You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
3.2 KiB
3.2 KiB
Neural networks in openpilot
To view the architecture of the ONNX networks, you can use netron
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
- Each 256 * 512 image is represented in YUV420 with 6 channels : 6 * 128 * 256
- Two consecutive images (256 * 512 * 3 in RGB) recorded at 20 Hz : 393216 = 2 * 6 * 128 * 256
- 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
- Each 256 * 512 image is represented in YUV420 with 6 channels : 6 * 128 * 256
- Two consecutive images (256 * 512 * 3 in RGB) recorded at 20 Hz : 393216 = 2 * 6 * 128 * 256
- 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 * 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 W = 1440 H = 960 luminance channel (Y) from the planar YUV420 format:
- full input size is 1440 * 960 = 1382400
- normalized ranging from 0.0 to 1.0 in float32 (onnx runner) or ranging from 0 to 255 in uint8 (snpe runner)
- camera calibration angles (roll, pitch, yaw) from liveCalibration: 3 x float32 inputs
output format
- 84 x float32 outputs = 2 + 41 * 2 (parsing example)
- for each person in the front seats (2 * 41)
- 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
- face occluded probability: 1
- touching wheel probability: 1
- paying attention probability: 1
- (deprecated) distracted probabilities: 2
- using phone probability: 1
- distracted probability: 1
- face pose: 12 = 6 + 6
- common outputs 2
- poor camera vision probability: 1
- left hand drive probability: 1
- for each person in the front seats (2 * 41)