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README.md

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#
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##
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## B站同步视频讲解
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- https://www.bilibili.com/video/BV1Xw411f7FW
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- 相关PPTX下载:http://zifuture.com:1556/fs/sxai/tensorRT.pptx
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3. TensorRT.vcxproj文件中,修改`<Import Project="$(VCTargetsPath)\BuildCustomizations\CUDA 10.0.targets" />`为你配置的CUDA路径
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4. TensorRT.vcxproj文件中,修改`<CodeGeneration>compute_61,sm_61</CodeGeneration>`为你显卡配备的计算能力
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- 根据型号参考这里:https://developer.nvidia.com/zh-cn/cuda-gpus#compute
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5. 配置依赖,或者下载依赖到lean中。配置VC++目录->包含目录和引用目录
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5. 配置依赖或者下载依赖到lean中。配置VC++目录->包含目录和引用目录
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6. 配置环境,调试->环境,设置PATH路径
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7. 编译并运行案例
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bboxes = yolo.commit(image).get()
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```
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- Pytorch无缝对接
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- Pytorch的无缝对接
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```python
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model = models.resnet18(True).eval().to(device)
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trt_model = tp.convert_torch_to_trt(model, input)
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// 加载图像
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auto image = imread("demo.jpg");
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// 获取模型的输入和输出tensor节点,可以根据名字或者索引获取第几个
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// 获取模型的输入和输出tensor节点,可以根据名字或者索引获取具体第几个
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auto input = engine->input(0);
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auto output = engine->output(0);
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