Merge branch 'develop' of https://github.com/PaddlePaddle/paddle-mobile into develop

src/fpga/api.cpp

@@ -22,7 +22,7 @@ limitations under the License. */
 #include "fpga/filter.h"
 #include "fpga/image.h"
 #define FPGA_TEST_MODE
-// #define PADDLE_MOBILE_OS_LINUX
+#define PADDLE_MOBILE_OS_LINUX
 
 namespace paddle_mobile {
 namespace fpga {

test/fpga/test_resnet50.cpp

@@ -11,34 +11,107 @@ distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
-
+#include <fstream>
 #include "../test_include.h"
-static const char *g_resnet_combine = "../models/resnet50";
+#include "fpga/api.h"
+void readStream(std::string filename, float *buf) {
+  std::ifstream in;
+  in.open(filename, std::ios::in);
+  if (!in.is_open()) {
+    std::cout << "open File Failed." << std::endl;
+    return;
+  }
+  string strOne;
+  int i = 0;
+  while (!in.eof()) {
+    in >> buf[i];
+    i++;
+  }
+  in.close();
+}
 
+void convert_to_chw(int16_t **data_in, int channel, int height, int width,
+                    int16_t *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)++);
+      }
+    }
+  }
+}
+
+void dump(std::string filename, const Tensor input_tensor) {
+  auto dataptr = input_tensor.data<float>();
+  std::ofstream out(filename.c_str());
+  float result = 0;
+  for (int i = 0; i < input_tensor.numel(); ++i) {
+    result = paddle_mobile::fpga::fp16_2_fp32(dataptr[i]);
+    out << result << std::endl;
+  }
+  out.close();
+}
+void dump_stride(std::string filename, const Tensor input_tensor,
+                 const int dumpnum) {
+  int c = (input_tensor.dims())[1];
+  int h = (input_tensor.dims())[2];
+  int w = (input_tensor.dims())[3];
+  auto data_ptr = input_tensor.data<float>();
+  int16_t *data_tmp = (int16_t *)malloc(c * h * w * sizeof(int16_t));
+  int16_t *data_ptr_16 = (int16_t *)data_ptr;
+  convert_to_chw(&data_ptr_16, c, h, w, data_tmp);
+  // const int16_t *dataptr = input_tensor.data<int16_t>();
+  std::ofstream out(filename.c_str());
+  float result = 0;
+  int stride = input_tensor.numel() / dumpnum;
+  stride = stride > 0 ? stride : 1;
+  for (int i = 0; i < input_tensor.numel(); i += stride) {
+    result = paddle_mobile::fpga::fp16_2_fp32(data_tmp[i]);
+    out << result << std::endl;
+  }
+  out.close();
+  free(data_tmp);
+}
+static const char *g_resnet50 = "../models/resnet50";
+const std::string g_image_src_float = "../images/image_src_float";
 int main() {
-  DLOG << paddle_mobile::fpga::open_device();
+  paddle_mobile::fpga::open_device();
   paddle_mobile::PaddleMobile<paddle_mobile::FPGA> paddle_mobile;
-  //  if (paddle_mobile.Load(std::string(g_resnet_combine) + "/model",
-  //                         std::string(g_resnet_combine) + "/params", true)) {
-  if (paddle_mobile.Load(std::string(g_resnet_combine), true)) {
-    std::vector<int64_t> dims{1, 3, 224, 224};
+  if (paddle_mobile.Load(std::string(g_resnet50), true)) {
     Tensor input_tensor;
     SetupTensor<float>(&input_tensor, {1, 3, 224, 224}, static_cast<float>(0),
                        static_cast<float>(1));
-
-    std::vector<float> input(input_tensor.data<float>(),
-                             input_tensor.data<float>() + input_tensor.numel());
-
+    readStream(g_image_src_float,
+               input_tensor.mutable_data<float>({1, 3, 224, 224}));
     paddle_mobile.FeedData(input_tensor);
-    for (int i = 0; i < 1000; i++) {
-      paddle_mobile.Predict_To(-1);
-      if (i % 100 == 0) std::cout << i << std::endl;
-    }
+    paddle_mobile.Predict_To(-1);
+    /*for(int i = 0; i < 73; i++)
+    {
+      auto tensor_ptr = paddle_mobile.FetchResult(i);
+      std::string saveName = "resnet50_result_" + std::to_string (i);
+      paddle_mobile::fpga::fpga_invalidate((*tensor_ptr).data<float>(),
+    tensor_ptr->numel()); dump_stride(saveName, (*tensor_ptr), 20);
+      //dump(saveName, (*tensor_ptr));
+    }*/
 
-    //    paddle_mobile.Predict_From(73);
-    //    paddle_mobile.Predict_From_To(72, 73);
-
-    DLOG << "Computation done";
+    /*std::shared_ptr<Tensor> output_tensor = paddle_mobile.FetchResult(73);
+    (*output_tensor).dump<float>("resnet50_result_73");
+    output_tensor = paddle_mobile.FetchResult(74);
+    (*output_tensor).dump<float>("resnet50_result_74");*/
+    std::shared_ptr<Tensor> output_tensor = paddle_mobile.FetchResult(74);
+    float max = 0;
+    auto data_ptr = output_tensor->data<float>();
+    int maximumIdx = 0;
+    for (int i = 0; i < (*output_tensor).numel(); i++) {
+      if (data_ptr[i] > max) {
+        maximumIdx = i;
+        max = data_ptr[i];
+      }
+    }
+    std::cout << "index : " << maximumIdx << ",    value : " << max
+              << std::endl;
+    std::cout << "Computation done" << std::endl;
     return 0;
   }
 }