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build_triton_engine.sh
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build_triton_engine.sh
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#!/bin/bash
IS_JETSON_PLATFORM=`uname -i | grep aarch64`
export PATH=$PATH:/usr/src/tensorrt/bin
if [ ! ${IS_JETSON_PLATFORM} ]; then
wget --content-disposition 'https://api.ngc.nvidia.com/v2/resources/org/nvidia/team/tao/tao-converter/v5.1.0_8.6.3.1_x86/files?redirect=true&path=tao-converter' -O tao-converter
else
wget --content-disposition 'https://api.ngc.nvidia.com/v2/resources/org/nvidia/team/tao/tao-converter/v5.1.0_jp6.0_aarch64/files?redirect=true&path=tao-converter' -O tao-converter
fi
chmod 755 tao-converter
#detection
#dssd
echo "Building Model dssd..."
mkdir -p models/dssd/1
trtexec --onnx=./models/dssd/dssd_resnet18_epoch_118.onnx --int8 --calib=./models/dssd/dssd_cal.bin --saveEngine=./models/dssd/1/dssd_resnet18_epoch_118.onnx_b4_gpu0_int8.engine --minShapes=Input:1x3x544x960 --optShapes=Input:2x3x544x960 --maxShapes=Input:4x3x544x960&
#efficientdet
echo "Building Model efficientdet..."
mkdir -p models/efficientdet/1
trtexec --onnx=./models/efficientdet/d0_avlp_544_960.onnx --int8 --calib=./models/efficientdet/d0_avlp_544_960.cal --saveEngine=./models/efficientdet/1/d0_avlp_544_960.onnx_b1_gpu0_int8.engine&
#frcnn
echo "Building Model frcnn..."
mkdir -p models/frcnn/1
trtexec --onnx=./models/frcnn/frcnn_kitti_resnet18.epoch24_trt8.onnx --int8 --calib=./models/frcnn/cal_frcnn_20230707_cal.bin --saveEngine=./models/frcnn/1/frcnn_kitti_resnet18.epoch24_trt8.onnx_b4_gpu0_int8.engine --minShapes=input_image:1x3x544x960 --optShapes=input_image:2x3x544x960 --maxShapes=input_image:4x3x544x960&
#retinanet
echo "Building Model retinanet..."
mkdir -p models/retinanet/1
trtexec --onnx=./models/retinanet/retinanet_resnet18_epoch_080_its.onnx --int8 --calib=./models/retinanet/retinanet_resnet18_epoch_080_its_tao5.cal --saveEngine=./models/retinanet/1/retinanet_resnet18_epoch_080_its.onnx_b4_gpu0_int8.engine --minShapes=Input:1x3x544x960 --optShapes=Input:2x3x544x960 --maxShapes=Input:4x3x544x960&
#ssd
echo "Building Model ssd..."
mkdir -p models/ssd/1
trtexec --onnx=./models/ssd/ssd_resnet18_epoch_074.onnx --int8 --calib=./models/ssd/ssd_cal.bin --saveEngine=./models/ssd/1/ssd_resnet18_epoch_074.onnx_b4_gpu0_int8.engine --minShapes=Input:1x3x544x960 --optShapes=Input:2x3x544x960 --maxShapes=Input:4x3x544x960&
#yolov3
echo "Building Model yolov3..."
mkdir -p models/yolov3/1
trtexec --onnx=./models/yolov3/yolov3_resnet18_398.onnx --int8 --calib=./models/yolov3/cal.bin.trt8517 --saveEngine=./models/yolov3/1/yolov3_resnet18_398.onnx_b4_gpu0_int8.engine --minShapes=Input:1x3x544x960 --optShapes=Input:2x3x544x960 --maxShapes=Input:4x3x544x960 --layerPrecisions=cls/Sigmoid:fp32,cls/Sigmoid_1:fp32,box/Sigmoid_1:fp32,box/Sigmoid:fp32,cls/Reshape_reshape:fp32,box/Reshape_reshape:fp32,Transpose2:fp32,sm_reshape:fp32,encoded_sm:fp32,conv_big_object:fp32,cls/mul:fp32,box/concat_concat:fp32,box/add_1:fp32,box/mul_4:fp32,box/add:fp32,box/mul_6:fp32,box/sub_1:fp32,box/add_2:fp32,box/add_3:fp32,yolo_conv1_6:fp32,yolo_conv1_6_lrelu:fp32,yolo_conv2:fp32,Resize1:fp32,yolo_conv1_5_lrelu:fp32,encoded_bg:fp32,yolo_conv4_lrelu:fp32,yolo_conv4:fp32&
#yolov4
echo "Building Model yolov4..."
