[docs] fixed prebuilt libs typos and update readme

CMakeLists.txt

@@ -133,7 +133,13 @@ if(ENABLE_PADDLE_BACKEND)
   add_definitions(-DENABLE_PADDLE_BACKEND)
   list(APPEND ALL_DEPLOY_SRCS ${DEPLOY_PADDLE_SRCS})
   include(external/paddle_inference.cmake)
-  list(APPEND DEPEND_LIBS external_paddle_inference external_dnnl external_omp)
+  if(NOT APPLE)
+    list(APPEND DEPEND_LIBS external_paddle_inference external_dnnl external_omp)
+  else()
+    # no third parties libs(mkldnn and omp) need to
+    # link into paddle_inference on MacOS OSX.
+    list(APPEND DEPEND_LIBS external_paddle_inference)
+  endif()
 endif()
 
 if(WITH_GPU)

FastDeploy.cmake.in

@@ -33,13 +33,16 @@ if(ENABLE_PADDLE_BACKEND)
     set(DNNL_LIB "${CMAKE_CURRENT_LIST_DIR}/third_libs/install/paddle_inference/third_party/install/mkldnn/lib/mkldnn.lib")
     set(IOMP_LIB "${CMAKE_CURRENT_LIST_DIR}/third_libs/install/paddle_inference/third_party/install/mklml/lib/libiomp5md.lib")
   elseif(APPLE)
-    set(DNNL_LIB "")
-    set(IOMP_LIB "")
+    message(STATUS "No third parties libs(mkldnn and omp) need to link into paddle_inference on MacOS OSX.")
   else()
     set(DNNL_LIB "${CMAKE_CURRENT_LIST_DIR}/third_libs/install/paddle_inference/third_party/install/mkldnn/lib/libmkldnn.so.0")
     set(IOMP_LIB "${CMAKE_CURRENT_LIST_DIR}/third_libs/install/paddle_inference/third_party/install/mklml/lib/libiomp5.so")
   endif()
-  list(APPEND FASTDEPLOY_LIBS ${PADDLE_LIB} ${DNNL_LIB} ${IOMP_LIB})
+  if(NOT APPLE)
+    list(APPEND FASTDEPLOY_LIBS ${PADDLE_LIB} ${DNNL_LIB} ${IOMP_LIB})
+  else()
+    list(APPEND FASTDEPLOY_LIBS ${PADDLE_LIB})
+  endif()
 endif()
 
 if(WITH_GPU)

csrc/fastdeploy/backends/common/multiclass_nms.cc

@@ -162,11 +162,11 @@ void MultiClassNMS::Compute(const float* boxes_data, const float* scores_data,
   int64_t out_dim = box_dim + 2;
 
   int num_nmsed_out = 0;
-  FDASSERT(score_size == 3, "Require rank of input scores be 3, but now it's " +
-                                std::to_string(score_size) + ".");
+  FDASSERT(score_size == 3,
+           "Require rank of input scores be 3, but now it's %d.", score_size);
   FDASSERT(boxes_dim[2] == 4,
-           "Require the 3-dimension of input boxes be 4, but now it's " +
-               std::to_string(boxes_dim[2]) + ".");
+           "Require the 3-dimension of input boxes be 4, but now it's %lld.",
+           box_dim);
   out_num_rois_data.resize(batch_size);
 
   std::vector<std::map<int, std::vector<int>>> all_indices;

csrc/fastdeploy/backends/ort/ops/multiclass_nms.cc

@@ -178,11 +178,11 @@ void MultiClassNmsKernel::Compute(OrtKernelContext* context) {
   int64_t out_dim = box_dim + 2;
 
   int num_nmsed_out = 0;
-  FDASSERT(score_size == 3, "Require rank of input scores be 3, but now it's " +
-                                std::to_string(score_size) + ".");
+  FDASSERT(score_size == 3,
+           "Require rank of input scores be 3, but now it's %d.", score_size);
   FDASSERT(boxes_dim[2] == 4,
-           "Require the 3-dimension of input boxes be 4, but now it's " +
-               std::to_string(boxes_dim[2]) + ".");
+           "Require the 3-dimension of input boxes be 4, but now it's %lld.",
+           box_dim);
   std::vector<int64_t> out_num_rois_dims = {batch_size};
   OrtValue* out_num_rois = ort_.KernelContext_GetOutput(
       context, 2, out_num_rois_dims.data(), out_num_rois_dims.size());

