From f98182e1b1eadbd79fe3ff384921ce598acaf71e Mon Sep 17 00:00:00 2001 From: East-Face <35283869+East-Face@users.noreply.github.com> Date: Tue, 13 Dec 2022 17:22:07 +0800 Subject: [PATCH] UNet: adapted to Pytorch-UNet v3.0 (#1169) * add deconvlution * Delete unet/samples directory * Update README.md Co-authored-by: liuxiangd --- unet/README.md | 6 +- unet/unet.cpp | 582 ++++++++++++++++++++++++++----------------------- 2 files changed, 310 insertions(+), 278 deletions(-) diff --git a/unet/README.md b/unet/README.md index fb2d01e..936ca4e 100644 --- a/unet/README.md +++ b/unet/README.md @@ -58,8 +58,10 @@ the speed of tensorRT engine is much faster ---- | ----- | ------ 816x672 | 816x672 | 816x672 58ms | 43ms (batchsize 8) | 14ms (batchsize 8) - - +# test img +``` +wget https://raw.githubusercontent.com/wang-xinyu/tensorrtx/f60dcc7bec28846cd973fc95ac829c4e57a11395/unet/samples/0cdf5b5d0ce1_01.jpg +``` # Further development 1. add INT8 calibrator
diff --git a/unet/unet.cpp b/unet/unet.cpp index a075f2e..a3a6cdc 100644 --- a/unet/unet.cpp +++ b/unet/unet.cpp @@ -4,349 +4,379 @@ #include "logging.h" #include "common.hpp" + #define DEVICE 0 // #define USE_FP16 // comment out this if want to 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 = 816; -static const int INPUT_W = 672; -static const int OUTPUT_SIZE = 672*816; +static const int INPUT_H = 640; +static const int INPUT_W = 959; +static const int OUTPUT_SIZE = INPUT_H * INPUT_W * cls; 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; + 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; } -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"], weightMap[lname + ".double_conv.0.bias"]); - 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"], weightMap[lname + ".double_conv.3.bias"]); - 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* 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; } -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; +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; } -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; - } - Weights emptywts{DataType::kFLOAT, nullptr, 0}; - Weights deconvwts1{DataType::kFLOAT, deval, resize * 2 * 2}; - IDeconvolutionLayer* deconv1 = network->addDeconvolutionNd(input1, resize, DimsHW{2, 2}, deconvwts1, emptywts); - deconv1->setStrideNd(DimsHW{2, 2}); - deconv1->setNbGroups(resize); - 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]; - // IPoolingLayer* pool1 = network->addPooling(dcov1, PoolingType::kMAX, DimsHW{2, 2}); - // pool1->setStrideNd(DimsHW{2, 2}); - // dcov1->add_pading - 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; - } - // 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); + + 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* outConv(INetworkDefinition *network, std::map& weightMap, ITensor& input, int outch, std::string lname) { - // Weights emptywts{DataType::kFLOAT, nullptr, 0}; - IConvolutionLayer* conv1 = network->addConvolutionNd(input, 1, 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; +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; } -ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) { - 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); +ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, std::string wtsPath) { + INetworkDefinition* network = builder->createNetworkV2(0U); - std::map weightMap = loadWeights("../unet.wts"); - Weights emptywts{DataType::kFLOAT, nullptr, 0}; + // 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); - // 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 x5 = down(network, weightMap, *x4->getOutput(0), 512, 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"); - std::cout << "set name out" << std::endl; - x10->getOutput(0)->setName(OUTPUT_BLOB_NAME); - network->markOutput(*x10->getOutput(0)); + std::map weightMap = loadWeights(wtsPath); + Weights emptywts{ DataType::kFLOAT, nullptr, 0 }; - // Build engine - builder->setMaxBatchSize(maxBatchSize); - config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB + // 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"); + + 