadata about the scene : 80 = 1 + 35 + 12 + 32
* Probability[^1] that openpilot is engaged : 1
* Probabilities[^1] of various things happening between now and 2,4,6,8,10s : 35 = 5 * 7
* Disengage of openpilot with gas pedal
* Disengage of openpilot with brake pedal
* Override of openpilot steering
* 3m/(s*s) of deceleration
* 4m/(s*s) of deceleration
* 5m/(s*s) of deceleration
* Probabilities[^1] of left or right blinker being active at 0,2,4,6,8,10s : 12 = 6 * 2
* Probabilities[^1] that each of the 8 desires is being executed at 0,2,4,6s : 32 = 4 * 8
* **pose** [^2]
* predicted mean and standard deviation of current translation and rotation rates : 12 = 2 * 6
* x,y,z velocity in current frame (meters/s)
* roll, pitch , yaw rates in current frame (radians/s)
* **recurrent state**
* The recurrent state vector that is fed back into the GRU for temporal context : 512
[^1]: All probabilities are in logits, so you need to apply sigmoid or softmax functions to get actual probabilities
[^2]: These outputs come directly from the vision blocks, they do not have access to temporal state or the desire input
## 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](https://github.com/commaai/openpilot/blob/master/selfdrive/modeld/models/dmonitoring.cc#L165))
* 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
