change format

test/fpga/test_marker_api.cpp

@@ -45,65 +45,67 @@ void readStream(std::string filename, char *buf) {
   in.close();
 }
 signed char float_to_int8(float fdata) {
-    if (fdata < 0.0) {
-        fdata -= 0.5;
-    } else {
-        fdata += 0.5;
-    }
-    return (signed char)fdata;
+  if (fdata < 0.0) {
+    fdata -= 0.5;
+  } else {
+    fdata += 0.5;
+  }
+  return (signed char)fdata;
 }
 void quantize(float **data_in, int data_size) {
-    float *tmp = *data_in;
-    signed char *tmp_data =
-            (signed char *)paddle_mobile::fpga::fpga_malloc(data_size * sizeof(char));
-    for (int i = 0; i < data_size; i++) {
-        tmp_data[i] = float_to_int8((*data_in)[i] + 128);
-    }
-    *data_in = (float *)tmp_data;  // NOLINT
-    paddle_mobile::fpga::fpga_free(tmp);
+  float *tmp = *data_in;
+  signed char *tmp_data =
+      (signed char *)paddle_mobile::fpga::fpga_malloc(data_size * sizeof(char));
+  for (int i = 0; i < data_size; i++) {
+    tmp_data[i] = float_to_int8((*data_in)[i] + 128);
+  }
+  *data_in = (float *)tmp_data;  // NOLINT
+  paddle_mobile::fpga::fpga_free(tmp);
 }
 
 void convert_to_chw(float **data_in, int channel, int height, int width,
                     float *data_tmp) {
-    int64_t amount_per_side = width * height;
-    for (int h = 0; h < height; h++) {
-        for (int w = 0; w < width; w++) {
-            for (int c = 0; c < channel; c++) {
-                *(data_tmp + c * amount_per_side + width * h + w) = *((*data_in)++);
-            }
-        }
+  int64_t amount_per_side = width * height;
+  for (int h = 0; h < height; h++) {
+    for (int w = 0; w < width; w++) {
+      for (int c = 0; c < channel; c++) {
+        *(data_tmp + c * amount_per_side + width * h + w) = *((*data_in)++);
+      }
     }
+  }
 }
 
-void dump_stride_float(std::string filename, paddle_mobile::PaddleTensor input_tensor) {
-    auto data_ptr = reinterpret_cast<float *>(input_tensor.data.data());
-    int c = (input_tensor.shape)[1];
-    int h = (input_tensor.shape)[2];
-    int w = (input_tensor.shape)[3];
-    int n = (input_tensor.shape)[0];
-    float *data_tmp =
-            reinterpret_cast<float *>(malloc(c * h * w * sizeof(float)));
-    //convert_to_chw(&data_ptr, c, h, w, data_tmp);
-    std::ofstream out(filename.c_str());
-    float result = 0;
-    int datasize = abs(c * h * w * n);
-    if (datasize == 0) {
-        std::cout << "wrong dump data size" << std::endl;
-        return;
-    }
-    for (int i = 0; i < datasize; i++) {
-        result = data_ptr[i];
-        out << result << std::endl;
-    }
-    out.close();
+void dump_stride_float(std::string filename,
+                       paddle_mobile::PaddleTensor input_tensor) {
+  auto data_ptr = reinterpret_cast<float *>(input_tensor.data.data());
+  int c = (input_tensor.shape)[1];
+  int h = (input_tensor.shape)[2];
+  int w = (input_tensor.shape)[3];
+  int n = (input_tensor.shape)[0];
+  float *data_tmp =
+      reinterpret_cast<float *>(malloc(c * h * w * sizeof(float)));
+  // convert_to_chw(&data_ptr, c, h, w, data_tmp);
+  std::ofstream out(filename.c_str());
+  float result = 0;
+  int datasize = abs(c * h * w * n);
+  if (datasize == 0) {
+    std::cout << "wrong dump data size" << std::endl;
+    return;
+  }
+  for (int i = 0; i < datasize; i++) {
+    result = data_ptr[i];
+    out << result << std::endl;
+  }
+  out.close();
 }
 
