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Initial commit for AXERA AI accelerators (#22206)
* feat: Initial AXERA detector * chore: update pip install URL for axengine package * Update docker/main/Dockerfile Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * Update docs/docs/configuration/object_detectors.md Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> * Update AXERA section in installation.md Removed details section for AXERA accelerators in installation guide. * Update axmodel download URL to Hugging Face --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> Co-authored-by: shizhicheng <shizhicheng@axera-tech.com>
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GuoQing Liu
GitHub
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@ -266,6 +266,12 @@ RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
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RUN --mount=type=bind,from=wheels,source=/wheels,target=/deps/wheels \
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pip3 install -U /deps/wheels/*.whl
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# Install Axera Engine
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RUN pip3 install https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3-frigate/axengine-0.1.3-py3-none-any.whl
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ENV PATH="${PATH}:/usr/bin/axcl"
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ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/lib/axcl"
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# Install MemryX runtime (requires libgomp (OpenMP) in the final docker image)
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RUN --mount=type=bind,source=docker/main/install_memryx.sh,target=/deps/install_memryx.sh \
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bash -c "bash /deps/install_memryx.sh"
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@ -49,6 +49,11 @@ Frigate supports multiple different detectors that work on different types of ha
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- [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs.
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**AXERA** <CommunityBadge />
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- [AXEngine](#axera): axmodels can run on AXERA AI acceleration.
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**For Testing**
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- [CPU Detector (not recommended for actual use](#cpu-detector-not-recommended): Use a CPU to run tflite model, this is not recommended and in most cases OpenVINO can be used in CPU mode with better results.
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@ -1478,6 +1483,41 @@ model:
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input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
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```
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## AXERA
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Hardware accelerated object detection is supported on the following SoCs:
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- AX650N
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- AX8850N
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This implementation uses the [AXera Pulsar2 Toolchain](https://huggingface.co/AXERA-TECH/Pulsar2).
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See the [installation docs](../frigate/installation.md#axera) for information on configuring the AXEngine hardware.
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### Configuration
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When configuring the AXEngine detector, you have to specify the model name.
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#### yolov9
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A yolov9 model is provided in the container at `/axmodels` and is used by this detector type by default.
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Use the model configuration shown below when using the axengine detector with the default axmodel:
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```yaml
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detectors:
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axengine:
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type: axengine
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model:
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path: frigate-yolov9-tiny
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model_type: yolo-generic
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width: 320
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height: 320
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tensor_format: bgr
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labelmap_path: /labelmap/coco-80.txt
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```
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# Models
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Some model types are not included in Frigate by default.
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@ -1571,12 +1611,12 @@ YOLOv9 model can be exported as ONNX using the command below. You can copy and p
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```sh
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docker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=320 --output . -f- <<'EOF'
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FROM python:3.11 AS build
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RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
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COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
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RUN apt-get update && apt-get install --no-install-recommends -y cmake libgl1 && rm -rf /var/lib/apt/lists/*
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COPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/
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WORKDIR /yolov9
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ADD https://github.com/WongKinYiu/yolov9.git .
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RUN uv pip install --system -r requirements.txt
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RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier>=0.4.1 onnxscript
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RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier==0.4.* onnxscript
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ARG MODEL_SIZE
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ARG IMG_SIZE
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ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt
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@ -103,6 +103,10 @@ Frigate supports multiple different detectors that work on different types of ha
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- [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs to provide efficient object detection.
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**AXERA** <CommunityBadge />
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- [AXEngine](#axera): axera models can run on AXERA NPUs via AXEngine, delivering highly efficient object detection.
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:::
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### Hailo-8
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@ -288,6 +292,14 @@ The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms fo
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| ssd mobilenet | ~ 25 ms |
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| yolov5m | ~ 118 ms |
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### AXERA
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- **AXEngine** Default model is **yolov9**
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| Name | AXERA AX650N/AX8850N Inference Time |
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| ---------------- | ----------------------------------- |
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| yolov9-tiny | ~ 4 ms |
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## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
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This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.
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@ -308,4 +320,4 @@ Basically - When you increase the resolution and/or the frame rate of the stream
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YES! The Coral does not help with decoding video streams.
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Decompressing video streams takes a significant amount of CPU power. Video compression uses key frames (also known as I-frames) to send a full frame in the video stream. The following frames only include the difference from the key frame, and the CPU has to compile each frame by merging the differences with the key frame. [More detailed explanation](https://support.video.ibm.com/hc/en-us/articles/18106203580316-Keyframes-InterFrame-Video-Compression). Higher resolutions and frame rates mean more processing power is needed to decode the video stream, so try and set them on the camera to avoid unnecessary decoding work.
