add an anomaly detection network efficient_ad

efficient_ad/CMakeLists.txt

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+cmake_minimum_required(VERSION 3.12)
+project(EfficientAD-M)
+
+add_definitions(-w)
+add_definitions(-D API_EXPORTS)
+set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_BUILD_TYPE "Debug")
+set(CMAKE_CUDA_ARCHITECTURES 61 75 86 89)
+set(THREADS_PREFER_PTHREAD_FLAG ON)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /od")
+
+### nvcc
+set(CMAKE_CUDA_COMPILER "D:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.8/bin/nvcc.exe")
+enable_language(CUDA)
+### cuda
+include_directories("D:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.8/include")
+link_directories("D:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.8/lib/x64")
+### tensorrt
+set(TRT_DIR "D:/Program Files/NVIDIA GPU Computing Toolkit/TensorRT-8.5.3.1/")
+include_directories(${TRT_DIR}/include)
+link_directories(${TRT_DIR}/lib)
+### opencv
+set(OpenCV_DIR "E:/OpenCV/OpenCV_4.6.0/opencv/build")
+find_package(OpenCV)
+include_directories(${OpenCV_INCLUDE_DIRS})
+### dirent
+include_directories("E:/SDK/dirent-1.24/include")
+
+include_directories(${PROJECT_SOURCE_DIR}/src/)
+file(GLOB_RECURSE SRCS ${PROJECT_SOURCE_DIR}/src/*.cpp ${PROJECT_SOURCE_DIR}/src/*.cu)
+
+add_executable(efficientAD_det "./efficientAD_det.cpp" ${SRCS})
+target_link_libraries(efficientAD_det nvinfer
+                                      cudart
+                                      nvinfer_plugin
+                                      ${OpenCV_LIBS}
+                                      )

efficient_ad/datas/models/gen_wts.py

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+import torch
+import struct
+import sys
+
+# Initialize
+pt_file = sys.argv[1]
+# Load model
+model = torch.load(pt_file, map_location=torch.device('cpu'))['model'].float()  # load to FP32
+model.to(device).eval()
+
+with open(pt_file.split('.')[0] + '.wts', 'w') as f:
+    f.write('{}\n'.format(len(model.state_dict().keys())))
+    for k, v in model.state_dict().items():
+        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')