mkdir -p models/yolov4/1
trtexec --onnx=./models/yolov4/yolov4_resnet18_epoch_080.onnx --best --calib=./models/yolov4/cal_trt861.bin --saveEngine=./models/yolov4/1/yolov4_resnet18_epoch_080.onnx_b4_gpu0_int8.engine --minShapes=Input:1x3x544x960 --optShapes=Input:2x3x544x960 --maxShapes=Input:4x3x544x960&
#yolov4-tiny
echo "Building Model yolov4-tiny..."
mkdir -p models/yolov4-tiny/1
trtexec --onnx=./models/yolov4-tiny/yolov4_cspdarknet_tiny_397.onnx --int8 --calib=./models/yolov4-tiny/cal.bin.trt8517 --saveEngine=./models/yolov4-tiny/1/yolov4_cspdarknet_tiny_397.onnx_b4_gpu0_int8.engine --minShapes=Input:1x3x544x960 --optShapes=Input:2x3x544x960 --maxShapes=Input:4x3x544x960&
#yolov5
#echo "Building Model yolov5..."
#mkdir -p models/yolov5/1
#/usr/src/tensorrt/bin/trtexec --fp16 --onnx=./models/yolov5/yolov5s.onnx \
# --saveEngine=./models/yolov5/1/yolov5s.onnx_b4_gpu0_fp16.engine --minShapes=images:1x3x672x672 \
# --optShapes=images:4x3x672x672 --maxShapes=images:4x3x672x672 --shapes=images:4x3x672x672 --workspace=10000
#for classifcaiton
#multi_task
#mkdir -p models/multi_task/1
#./tao-converter -k nvidia_tlt -t fp16 -b 4 -d 3,80,60 -e models/multi_task/1/abc.etlt_b4_gpu0_fp16.engine \
# models/multi_task/abc.etlt
#for instance segmentation
#for peopleSegNet
echo "Building Model peopleSegNet..."
mkdir -p models/peopleSegNet/1
./tao-converter -k nvidia_tlt -t int8 -c models/peopleSegNet/peoplesegnet_resnet50_int8.txt -b 4 -d 3,576,960 \
-e models/peopleSegNet/1/peoplesegnet_resnet50.etlt_b4_gpu0_int8.engine models/peopleSegNet/peoplesegnet_resnet50.etlt&
#for segmentation
#peopleSemSegNet
echo "Building Model peopleSemSegNet..."
mkdir -p models/peopleSemSegNet_vanilla/1
trtexec --onnx=./models/peopleSemSegNet_vanilla/peoplesemsegnet_vanilla_unet_dynamic_etlt_int8_fp16.onnx --int8 \
--calib=./models/peopleSemSegNet_vanilla/peoplesemsegnet_vanilla_unet_dynamic_etlt_int8.cache --saveEngine=./models/peopleSemSegNet_vanilla/1/peoplesemsegnet_vanilla_unet_dynamic_etlt_int8_fp16.onnx_b4_gpu0_int8.engine \
--minShapes="input_1:0":1x3x544x960 --optShapes="input_1:0":4x3x544x960 --maxShapes="input_1:0":4x3x544x960&
mkdir -p models/peopleSemSegNet_shuffle/1
trtexec --onnx=./models/peopleSemSegNet_shuffle/peoplesemsegnet_shuffleseg.onnx --int8 \
--calib=./models/peopleSemSegNet_shuffle/peoplesemsegnet_shuffleseg_cache.txt --saveEngine=./models/peopleSemSegNet_shuffle/1/peoplesemsegnet_shuffleseg.onnx_b4_gpu0_int8.engine \
--minShapes="input_2:0":1x3x544x960 --optShapes="input_2:0":4x3x544x960 --maxShapes="input_2:0":4x3x544x960&
#unet
echo "Building Model unet..."
