58 lines
1.6 KiB
Bash
58 lines
1.6 KiB
Bash
# python3 convert_yolov5_p6_rknn.py \
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# --pt /data6/zhangw/yolov5/runs/train/YNDC_20251010_yolov5_optimize/weights/best.pt \
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# --onnx /data6/zhangw/yolov5/runs/train/YNDC_20251010_yolov5_optimize/weights/yolov5s6_p6.onnx \
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# --rknn /data6/zhangw/yolov5/runs/train/YNDC_20251010_yolov5_optimize/weights/yolov5s6_p6.rknn \
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# --platform rk3576 \
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# --img-size 640 \
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# --classes 5
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#!/bin/bash
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set -e
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# -------------------------------
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# 路径配置
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# -------------------------------
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PT_PATH="/data6/zhangw/yolov5/runs/train/YNDC_20251010_yolov5_optimize/weights/best.pt"
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ONNX_PATH="/data6/zhangw/yolov5/runs/train/YNDC_20251010_yolov5_optimize/weights/best.onnx"
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RKNN_PATH="/data6/zhangw/Projects/output_rknn/yolov5_best.rknn"
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IMG_SIZE=640
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YOLOV5_RKNN_PY="/data6/zhangw/Projects/rknn_model_zoo/examples/yolov5/python/yolov5.py"
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# -------------------------------
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echo "====== Step 1: Export ONNX ======"
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# -------------------------------
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python export.py \
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--weights $PT_PATH \
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--include onnx \
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--imgsz $IMG_SIZE \
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--opset 12 \
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--device cpu
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echo "✔ ONNX 导出成功：$ONNX_PATH"
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# -------------------------------
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echo "====== Step 2: Simplify ONNX ======"
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# -------------------------------
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python -m onnxsim $ONNX_PATH $ONNX_PATH
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echo "✔ ONNX 简化完成"
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# -------------------------------
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echo "====== Step 3: Convert to RKNN ======"
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# -------------------------------
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python3 $YOLOV5_RKNN_PY \
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--model_path $ONNX_PATH \
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--img_size $IMG_SIZE \
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--rknn_out $RKNN_PATH \
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--dataset /data6/zhangw/yolov5/data/coco128.yaml
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echo "✔ RKNN 模型已生成：$RKNN_PATH"
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echo "全部完成！"
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