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

Rearrange tensorrt files into a docker support folder.

Dockerfile

@@ -256,16 +256,21 @@ COPY --from=rootfs / /
 # Build TensorRT-specific library
 FROM nvcr.io/nvidia/tensorrt:23.05-py3 AS trt-deps
 
-RUN --mount=type=bind,source=docker/tensorrt_libyolo.sh,target=/tensorrt_libyolo.sh \
+RUN --mount=type=bind,source=docker/support/tensorrt_detector/tensorrt_libyolo.sh,target=/tensorrt_libyolo.sh \
     /tensorrt_libyolo.sh
 
 # Frigate w/ TensorRT Support as separate image
 FROM frigate AS frigate-tensorrt
 
+ENV YOLO_MODELS="yolov7-tiny-416"
+
 COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
+COPY --from=trt-deps /usr/local/src/tensorrt_demos /usr/local/src/tensorrt_demos
+COPY docker/support/tensorrt_detector/rootfs/ /
+
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
     pip3 install -U /deps/trt-wheels/*.whl && \
-    ln -s libnvrtc.so.11.2 /usr/local/lib/python3.9/dist-packages/nvidia/cuda_nvrtc/lib/libnvrtc.so && \
+    ln -s libnvrtc.so.12.1 /usr/local/lib/python3.9/dist-packages/nvidia/cuda_nvrtc/lib/libnvrtc.so && \
     ldconfig
 
 # Dev Container w/ TRT

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

@@ -0,0 +1,45 @@
+#!/command/with-contenv bash
+# shellcheck shell=bash
+# Prepare the logs folder for s6-log
+
+set -o errexit -o nounset -o pipefail
+
+OUTPUT_FOLDER?=/media/frigate/model_cache/tensorrt
+
+# Create output folder
+mkdir -p ${OUTPUT_FOLDER}
+
+FIRST_MODEL=true
+MODEL_CONVERT=""
+
+for model in ${YOLO_MODELS//,/ }
+do
+    if ![[ -f ${OUTPUT_FOLDER}/${model}.trt ]]; then
+        if [[FIRST_MODEL == true]]; then
+            MODEL_CONVERT="${model}"
+            FIRST_MODEL=false
+        else
+            MODEL_CONVERT+=",{$model}"
+        fi
+    fi
+done
+
+if [[${MODEL_CONVERT} == ""]]; then
+    echo "No models to convert."
+    exit 0;
+fi
+
+echo "Generating the following TRT Models: ${YOLO_MODELS}"
+
+# Build trt engine
+cd /usr/local/src/tensorrt_demos/yolo
+
+# Download yolo weights
+./download_yolo.sh $MODEL_CONVERT
+
+for model in ${MODEL_CONVERT//,/ }
+do
+    python3 yolo_to_onnx.py -m ${model}
+    python3 onnx_to_tensorrt.py -m ${model}
+    cp /tmp/tensorrt_demos-conditional_download/yolo/${model}.trt ${OUTPUT_FOLDER}/${model}.trt;
+done

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

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

docker/support/tensorrt_detector/rootfs/etc/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 +1,16 @@
+#!/bin/bash
+
+set -euxo pipefail
+
+# 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
+cp libyolo_layer.so ../yolo/libyolo_layer.so
+
+# Store yolo scripts for later conversion
+cd ../
+mkdir -p /usr/local/src/tensorrt_demos
+cp -a yolo /usr/local/src/tensorrt_demos/

docker/tensorrt_libyolo.sh

@@ -1,10 +0,0 @@
-#!/bin/bash
-
-set -euxo pipefail
-
-# Clone tensorrt_demos repo
-git clone --depth 1 https://github.com/yeahme49/tensorrt_demos.git
-
-# Build libyolo
-cd ./tensorrt_demos/plugins && make all
-cp libyolo_layer.so /usr/local/lib/libyolo_layer.so

docker/tensorrt_models.sh

@@ -1,32 +0,0 @@
-#!/bin/bash
-
-set -euxo pipefail
-
-OUTPUT_FOLDER=/trt-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}
-
-# Clone tensorrt_demos repo
-# git clone --depth 1 https://github.com/NateMeyer/tensorrt_demos.git -b conditional_download /tmp/tensorrt_demos
-cd /tmp/ && wget -qO tensorrt_demos.zip https://github.com/NateMeyer/tensorrt_demos/archive/refs/heads/conditional_download.zip
-unzip tensorrt_demos.zip
-
-cp /usr/local/lib/libyolo_layer.so /tmp/tensorrt_demos-conditional_download/plugins/libyolo_layer.so
-
-# Download yolo weights
-cd /tmp/tensorrt_demos-conditional_download/yolo && ./download_yolo.sh $YOLO_MODELS
-
-# Build trt engine
-cd /tmp/tensorrt_demos-conditional_download/yolo
-
-for model in ${YOLO_MODELS//,/ }
-do
-    python3 yolo_to_onnx.py -m ${model}
-    python3 onnx_to_tensorrt.py -m ${model}
-    cp /tmp/tensorrt_demos-conditional_download/yolo/${model}.trt ${OUTPUT_FOLDER}/${model}.trt;
-done
-
-# Cleanup repo
-rm -r /tmp/tensorrt_demos-conditional_download