diff --git a/unet/CMakeLists.txt b/unet/CMakeLists.txt index 6194e7b..441e0ad 100644 --- a/unet/CMakeLists.txt +++ b/unet/CMakeLists.txt @@ -13,8 +13,8 @@ include_directories(/usr/local/cuda/include/) link_directories(/usr/local/cuda/lib64/) # tensorrt -include_directories(/workspace/TensorRT-8.4.1.5/include/) -link_directories(/workspace/TensorRT-8.4.1.5/lib/) +include_directories(/workspace/TensorRT-7.2.3.4/include/) +link_directories(/workspace/TensorRT-7.2.3.4/lib/) # opencv library find_package(OpenCV) diff --git a/unet/README.md b/unet/README.md index 936ca4e..108e619 100644 --- a/unet/README.md +++ b/unet/README.md @@ -1,68 +1,61 @@ # UNet -This is a TensorRT version UNet, inspired by [tensorrtx](https://github.com/wang-xinyu/tensorrtx) and [pytorch-unet](https://github.com/milesial/Pytorch-UNet).
-You can generate TensorRT engine file using this script and customize some params and network structure based on network you trained (FP32/16 precision, input size, different conv, activation function...)
-# Requirements +Pytorch model from [Pytorch-UNet](https://github.com/milesial/Pytorch-UNet). -TensorRT 7.x or 8.x (you need to install tensorrt first)
-Python
-opencv
-cmake
+## Contributors -# Train .pth file and convert .wts + + + + -## Create env +## Requirements +Please use TensorRT 7.x. + +There is a bug with TensorRT 8.x, we are working on it. + +## Build and Run + +1. Generate .wts ``` -pip install -r requirements.txt -``` - -## Train .pth file - -Train your dataset by following [Pytorch-UNet](https://github.com/milesial/Pytorch-UNet) and generate .pth file.
- -Please set bilinear=False, i.e. `UNet(n_channels=3, n_classes=1, bilinear=False)`, because TensorRT doesn't support Upsample layer. - -## Convert .pth to .wts - -``` -cp tensorrtx/unet/gen_wts.py Pytorch-UNet/ +cp {path-of-tensorrtx}/unet/gen_wts.py Pytorch-UNet/ cd Pytorch-UNet/ -python gen_wts.py +wget https://github.com/milesial/Pytorch-UNet/releases/download/v3.0/unet_carvana_scale0.5_epoch2.pth +python gen_wts.py unet_carvana_scale0.5_epoch2.pth ``` -# Generate engine file and infer - -Build: +2. Generate TensorRT engine ``` cd tensorrtx/unet/ mkdir build cd build cmake .. make +cp {path-of-Pytorch-UNet}/unet.wts . +./unet -s ``` -Generate TensorRT engine file: -``` -unet -s -``` -Inference on images in a folder: -``` -unet -d ../samples -``` - -# Benchmark -the speed of tensorRT engine is much faster - - pytorch | TensorRT FP32 | TensorRT FP16 - ---- | ----- | ------ - 816x672 | 816x672 | 816x672 - 58ms | 43ms (batchsize 8) | 14ms (batchsize 8) -# test img +3. Run inference ``` wget https://raw.githubusercontent.com/wang-xinyu/tensorrtx/f60dcc7bec28846cd973fc95ac829c4e57a11395/unet/samples/0cdf5b5d0ce1_01.jpg +./unet -d 0cdf5b5d0ce1_01.jpg ``` -# Further development -1. add INT8 calibrator
-2. add custom plugin
+4. Check result.jpg + +

+ +

+ +# Benchmark + +Pytorch | TensorRT FP32 | TensorRT FP16 +---- | ----- | ------ +816x672 | 816x672 | 816x672 +58ms | 43ms (batchsize 8) | 14ms (batchsize 8) + +## More Information + +See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx) + diff --git a/unet/common.hpp b/unet/common.hpp index ac2ec89..eb291c5 100644 --- a/unet/common.hpp +++ b/unet/common.hpp @@ -6,7 +6,6 @@ #include #include #include -#include #include "NvInfer.h" #define CHECK(status) \ @@ -95,46 +94,5 @@ IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map& weightMap, ITensor& input, int outch, int ksize, int s, int g, std::string lname) { - Weights emptywts{DataType::kFLOAT, nullptr, 0}; - int p = ksize / 2; - IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap[lname + ".conv.weight"], emptywts); - assert(conv1); - conv1->setStrideNd(DimsHW{s, s}); - conv1->setPaddingNd(DimsHW{p, p}); - conv1->setNbGroups(g); - IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".bn", 1e-3); - - // hard_swish = x * hard_sigmoid - auto hsig = network->addActivation(*bn1->getOutput(0), ActivationType::kHARD_SIGMOID); - assert(hsig); - hsig->setAlpha(1.0 / 6.0); - hsig->setBeta(0.5); - auto ew = network->addElementWise(*bn1->getOutput(0), *hsig->getOutput(0), ElementWiseOperation::kPROD); - assert(ew); - return ew; -} - -int read_files_in_dir(const char *p_dir_name, std::vector &file_names) { - DIR *p_dir = opendir(p_dir_name); - if (p_dir == nullptr) { - return -1; - } - - struct dirent* p_file = nullptr; - while ((p_file = readdir(p_dir)) != nullptr) { - if (strcmp(p_file->d_name, ".") != 0 && - strcmp(p_file->d_name, "..") != 0) { - //std::string cur_file_name(p_dir_name); - //cur_file_name += "/"; - //cur_file_name += p_file->d_name; - std::string cur_file_name(p_file->d_name); - file_names.push_back(cur_file_name); - } - } - - closedir(p_dir); - return 0; -} - #endif + diff --git a/unet/gen_wts.py b/unet/gen_wts.py index f5fc683..b6a74b5 100644 --- a/unet/gen_wts.py +++ b/unet/gen_wts.py @@ -1,36 +1,24 @@ import torch -from torch import nn -import torchvision -import os +import sys import struct -from torchsummary import summary def main(): - print('cuda device count: ', torch.cuda.device_count()) - net = torch.load('ori_unet.pth') - net = net.to('cuda:0') - net = net.eval() - print('model: ', net) - #print('state dict: ', net.state_dict().keys()) - tmp = torch.ones(1, 3, 224, 224).to('cuda:0') - print('input: ', tmp) - out = net(tmp) + device = torch.device('cpu') + state_dict = torch.load(sys.argv[1], map_location=device) - print('output:', out) - - summary(net, (3, 224, 224)) - #return - f = open("unet.wts", 'w') - f.write("{}\n".format(len(net.state_dict().keys()))) - for k,v in net.state_dict().items(): - print('key: ', k) - print('value: ', v.shape) - vr = v.reshape(-1).cpu().numpy() - f.write("{} {}".format(k, len(vr))) - for vv in vr: - f.write(" ") - f.write(struct.pack(">f", float(vv)).hex()) - f.write("\n") + f = open("unet.wts", 'w') + f.write("{}\n".format(len(state_dict.keys()))) + for k, v in state_dict.items(): + print('key: ', k) + print('value: ', v.shape) + vr = v.reshape(-1).cpu().numpy() + f.write("{} {}".format(k, len(vr))) + for vv in vr: + f.write(" ") + f.write(struct.pack(">f", float(vv)).hex()) + f.write("\n") + f.close() if __name__ == '__main__': - main() + main() + diff --git a/unet/unet.cpp b/unet/unet.cpp index a3a6cdc..4018a03 100644 --- a/unet/unet.cpp +++ b/unet/unet.cpp @@ -4,14 +4,12 @@ #include "logging.h" #include "common.hpp" - #define DEVICE 0 -// #define USE_FP16 // comment out this if want to use FP16 +#define USE_FP32 // USE_FP32 or USE_FP16 #define CONF_THRESH 0.5 #define BATCH_SIZE 1 #define cls 2 #define BILINEAR false -using namespace nvinfer1; // stuff we know about the network and the input/output blobs static const int INPUT_H = 640; @@ -21,362 +19,283 @@ const char* INPUT_BLOB_NAME = "data"; const char* OUTPUT_BLOB_NAME = "prob"; static Logger gLogger; -cv::Mat preprocess_img(cv::Mat& img) { - int w, h, x, y; - float r_w = INPUT_W / (img.cols * 1.0); - float r_h = INPUT_H / (img.rows * 1.0); - if (r_h > r_w) { - w = INPUT_W; - h = r_w * img.rows; - x = 0; - y = (INPUT_H - h) / 2; - } - else { - w = r_h * img.cols; - h = INPUT_H; - x = (INPUT_W - w) / 2; - y = 0; - } - cv::Mat re(h, w, CV_8UC3); - cv::resize(img, re, re.size(), 0, 0, cv::INTER_CUBIC); - cv::Mat out(INPUT_H, INPUT_W, CV_8UC3, cv::Scalar(128, 128, 128)); - re.copyTo(out(cv::Rect(x, y, re.cols, re.rows))); - return out; -} +using namespace