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Decompressing video streams takes a significant amount of CPU power. Video compression uses key frames (also known as I-frames) to send a full frame in the video stream. The following frames only include the difference from the key frame, and the CPU has to compile each frame by merging the differences with the key frame. [More detailed explanation](https://support.video.ibm.com/hc/en-us/articles/18106203580316-Keyframes-InterFrame-Video-Compression). Higher resolutions and frame rates mean more processing power is needed to decode the video stream, so try and set them on the camera to avoid unnecessary decoding work.
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@ -439,6 +439,39 @@ or add these options to your `docker run` command:
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Next, you should configure [hardware object detection](/configuration/object_detectors#synaptics) and [hardware video processing](/configuration/hardware_acceleration_video#synaptics).
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### AXERA
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AXERA accelerators are available in an M.2 form factor, compatible with both Raspberry Pi and Orange Pi. This form factor has also been successfully tested on x86 platforms, making it a versatile choice for various computing environments.
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#### Installation
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Using AXERA accelerators requires the installation of the AXCL driver. We provide a convenient Linux script to complete this installation.
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Follow these steps for installation:
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1. Copy or download [this script](https://github.com/ivanshi1108/assets/releases/download/v0.16.2/user_installation.sh).
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2. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
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3. Run the script with `./user_installation.sh`
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#### Setup
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To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
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Next, grant Docker permissions to access your hardware by adding the following lines to your `docker-compose.yml` file:
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```yaml
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devices:
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- /dev/axcl_host
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- /dev/ax_mmb_dev
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- /dev/msg_userdev
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```
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If you are using `docker run`, add this option to your command `--device /dev/axcl_host --device /dev/ax_mmb_dev --device /dev/msg_userdev`
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#### Configuration
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Finally, configure [hardware object detection](/configuration/object_detectors#axera) to complete the setup.
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## Docker
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Running through Docker with Docker Compose is the recommended install method.
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86
frigate/detectors/plugins/axengine.py
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86
frigate/detectors/plugins/axengine.py
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@ -0,0 +1,86 @@
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import logging
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import os.path
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import re
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import urllib.request
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from typing import Literal
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import axengine as axe
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from frigate.const import MODEL_CACHE_DIR
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
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from frigate.util.model import post_process_yolo
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logger = logging.getLogger(__name__)
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DETECTOR_KEY = "axengine"
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supported_models = {
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ModelTypeEnum.yologeneric: "frigate-yolov9-.*$",
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}
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model_cache_dir = os.path.join(MODEL_CACHE_DIR, "axengine_cache/")
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class AxengineDetectorConfig(BaseDetectorConfig):
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type: Literal[DETECTOR_KEY]
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class Axengine(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, config: AxengineDetectorConfig):
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logger.info("__init__ axengine")
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super().__init__(config)
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self.height = config.model.height
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self.width = config.model.width
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model_path = config.model.path or "frigate-yolov9-tiny"
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model_props = self.parse_model_input(model_path)
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self.session = axe.InferenceSession(model_props["path"])
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def __del__(self):
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pass
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def parse_model_input(self, model_path):
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model_props = {}
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model_props["preset"] = True
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model_matched = False
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for model_type, pattern in supported_models.items():
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if re.match(pattern, model_path):
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model_matched = True
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model_props["model_type"] = model_type
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if model_matched:
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model_props["filename"] = model_path + ".axmodel"
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model_props["path"] = model_cache_dir + model_props["filename"]
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if not os.path.isfile(model_props["path"]):
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self.download_model(model_props["filename"])
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else:
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supported_models_str = ", ".join(model[1:-1] for model in supported_models)
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raise Exception(
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f"Model {model_path} is unsupported. Provide your own model or choose one of the following: {supported_models_str}"
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)
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return model_props
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def download_model(self, filename):
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if not os.path.isdir(model_cache_dir):
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os.mkdir(model_cache_dir)
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HF_ENDPOINT = os.environ.get("HF_ENDPOINT", "https://huggingface.co")
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urllib.request.urlretrieve(
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f"{HF_ENDPOINT}/AXERA-TECH/frigate-resource/resolve/axmodel/{filename}",
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model_cache_dir + filename,
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)
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def detect_raw(self, tensor_input):
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results = None
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results = self.session.run(None, {"images": tensor_input})
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if self.detector_config.model.model_type == ModelTypeEnum.yologeneric:
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return post_process_yolo(results, self.width, self.height)
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else:
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raise ValueError(
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f'Model type "{self.detector_config.model.model_type}" is currently not supported.'
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)
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