forked from Github/frigate
Community Supported Boards Framework (#7114)
* Make main frigate build non rpi specific and build rpi using base image * Add boards to sidebar * Fix docker build * Fix docs build * Update pr branch for testing * remove target from rpi build * Remove manual build * Add push build for rpi * fix typos, improve wording * Add arm build for rpi * Cleanup and add default github ref name * Cleanup docker build file system * Setup to use docker bake * Add ci/cd for bake * Fix path * Fix devcontainer * Set targets * Fix build * Fix syntax * Add wheels target * Move dev container to trt * Update key and fix rpi local * Move requirements files and set intermediate targets * Add back --load * Update docs for community board development * Update installation docs to reflect different builds available * Update docs with official and community supported headers * Update codeowners docs * Update docs * Assemble main and standard builds * Change order of pushes * Remove community board after successful build * Fix rpi bake file names
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/usr/local/lib
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/usr/local/lib/python3.9/dist-packages/nvidia/cudnn/lib
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/usr/local/lib/python3.9/dist-packages/nvidia/cuda_runtime/lib
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/usr/local/lib/python3.9/dist-packages/nvidia/cublas/lib
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/usr/local/lib/python3.9/dist-packages/nvidia/cuda_nvrtc/lib
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/usr/local/lib/python3.9/dist-packages/tensorrt
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53
docker/tensorrt/detector/rootfs/etc/s6-overlay/s6-rc.d/trt-model-prepare/run
Executable file
53
docker/tensorrt/detector/rootfs/etc/s6-overlay/s6-rc.d/trt-model-prepare/run
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#!/command/with-contenv bash
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# shellcheck shell=bash
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# Generate models for the TensorRT detector
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set -o errexit -o nounset -o pipefail
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MODEL_CACHE_DIR=${MODEL_CACHE_DIR:-"/config/model_cache/tensorrt"}
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OUTPUT_FOLDER="${MODEL_CACHE_DIR}/${TRT_VER}"
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# Create output folder
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mkdir -p ${OUTPUT_FOLDER}
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FIRST_MODEL=true
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MODEL_CONVERT=""
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for model in ${YOLO_MODELS//,/ }
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do
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# Remove old link in case path/version changed
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rm -f ${MODEL_CACHE_DIR}/${model}.trt
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if [[ ! -f ${OUTPUT_FOLDER}/${model}.trt ]]; then
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if [[ ${FIRST_MODEL} = true ]]; then
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MODEL_CONVERT="${model}"
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FIRST_MODEL=false;
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else
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MODEL_CONVERT+=",${model}";
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fi
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else
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ln -s ${OUTPUT_FOLDER}/${model}.trt ${MODEL_CACHE_DIR}/${model}.trt
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fi
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done
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if [[ -z ${MODEL_CONVERT} ]]; then
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echo "No models to convert."
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exit 0
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fi
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echo "Generating the following TRT Models: ${MODEL_CONVERT}"
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# Build trt engine
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cd /usr/local/src/tensorrt_demos/yolo
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# Download yolo weights
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./download_yolo.sh $MODEL_CONVERT > /dev/null
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for model in ${MODEL_CONVERT//,/ }
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do
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echo "Converting ${model} model"
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python3 yolo_to_onnx.py -m ${model} > /dev/null
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python3 onnx_to_tensorrt.py -m ${model} > /dev/null
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cp ${model}.trt ${OUTPUT_FOLDER}/${model}.trt
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ln -s ${OUTPUT_FOLDER}/${model}.trt ${MODEL_CACHE_DIR}/${model}.trt
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done
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@@ -0,0 +1 @@
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oneshot
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@@ -0,0 +1 @@
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/etc/s6-overlay/s6-rc.d/trt-model-prepare/run
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18
docker/tensorrt/detector/tensorrt_libyolo.sh
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docker/tensorrt/detector/tensorrt_libyolo.sh
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#!/bin/bash
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set -euxo pipefail
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SCRIPT_DIR="/usr/local/src/tensorrt_demos"
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# Clone tensorrt_demos repo
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git clone --depth 1 https://github.com/NateMeyer/tensorrt_demos.git -b conditional_download
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# Build libyolo
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cd ./tensorrt_demos/plugins && make all
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cp libyolo_layer.so /usr/local/lib/libyolo_layer.so
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# Store yolo scripts for later conversion
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cd ../
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mkdir -p ${SCRIPT_DIR}/plugins
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cp plugins/libyolo_layer.so ${SCRIPT_DIR}/plugins/libyolo_layer.so
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cp -a yolo ${SCRIPT_DIR}/
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