Reimplement support for rknn detector

.github/workflows/ci.yml

@@ -69,15 +69,15 @@ jobs:
  rpi.tags=${{ steps.setup.outputs.image-name }}-rpi
  *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64
  *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64,mode=max
- #- name: Build and push RockChip build
- # uses: docker/bake-action@v3
- # with:
- # push: true
- # targets: rk
- # files: docker/rockchip/rk.hcl
- # set: |
- # rk.tags=${{ steps.setup.outputs.image-name }}-rk
- # *.cache-from=type=gha
+ - name: Build and push Rockchip build
+ uses: docker/bake-action@v3
+ with:
+ push: true
+ targets: rk
+ files: docker/rockchip/rk.hcl
+ set: |
+ rk.tags=${{ steps.setup.outputs.image-name }}-rk
+ *.cache-from=type=gha
  jetson_jp4_build:
  runs-on: ubuntu-latest
  name: Jetson Jetpack 4

docker/rockchip/Dockerfile

@@ -18,10 +18,7 @@ RUN --mount=type=bind,from=rk-wheels,source=/rk-wheels,target=/deps/rk-wheels \
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
 
-ADD https://github.com/MarcA711/rknpu2/releases/download/v1.5.2/librknnrt_rk356x.so /usr/lib/
-ADD https://github.com/MarcA711/rknpu2/releases/download/v1.5.2/librknnrt_rk3588.so /usr/lib/
-
-# TODO removed models, other models support may need to be added back in
+ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.0.0/librknnrt.so /usr/lib/
 
 RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffmpeg
 RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffprobe

docker/rockchip/requirements-wheels-rk.txt

@@ -1,2 +1 @@
-hide-warnings == 0.17
-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
+rknn-toolkit-lite2 @ https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.0.0/rknn_toolkit_lite2-2.0.0b0-cp39-cp39-linux_aarch64.whl

docs/docs/configuration/hardware_acceleration.md

@@ -366,7 +366,7 @@ Hardware accelerated video de-/encoding is supported on all Rockchip SoCs using
 