efficient_ad/efficientAD_det.cpp

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+#include <cuda_runtime.h>
+
+#include <chrono>
+#include <cmath>
+#include <cstdint>
+#include <iostream>
+#include <opencv2/opencv.hpp>
+
+#include "config.h"
+#include "cuda_utils.h"
+#include "logging.h"
+#include "model.h"
+#include "postprocess.h"
+#include "utils.h"
+
+using namespace nvinfer1;
+
+static Logger gLogger;
+// const static int kOutputSize = kMaxNumOutputBbox * sizeof(Detection) / sizeof(float) + 1;
+const static int kInputSize = 3 * 256 * 256;
+const static int kOutputSize = 1 * 256 * 256;
+
+bool parse_args(int argc, char** argv, std::string& wts, std::string& engine, float& gd, float& gw, std::string& img_dir) {
+  if (argc != 4) return false;
+  if (std::string(argv[1]) == "-s") {
+    wts = std::string(argv[2]);
+    engine = std::string(argv[3]);
+  } else if (std::string(argv[1]) == "-d") {
+    engine = std::string(argv[2]);
+    img_dir = std::string(argv[3]);
+  } else {
+    return false;
+  }
+  return true;
+}
+
+void prepare_infer_buffers(ICudaEngine* engine, float** gpu_input_buffer, float** gpu_output_buffer, float** cpu_output_buffer) {
+  // assert(engine->getNbIOTensors() == 2);
+  assert(engine->getNbBindings() == 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(kInputTensorName);
+  const int outputIndex = engine->getBindingIndex(kOutputTensorName);
+  // nvinfer1::Dims outputDims = engine->getBindingDimensions(outputIndex);
+  assert(inputIndex == 0);
+  assert(outputIndex == 1);
+
+  // Create GPU in/output buffers on device
+  CUDA_CHECK(cudaMalloc((void**)gpu_input_buffer, kBatchSize * 3 * kInputH * kInputW * sizeof(float)));
+  CUDA_CHECK(cudaMalloc((void**)gpu_output_buffer, kBatchSize * 1 * kOutputSize * sizeof(float)));  // 3 or 1 ??
+  // Create CPU output buffers on host
+  *cpu_output_buffer = new float[kBatchSize * kOutputSize];
+}
+
+void preprocessImg(cv::Mat& img, int newh, int neww) {
+  cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
+  cv::resize(img, img, cv::Size(neww, newh));
+  img.convertTo(img, CV_32FC3);
+  // ImageNet normalize
+  img /= 255.0f;
+  img -= cv::Scalar(0.485, 0.456, 0.406);
+  img /= cv::Scalar(0.229, 0.224, 0.225);
+}
+
+void infer(IExecutionContext& context, cudaStream_t& stream, std::vector<void*>& gpu_buffers, std::vector<float>& cpu_input_data,
+           std::vector<float>& cpu_output_data, int batchsize) {
+  // copy input data from host (CPU) to device (GPU)
+  CUDA_CHECK(cudaMemcpyAsync(gpu_buffers[0], cpu_input_data.data(), cpu_input_data.size() * sizeof(float), cudaMemcpyHostToDevice, stream));
+  // execute inference using context provided by engine
+  context.enqueue(batchsize, gpu_buffers.data(), stream, nullptr);
+  // copy output back from device (GPU) to host (CPU)
+  CUDA_CHECK(cudaMemcpyAsync(cpu_output_data.data(), gpu_buffers[1], batchsize * kOutputSize * sizeof(float), cudaMemcpyDeviceToHost, stream));
+  // synchronize the stream to prevent issues (block CUDA and wait for CUDA operations to be completed)
+  cudaStreamSynchronize(stream);
+}
+
+void serialize_engine(unsigned int max_batchsize, float& gd, float& gw, std::string& wts_name, std::string& engine_name) {
+  // 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 = nullptr;
+  engine = build_efficientAD_engine(max_batchsize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
+  assert(engine != nullptr);
+
+  // Serialize the engine
+  IHostMemory* serialized_engine = engine->serialize();
+  assert(serialized_engine != nullptr);
+
+  // Save engine to file
+  std::ofstream p(engine_name, std::ios::binary);
+  if (!p) {
+    std::cerr << "Could not open plan output file" << std::endl;
+    assert(false);
+  }
+  p.write(reinterpret_cast<const char*>(serialized_engine->data()), serialized_engine->size());
+
+  // Close everything down
+  engine->destroy();
+  config->destroy();
+  serialized_engine->destroy();
+  builder->destroy();
+}
+
+void deserialize_engine(std::string& engine_name, IRuntime** runtime, ICudaEngine** engine, IExecutionContext** context) {
+  std::ifstream file(engine_name, std::ios::binary);
+  if (!file.good()) {
+    std::cerr << "read " << engine_name << " error!" << std::endl;
+    assert(false);
+  }
+  size_t size = 0;
+  file.seekg(0, file.end);
+  size = file.tellg();
+  file.seekg(0, file.beg);
+  char* serialized_engine = new char[size];
+  assert(serialized_engine);
+  file.read(serialized_engine, size);
+  file.close();
+
+  *runtime = createInferRuntime(gLogger);
+  assert(*runtime);
+  *engine = (*runtime)->deserializeCudaEngine(serialized_engine, size);
+  assert(*engine != nullptr);
+  *context = (*engine)->createExecutionContext();
+  assert(*context);
+
+  delete[] serialized_engine;
+}
+
+int main(int argc, char** argv) {
+  cudaSetDevice(kGpuId);
+
+  std::string wts_name = "";
+  std::string engine_name = "";
+  float gd = 1.0f, gw = 1.0f;
+  std::string img_dir;
+
+  if (!parse_args(argc, argv, wts_name, engine_name, gd, gw, img_dir)) {
+    std::cerr << "arguments not right!" << std::endl;
+    std::cerr << "./efficientad_det -s [.wts] [.engine]  // serialize model to plan file" << std::endl;
+    std::cerr << "./efficientad_det -d [.engine] [../../datas/images/...]  // deserialize plan file and run inference" << std::endl;
+    return -1;
+  }
+
+  // Create a model using the API directly and serialize it to a file
+  if (!wts_name.empty()) {
+    serialize_engine(kBatchSize, gd, gw, wts_name, engine_name);
+    return 0;
+  }
+
+  // Deserialize the engine from file
+  IRuntime* runtime = nullptr;
+  ICudaEngine* engine = nullptr;
+  IExecutionContext* context = nullptr;
+  deserialize_engine(engine_name, &runtime, &engine, &context);
+
+  // create CUDA stream for simultaneous CUDA operations
+  cudaStream_t stream;
+  CUDA_CHECK(cudaStreamCreate(&stream));
+
+  // prepare cpu and gpu buffers
+  void *gpu_input_buffer, *gpu_output_buffer;
+  CUDA_CHECK(cudaMalloc(&gpu_input_buffer, kBatchSize * 3 * kInputH * kInputW * sizeof(float)));
+  CUDA_CHECK(cudaMalloc(&gpu_output_buffer, kBatchSize * 1 * kOutputSize * sizeof(float)));  // 3 or 1 ??
+  std::vector<void*> gpu_buffers = {gpu_input_buffer, gpu_output_buffer};
+  std::vector<float> cpu_input_data(kBatchSize * kInputSize, 0);
+  std::vector<float> cpu_output_data(kBatchSize * kOutputSize, 0);
+
+  // read images from directory
+  std::vector<std::string> file_names;
+  if (read_files_in_dir(img_dir.c_str(), file_names) < 0) {
+    std::cerr << "read_files_in_dir failed." << std::endl;
+    return -1;
+  }
+
+  std::vector<cv::Mat> originImg_batch;
+  for (size_t i = 0; i < file_names.size(); i += kBatchSize) {
+    // get a batch of images
+    std::vector<cv::Mat> img_batch;
+    std::vector<std::string> img_name_batch;
+
+    for (size_t j = i; j < i + kBatchSize && j < file_names.size(); j++) {
+      cv::Mat img = cv::imread(img_dir + "/" + file_names[j]);
+      originImg_batch.push_back(img.clone());
+      preprocessImg(img, kInputW, kInputH);
+      assert(img.cols * img.rows * 3 == 3 * 256 * 256);
+      for (int c = 0; c < 3; c++) {
+        for (int h = 0; h < img.rows; h++) {
+          for (int w = 0; w < img.cols; w++) {
+            cpu_input_data[c * img.rows * img.cols +
+                           h * img.cols +
+                           w] = img.at<cv::Vec3f>(h, w)[c];
+          }
+        }
+      }
+      img_batch.push_back(img);
+      img_name_batch.push_back(file_names[j]);
+    }
+
+    // Run inference
+    auto start = std::chrono::system_clock::now();
+    // infer(*context, stream, (void**)gpu_buffers, cpu_input_data, cpu_output_buffer, kBatchSize);
+    infer(*context, stream, gpu_buffers, cpu_input_data, cpu_output_data, kBatchSize);  // change to save into vec `cpu_output_data`
+    auto end = std::chrono::system_clock::now();
+    std::cout << "inference time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
+
+    // postProcess
+    cv::Mat img_1(256, 256, CV_8UC1);
+    for (int row = 0; row < 256; row++) {
+      for (int col = 0; col < 256; col++) {
+        float value = cpu_output_data[row * 256 + col];
+        if (value < 0)  // clip(0,1)
+          value = 0;
+        else if (value > 1)
+          value = 1;
+        img_1.at<uchar>(row, col) = static_cast<uchar>(value * 255);
+      }
+    }
+
+    cv::Mat HeatMap, colorMap;
+    // genHeatMap(img_batch[0], img_1, HeatMap);
+    cv::applyColorMap(img_1, colorMap, cv::COLORMAP_JET);
+    cv::resize(originImg_batch[i], originImg_batch[i], cv::Size(256, 256));
+    cv::cvtColor(originImg_batch[i], originImg_batch[i], cv::COLOR_RGB2BGR);
+    cv::addWeighted(originImg_batch[i], 0.5, colorMap, 0.5, 0, HeatMap);
+
+    // Save images
+    for (size_t j = 0; j < img_batch.size(); j++) {
+      cv::imwrite("_output" + img_name_batch[j], img_1);
+      cv::imwrite("_heatmap" + img_name_batch[j], HeatMap);
+    }
+  }
+
+  // Release stream and buffers
+  cudaStreamDestroy(stream);
+  CUDA_CHECK(cudaFree(gpu_buffers[0]));
+  CUDA_CHECK(cudaFree(gpu_buffers[1]));
+
+  // Destroy the engine
+  context->destroy();
+  engine->destroy();
+  runtime->destroy();
+
+  return 0;
+}