-void dump_stride(std::string filename, paddle_mobile::PaddleTensor input_tensor) {
-    if (input_tensor.dtypeid == typeid(float)) {
-        dump_stride_float(filename, input_tensor);
-    } else {
-        std::cout << "only support dumping float data" << std::endl;
-    }
+void dump_stride(std::string filename,
+                 paddle_mobile::PaddleTensor input_tensor) {
+  if (input_tensor.dtypeid == typeid(float)) {
+    dump_stride_float(filename, input_tensor);
+  } else {
+    std::cout << "only support dumping float data" << std::endl;
+  }
 }
 PaddleMobileConfig GetConfig() {
   PaddleMobileConfig config;
@@ -119,64 +121,63 @@ PaddleMobileConfig GetConfig() {
   return config;
 }
 PaddleMobileConfig GetConfig1() {
-    PaddleMobileConfig config;
-    config.precision = PaddleMobileConfig::FP32;
-    config.device = PaddleMobileConfig::kFPGA;
-    config.prog_file = g_model1;
-    config.param_file = g_param1;
-    config.thread_num = 1;
-    config.batch_size = 1;
-    config.optimize = true;
-    config.lod_mode = true;
-    config.quantification = false;
-    return config;
+  PaddleMobileConfig config;
+  config.precision = PaddleMobileConfig::FP32;
+  config.device = PaddleMobileConfig::kFPGA;
+  config.prog_file = g_model1;
+  config.param_file = g_param1;
+  config.thread_num = 1;
+  config.batch_size = 1;
+  config.optimize = true;
+  config.lod_mode = true;
+  config.quantification = false;
+  return config;
 }
 
 int main() {
   open_device();
 
-
   PaddleMobileConfig config1 = GetConfig1();
   auto predictor1 =
-            CreatePaddlePredictor<PaddleMobileConfig,
-                    PaddleEngineKind::kPaddleMobile>(config1);
+      CreatePaddlePredictor<PaddleMobileConfig,
+                            PaddleEngineKind::kPaddleMobile>(config1);
 
   std::cout << "Finishing loading model" << std::endl;
-for(int i = 0; i < 1; ++i){
-
-  int img_length1 = 144 * 14 * 14;
-  auto img1 = reinterpret_cast<float *>(fpga_malloc(img_length1 * sizeof(float)));
-  readStream(g_image1, reinterpret_cast<char *>(img1));
-
-  std::cout << "Finishing initializing data" << std::endl;
-  struct PaddleTensor  t_img1;
-
-  t_img1.dtypeid = typeid(float);
-  t_img1.layout = LAYOUT_HWC;
-  t_img1.shape = std::vector<int>({1, 14, 14, 144});
-  t_img1.name = "Image information";
-  t_img1.data.Reset(img1, img_length1 * sizeof(float));
-  predictor1->FeedPaddleTensors({t_img1});
-
-  std::cout << "Finishing feeding data " << std::endl;
-
-  predictor1->Predict_From_To(0, -1);
-  std::cout << "Finishing predicting " << std::endl;
-
-  std::vector<paddle_mobile::PaddleTensor> v1;        // No need to initialize v
-  predictor1->FetchPaddleTensors(&v1);  // Old data in v will be cleared
-  std::cout << "Output number is " << v1.size() << std::endl;
-  for (int fetchNum = 0; fetchNum < v1.size(); fetchNum++) {
+  for (int i = 0; i < 1; ++i) {
+    int img_length1 = 144 * 14 * 14;
+    auto img1 =
+        reinterpret_cast<float *>(fpga_malloc(img_length1 * sizeof(float)));
+    readStream(g_image1, reinterpret_cast<char *>(img1));
+
+    std::cout << "Finishing initializing data" << std::endl;
+    struct PaddleTensor t_img1;
+
+    t_img1.dtypeid = typeid(float);
+    t_img1.layout = LAYOUT_HWC;
+    t_img1.shape = std::vector<int>({1, 14, 14, 144});
+    t_img1.name = "Image information";
+    t_img1.data.Reset(img1, img_length1 * sizeof(float));
+    predictor1->FeedPaddleTensors({t_img1});
+
+    std::cout << "Finishing feeding data " << std::endl;
+
+    predictor1->Predict_From_To(0, -1);
+    std::cout << "Finishing predicting " << std::endl;
+
+    std::vector<paddle_mobile::PaddleTensor> v1;  // No need to initialize v
+    predictor1->FetchPaddleTensors(&v1);  // Old data in v will be cleared
+    std::cout << "Output number is " << v1.size() << std::endl;
+    for (int fetchNum = 0; fetchNum < v1.size(); fetchNum++) {
       std::string dumpName = "marker2_api_fetch_" + std::to_string(fetchNum);
       dump_stride(dumpName, v1[fetchNum]);
+    }
   }
-}
-/////////////////////////////////////
+  /////////////////////////////////////
 
   PaddleMobileConfig config = GetConfig();
   auto predictor =
-  CreatePaddlePredictor<PaddleMobileConfig,
-  PaddleEngineKind::kPaddleMobile>(config);
+      CreatePaddlePredictor<PaddleMobileConfig,
+                            PaddleEngineKind::kPaddleMobile>(config);
 
   std::cout << "Finishing loading model" << std::endl;
 
