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https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
Include libraries and .rknn models for other Rockchip SoCs (#8649)
* support for other yolov models and config checks * apply code formatting * Information about core mask and inference speed * update rknn postprocess and remove params * update model selection * Apply suggestions from code review Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * support rknn on all socs * apply changes from review and fix post process bug * apply code formatting * update tip in object_detectors docs --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
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Marc Altmann
GitHub
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c6208b266b
@ -9,11 +9,6 @@ COPY docker/rockchip/requirements-wheels-rk.txt /requirements-wheels-rk.txt
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RUN sed -i "/https:\/\//d" /requirements-wheels.txt
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RUN pip3 wheel --wheel-dir=/rk-wheels -c /requirements-wheels.txt -r /requirements-wheels-rk.txt
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FROM wget as rk-downloads
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RUN wget -qO librknnrt.so https://github.com/MarcA711/rknpu2/raw/master/runtime/RK3588/Linux/librknn_api/aarch64/librknnrt.so
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RUN wget -qO ffmpeg https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/latest/ffmpeg
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RUN wget -qO ffprobe https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/latest/ffprobe
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RUN wget -qO yolov8n-320x320.rknn https://github.com/MarcA711/rknn-models/releases/download/latest/yolov8n-320x320.rknn
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FROM deps AS rk-deps
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ARG TARGETARCH
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@ -22,12 +17,16 @@ RUN --mount=type=bind,from=rk-wheels,source=/rk-wheels,target=/deps/rk-wheels \
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WORKDIR /opt/frigate/
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COPY --from=rootfs / /
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COPY --from=rk-downloads /rootfs/librknnrt.so /usr/lib/
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COPY --from=rk-downloads /rootfs/yolov8n-320x320.rknn /models/
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ADD https://github.com/MarcA711/rknpu2/releases/download/v1.5.2/librknnrt_rk356x.so /usr/lib/
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ADD https://github.com/MarcA711/rknpu2/releases/download/v1.5.2/librknnrt_rk3588.so /usr/lib/
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ADD https://github.com/MarcA711/rknn-models/releases/download/v1.5.2-rk3562/yolov8n-320x320-rk3562.rknn /models/rknn/
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ADD https://github.com/MarcA711/rknn-models/releases/download/v1.5.2-rk3566/yolov8n-320x320-rk3566.rknn /models/rknn/
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ADD https://github.com/MarcA711/rknn-models/releases/download/v1.5.2-rk3568/yolov8n-320x320-rk3568.rknn /models/rknn/
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ADD https://github.com/MarcA711/rknn-models/releases/download/v1.5.2-rk3588/yolov8n-320x320-rk3588.rknn /models/rknn/
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RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffmpeg
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RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffprobe
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COPY --from=rk-downloads /rootfs/ffmpeg /usr/lib/btbn-ffmpeg/bin/
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COPY --from=rk-downloads /rootfs/ffprobe /usr/lib/btbn-ffmpeg/bin/
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RUN chmod +x /usr/lib/btbn-ffmpeg/bin/ffmpeg
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RUN chmod +x /usr/lib/btbn-ffmpeg/bin/ffprobe
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ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/latest/ffmpeg /usr/lib/btbn-ffmpeg/bin/
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ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/latest/ffprobe /usr/lib/btbn-ffmpeg/bin/
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@ -1,2 +1,2 @@
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hide-warnings == 0.17
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rknn-toolkit-lite2 @ https://github.com/MarcA711/rknn-toolkit2/raw/master/rknn_toolkit_lite2/packages/rknn_toolkit_lite2-1.5.2-cp39-cp39-linux_aarch64.whl
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rknn-toolkit-lite2 @ https://github.com/MarcA711/rknn-toolkit2/releases/download/v1.5.2/rknn_toolkit_lite2-1.5.2-cp39-cp39-linux_aarch64.whl
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@ -295,16 +295,16 @@ To verify that the integration is working correctly, start Frigate and observe t
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## Rockchip RKNN-Toolkit-Lite2
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This detector is only available if one of the following Rockchip SoCs is used:
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- RK3566/RK3568
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- RK3588/RK3588S
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- RV1103/RV1106
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- RK3568
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- RK3566
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- RK3562
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These SoCs come with a NPU that will highly speed up detection.
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### Setup
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RKNN support is provided using the `-rk` suffix for the docker image. Moreover, privileged mode must be enabled by adding the `--privileged` flag to your docker run command or `privileged: true` to your `docker-compose.yml` file.
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Use a frigate docker image with `-rk` suffix and enable privileged mode by adding the `--privileged` flag to your docker run command or `privileged: true` to your `docker-compose.yml` file.
