Upgrade TensorRT to 8.5.3 (#7006)

* Update to latest tensorrt (8.6.1) release

* Build trt libyolo_layer.so in container

* Update tensorrt_models script to convert models from the frigate container

* Fix typo in model script

* Fix paths to yolo lib and models folder

* Add S6 scripts to test and convert specified TensortRT models at startup.

Rearrange tensorrt files into a docker support folder.

* Update TensorRT documentation to reflect the new model conversion process and minimum HW support.

* Fix model_cache path to live in config directory

* Move tensorrt s6 files to the correct directory

* Fix issues in model generation script

* Disable global timeout for s6 services

* Add version folder to tensorrt model_cache path

* Include TensorRT version 8.5.3

* Add numpy requirement prior to removal of np.bool

* This TRT version uses a mixture of cuda dependencies

* Redirect stdout from noisy model conversion

docker/install_deps.sh

@@ -68,7 +68,7 @@ if [[ "${TARGETARCH}" == "arm64" ]]; then
         libva-drm2 mesa-va-drivers
 fi
 
-apt-get purge gnupg apt-transport-https wget xz-utils -y
+apt-get purge gnupg apt-transport-https xz-utils -y
 apt-get clean autoclean -y
 apt-get autoremove --purge -y
 rm -rf /var/lib/apt/lists/*

docker/support/tensorrt_detector/rootfs/etc/ld.so.conf.d/cuda_tensorrt.conf

@@ -1,3 +1,4 @@
+/usr/local/lib
 /usr/local/lib/python3.9/dist-packages/nvidia/cudnn/lib
 /usr/local/lib/python3.9/dist-packages/nvidia/cuda_runtime/lib
 /usr/local/lib/python3.9/dist-packages/nvidia/cublas/lib

docker/support/tensorrt_detector/rootfs/etc/s6-overlay/s6-rc.d/trt-model-prepare/run

@@ -0,0 +1,53 @@
+#!/command/with-contenv bash
+# shellcheck shell=bash
+# Generate models for the TensorRT detector
+
+set -o errexit -o nounset -o pipefail
+
+MODEL_CACHE_DIR=${MODEL_CACHE_DIR:-"/config/model_cache/tensorrt"}
+OUTPUT_FOLDER="${MODEL_CACHE_DIR}/${TRT_VER}"
+
+# Create output folder
+mkdir -p ${OUTPUT_FOLDER}
+
+FIRST_MODEL=true
+MODEL_CONVERT=""
+
+for model in ${YOLO_MODELS//,/ }
+do
+    # Remove old link in case path/version changed
+    rm -f ${MODEL_CACHE_DIR}/${model}.trt
+    
+    if [[ ! -f ${OUTPUT_FOLDER}/${model}.trt ]]; then
+        if [[ ${FIRST_MODEL} = true ]]; then
+            MODEL_CONVERT="${model}"
+            FIRST_MODEL=false;
+        else
+            MODEL_CONVERT+=",${model}";
+        fi
+    else
+        ln -s ${OUTPUT_FOLDER}/${model}.trt ${MODEL_CACHE_DIR}/${model}.trt
+    fi
+done
+
+if [[ -z ${MODEL_CONVERT} ]]; then
+    echo "No models to convert."
+    exit 0
+fi
+
+echo "Generating the following TRT Models: ${MODEL_CONVERT}"
+
+# Build trt engine
+cd /usr/local/src/tensorrt_demos/yolo
+
+# Download yolo weights
+./download_yolo.sh $MODEL_CONVERT > /dev/null
+
+for model in ${MODEL_CONVERT//,/ }
+do
+    echo "Converting ${model} model"
+    python3 yolo_to_onnx.py -m ${model} > /dev/null
+    python3 onnx_to_tensorrt.py -m ${model} > /dev/null
+    cp ${model}.trt ${OUTPUT_FOLDER}/${model}.trt
+    ln -s ${OUTPUT_FOLDER}/${model}.trt ${MODEL_CACHE_DIR}/${model}.trt
+done

docker/support/tensorrt_detector/rootfs/etc/s6-overlay/s6-rc.d/trt-model-prepare/type

@@ -0,0 +1 @@
+oneshot

docker/support/tensorrt_detector/rootfs/etc/s6-overlay/s6-rc.d/trt-model-prepare/up

@@ -0,0 +1 @@
+/etc/s6-overlay/s6-rc.d/trt-model-prepare/run

docker/support/tensorrt_detector/tensorrt_libyolo.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+set -euxo pipefail
+
+SCRIPT_DIR="/usr/local/src/tensorrt_demos"
+
+# Clone tensorrt_demos repo
+git clone --depth 1 https://github.com/NateMeyer/tensorrt_demos.git -b conditional_download
+
+# Build libyolo
+cd ./tensorrt_demos/plugins && make all
+cp libyolo_layer.so /usr/local/lib/libyolo_layer.so
+
+# Store yolo scripts for later conversion
+cd ../
+mkdir -p ${SCRIPT_DIR}/plugins
+cp plugins/libyolo_layer.so ${SCRIPT_DIR}/plugins/libyolo_layer.so
+cp -a yolo ${SCRIPT_DIR}/

docker/tensorrt_models.sh

@@ -1,34 +0,0 @@
-#!/bin/bash
-
-set -euxo pipefail
-
-CUDA_HOME=/usr/local/cuda
-LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
-OUTPUT_FOLDER=/tensorrt_models
-echo "Generating the following TRT Models: ${YOLO_MODELS:="yolov4-tiny-288,yolov4-tiny-416,yolov7-tiny-416"}"
-
-# Create output folder
-mkdir -p ${OUTPUT_FOLDER}
-
-# Install packages
-pip install --upgrade pip && pip install onnx==1.9.0 protobuf==3.20.3
-
-# Clone tensorrt_demos repo
-git clone --depth 1 https://github.com/yeahme49/tensorrt_demos.git /tensorrt_demos
-
-# Build libyolo
-cd /tensorrt_demos/plugins && make all
-cp libyolo_layer.so ${OUTPUT_FOLDER}/libyolo_layer.so
-
-# Download yolo weights
-cd /tensorrt_demos/yolo && ./download_yolo.sh
-
-# Build trt engine
-cd /tensorrt_demos/yolo
-
-for model in ${YOLO_MODELS//,/ }
-do
-    python3 yolo_to_onnx.py -m ${model}
-    python3 onnx_to_tensorrt.py -m ${model}
-    cp /tensorrt_demos/yolo/${model}.trt ${OUTPUT_FOLDER}/${model}.trt;
-done