mirror of https://github.com/commaai/openpilot.git
da952e9b64
* squash * bump tg * bump tg * debump tinygrad * bump tinygrad * bump tg * Skip init iteration * fixes * cleanups * skip first test sample * typos * linter unhappy * update cpu usage * OPENCL just zeros for now * imports * Try printing * Runs again, but slower * unused import * Allow more buffer with tg and all on gpu * bump tinygrad --------- Co-authored-by: Adeeb Shihadeh <adeebshihadeh@gmail.com> Co-authored-by: Bruce Wayne <harald.the.engineer@gmail.com> |
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| .. | ||
| README.md | ||
| __init__.py | ||
| commonmodel.cc | ||
| commonmodel.h | ||
| commonmodel.pxd | ||
| commonmodel_pyx.pxd | ||
| commonmodel_pyx.pyx | ||
| dmonitoring_model.current | ||
| dmonitoring_model.onnx | ||
| supercombo.onnx | ||
README.md
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)