mkdir -p models/unet/1
trtexec --onnx=./models/unet/unet_resnet18.onnx --int8 --calib=./models/unet/unet_cal.bin --saveEngine=./models/unet/1/unet_resnet18.onnx_b4_gpu0_int8.engine --minShapes="input_1:0":1x3x320x320 --optShapes="input_1:0":2x3x320x320 --maxShapes="input_1:0":4x3x320x320&
#citysemsegformer
echo "Building Model citysemsegformer..."
mkdir -p models/citysemsegformer/1 && \
trtexec --onnx=./models/citysemsegformer/citysemsegformer.onnx --fp16 \
--saveEngine=./models/citysemsegformer/1/citysemsegformer.onnx_b1_gpu0_fp16.engine \
--minShapes="input":1x3x1024x1820 --optShapes="input":1x3x1024x1820 --maxShapes="input":1x3x1024x1820&
#bodypose2d
echo "Building Model bodypose2d..."
mkdir -p models/bodypose2d/1
./tao-converter -k nvidia_tlt -t fp16 -p input_1:0,1x288x384x3,32x288x384x3,32x288x384x3 \
-e models/bodypose2d/1/model.etlt_b32_gpu0_fp16.engine models/bodypose2d/model.etlt&
#gesture
echo "Building Model gesture..."
mkdir -p models/gesture/1
./tao-converter -k nvidia_tlt -t int8 -c models/gesture/int8_calibration.txt -p \
input_1,1x3x160x160,8x3x160x160,8x3x160x160 -e models/gesture/1/gesture.etlt_b8_gpu0_int8.engine models/gesture/gesture.etlt&
#facenet
echo "Building Model facenet..."
mkdir -p models/facenet/1
./tao-converter -k nvidia_tlt -t int8 -c models/facenet/facenet_cal.txt -b 16 -d 3,416,736 \
-e models/facenet/1/facenet.etlt_b16_gpu0_int8.engine models/facenet/facenet.etlt&
#gesture
echo "Building Model faciallandmark..."
mkdir -p models/faciallandmark/1
./tao-converter -k nvidia_tlt -t int8 -c models/faciallandmark/fpenet_cal.txt -b 4 -p \
input_face_images,1x1x80x80,2x1x80x80,4x1x80x80 -e models/faciallandmark/1/faciallandmark.etlt_b4_gpu0_int8.engine models/faciallandmark/faciallandmark.etlt&
#peoplenet_transformer
echo "Building Model peoplenet_transformer"
mkdir -p models/peoplenet_transformer/1
trtexec --onnx=./models/peoplenet_transformer/resnet50_peoplenet_transformer_op17.onnx --fp16 \
--saveEngine=./models/peoplenet_transformer/1/resnet50_peoplenet_transformer_op17.onnx_b1_gpu0_fp16.engine \
--minShapes="inputs":1x3x544x960 --optShapes="inputs":1x3x544x960 --maxShapes="inputs":1x3x544x960&
#retail_object_detection_100
echo "Building Model retail_object_detection_100"
mkdir -p models/retail_object_detection_100/1
trtexec --minShapes=input:1x416x416x3 --optShapes=input:1x416x416x3 --maxShapes=input:1x416x416x3 \
--fp16 --saveEngine=models/retail_object_detection_100/1/retail_detector_100.onnx_b1_gpu0_fp16.engine \
--onnx=models/retail_object_detection_100/retail_detector_100.onnx --workspace=100000&
#reidentificationnet
echo "Building Model reidentificationnet"
mkdir -p models/reidentificationnet/1
trtexec --minShapes=input:1x3x256x128 --optShapes=input:8x3x256x128 --maxShapes=input:16x3x256x128 \
--fp16 --saveEngine=models/reidentificationnet/1/resnet50_market1501_aicity156.onnx_b16_gpu0_fp16.engine \
--onnx=models/reidentificationnet/resnet50_market1501_aicity156.onnx --workspace=100000&
#retail_object_detection_binary_effdet
echo "Building Model retail_object_detection_binary_effdet"
