## 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: 799906 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 buffer to command model to execute certain actions, bit needs to be sent for the past 5 seconds (at 20FPS) : 100 * 8
* **traffic convention**
* one-hot encoded vector to tell model whether traffic is right-hand or left-hand traffic : 2
* **feature buffer**
* A buffer of intermediate features that gets appended to the current feature to form a 5 seconds temporal context (at 20FPS) : 99 * 128
### Supercombo output format (Full size: XXX x float32)
Read [here ](https://github.com/commaai/openpilot/blob/90af436a121164a51da9fa48d093c29f738adf6a/selfdrive/modeld/models/driving.h#L236 ) 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](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
