Comments (7)
xinsuinizhuan
commented on May 14, 2024
1
另外，预处理和解码都是在cpu上跑的，预处理和后处理优化完后，可以跑的更快。
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Linaom1214
commented on May 14, 2024
像大神的：https://github.com/wang-xinyu/tensorrtx/tree/master/yolov5
本项目就有C++实现呀，tensorrtx 是真大佬，我这是采用onnx到trt 本质上和tensorRT Pro的实现方式一样
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Linaom1214
commented on May 14, 2024
另外，推理和解码都是在cpu上跑的，预处理和后处理，需要优化的地方还比较多。
这里只是简单的代码实现，您可以基于此二次开发
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Linaom1214
commented on May 14, 2024
另外，推理和解码都是在cpu上跑的，预处理和后处理，需要优化的地方还比较多。
这里只是简单的代码实现，您可以基于此二次开发
补充一句，这里的已经将解码过程放在了模型里，后处理只有nms
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Linaom1214
commented on May 14, 2024
Jun
更新了新的end2end 方案，无需第三库更简洁高效，enjoy it!
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xinsuinizhuan
commented on May 14, 2024
Jun
更新了新的end2end 方案，无需第三库更简洁高效，enjoy it!
地址发一下！
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Linaom1214
commented on May 14, 2024
Jun
更新了新的end2end 方案，无需第三库更简洁高效，enjoy it!地址发一下！
https://github.com/Linaom1214/TensorRT-For-YOLO-Series/blob/main/Examples.ipynb
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Related Issues (20)
- YOLOv7 Tensorrt converted model inference is equal to PyTorch model HOT 3
- int8 vs fp16 加速倍数能有多少？ HOT 1
- How to use engine in a process or a thread HOT 4
- how to deploy in multiple nvidia card, such as a computer with 8 3060 card?
- Add dynamic batch support for converting from onnx to .engine?
- auto in_dims = engine->getBindingDimensions(engine->getBindingIndex("image_arrays")); HOT 1
- En715 Jetson xaiver Nx Yolov7.trt Not detect HOT 2
- yolov7，official，int8，onnx-> trt报错 HOT 3
- c++ endtoend 关于预测的置信度绘制 HOT 4
- memory leak: Destroy function does not work
- Detection duplicates with fp16 on Jetson Nano (TensorRT v8.2.1.8) HOT 2
- Support for windows?
- License? HOT 4
- 关于V8 tensorrt 出现乱框的情况 HOT 33
- TensorRT Conversion Issue "TypeError: pybind11::init(): factory function returned nullptr" HOT 2
- yolox 自己训练的模型 trt推理 位置不对 HOT 1
- int8量化的时候，输入是多个，怎么修改呢？ calib_shape = [calib_batch_size] + list(inputs[0].shape[1:])不对吧 HOT 4
- Error Code 1: Serialization (Serialization assertion creator failed.Cannot deserialize plugin since corresponding IPluginCreator not found in Plugin Registry) HOT 2
- wrong confidence score (negative confidence score) on Jetson Nano inference HOT 3
- usage example for image_batch.py HOT 2
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