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https://github.com/blakeblackshear/frigate.git
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44d8cdbba1
* ROCm AMD/GPU based build and detector, WIP * detectors/rocm: separate yolov8 postprocessing into own function; fix box scaling; use cv2.dnn.blobForImage for preprocessing; assert on required model parameters * AMD/ROCm: add couple of more ultralytics models; comments * docker/rocm: make imported model files readable by all * docker/rocm: readme about running on AMD GPUs * docker/rocm: updated README * docker/rocm: updated README * docker/rocm: updated README * detectors/rocm: separated preprocessing functions into yolo_utils.py * detector/plugins: added onnx cpu plugin * docker/rocm: updated container with limite label sets * example detectors view * docker/rocm: updated README.md * docker/rocm: update README.md * docker/rocm: do not set HSA_OVERRIDE_GFX_VERSION at all for the general version as the empty value broke rocm * detectors: simplified/optimized yolov8_postprocess * detector/yolo_utils: indentation, remove unused variable * detectors/rocm: default option to conserve cpu usage at the expense of latency * detectors/yolo_utils: use nms to prefilter overlapping boxes if too many detected * detectors/edgetpu_tfl: add support for yolov8 * util/download_models: script to download yolov8 model files * docker/main: add download-models overlay into s6 startup * detectors/rocm: assume models are in /config/model_cache/yolov8/ * docker/rocm: compile onnx files into mxr files at startup * switch model download into bash script * detectors/rocm: automatically override HSA_OVERRIDE_GFX_VERSION for couple of known chipsets * docs: rocm detector first notes * typos * describe builds (harakas temporary) * docker/rocm: also build a version for gfx1100 * docker/rocm: use cp instead of tar * docker.rocm: remove README as it is now in detector config * frigate/detectors: renamed yolov8_preprocess->preprocess, pass input tensor element type * docker/main: use newer openvino (2023.3.0) * detectors: implement class aggregation * update yolov8 model * add openvino/yolov8 support for label aggregation * docker: remove pointless s6/timeout-up files * Revert "detectors: implement class aggregation" This reverts commitdcfe6bbf6f. * detectors/openvino: remove class aggregation * detectors: increase yolov8 postprocessing score trershold to 0.5 * docker/rocm: separate rocm distributed files into its own build stage * Update object_detectors.md * updated CODEOWNERS file for rocm * updated build names for documentation * Revert "docker/main: use newer openvino (2023.3.0)" This reverts commitdee95de908. * reverrted openvino detector * reverted edgetpu detector * scratched rocm docs from any mention of edgetpu or openvino * Update docs/docs/configuration/object_detectors.md Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * renamed frigate.detectors.yolo_utils.py -> frigate.detectors.util.py * clarified rocm example performance * Improved wording and clarified text * Mentioned rocm detector for AMD GPUs * applied ruff formating * applied ruff suggested fixes * docker/rocm: fix missing argument resulting in larger docker image sizes * docs/configuration/object_detectors: fix links to yolov8 release files --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
import glob
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import logging
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import numpy as np
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from typing_extensions import Literal
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import BaseDetectorConfig
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from frigate.detectors.util import preprocess, yolov8_postprocess
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logger = logging.getLogger(__name__)
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DETECTOR_KEY = "onnx"
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class ONNXDetectorConfig(BaseDetectorConfig):
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type: Literal[DETECTOR_KEY]
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class ONNXDetector(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, detector_config: ONNXDetectorConfig):
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try:
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import onnxruntime
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logger.info("ONNX: loaded onnxruntime module")
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except ModuleNotFoundError:
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logger.error(
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"ONNX: module loading failed, need 'pip install onnxruntime'?!?"
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)
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raise
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assert (
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detector_config.model.model_type == "yolov8"
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), "ONNX: detector_config.model.model_type: only yolov8 supported"
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assert (
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detector_config.model.input_tensor == "nhwc"
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), "ONNX: detector_config.model.input_tensor: only nhwc supported"
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if detector_config.model.input_pixel_format != "rgb":
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logger.warn(
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"ONNX: detector_config.model.input_pixel_format: should be 'rgb' for yolov8, but '{detector_config.model.input_pixel_format}' specified!"
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)
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assert detector_config.model.path is not None, (
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"ONNX: No model.path configured, please configure model.path and model.labelmap_path; some suggestions: "
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+ ", ".join(glob.glob("/config/model_cache/yolov8/*.onnx"))
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+ " and "
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+ ", ".join(glob.glob("/config/model_cache/yolov8/*_labels.txt"))
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)
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path = detector_config.model.path
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logger.info(f"ONNX: loading {detector_config.model.path}")
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self.model = onnxruntime.InferenceSession(path)
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logger.info(f"ONNX: {path} loaded")
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def detect_raw(self, tensor_input):
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model_input_name = self.model.get_inputs()[0].name
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model_input_shape = self.model.get_inputs()[0].shape
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tensor_input = preprocess(tensor_input, model_input_shape, np.float32)
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tensor_output = self.model.run(None, {model_input_name: tensor_input})[0]
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return yolov8_postprocess(model_input_shape, tensor_output)
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