 ### Prerequisites
 
-Make sure that you use a linux distribution that comes with the rockchip BSP kernel 5.10 or 6.1 and supports VPU. To check, enter the following commands:
+Make sure that you use a linux distribution that comes with the rockchip BSP kernel 5.10 or 6.1 and rkvdec2 driver. To check, enter the following commands:
 
 ```
 $ uname -r

docs/docs/configuration/object_detectors.md

@@ -302,3 +302,109 @@ Replace `<your_codeproject_ai_server_ip>` and `<port>` with the IP address and p
 To verify that the integration is working correctly, start Frigate and observe the logs for any error messages related to CodeProject.AI. Additionally, you can check the Frigate web interface to see if the objects detected by CodeProject.AI are being displayed and tracked properly.
 
 # Community Supported Detectors
+
+## Rockchip platform
+
+Hardware accelerated object detection is supported on the following SoCs:
+
+- RK3562
+- RK3566
+- RK3568
+- RK3576
+- RK3588
+
+This implementation uses the [Rockchip's RKNN-Toolkit2](https://github.com/airockchip/rknn-toolkit2/) Currently, only [Yolo-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) is supported as object detection model.
+
+### Prerequisites
+
+Make sure that you use a linux distribution that comes with the rockchip BSP kernel 5.10 or 6.1 and rknpu driver. To check, enter the following commands:
+
+```
+$ uname -r
+5.10.xxx-rockchip # or 6.1.xxx; the -rockchip suffix is important
+$ ls /dev/dri
+by-path card0 card1 renderD128 renderD129 # should list renderD129
+$ sudo cat /sys/kernel/debug/rknpu/version
+RKNPU driver: v0.9.2 # or later version
+```
+
+I recommend [Joshua Riek's Ubuntu for Rockchip](https://github.com/Joshua-Riek/ubuntu-rockchip), if your board is supported.
+
+### Setup
+
+Follow Frigate's default installation instructions, but use a docker image with `-rk` suffix for example `ghcr.io/blakeblackshear/frigate:stable-rk`.
+
+Next, you need to grant docker permissions to access your hardware:
+
+- During the configuration process, you should run docker in privileged mode to avoid any errors due to insufficient permissions. To do so, add `privileged: true` to your `docker-compose.yml` file or the `--privileged` flag to your docker run command.
+- After everything works, you should only grant necessary permissions to increase security. Add the lines below to your `docker-compose.yml` file or the following options to your docker run command: `--security-opt systempaths=unconfined --security-opt apparmor=unconfined --device /dev/dri:/dev/dri`:
+
+```yaml
+security_opt:
+ - apparmor=unconfined
+ - systempaths=unconfined
+devices:
+ - /dev/dri:/dev/dri
+```
+
+### Configuration
+
+This `config.yml` shows all relevant options to configure the detector and explains them. All values shown are the default values (except for two). Lines that are required at least to use the detector are labeled as required, all other lines are optional.
+
+```yaml
+detectors: # required
+ rknn: # required
+ type: rknn # required
+ # number of NPU cores to use
+ # 0 means choose automatically
+ # increase for better performance if you have a multicore NPU e.g. set to 3 on rk3588
+ num_cores: 0
+
+model: # required
+ # name of model (will be automatically downloaded) or path to your own .rknn model file
+ # possible values are:
+ # - deci-fp16-yolonas_s
+ # - deci-fp16-yolonas_m
+ # - deci-fp16-yolonas_l
+ # - /config/model_cache/your_custom_model.rknn
+ path: deci-fp16-yolonas_s
+ # width and height of detection frames
+ width: 320
+ height: 320
+ # pixel format of detection frame
+ # default value is rgb but yolo models usually use bgr format
+ input_pixel_format: bgr # required
+ # shape of detection frame
+ input_tensor: nhwc
+```
+
+### Choosing a model
+
+:::warning
+
+yolo-nas models use weights from DeciAI. These weights are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html 
+
+:::
+
+The inference time was determined on a rk3588 with 3 NPU cores.
+
+| Model | Size in mb | Inference time in ms |
+| ------------------- | ---------- | -------------------- |
+| deci-fp16-yolonas_s | 24 | 25 |
+| deci-fp16-yolonas_m | 62 | 35 |
+| deci-fp16-yolonas_l | 81 | 45 |
+
+:::tip
+
+You can get the load of your NPU with the following command:
+
+```bash
+$ cat /sys/kernel/debug/rknpu/load
+>> NPU load: Core0: 0%, Core1: 0%, Core2: 0%,
+```
+
+:::
+
+- By default the rknn detector uses the yolonas_s model (`model: path: default-fp16-yolonas_s`). This model comes with the image, so no further steps than those mentioned above are necessary and no download happens.
+- The other choices are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
+- Finally, you can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.

frigate/detectors/detection_api.py

@@ -72,7 +72,7 @@ def post_process_yolonas(self, output):
 
  def post_process(self, output):
  if self.detector_config.model.model_type == ModelTypeEnum.yolonas:
- return self.yolonas(output)
+ return self.post_process_yolonas(output)
  else:
  raise ValueError(
  f'Model type "{self.detector_config.model.model_type}" is currently not supported.'

frigate/detectors/plugins/rknn.py

@@ -1,118 +1,157 @@
 import logging
 import os.path
+import re
+import urllib.request
 from typing import Literal
 
-try:
- from hide_warnings import hide_warnings
-except: # noqa: E722
-
- def hide_warnings(func):
- pass
-
-
 from pydantic import Field
 
 from frigate.detectors.detection_api import DetectionApi
-from frigate.detectors.detector_config import BaseDetectorConfig
+from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
 
 logger = logging.getLogger(__name__)
 
 DETECTOR_KEY = "rknn"
 
-supported_socs = ["rk3562", "rk3566", "rk3568", "rk3588"]
+supported_socs = ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"]
+
+supported_models = {ModelTypeEnum.yolonas: "^deci-fp16-yolonas_[sml]$"}
+
+model_chache_dir = "/config/model_cache/rknn_cache/"
 
 
 class RknnDetectorConfig(BaseDetectorConfig):
  type: Literal[DETECTOR_KEY]