nvinfer1; ILayer* doubleConv(INetworkDefinition* network, std::map& weightMap, ITensor& input, int outch, int ksize, std::string lname, int midch) { - Weights emptywts{ DataType::kFLOAT, nullptr, 0 }; - // int p = ksize / 2; - // if (midch==NULL){ - // midch = outch; - // } - IConvolutionLayer* conv1 = network->addConvolutionNd(input, midch, DimsHW{ ksize, ksize }, weightMap[lname + ".double_conv.0.weight"], emptywts); - conv1->setStrideNd(DimsHW{ 1, 1 }); - conv1->setPaddingNd(DimsHW{ 1, 1 }); - conv1->setNbGroups(1); - IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".double_conv.1", 0); - IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU); - IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{ 3, 3 }, weightMap[lname + ".double_conv.3.weight"], emptywts); - conv2->setStrideNd(DimsHW{ 1, 1 }); - conv2->setPaddingNd(DimsHW{ 1, 1 }); - conv2->setNbGroups(1); - IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + ".double_conv.4", 0); - IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kLEAKY_RELU); - assert(relu2); - return relu2; + Weights emptywts{ DataType::kFLOAT, nullptr, 0 }; + IConvolutionLayer* conv1 = network->addConvolutionNd(input, midch, DimsHW{ ksize, ksize }, weightMap[lname + ".double_conv.0.weight"], emptywts); + conv1->setStrideNd(DimsHW{ 1, 1 }); + conv1->setPaddingNd(DimsHW{ 1, 1 }); + conv1->setNbGroups(1); + IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".double_conv.1", 0); + IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU); + IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{ 3, 3 }, weightMap[lname + ".double_conv.3.weight"], emptywts); + conv2->setStrideNd(DimsHW{ 1, 1 }); + conv2->setPaddingNd(DimsHW{ 1, 1 }); + conv2->setNbGroups(1); + IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + ".double_conv.4", 0); + IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kLEAKY_RELU); + assert(relu2); + return relu2; } ILayer* down(INetworkDefinition* network, std::map& weightMap, ITensor& input, int outch, int p, std::string lname) { - - IPoolingLayer* pool1 = network->addPoolingNd(input, PoolingType::kMAX, DimsHW{ 2, 2 }); - assert(pool1); - ILayer* dcov1 = doubleConv(network, weightMap, *pool1->getOutput(0), outch, 3, lname + ".maxpool_conv.1", outch); - assert(dcov1); - return dcov1; + IPoolingLayer* pool1 = network->addPoolingNd(input, PoolingType::kMAX, DimsHW{ 2, 2 }); + assert(pool1); + ILayer* dcov1 = doubleConv(network, weightMap, *pool1->getOutput(0), outch, 3, lname + ".maxpool_conv.1", outch); + assert(dcov1); + return dcov1; } - ILayer* up(INetworkDefinition* network, std::map& weightMap, ITensor& input1, ITensor& input2, int resize, int outch, int midch, std::string lname) { - float* deval = reinterpret_cast(malloc(sizeof(float) * resize * 2 * 2)); - for (int i = 0; i < resize * 2 * 2; i++) { - deval[i] = 1.0; - } + if (BILINEAR) { + // add upsample bilinear + IResizeLayer* deconv1 = network->addResize(input1); + auto outdims = input2.getDimensions(); + deconv1->setOutputDimensions(outdims); + deconv1->setResizeMode(ResizeMode::kLINEAR); + deconv1->setAlignCorners(true); - if (BILINEAR) { - // add upsample bilinear - IResizeLayer* deconv1 = network->addResize(input1); - auto outdims = input2.getDimensions(); - deconv1->setOutputDimensions(outdims); - deconv1->setResizeMode(ResizeMode::kLINEAR); - deconv1->setAlignCorners(true); + int diffx = input2.getDimensions().d[1] - deconv1->getOutput(0)->getDimensions().d[1]; + int diffy = input2.getDimensions().d[2] - deconv1->getOutput(0)->getDimensions().d[2]; - int diffx = input2.getDimensions().d[1] - deconv1->getOutput(0)->getDimensions().d[1]; - int diffy = input2.getDimensions().d[2] - deconv1->getOutput(0)->getDimensions().d[2]; - - ILayer* pad1 = network->addPaddingNd(*deconv1->getOutput(0), DimsHW{ diffx / 2, diffy / 2 }, DimsHW{ diffx - (diffx / 2), diffy - (diffy / 2) }); - // dcov1->setPaddingNd(DimsHW{diffx / 2, diffx - diffx / 2},DimsHW{diffy / 2, diffy - diffy / 2}); - ITensor* inputTensors[] = { &input2,pad1->getOutput(0) }; - auto cat = network->addConcatenation(inputTensors, 2); - assert(cat); - if (midch == 64) { - ILayer* dcov1 = doubleConv(network, weightMap, *cat->getOutput(0), outch, 3, lname + ".conv", outch); - assert(dcov1); - return dcov1; - } - else { - int midch1 = outch / 2; - ILayer* dcov1 = doubleConv(network, weightMap, *cat->getOutput(0), midch1, 3, lname + ".conv", outch); - assert(dcov1); - return dcov1; - } - } - else { - /*Weights emptywts{ DataType::kFLOAT, nullptr, 0 }; - Weights deconvwts1{ DataType::kFLOAT, deval, resize * 2 * 2 };*/ - // weightMap[lname + ".up.weight"], weightMap[lname + ".up.bias"] - IDeconvolutionLayer* deconv1 = network->addDeconvolutionNd(input1, resize, DimsHW{ 2, 2 }, weightMap[lname + ".up.weight"], weightMap[lname + ".up.bias"]); - deconv1->setStrideNd(DimsHW{ 2, 2 }); - deconv1->setNbGroups(1); - //weightMap["deconvwts." + lname] = deconvwts1; - - int diffx = input2.getDimensions().d[1] - deconv1->getOutput(0)->getDimensions().d[1]; - int diffy = input2.getDimensions().d[2] - deconv1->getOutput(0)->getDimensions().d[2]; - - ILayer* pad1 = network->addPaddingNd(*deconv1->getOutput(0), DimsHW{ diffx / 2, diffy / 2 }, DimsHW{ diffx - (diffx / 2), diffy - (diffy / 2) }); - // dcov1->setPaddingNd(DimsHW{diffx / 2, diffx - diffx / 2},DimsHW{diffy / 2, diffy - diffy / 2}); - ITensor* inputTensors[] = { &input2,pad1->getOutput(0) }; - auto cat = network->addConcatenation(inputTensors, 2); - assert(cat); - ILayer* dcov1 = doubleConv(network, weightMap, *cat->getOutput(0), midch, 3, lname + ".conv", outch); - assert(dcov1); - return dcov1; - } + ILayer* pad1 = network->addPaddingNd(*deconv1->getOutput(0), DimsHW{ diffx / 2, diffy / 2 }, DimsHW{ diffx - (diffx / 2), diffy - (diffy / 2) }); + // dcov1->setPaddingNd(DimsHW{diffx / 2, diffx - diffx / 2},DimsHW{diffy / 2, diffy - diffy / 2}); + ITensor* inputTensors[] = { &input2,pad1->getOutput(0) }; + auto cat = network->addConcatenation(inputTensors, 2); + assert(cat); + if (midch == 64) { + ILayer* dcov1 = doubleConv(network, weightMap, *cat->getOutput(0), outch, 3, lname + ".conv", outch); + assert(dcov1); + return dcov1; + } else { + int midch1 = outch / 2; + ILayer* dcov1 = doubleConv(network, weightMap, *cat->getOutput(0), midch1, 3, lname + ".conv", outch); + assert(dcov1); + return dcov1; + } + } else { + IDeconvolutionLayer* deconv1 = network->addDeconvolutionNd(input1, resize, DimsHW{ 2, 2 }, weightMap[lname + ".up.weight"], weightMap[lname + ".up.bias"]); + deconv1->setStrideNd(DimsHW{ 2, 2 }); + deconv1->setNbGroups(1); + int diffx = input2.getDimensions().d[1] - deconv1->getOutput(0)->getDimensions().d[1]; + int diffy = input2.getDimensions().d[2] - deconv1->getOutput(0)->getDimensions().d[2]; + ILayer* pad1 = network->addPaddingNd(*deconv1->getOutput(0), DimsHW{ diffx / 2, diffy / 2 }, DimsHW{ diffx - (diffx / 2), diffy - (diffy / 2) }); + // dcov1->setPaddingNd(DimsHW{diffx / 2, diffx - diffx / 2},DimsHW{diffy / 2, diffy - diffy / 2}); + ITensor* inputTensors[] = { &input2,pad1->getOutput(0) }; + auto cat = network->addConcatenation(inputTensors, 2); + assert(cat); + ILayer* dcov1 = doubleConv(network, weightMap, *cat->getOutput(0), midch, 3, lname + ".conv", outch); + assert(dcov1); + return dcov1; + } } - ILayer* outConv(INetworkDefinition* network, std::map& weightMap, ITensor& input, int outch, std::string lname) { - // Weights emptywts{DataType::kFLOAT, nullptr, 