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### Configuration
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@ -376,3 +376,16 @@ $ cat /sys/kernel/debug/rknpu/load
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model:
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path: /config/model_cache/rknn/my-rknn-model.rknn
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```
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:::tip
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When you have a multicore NPU, you can enable all cores to reduce inference times. You should consider activating all cores if you use a larger model like yolov8l. If your NPU has 3 cores (like rk3588/S SoCs), you can enable all 3 cores using:
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```yaml
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detectors:
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rknn:
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type: rknn
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core_mask: 0b111
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```
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:::
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@ -103,7 +103,7 @@ Frigate supports SBCs with the following Rockchip SoCs:
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- RV1103/RV1106
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- RK3562
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Using the yolov8n model and an Orange Pi 5 Plus with RK3588 SoC inference speeds vary between 25-40 ms.
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Using the yolov8n model and an Orange Pi 5 Plus with RK3588 SoC inference speeds vary between 20 - 25 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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@ -22,7 +22,9 @@ logger = logging.getLogger(__name__)
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DETECTOR_KEY = "rknn"
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yolov8_rknn_models = {
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supported_socs = ["rk3562", "rk3566", "rk3568", "rk3588"]
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yolov8_suffix = {
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"default-yolov8n": "n",
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"default-yolov8s": "s",
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"default-yolov8m": "m",
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@ -40,28 +42,57 @@ class Rknn(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, config: RknnDetectorConfig):
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# find out SoC
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try:
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with open("/proc/device-tree/compatible") as file:
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soc = file.read().split(",")[-1].strip("\x00")
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except FileNotFoundError:
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logger.error("Make sure to run docker in privileged mode.")
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raise Exception("Make sure to run docker in privileged mode.")
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if soc not in supported_socs:
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logger.error(
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"Your SoC is not supported. Your SoC is: {}. Currently these SoCs are supported: {}.".format(
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soc, supported_socs
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)
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)
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raise Exception(
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"Your SoC is not supported. Your SoC is: {}. Currently these SoCs are supported: {}.".format(
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soc, supported_socs
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)
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)
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if "rk356" in soc:
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os.rename("/usr/lib/librknnrt_rk356x.so", "/usr/lib/librknnrt.so")
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elif "rk3588" in soc:
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os.rename("/usr/lib/librknnrt_rk3588.so", "/usr/lib/librknnrt.so")
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self.model_path = config.model.path or "default-yolov8n"
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self.core_mask = config.core_mask
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self.height = config.model.height
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self.width = config.model.width
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if self.model_path in yolov8_rknn_models:
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if self.model_path in yolov8_suffix:
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if self.model_path == "default-yolov8n":
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self.model_path = "/models/yolov8n-320x320.rknn"
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self.model_path = "/models/rknn/yolov8n-320x320-{soc}.rknn".format(
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soc=soc
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)
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else:
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model_suffix = yolov8_rknn_models[self.model_path]
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model_suffix = yolov8_suffix[self.model_path]
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self.model_path = (
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"/config/model_cache/rknn/yolov8{}-320x320.rknn".format(
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model_suffix
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"/config/model_cache/rknn/yolov8{suffix}-320x320-{soc}.rknn".format(
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suffix=model_suffix, soc=soc
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)
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)
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os.makedirs("/config/model_cache/rknn", exist_ok=True)
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if not os.path.isfile(self.model_path):
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logger.info("Downloading yolov8{} model.".format(model_suffix))
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logger.info(
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"Downloading yolov8{suffix} model.".format(suffix=model_suffix)
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)
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urllib.request.urlretrieve(
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"https://github.com/MarcA711/rknn-models/releases/download/latest/yolov8{}-320x320.rknn".format(
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model_suffix
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"https://github.com/MarcA711/rknn-models/releases/download/v1.5.2-{soc}/yolov8{suffix}-320x320-{soc}.rknn".format(
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soc=soc, suffix=model_suffix
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),
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self.model_path,
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)
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@ -140,10 +171,10 @@ class Rknn(DetectionApi):
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boxes = np.transpose(
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np.vstack(
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(
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results[:, 1] - 0.5 * results[:, 3],
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results[:, 0] - 0.5 * results[:, 2],
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results[:, 3] + 0.5 * results[:, 3],
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results[:, 2] + 0.5 * results[:, 2],
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(results[:, 1] - 0.5 * results[:, 3]) / self.height,
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(results[:, 0] - 0.5 * results[:, 2]) / self.width,
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(results[:, 1] + 0.5 * results[:, 3]) / self.height,
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(results[:, 0] + 0.5 * results[:, 2]) / self.width,
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)
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)
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)
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