efficient_ad/src/config.h

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+#pragma once
+
+/* --------------------------------------------------------
+ * These configs are related to tensorrt model, if these are changed,
+ * please re-compile and re-serialize the tensorrt model.
+ * --------------------------------------------------------*/
+
+// For INT8, you need prepare the calibration dataset, please refer to
+#define USE_FP32  // set USE_INT8 or USE_FP16 or USE_FP32
+
+// These are used to define input/output tensor names,
+// you can set them to whatever you want.
+const static char* kInputTensorName = "data";
+const static char* kOutputTensorName = "prob";
+
+constexpr static int kBatchSize = 1;
+
+// input width and height must by divisible by 32
+constexpr static int kInputH = 256;
+constexpr static int kInputW = 256;
+
+/* --------------------------------------------------------
+ * These configs are NOT related to tensorrt model, if these are changed,
+ * please re-compile, but no need to re-serialize the tensorrt model.
+ * --------------------------------------------------------*/
+
+// default GPU_id
+const static int kGpuId = 0;
+
+// If your image size is larger than 4096 * 3112, please increase this value
+const static int kMaxInputImageSize = 4096 * 3112;
+

efficient_ad/src/cuda_utils.h

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+#ifndef TRTX_CUDA_UTILS_H_
+#define TRTX_CUDA_UTILS_H_
+
+#include <cuda_runtime_api.h>
+
+#ifndef CUDA_CHECK
+#define CUDA_CHECK(callstr)\
+    {\
+        cudaError_t error_code = callstr;\
+        if (error_code != cudaSuccess) {\
+            std::cerr << "CUDA error " << error_code << " at " << __FILE__ << ":" << __LINE__;\
+            assert(0);\
+        }\
+    }
+#endif  // CUDA_CHECK
+
+#endif  // TRTX_CUDA_UTILS_H_
+