mkdir -p models/retail_object_detection_binary_effdet/1
trtexec --onnx=models/retail_object_detection_binary_effdet/retail_detector_binary.onnx \
--saveEngine=models/retail_object_detection_binary_effdet/1/retail_detector_binary.onnx_b1_gpu0_fp16.engine \
--minShapes=input:1x416x416x3 --optShapes=input:1x416x416x3 --maxShapes=input:1x416x416x3 --workspace=102400 \
--fp16 --sparsity=enable
#retail_object_detection_binary_dino
echo "Building Model retail_object_detection_binary_dino"
mkdir -p models/retail_object_detection_binary_dino/1
trtexec --onnx=models/retail_object_detection_binary_dino/retail_object_detection_dino_binary.onnx \
--saveEngine=models/retail_object_detection_binary_dino/1/retail_object_detection_dino_binary.onnx_b1_gpu0_fp32.engine \
--minShapes=inputs:1x3x544x960 --optShapes=inputs:1x3x544x960 --maxShapes=inputs:1x3x544x960 --workspace=102400 \
--sparsity=enable&
#retail_object_detection_meta
echo "Building Model retail_object_detection_meta"
mkdir -p models/retail_object_detection_meta/1
trtexec --onnx=models/retail_object_detection_meta/retail_object_detection_dino_meta.onnx \
--saveEngine=models/retail_object_detection_meta/1/retail_object_detection_dino_meta.onnx_b1_gpu0_fp16.engine \
--minShapes=inputs:1x3x544x960 --optShapes=inputs:1x3x544x960 --maxShapes=inputs:1x3x544x960 --workspace=102400 \
--fp16 --sparsity=enable&
#retail_object_recognition
echo "Building Model retail_object_recognition"
mkdir -p models/retail_object_recognition/1
trtexec --onnx=models/retail_object_recognition/retail_object_recognition.onnx \
--saveEngine=models/retail_object_recognition/1/retail_object_recognition.onnx_b16_gpu0_fp16.engine \
--minShapes=inputs:1x3x224x224 --optShapes=inputs:16x3x224x224 --maxShapes=inputs:16x3x224x224 --workspace=102400 \
--fp16 --sparsity=enable&
#peoplenet
echo "Building Model peoplenet..."
mkdir -p models/peoplenet/1
trtexec --onnx=./models/peoplenet/resnet34_peoplenet_int8.onnx --int8 \
--calib=./models/peoplenet/resnet34_peoplenet_int8.txt --saveEngine=./models/peoplenet/1/resnet34_peoplenet_int8.onnx_b1_gpu0_int8.engine \
--minShapes="input_1:0":1x3x544x960 --optShapes="input_1:0":1x3x544x960 --maxShapes="input_1:0":1x3x544x960&
#poseclassificationnet
echo "Building Model poseclassificationnet..."
mkdir -p models/poseclassificationnet/1
trtexec --onnx=./models/poseclassificationnet/st-gcn_3dbp_nvidia.onnx --fp16 \
--saveEngine=./models/poseclassificationnet/1/st-gcn_3dbp_nvidia.onnx_b4_gpu0_fp16.engine \
--minShapes="input":1x3x300x34x1 --optShapes="input":4x3x300x34x1 --maxShapes="input":4x3x300x34x1&
#bodypose3dnet
echo "Building Model bodypose3dnet..."
mkdir -p models/bodypose3dnet/1
./tao-converter -k tlt_encode -t fp16 -d 3,256,192 -b 8 \
-p input0,1x3x256x192,8x3x256x192,8x3x256x192 \
-p k_inv,1x3x3,8x3x3,8x3x3 \
-p t_form_inv,1x3x3,8x3x3,8x3x3 \
-p scale_normalized_mean_limb_lengths,1x36,8x36,8x36 \
-p mean_limb_lengths,1x36,8x36,8x36 \
-e models/bodypose3dnet/1/bodypose3dnet_accuracy.etlt_b8_gpu0_fp16.engine \
models/bodypose3dnet/bodypose3dnet_accuracy.etlt&