Merge pull request #444 from PINTO0309/YOLOv9-shoulder-elbow-knee

YOLOv9 shoulder elbow knee

463_YOLOv9-Shoulder-Elbow-Knee/download_c.sh

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+#!/bin/bash
+
+curl "https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/463_YOLOv9-Shoulder-Elbow-Knee/resources_c.tar.gz" -o resources.tar.gz
+tar -zxvf resources.tar.gz
+rm resources.tar.gz
+
+echo Download finished.

463_YOLOv9-Shoulder-Elbow-Knee/download_e_nopost.sh

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+#!/bin/bash
+
+curl "https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/463_YOLOv9-Shoulder-Elbow-Knee/resources_e_nopost.tar.gz" -o resources.tar.gz
+tar -zxvf resources.tar.gz
+rm resources.tar.gz
+
+echo Download finished.

463_YOLOv9-Shoulder-Elbow-Knee/download_e_withpost.sh

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+#!/bin/bash
+
+curl "https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/463_YOLOv9-Shoulder-Elbow-Knee/resources_e_withpost.tar.gz" -o resources.tar.gz
+tar -zxvf resources.tar.gz
+rm resources.tar.gz
+
+echo Download finished.

463_YOLOv9-Shoulder-Elbow-Knee/download_s.sh

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+#!/bin/bash
+
+curl "https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/463_YOLOv9-Shoulder-Elbow-Knee/resources_s.tar.gz" -o resources.tar.gz
+tar -zxvf resources.tar.gz
+rm resources.tar.gz
+
+echo Download finished.

463_YOLOv9-Shoulder-Elbow-Knee/download_t.sh

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+#!/bin/bash
+
+curl "https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/463_YOLOv9-Shoulder-Elbow-Knee/resources_t.tar.gz" -o resources.tar.gz
+tar -zxvf resources.tar.gz
+rm resources.tar.gz
+
+echo Download finished.

463_YOLOv9-Shoulder-Elbow-Knee/url.txt

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+https://github.com/WongKinYiu/yolov9
+
+https://github.com/PINTO0309/onnx2tf
+https://github.com/PINTO0309/simple-onnx-processing-tools
+https://github.com/PINTO0309/tflite-input-output-rewriter
+

463_YOLOv9-Shoulder-Elbow-Knee/yolov9/export_onnx.sh

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+TYPE=e
+# RELU= or RELU=-relu
+RELU=
+RELUS=$(echo ${RELU} | sed 's/-/_/g')
+MODEL_NAME=yolov9_${TYPE}_sek
+SUFFIX="0100_1x3x"
+# best-t.pt
+# best-t-relu.pt
+# best-e.pt
+# best-e-relu.pt
+MODEL_PATH=best-${TYPE}-sek.pt #best-${TYPE}${RELU}.pt
+
+RESOLUTIONS=(
+    "128 160"
+    "128 256"
+    "192 320"
+    "192 416"
+    "192 640"
+    "192 800"
+    "256 320"
+    "256 416"
+    "256 448"
+    "256 640"
+    "256 800"
+    "256 960"
+    "288 1280"
+    "288 480"
+    "288 640"
+    "288 800"
+    "288 960"
+    "320 320"
+    "384 1280"
+    "384 480"
+    "384 640"
+    "384 800"
+    "384 960"
+    "416 416"
+    "480 1280"
+    "480 640"
+    "480 800"
+    "480 960"
+    "512 512"
+    "512 640"
+    "512 896"
+    "544 1280"
+    "544 800"
+    "544 960"
+    "640 640"
+    "736 1280"
+    "576 1024"
+    "384 672"
+)
+
+python reparameterization${RELUS}.py \
+--type ${TYPE} \
+--cfg ./models/detect/gelan-${TYPE}${RELU}.yaml \
+--weights ${MODEL_PATH} \
+--save ${MODEL_NAME}${RELUS}.pt
+
+for((i=0; i<${#RESOLUTIONS[@]}; i++))
+do
+    RESOLUTION=(`echo ${RESOLUTIONS[i]}`)
+    H=${RESOLUTION[0]}
+    W=${RESOLUTION[1]}
+
+    python export.py \
+    --data data/original.yaml \
+    --weights ${MODEL_NAME}${RELUS}.pt \
+    --imgsz ${H} ${W} \
+    --batch-size 1 \
+    --device cpu \
+    --opset 13 \
+    --include onnx
+
+    mv ${MODEL_NAME}${RELUS}.onnx ${MODEL_NAME}${RELUS}_${SUFFIX}${H}x${W}.onnx
+
+    sng4onnx \
+    --input_onnx_file_path ${MODEL_NAME}${RELUS}_${SUFFIX}${H}x${W}.onnx \
+    --output_onnx_file_path ${MODEL_NAME}${RELUS}_${SUFFIX}${H}x${W}.onnx
+
+    onnxsim ${MODEL_NAME}${RELUS}_${SUFFIX}${H}x${W}.onnx ${MODEL_NAME}${RELUS}_${SUFFIX}${H}x${W}.onnx
+    onnxsim ${MODEL_NAME}${RELUS}_${SUFFIX}${H}x${W}.onnx ${MODEL_NAME}${RELUS}_${SUFFIX}${H}x${W}.onnx
+    onnxsim ${MODEL_NAME}${RELUS}_${SUFFIX}${H}x${W}.onnx ${MODEL_NAME}${RELUS}_${SUFFIX}${H}x${W}.onnx
+done
+
+python export.py \
+--data data/original.yaml \
+--weights ${MODEL_NAME}${RELUS}.pt \
+--device cpu \
+--opset 13 \
+--include onnx \
+--dynamic
+mv ${MODEL_NAME}${RELUS}.onnx ${MODEL_NAME}${RELUS}_Nx3HxW.onnx
+onnxsim ${MODEL_NAME}${RELUS}_Nx3HxW.onnx ${MODEL_NAME}${RELUS}_Nx3HxW.onnx