- core_mask: int = Field(default=0, ge=0, le=7, title="Core mask for NPU.")
+ num_cores: int = Field(default=0, ge=0, le=3, title="Number of NPU cores to use.")
+ purge_model_cache: bool = Field(default=True)
 
 
 class Rknn(DetectionApi):
  type_key = DETECTOR_KEY
 
  def __init__(self, config: RknnDetectorConfig):
- # find out SoC
+ super().__init__(config)
+ self.height = config.model.height
+ self.width = config.model.width
+ core_mask = 2**config.num_cores - 1
+ soc = self.get_soc()
+
+ model_props = self.parse_model_input(config.model.path, soc)
+
+ if model_props["preset"]:
+ config.model.model_type = model_props["model_type"]
+
+ if model_props["model_type"] == ModelTypeEnum.yolonas:
+ logger.info("""
+ You are using yolo-nas with weights from DeciAI.
+ These weights are subject to their license and can't be used commercially.
+ For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
+ """)
+
+ from rknnlite.api import RKNNLite
+
+ self.rknn = RKNNLite(verbose=False)
+ if self.rknn.load_rknn(model_props["path"]) != 0:
+ logger.error("Error initializing rknn model.")
+ if self.rknn.init_runtime(core_mask=core_mask) != 0:
+ logger.error(
+ "Error initializing rknn runtime. Do you run docker in privileged mode?"
+ )
+
+ def __del__(self):
+ self.rknn.release()
+
+ def get_soc(self):
  try:
  with open("/proc/device-tree/compatible") as file:
  soc = file.read().split(",")[-1].strip("\x00")
  except FileNotFoundError:
- logger.error("Make sure to run docker in privileged mode.")
  raise Exception("Make sure to run docker in privileged mode.")
 
  if soc not in supported_socs:
- logger.error(
- "Your SoC is not supported. Your SoC is: {}. Currently these SoCs are supported: {}.".format(
- soc, supported_socs
- )
- )
  raise Exception(
- "Your SoC is not supported. Your SoC is: {}. Currently these SoCs are supported: {}.".format(
- soc, supported_socs
- )
+ f"Your SoC is not supported. Your SoC is: {soc}. Currently these SoCs are supported: {supported_socs}."
  )
 
- if not os.path.isfile("/usr/lib/librknnrt.so"):
- if "rk356" in soc:
- os.rename("/usr/lib/librknnrt_rk356x.so", "/usr/lib/librknnrt.so")
- elif "rk3588" in soc:
- os.rename("/usr/lib/librknnrt_rk3588.so", "/usr/lib/librknnrt.so")
-
- self.core_mask = config.core_mask
- self.height = config.model.height
- self.width = config.model.width
-
- if True:
- os.makedirs("/config/model_cache/rknn", exist_ok=True)
-
- if (config.model.width != 320) or (config.model.height != 320):
- logger.error(
- "Make sure to set the model width and height to 320 in your config.yml."
+ return soc
+
+ def parse_model_input(self, model_path, soc):
+ model_props = {}
+
+ # find out if user provides his own model
+ # user provided models should be a path and contain a "/"
+ if "/" in model_path:
+ model_props["preset"] = False
+ model_props["path"] = model_path
+ else:
+ model_props["preset"] = True
+
+ """
+ Filenames follow this pattern:
+ origin-quant-basename-soc-tk_version-rev.rknn
+ origin: From where comes the model? default: upstream repo; rknn: modifications from airockchip
+ quant: i8 or fp16
+ basename: e.g. yolonas_s
+ soc: e.g. rk3588
+ tk_version: e.g. v2.0.0
+ rev: e.g. 1
+
+ Full name could be: default-fp16-yolonas_s-rk3588-v2.0.0-1.rknn
+ """
+
+ model_matched = False
+
+ for model_type, pattern in supported_models.items():
+ if re.match(pattern, model_path):
+ model_matched = True
+ model_props["model_type"] = model_type
+
+ if model_matched:
+ model_props["filename"] = model_path + f"-{soc}-v2.0.0-1.rknn"
+
+ model_props["path"] = model_chache_dir + model_props["filename"]
+
+ if not os.path.isfile(model_props["path"]):
+ self.download_model(model_props["filename"])
+ else:
+ supported_models_str = ", ".join(
+ model[1:-1] for model in supported_models
  )
  raise Exception(
- "Make sure to set the model width and height to 320 in your config.yml."
+ f"Model {model_path} is unsupported. Provide your own model or choose one of the following: {supported_models_str}"
  )
 
- if config.model.input_pixel_format != "bgr":
- logger.error(
- 'Make sure to set the model input_pixel_format to "bgr" in your config.yml.'
- )
- raise Exception(
- 'Make sure to set the model input_pixel_format to "bgr" in your config.yml.'
- )
+ return model_props
 
- if config.model.input_tensor != "nhwc":
- logger.error(
- 'Make sure to set the model input_tensor to "nhwc" in your config.yml.'
- )
- raise Exception(
- 'Make sure to set the model input_tensor to "nhwc" in your config.yml.'
- )
+ def download_model(self, filename):
+ if not os.path.isdir(model_chache_dir):
+ os.mkdir(model_chache_dir)
 
- from rknnlite.api import RKNNLite
+ urllib.request.urlretrieve(
+ f"https://github.com/MarcA711/rknn-models/releases/download/v2.0.0/{filename}",
+ model_chache_dir + filename,
+ )
 
- self.rknn = RKNNLite(verbose=False)
- if self.rknn.load_rknn(self.model_path) != 0:
- logger.error("Error initializing rknn model.")
- if self.rknn.init_runtime(core_mask=self.core_mask) != 0:
- logger.error(
- "Error initializing rknn runtime. Do you run docker in privileged mode?"
+ def check_config(self, config):
+ if (config.model.width != 320) or (config.model.height != 320):
+ raise Exception(
+ "Make sure to set the model width and height to 320 in your config.yml."
  )
 
- raise Exception(
- "RKNN does not currently support any models. Please see the docs for more info."
- )
-
- def __del__(self):
- self.rknn.release()
+ if config.model.input_pixel_format != "bgr":
+ raise Exception(
+ 'Make sure to set the model input_pixel_format to "bgr" in your config.yml.'
+ )
 
- @hide_warnings
- def inference(self, tensor_input):
- return self.rknn.inference(inputs=tensor_input)
+ if config.model.input_tensor != "nhwc":
+ raise Exception(
+ 'Make sure to set the model input_tensor to "nhwc" in your config.yml.'
+ )
 
  def detect_raw(self, tensor_input):
- output = self.inference(
+ output = self.rknn.inference(
  [
  tensor_input,
  ]
  )
- return self.postprocess(output[0])
+ return self.post_process(output)