tensorrt deeplabv3+ 部署踩坑
deeplabv3+模型转onnx模型
一开始我用的paddlepaddle的实现，转换成onnx模型再转trt模型推理需要2s左右
后面我用得pytorch的deeplabv3+实现
https://github.com/yassouali/pytorch-segmentation
转换成onnx模型再转trt模型推理也在2s左右
后面参考tensorrtx项目和一个识别指针的项目使用c++手写deeplabv3+网络，然后再用gen_wrt.py把权重导出来再手动塞到c++手写的模型中，发现也是2s左右。
最后使用trt的打印每层执行时间发现在下采样部分特别慢，后面我把python里的和c++里的上采样使用近邻上采样替换掉双线性下采样后就快了，大概在20ms左右。
一开始这个应该是trt不支持双线性下采样，转过来的onnx模型使用卷积操作代替上采样所以很慢，网上百度，trt实现双线性下采样需要自己写插件，后面查看文档发现kLINEAR有写支持bilinear，但是我没找到api指定bilinear。
nvinfer1::ResizeMode只支持两种参数
trt文档 https://docs.nvidia.com/deeplearning/tensorrt/api/c_api/classnvinfer1_1_1_i_profiler.html
双线性上采样插件代码参考
/*
* Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*/
#include "ResizeBilinear.hpp"
#include
#include
// TODO: Move this to a common header
inline bool is_CHW(nvinfer1::Dims const& dims) {
return (dims.nbDims == 3 &&
dims.type[0] == nvinfer1::DimensionType::kCHANNEL &&
dims.type[1] == nvinfer1::DimensionType::kSPATIAL &&
dims.type[2] == nvinfer1::DimensionType::kSPATIAL);
}
nvinfer1::Dims ResizeBilinearPlugin::getOutputDimensions(int index,
const nvinfer1::Dims *inputDims,
int nbInputs) {
assert(nbInputs == 1);
nvinfer1::Dims const& input = inputDims[0];
assert(is_CHW(input));
assert(_ndims == 2);
assert(index == 0);
nvinfer1::Dims output;
output.nbDims = input.nbDims;
int s = 0;
for( int d=0; dgetOutputDimensions(0, &this->getInputDims(0), 1);
assert(is_CHW(this->getInputDims(0)));
assert(is_CHW(_output_dims));
assert(_ndims == 2);
return 0;
}
template
__global__
void resize_bilinear_kernel_2d(int nbatch,
float2 scale,
int2 isize,
int2 osize,
Data const* idata, int istride, int ibatchstride,
Data* odata, int ostride, int obatchstride) {
int x0 = threadIdx.x + blockIdx.x * blockDim.x;
int y0 = threadIdx.y + blockIdx.y * blockDim.y;
int z0 = blockIdx.z;
for( int batch=z0; batchgetInputDims(0);
int nchan = input_dims.d[0];
switch( _ndims ) {
case 2: {
float2 scale = {_scale[1], _scale[0]};
int2 isize = {input_dims.d[2], input_dims.d[1]};
int2 osize = {_output_dims.d[2], _output_dims.d[1]};
int istride = input_dims.d[2];
int ostride = _output_dims.d[2];
int ibatchstride = input_dims.d[1] * istride;
int obatchstride = _output_dims.d[1] * ostride;
dim3 block(16, 16);
dim3 grid((osize.x - 1) / block.x + 1,
(osize.y - 1) / block.y + 1,
std::min(batchSize * nchan, 65535));
if (getDataType()==nvinfer1::DataType::kFLOAT) {
resize_bilinear_kernel_2d<<>>
(batchSize * nchan, scale, isize, osize,
static_cast( inputs[0]), istride, ibatchstride,
static_cast(outputs[0]), ostride, obatchstride);
} else {
resize_bilinear_kernel_2d<<>>
(batchSize * nchan, scale, isize, osize,
static_cast<__half const*>( inputs[0]), istride, ibatchstride,
static_cast<__half* >(outputs[0]), ostride, obatchstride);
}
return cudaGetLastError() != cudaSuccess;
}
default: return -1;
}
}
https://github.com/chenjun2hao/onnx-tensorrt/commit/b5f9e9219cbc21ab1262018f42f12d8319217500