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a8ec6f4cfd
* 1456d261-d232-4654-8885-4d9fde883894/440 6b7d7cec-ead8-40f3-86cc-86d52c9b03fe/300
* compute only 9 tokens: 1456d261-d232-4654-8885-4d9fde883894/440 6b7d7cec-ead8-40f3-86cc-86d52c9b03fe/300
* tinygrad: cleanup gather
* 1456d261-d232-4654-8885-4d9fde883894/440 6b7d7cec-ead8-40f3-86cc-86d52c9b03fe/700
* empty commit for tests
* bump tinygrad
* dont use tinygrad matmul for now
* bump tinygrad
* 1456d261-d232-4654-8885-4d9fde883894/440 e63ab895-2222-4abd-a9a5-af86bb70e260/700
* float16 1456d261-d232-4654-8885-4d9fde883894/440 e63ab895-2222-4abd-a9a5-af86bb70e260/700
* increase steer rate cost
* Revert "increase steer rate cost"
This reverts commit 74ce9ab9be7ef17ecfec931f96851b12f37f2336.
* fork tinygrad
* empty commit for tests
* basics
* Kinda works
* new lat
* new tuning
* Move LATMPCN so scons compiles
* Update long weights
* Add tinygrad optim
* Update model ref
* update weights
* Update ref
* Try
* Error message for field ignore
* update model regf
* ref commit
* Fix onnx test
Co-authored-by: Yassine Yousfi <yyousfi1@binghamton.edu>
old-commit-hash: cb0b7375b7
Neural networks in openpilot
To view the architecture of the ONNX networks, you can use netron
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
- 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 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: 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 (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)
- 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
- face pose: 12 = 6 + 6