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导出int8的完整流程 #28
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支持,该 repo 沿用的是 tensorRT_Pro 中的 INT8 量化方案,采用的是 TRT C++ API 实现 PTQ 量化,因此你只需要在量化时指定 calibration dataset 路径即可,使用示例如下: TRT::compile(
mode, // FP32、FP16、INT8
test_batch_size, // max batch size
onnx_file, // source
model_file, // save to
{},
int8process,
"inference",
"calibration_dataset", // 指定校准数据集路径
"calib.entropy.cache", // 指定保存的校准文件名
"Calibrator::Entropy", // 指定校准器,目前仅支持 Entropy 和 MinMax
); |
谢谢,现在提示这个错误,我用的tensorrt版本是tensorrt-8.6.1,是onnx-tensorrt版本不对是吗 [2024-06-10 12:27:13][error][trt_builder.cpp:30]:NVInfer: /home/yr/yr/code/cv/object_detection/tensorRT_Pro-YOLOv8/src/tensorRT/onnx_parser/ModelImporter.cpp:739: --- End node --- |
算子解析问题,提示说这个版本的 TensorRT 不支持动态 shape 的 GatherElements 算子,默认使用的 onnxparser 版本是 8.0 的,你可能需要手动替换 onnx_parser 具体可以参考:RT-DETR推理详解及部署实现 |
好的谢谢 |
你好,文档中没有看到转换为int8并实现推理的完整流程,能否支持该功能
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