0}; - - IConvolutionLayer* conv1 = network->addConvolutionNd(input, cls, DimsHW{ 1, 1 }, weightMap[lname + ".conv.weight"], weightMap[lname + ".conv.bias"]); - assert(conv1); - conv1->setStrideNd(DimsHW{ 1, 1 }); - conv1->setPaddingNd(DimsHW{ 0, 0 }); - conv1->setNbGroups(1); - return conv1; + // Weights emptywts{DataType::kFLOAT, nullptr, 0}; + IConvolutionLayer* conv1 = network->addConvolutionNd(input, cls, DimsHW{ 1, 1 }, weightMap[lname + ".conv.weight"], weightMap[lname + ".conv.bias"]); + assert(conv1); + conv1->setStrideNd(DimsHW{ 1, 1 }); + conv1->setPaddingNd(DimsHW{ 0, 0 }); + conv1->setNbGroups(1); + return conv1; } +ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, std::string wts_path) { + INetworkDefinition* network = builder->createNetworkV2(0U); -ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, std::string wtsPath) { - INetworkDefinition* network = builder->createNetworkV2(0U); + // Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME + ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W }); + assert(data); - // Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME - ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W }); - assert(data); + std::map weightMap = loadWeights(wts_path); + Weights emptywts{ DataType::kFLOAT, nullptr, 0 }; - std::map weightMap = loadWeights(wtsPath); - Weights emptywts{ DataType::kFLOAT, nullptr, 0 }; + // build network + auto x1 = doubleConv(network, weightMap, *data, 64, 3, "inc", 64); + auto x2 = down(network, weightMap, *x1->getOutput(0), 128, 1, "down1"); + auto x3 = down(network, weightMap, *x2->getOutput(0), 256, 1, "down2"); + auto x4 = down(network, weightMap, *x3->getOutput(0), 512, 1, "down3"); + auto channel = 512; + if (!BILINEAR) { + channel = 1024; + } + auto x5 = down(network, weightMap, *x4->getOutput(0), channel, 1, "down4"); + ILayer* x6 = up(network, weightMap, *x5->getOutput(0), *x4->getOutput(0), 512, 512, 512, "up1"); + ILayer* x7 = up(network, weightMap, *x6->getOutput(0), *x3->getOutput(0), 256, 256, 256, "up2"); + ILayer* x8 = up(network, weightMap, *x7->getOutput(0), *x2->getOutput(0), 128, 128, 128, "up3"); + ILayer* x9 = up(network, weightMap, *x8->getOutput(0), *x1->getOutput(0), 64, 64, 64, "up4"); + ILayer* x10 = outConv(network, weightMap, *x9->getOutput(0), OUTPUT_SIZE, "outc"); - // build network - auto x1 = doubleConv(network, weightMap, *data, 64, 3, "inc", 64); - auto x2 = down(network, weightMap, *x1->getOutput(0), 128, 1, "down1"); - auto x3 = down(network, weightMap, *x2->getOutput(0), 256, 1, "down2"); - auto x4 = down(network, weightMap, *x3->getOutput(0), 512, 1, "down3"); - auto channel = 512; - if (!BILINEAR) - { - channel = 1024; - } - auto x5 = down(network, weightMap, *x4->getOutput(0), channel, 1, "down4"); - ILayer* x6 = up(network, weightMap, *x5->getOutput(0), *x4->getOutput(0), 512, 512, 512, "up1"); - ILayer* x7 = up(network, weightMap, *x6->getOutput(0), *x3->getOutput(0), 256, 256, 256, "up2"); - ILayer* x8 = up(network, weightMap, *x7->getOutput(0), *x2->getOutput(0), 128, 128, 128, "up3"); - ILayer* x9 = up(network, weightMap, *x8->getOutput(0), *x1->getOutput(0), 64, 64, 64, "up4"); - ILayer* x10 = outConv(network, weightMap, *x9->getOutput(0), OUTPUT_SIZE, "outc"); + x10->getOutput(0)->setName(OUTPUT_BLOB_NAME); + network->markOutput(*x10->getOutput(0)); - std::cout << "set name out" << std::endl; - x10->getOutput(0)->setName(OUTPUT_BLOB_NAME); - network->markOutput(*x10->getOutput(0)); - - // Build engine - builder->setMaxBatchSize(maxBatchSize); - config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB + // Build engine + builder->setMaxBatchSize(maxBatchSize); + config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB #ifdef USE_FP16 - config->setFlag(BuilderFlag::kFP16); + config->setFlag(BuilderFlag::kFP16); #endif - std::cout << "Building engine, please wait for a while..." << std::endl; - ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config); - std::cout << "Build engine successfully!" << std::endl; + std::cout << "Building engine, please wait for a while..." << std::endl; + ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config); + std::cout << "Build engine successfully!" << std::endl; - // Don't need the network any more - network->destroy(); + // Don't need the network any more + network->destroy(); - // Release host memory - for (auto& mem : weightMap) - { - free((void*)(mem.second.values)); - } + // Release host memory + for (auto& mem : weightMap) { + free((void*)(mem.second.values)); + } - return engine; + return engine; } -void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream, std::string wtsPath) { - // Create builder - IBuilder* builder = createInferBuilder(gLogger); - IBuilderConfig* config = builder->createBuilderConfig(); +void APIToModel(unsigned int maxBatchSize, IHostMemory** model_stream, std::string wts_path) { + // Create builder + IBuilder* builder = createInferBuilder(gLogger); + IBuilderConfig* config = builder->createBuilderConfig(); - // Create model to populate the network, then set the outputs and create an engine - // ICudaEngine* engine = (CREATENET(NET))(maxBatchSize, builder, config, DataType::kFLOAT); - ICudaEngine* engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT, wtsPath); - assert(engine != nullptr); + // Create model to populate the network, then set the outputs and create an engine + ICudaEngine* engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT, wts_path); + assert(engine != nullptr); - // Serialize the engine - (*modelStream) = engine->serialize(); + // Serialize the engine + (*model_stream) = engine->serialize(); - // Close everything down - engine->destroy(); - builder->destroy(); + // Close everything down + engine->destroy(); + builder->destroy(); } void doInference(IExecutionContext& context, float* input, float* output, int batchSize) { - const ICudaEngine& engine = context.getEngine(); + const ICudaEngine& engine = context.getEngine(); - // Pointers to input and output device buffers to pass to engine. - // Engine requires exactly IEngine::getNbBindings() number of buffers. - assert(engine.getNbBindings() == 2); - void* buffers[2]; + // Pointers to input and output device buffers to pass to engine. + // Engine requires exactly IEngine::getNbBindings() number of buffers. + assert(engine.getNbBindings() == 2); + void* buffers[2]; - // In order to bind the buffers, we need to know the names of the input and output tensors. - // Note that indices are guaranteed to be less than IEngine::getNbBindings() - const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME); - const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME); + // In order to bind the buffers, we need to know the names of the input and output tensors. + // Note that indices are guaranteed to be less than IEngine::getNbBindings() + const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME); + const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME); - // Create GPU buffers on device - CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float))); - CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float))); + // Create GPU buffers on device + CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float))); + CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float))); - // Create stream - cudaStream_t stream; - CHECK(cudaStreamCreate(&stream)); + // Create stream + cudaStream_t stream; + CHECK(cudaStreamCreate(&stream)); - // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host - CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream)); - context.enqueue(batchSize, buffers, stream, nullptr); - CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream)); + // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host + CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream)); + context.enqueue(batchSize, buffers, stream, nullptr); + CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream)); - cudaStreamSynchronize(stream); + cudaStreamSynchronize(stream); - // Release stream and buffers - cudaStreamDestroy(stream); - CHECK(cudaFree(buffers[inputIndex])); - CHECK(cudaFree(buffers[outputIndex])); -} - -struct Detection { - float mask[INPUT_W * INPUT_H * 1]; -}; - -float sigmoid(float x) { - return (1 / (1 + exp(-x))); -} - -void process_cls_result(Detection& res, float* output) { - for (int i = 0; i < INPUT_W * INPUT_H * 1; i++) { - res.mask[i] = sigmoid(*(output + i)); - } + // Release stream and buffers + cudaStreamDestroy(stream); + CHECK(cudaFree(buffers[inputIndex])); + CHECK(cudaFree(buffers[outputIndex])); } int main(int argc, char** argv) { - cudaSetDevice(DEVICE); - // create a model using the API directly and serialize it to a stream - char* trtModelStream{ nullptr }; - size_t size{ 0 }; - std::string engine_name = "unet.engine"; - std::vector file_names; - std::string wtsPath = "..\\models\\unet_carvana_scale0.5_epoch2.wts"; - if (argc == 2 && std::string(argv[1]) == "-s") { - IHostMemory* modelStream{ nullptr }; - APIToModel(BATCH_SIZE, &modelStream, wtsPath); - assert(modelStream != nullptr); - std::ofstream p(engine_name, std::ios::binary); - if (!p) { - std::cerr << "could not open plan output file" << std::endl; - return -1; - } - p.write(reinterpret_cast(modelStream->data()), modelStream->size()); - modelStream->destroy(); - return 0; - } - else if (argc == 3 && std::string(argv[1]) == "-d") { - std::ifstream file(engine_name, std::ios::binary); - if (file.good()) { - file.seekg(0, file.end); - size = file.tellg(); - file.seekg(0, file.beg); - trtModelStream = new char[size]; - assert(trtModelStream); - file.read(trtModelStream, size); - file.close(); - cv::glob(argv[2], file_names); - } - } - else { - std::cerr << "arguments not right!" << std::endl; - std::cerr << "./unet -s // serialize model to plan file" << std::endl; - std::cerr << "./unet -d ../samples // deserialize plan file and run inference" << std::endl; - return -1; - } + cudaSetDevice(DEVICE); - //std::vector file_names; - //if (read_files_in_dir(argv[2], file_names) < 0) { - // std::cout << "read_files_in_dir failed." << std::endl; - // return -1; - //} + char* trt_model_stream = nullptr; + size_t size = 0; + std::string engine_name = "unet.engine"; + std::string wts_path = "unet.wts"; - // prepare input data --------------------------- - static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W]; - //for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++) - // data[i] = 1.0; - static float prob[BATCH_SIZE * OUTPUT_SIZE]; - IRuntime* runtime = createInferRuntime(gLogger); - assert(runtime != nullptr); - ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size); - assert(engine != nullptr); - IExecutionContext* context = engine->createExecutionContext(); - assert(context != nullptr); - delete[] trtModelStream; + if (argc == 2 && std::string(argv[1]) == "-s") { + // Create a TensorRT model and serialize it to a file + IHostMemory* model_stream{ nullptr }; + APIToModel(BATCH_SIZE, &model_stream, wts_path); + assert(model_stream != nullptr); + std::ofstream p(engine_name, std::ios::binary); + if (!p) { + std::cerr << "could not open plan output file" << std::endl; + return -1; + } + p.write(reinterpret_cast(model_stream->data()), model_stream->size()); + model_stream->destroy(); + return 0; + } else if (argc == 3 && std::string(argv[1]) == "-d") { + // Load engine file + std::ifstream file(engine_name, std::ios::binary); + if (file.good()) { + file.seekg(0, file.end); + size = file.tellg(); + file.seekg(0, file.beg); + trt_model_stream = new char[size]; + assert(trt_model_stream); + file.read(trt_model_stream, size); + file.close(); + } + } else { + std::cerr << "arguments not right!" << std::endl; + std::cerr << "./unet -s // serialize model to plan file" << std::endl; + std::cerr << "./unet -d ../samples // deserialize plan file and run inference" << std::endl; + return -1; + } + // Prepare input output data + static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W]; + static float prob[BATCH_SIZE * OUTPUT_SIZE]; - cv::Mat results = cv::Mat::zeros(INPUT_H, INPUT_W, CV_8UC3); - for (int f = 0; f < (int)file_names.size(); f++) - { + // Deserialize engine + IRuntime* runtime = createInferRuntime(gLogger); + assert(runtime != nullptr); + ICudaEngine* engine = runtime->deserializeCudaEngine(trt_model_stream, size); + assert(engine != nullptr); + IExecutionContext* context = engine->createExecutionContext(); + assert(context != nullptr); + delete[] trt_model_stream; - cv::Mat img = cv::imread(file_names[f]); - if (img.empty()) continue; - cv::Mat pr_img = preprocess_img(img); - //cv::resize(img, pr_img, cv::Size(INPUT_W, INPUT_H)); + cv::Mat img = cv::imread(argv[2]); - for (int i = 0; i < INPUT_H * INPUT_W; i++) { - data[i] = (pr_img.at(i)[2]) / 255.0; - data[i + INPUT_H * INPUT_W] = (pr_img.at(i)[1]) / 255.0; - data[i + 2 * INPUT_H * INPUT_W] = (pr_img.at(i)[0]) / 255.0; - } - // Run inference - auto start = std::chrono::system_clock::now(); - doInference(*context, data, prob, BATCH_SIZE); - auto end = std::chrono::system_clock::now(); - std::cout << std::chrono::duration_cast(end - start).count() << "ms" << std::endl; - for (int i = 0; i < INPUT_H * INPUT_W; i++) { - float fmax = 0.0; - int index = 0; - for (int j = 0; j < cls; j++) { - if (prob[i + j * INPUT_H * INPUT_W] > fmax) { - index = j; - fmax = prob[i + j * INPUT_H * INPUT_W]; - } - } + // Preprocess + cv::resize(img, img, cv::Size(INPUT_W, INPUT_H)); + for (int i = 0; i < INPUT_H * INPUT_W; i++) { + data[i] = (img.at(i)[2]) / 255.0; + data[i + INPUT_H * INPUT_W] = (img.at(i)[1]) / 255.0; + data[i + 2 * INPUT_H * INPUT_W] = (img.at(i)[0]) / 255.0; + } - if (index == 1) { - results.at(i) = cv::Vec3b(255, 255, 255); - } + // Run inference + auto start = std::chrono::system_clock::now(); + doInference(*context, data, prob, BATCH_SIZE); + auto end = std::chrono::system_clock::now(); + std::cout << std::chrono::duration_cast(end - start).count() << "ms" << std::endl; - else { - results.at(i) = cv::Vec3b(0, 0, 0); - } - } - cv::imshow(" results", results); - cv::imwrite(f + "_unet.jpg", results); + // Postprocess + cv::Mat result = cv::Mat::zeros(INPUT_H, INPUT_W, CV_8UC3); + for (int i = 0; i < INPUT_H * INPUT_W; i++) { + float fmax = prob[i]; + int index = 0; + for (int j = 1; j < cls; j++) { + if (prob[i + j * INPUT_H * INPUT_W] > fmax) { + index = j; + fmax = prob[i + j * INPUT_H * INPUT_W]; + } + } - cv::waitKey(0); - results = cv::Mat::zeros(INPUT_H, INPUT_W, CV_8UC3); + if (index == 1) { + result.at(i) = cv::Vec3b(255, 255, 255); + } + } - } + cv::imwrite("result.jpg", result); - // Destroy the engine - context->destroy(); - engine->destroy(); - runtime->destroy(); + // Destroy the engine + context->destroy(); + engine->destroy(); + runtime->destroy(); - return 0; + return 0; }