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59 lines
3.0 KiB
59 lines
3.0 KiB
## Neural networks in openpilot
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To view the architecture of the ONNX networks, you can use [netron](https://netron.app/)
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## Supercombo
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### Supercombo input format (Full size: 393738 x float32)
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* **image stream**
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* Two consecutive images (256 * 512 * 3 in RGB) recorded at 20 Hz : 393216 = 2 * 6 * 128 * 256
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* Each 256 * 512 image is represented in YUV420 with 6 channels : 6 * 128 * 256
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* 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]
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* Channel 4 represents the half-res U channel
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* Channel 5 represents the half-res V channel
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* **wide image stream**
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* Two consecutive images (256 * 512 * 3 in RGB) recorded at 20 Hz : 393216 = 2 * 6 * 128 * 256
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* Each 256 * 512 image is represented in YUV420 with 6 channels : 6 * 128 * 256
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* 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]
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* Channel 4 represents the half-res U channel
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* Channel 5 represents the half-res V channel
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* **desire**
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* one-hot encoded vector to command model to execute certain actions, bit only needs to be sent for 1 frame : 8
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* **traffic convention**
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* one-hot encoded vector to tell model whether traffic is right-hand or left-hand traffic : 2
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* **recurrent state**
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* The recurrent state vector that is fed back into the GRU for temporal context : 512
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### Supercombo output format (Full size: XXX x float32)
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Read [here](https://github.com/commaai/openpilot/blob/90af436a121164a51da9fa48d093c29f738adf6a/selfdrive/modeld/models/driving.h#L236) for more.
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## Driver Monitoring Model
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* .onnx model can be run with onnx runtimes
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* .dlc file is a pre-quantized model and only runs on qualcomm DSPs
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### input format
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* single image (640 * 320 * 3 in RGB):
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* full input size is 6 * 640/2 * 320/2 = 307200
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* represented in YUV420 with 6 channels:
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* 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]
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* Channel 4 represents the half-res U channel
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* Channel 5 represents the half-res V channel
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* normalized, ranging from -1.0 to 1.0
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### output format
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* 39 x float32 outputs ([parsing example](https://github.com/commaai/openpilot/blob/master/selfdrive/modeld/models/dmonitoring.cc#L165))
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* face pose: 12 = 6 + 6
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* face orientation [pitch, yaw, roll] in camera frame: 3
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* face position [dx, dy] relative to image center: 2
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* normalized face size: 1
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* standard deviations for above outputs: 6
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* face visible probability: 1
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* eyes: 20 = (8 + 1) + (8 + 1) + 1 + 1
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* eye position and size, and their standard deviations: 8
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* eye visible probability: 1
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* eye closed probability: 1
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* wearing sunglasses probability: 1
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* poor camera vision probability: 1
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* face partially out-of-frame probability: 1
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* (deprecated) distracted probabilities: 2
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* face covered probability: 1
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