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add converter to generate yolo4 models
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converters/yolo4/Dockerfile.l4t.tf15
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converters/yolo4/Dockerfile.l4t.tf15
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FROM nvcr.io/nvidia/l4t-tensorflow:r32.6.1-tf1.15-py3
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RUN apt-get update && apt-get install -y git sudo
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RUN git clone https://github.com/jkjung-avt/tensorrt_demos.git /tensorrt_demos
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RUN cd /tensorrt_demos/yolo && ./install_pycuda.sh
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RUN apt-get update && apt-get install -y cmake build-essential unzip
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ADD install_protobuf.sh /install_protobuf.sh
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RUN /install_protobuf.sh
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RUN pip3 install onnx==1.4.1
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RUN cd /tensorrt_demos/yolo && ./download_yolo.sh
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ADD run.sh /run.sh
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converters/yolo4/README.md
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converters/yolo4/README.md
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Following the https://github.com/jkjung-avt/tensorrt_demos#demo-5-yolov4
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A build.sh file will convert pre-trained yolov3 and yolov4 models through ONNX to TensorRT engines. The implementation with a "yolo_layer" plugin has been updated to speed up inference time of the yolov3/yolov4 models.
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Current "yolo_layer" plugin implementation is based on TensorRT's IPluginV2IOExt. It only works for TensorRT 6+. "yolo_layer" developed plugin by referencing similar plugin code by wang-xinyu and dongfangduoshou123. So big thanks to both of them.
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Output will be copied to the ./model folder
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## Available models
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| TensorRT engine | mAP @<br>IoU=0.5:0.95 | mAP @<br>IoU=0.5 | FPS on Nano |
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|:------------------------|:---------------------:|:------------------:|:-----------:|
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| yolov3-tiny-288 (FP16) | 0.077 | 0.158 | 35.8 |
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| yolov3-tiny-416 (FP16) | 0.096 | 0.202 | 25.5 |
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| yolov3-288 (FP16) | 0.331 | 0.601 | 8.16 |
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| yolov3-416 (FP16) | 0.373 | 0.664 | 4.93 |
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| yolov3-608 (FP16) | 0.376 | 0.665 | 2.53 |
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| yolov3-spp-288 (FP16) | 0.339 | 0.594 | 8.16 |
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| yolov3-spp-416 (FP16) | 0.391 | 0.664 | 4.82 |
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| yolov3-spp-608 (FP16) | 0.410 | 0.685 | 2.49 |
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| yolov4-tiny-288 (FP16) | 0.179 | 0.344 | 36.6 |
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| yolov4-tiny-416 (FP16) | 0.196 | 0.387 | 25.5 |
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| yolov4-288 (FP16) | 0.376 | 0.591 | 7.93 |
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| yolov4-416 (FP16) | 0.459 | 0.700 | 4.62 |
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| yolov4-608 (FP16) | 0.488 | 0.736 | 2.35 |
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| yolov4-csp-256 (FP16) | 0.336 | 0.502 | 12.8 |
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| yolov4-csp-512 (FP16) | 0.436 | 0.630 | 4.26 |
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| yolov4x-mish-320 (FP16) | 0.400 | 0.581 | 4.79 |
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| yolov4x-mish-640 (FP16) | 0.470 | 0.668 | 1.46 |
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Please update frigate/converters/yolo4/assets/run.sh to add necessary models
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Note:
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This will consume pretty significant amound of memory. You might consider extending swap on Jetson Nano
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Usage:
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cd ./frigate/converters/yolo4/
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./build.sh
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converters/yolo4/assets/install_protobuf.sh
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converters/yolo4/assets/install_protobuf.sh
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#!/bin/bash
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set -e
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folder=${HOME}/src
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mkdir -p $folder
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echo "** Install requirements"
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sudo apt-get install -y autoconf libtool
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echo "** Download protobuf-3.8.0 sources"
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cd $folder
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if [ ! -f protobuf-python-3.8.0.zip ]; then
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wget https://github.com/protocolbuffers/protobuf/releases/download/v3.8.0/protobuf-python-3.8.0.zip
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fi
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if [ ! -f protoc-3.8.0-linux-aarch_64.zip ]; then
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wget https://github.com/protocolbuffers/protobuf/releases/download/v3.8.0/protoc-3.8.0-linux-aarch_64.zip
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fi
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echo "** Install protoc"
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unzip protobuf-python-3.8.0.zip
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unzip protoc-3.8.0-linux-aarch_64.zip -d protoc-3.8.0
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sudo cp protoc-3.8.0/bin/protoc /usr/local/bin/protoc
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echo "** Build and install protobuf-3.8.0 libraries"
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export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp
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cd protobuf-3.8.0/
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./autogen.sh
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./configure --prefix=/usr/local
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make -j$(nproc)
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make check
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sudo make install
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sudo ldconfig
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echo "** Update python3 protobuf module"
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# remove previous installation of python3 protobuf module
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sudo apt-get install -y python3-pip
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sudo pip3 uninstall -y protobuf
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sudo pip3 install Cython
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cd python/
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python3 setup.py build --cpp_implementation
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python3 setup.py test --cpp_implementation
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sudo python3 setup.py install --cpp_implementation
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echo "** Build protobuf-3.8.0 successfully"
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converters/yolo4/assets/run.sh
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converters/yolo4/assets/run.sh
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#!/bin/bash
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set -xe
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cd /tensorrt_demos/plugins && make
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cd /tensorrt_demos/yolo
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for model in yolov4-tiny-288 \
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yolov4-tiny-416 \
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yolov4-288 \
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yolov4-416 ; do
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python3 yolo_to_onnx.py -m ${model}
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python3 onnx_to_tensorrt.py -m ${model}
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cp /tensorrt_demos/yolo/${model}.trt /model/${model}.trt
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done
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converters/yolo4/build.sh
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converters/yolo4/build.sh
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#!/bin/bash
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mkdir -p $(pwd)/model
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docker build --tag models.yolo4 --file ./Dockerfile.l4t.tf15 ./assets/
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sudo docker run --rm -it --name models.yolo4 \
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--mount type=tmpfs,target=/tmp/cache,tmpfs-size=1000000000 \
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-v $(pwd)/model:/model:rw \
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-v /tmp/argus_socket:/tmp/argus_socket \
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-e NVIDIA_VISIBLE_DEVICES=all \
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-e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video \
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--runtime=nvidia \
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--privileged \
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models.yolo4 /run.sh
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