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 #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) { - // Create builder - IBuilder* builder = createInferBuilder(gLogger); - IBuilderConfig* config = builder->createBuilderConfig(); +void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream, std::string wtsPath) { + // 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 = createEngine(maxBatchSize, builder, config, DataType::kFLOAT); - assert(engine != nullptr); + // 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); - // Serialize the engine - (*modelStream) = engine->serialize(); + // Serialize the engine + (*modelStream) = 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])); + // Release stream and buffers + cudaStreamDestroy(stream); + CHECK(cudaFree(buffers[inputIndex])); + CHECK(cudaFree(buffers[outputIndex])); } struct Detection { - float mask[INPUT_W * INPUT_H * 1]; + float mask[INPUT_W * INPUT_H * 1]; }; float sigmoid(float x) { - return (1 / (1 + exp(-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)); - } +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)); + } } 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"; - if (argc == 2 && std::string(argv[1]) == "-s") { - IHostMemory* modelStream{nullptr}; - APIToModel(BATCH_SIZE, &modelStream); - 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(); - } - } 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); + // 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; + } - 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; - } + //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; + //} - // 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; + // 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; - int fcount = 0; - for (int f = 0; f < (int)file_names.size(); f++) { - fcount++; - if (fcount < BATCH_SIZE && f + 1 != (int)file_names.size()) continue; - for (int b = 0; b < fcount; b++) { - cv::Mat img = cv::imread(std::string(argv[2]) + "/" + file_names[f - fcount + 1 + b]); - if (img.empty()) continue; - cv::Mat pr_img = preprocess_img(img); // letterbox BGR to RGB - // cv::imwrite("s_o" + file_names[f - fcount + 1 + b] + "_unet.jpg", pr_img); - int i = 0; - for (int row = 0; row < INPUT_H; ++row) { - uchar* uc_pixel = pr_img.data + row * pr_img.step; - for (int col = 0; col < INPUT_W; ++col) { - data[b * 3 * INPUT_H * INPUT_W + i] = (float)uc_pixel[2] / 255.0; - data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (float)uc_pixel[1] / 255.0; - data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (float)uc_pixel[0] / 255.0; - uc_pixel += 3; - ++i; - } - } - } - // 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; + cv::Mat results = cv::Mat::zeros(INPUT_H, INPUT_W, CV_8UC3); + for (int f = 0; f < (int)file_names.size(); f++) + { - std::vector batch_res(fcount); - for (int b = 0; b < fcount; b++) { - auto& res = batch_res[b]; - process_cls_result(res, &prob[b * OUTPUT_SIZE]); - } + 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)); - std::cout << fcount << std::endl; + 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]; + } + } - for (int b = 0; b < fcount; b++) { - auto& res = batch_res[b]; - float* mask = res.mask; - cv::Mat mask_mat = cv::Mat(INPUT_H, INPUT_W, CV_8UC1); - uchar* ptmp = NULL; - for (int i = 0; i < INPUT_H; i++) { - ptmp = mask_mat.ptr(i); - for (int j = 0; j < INPUT_W; j++){ - float * pixcel = mask + i * INPUT_W + j; - // std::cout << *pixcel << std::endl; - if (*pixcel > CONF_THRESH) { - ptmp[j] = 255; - } else { - ptmp[j]=0; - } - } - } + if (index == 1) { + results.at(i) = cv::Vec3b(255, 255, 255); + } - cv::imwrite("s_" + file_names[f - fcount + 1 + b] + "_unet.jpg", mask_mat); - } - fcount = 0; - } + else { + results.at(i) = cv::Vec3b(0, 0, 0); + } + } + cv::imshow(" results", results); + cv::imwrite(f + "_unet.jpg", results); - // Destroy the engine - context->destroy(); - engine->destroy(); - runtime->destroy(); + cv::waitKey(0); + results = cv::Mat::zeros(INPUT_H, INPUT_W, CV_8UC3); - return 0; + } + + // Destroy the engine + context->destroy(); + engine->destroy(); + runtime->destroy(); + + return 0; }