@@ -194,14 +195,14 @@ for(int i = 0; i < 1; ++i){
   t_img_info.data.Reset(img_info, 3 * sizeof(float));
 
   t_img.dtypeid = typeid(float);
-  //quantize(&img, img_length);
-  //t_img.dtypeid = typeid(int8_t);
+  // quantize(&img, img_length);
+  // t_img.dtypeid = typeid(int8_t);
   t_img.layout = LAYOUT_HWC;
   t_img.shape = std::vector<int>({1, 432, 1280, 3});
   t_img.name = "Image information";
   t_img.data.Reset(img, img_length * sizeof(float));
-  //t_img.data.Reset(img, img_length * sizeof(int8_t));
-// for(int i = 0; i < 100; ++i){
+  // t_img.data.Reset(img, img_length * sizeof(int8_t));
+  // for(int i = 0; i < 100; ++i){
   predictor->FeedPaddleTensors({t_img_info, t_img});
 
   std::cout << "Finishing feeding data " << std::endl;
@@ -209,8 +210,8 @@ for(int i = 0; i < 1; ++i){
   predictor->Predict_From_To(0, -1);
   std::cout << "Finishing predicting " << std::endl;
 
-  std::vector<paddle_mobile::PaddleTensor> v;        // No need to initialize v
-  predictor->FetchPaddleTensors(&v);  // Old data in v will be cleared
+  std::vector<paddle_mobile::PaddleTensor> v;  // No need to initialize v
+  predictor->FetchPaddleTensors(&v);           // Old data in v will be cleared
   std::cout << "Output number is " << v.size() << std::endl;
   for (int fetchNum = 0; fetchNum < v.size(); fetchNum++) {
     std::string dumpName = "marker_api_fetch_" + std::to_string(fetchNum);

test/fpga/test_mobilenet_api.cpp

@@ -19,8 +19,8 @@ limitations under the License. */
 #include <iostream>
 #include "../../src/io/paddle_inference_api.h"
 
-using namespace paddle_mobile;  //NOLINT
-using namespace paddle_mobile::fpga;  //NOLINT
+using namespace paddle_mobile;        // NOLINT
+using namespace paddle_mobile::fpga;  // NOLINT
 
 static const char *g_image = "../images/mobilenet_txtdata/1.txt";
 static const char *g_model = "../models/keycurve_l2_regular4_model/__model__";
@@ -119,7 +119,9 @@ PaddleMobileConfig GetConfig() {
 int main() {
   open_device();
   PaddleMobileConfig config = GetConfig();
-  auto predictor = CreatePaddlePredictor<paddle_mobile::PaddleMobileConfig, PaddleEngineKind::kPaddleMobile>(config);
+  auto predictor =
+      CreatePaddlePredictor<paddle_mobile::PaddleMobileConfig,
+                            PaddleEngineKind::kPaddleMobile>(config);
 
   std::cout << "Finishing loading model" << std::endl;
   int img_length = 256 * 416 * 3;

test/fpga/test_yolo_api.cpp

@@ -19,8 +19,8 @@ limitations under the License. */
 #include <iostream>
 #include "../../src/io/paddle_inference_api.h"
 
-using namespace paddle_mobile;  //NOLINT
-using namespace paddle_mobile::fpga;  //NOLINT
+using namespace paddle_mobile;        // NOLINT
+using namespace paddle_mobile::fpga;  // NOLINT
 
 static const char *g_image = "../images/yolo_test_txtimg/1.txt";
 static const char *g_model = "../models/yolo_bn_l2_model/__model__";
@@ -51,8 +51,7 @@ signed char float_to_int8(float fdata) {
 }
 void quantize(float **data_in, int data_size) {
   float *tmp = *data_in;
-  signed char *tmp_data =
-      (signed char *)fpga_malloc(data_size * sizeof(char));
+  signed char *tmp_data = (signed char *)fpga_malloc(data_size * sizeof(char));
   for (int i = 0; i < data_size; i++) {
     tmp_data[i] = float_to_int8((*data_in)[i] + 128);
   }
@@ -120,7 +119,9 @@ PaddleMobileConfig GetConfig() {
 int main() {
   open_device();
   PaddleMobileConfig config = GetConfig();
-  auto predictor = CreatePaddlePredictor<PaddleMobileConfig, PaddleEngineKind::kPaddleMobile>(config);
+  auto predictor =
+      CreatePaddlePredictor<PaddleMobileConfig,
+                            PaddleEngineKind::kPaddleMobile>(config);
 
   std::cout << "Finishing loading model" << std::endl;
   int img_length = 256 * 416 * 3;