AMD GPU support with the rocm detector and YOLOv8 pretrained model download #9762
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… box scaling; use cv2.dnn.blobForImage for preprocessing; assert on required model parameters
…al version as the empty value broke rocm
…ouple of known chipsets
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Here @NickM-27 |
NickM-27
requested changes
Feb 9, 2024
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
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also one other thing, need to add this to recommended_hardware docs as well |
Added mention: |
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just need to get the python checks happy |
What's the best way to do it? It just requires formatting changes? Can you apply it automagically? |
NickM-27
approved these changes
Feb 9, 2024
This was referenced Feb 10, 2024
mike2307
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Feb 11, 2024
…wnload (blakeblackshear#9762) * 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 commit dcfe6bb. * 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 commit dee95de. * 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>
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