csrc/fastdeploy/backends/ort/ort_backend.cc

@@ -191,8 +191,10 @@ void OrtBackend::CopyToCpu(const Ort::Value& value, FDTensor* tensor) {
            numel * sizeof(double));
     tensor->dtype = FDDataType::FP64;
   } else {
-    FDASSERT(false, "Unrecognized data type of " + std::to_string(data_type) +
-                        " while calling OrtBackend::CopyToCpu().");
+    FDASSERT(
+        false,
+        "Unrecognized data type of %d while calling OrtBackend::CopyToCpu().",
+        data_type);
   }
 }
 
@@ -237,9 +239,9 @@ bool OrtBackend::Infer(std::vector<FDTensor>& inputs,
 }
 
 TensorInfo OrtBackend::GetInputInfo(int index) {
-  FDASSERT(index < NumInputs(), "The index:" + std::to_string(index) +
-                                    " should less than the number of inputs:" +
-                                    std::to_string(NumInputs()) + ".");
+  FDASSERT(index < NumInputs(),
+           "The index: %d should less than the number of inputs: %d.", index,
+           NumInputs());
   TensorInfo info;
   info.name = inputs_desc_[index].name;
   info.shape.assign(inputs_desc_[index].shape.begin(),
@@ -250,9 +252,8 @@ TensorInfo OrtBackend::GetInputInfo(int index) {
 
 TensorInfo OrtBackend::GetOutputInfo(int index) {
   FDASSERT(index < NumOutputs(),
-           "The index:" + std::to_string(index) +
-               " should less than the number of outputs:" +
-               std::to_string(NumOutputs()) + ".");
+           "The index: %d should less than the number of outputs: %d.", index,
+           NumOutputs());
   TensorInfo info;
   info.name = outputs_desc_[index].name;
   info.shape.assign(outputs_desc_[index].shape.begin(),

csrc/fastdeploy/core/fd_type.cc

@@ -34,7 +34,7 @@ int FDDataTypeSize(const FDDataType& data_type) {
   } else if (data_type == FDDataType::UINT8) {
     return sizeof(uint8_t);
   } else {
-    FDASSERT(false, "Unexpected data type: " + Str(data_type));
+    FDASSERT(false, "Unexpected data type: %s", Str(data_type).c_str());
   }
   return -1;
 }

csrc/fastdeploy/fastdeploy_model.cc

@@ -89,12 +89,12 @@ bool FastDeployModel::InitRuntime() {
                 << Str(runtime_option.backend) << "." << std::endl;
       if (use_gpu) {
         FDASSERT(valid_gpu_backends.size() > 0,
-                 "There's no valid gpu backend for " + ModelName() + ".");
+                 "There's no valid gpu backend for %s.", ModelName().c_str());
         FDWARNING << "FastDeploy will choose " << Str(valid_gpu_backends[0])
                   << " for model inference." << std::endl;
       } else {
         FDASSERT(valid_gpu_backends.size() > 0,
-                 "There's no valid cpu backend for " + ModelName() + ".");
+                 "There's no valid cpu backend for %s.", ModelName().c_str());
         FDWARNING << "FastDeploy will choose " << Str(valid_cpu_backends[0])
                   << " for model inference." << std::endl;
       }

csrc/fastdeploy/function/reduce.cc

@@ -310,15 +310,16 @@ void ArgMinMax(const FDTensor& x, FDTensor* out, int64_t axis,
   const auto& x_dims = x.shape;
   int64_t x_rank = x_dims.size();
   FDASSERT(axis >= -x_rank,
-           "'axis'(%d) must be greater than or equal to -Rank(X)(%d).", axis,
-           -x_rank);
+           "'axis'(%lld) must be greater than or equal to -Rank(X)(%lld).",
+           axis, -x_rank);
   FDASSERT(axis < x_rank,
-           "'axis'(%d) must be less than or equal to Rank(X)(%d).", axis,
+           "'axis'(%lld) must be less than or equal to Rank(X)(%lld).", axis,
            x_rank);
   FDASSERT(output_dtype == FDDataType::INT32 || FDDataType::INT64,
            "The attribute of dtype in argmin/argmax must be [%s] or [%s], but "
            "received [%s].",
-           Str(FDDataType::INT32), Str(FDDataType::INT64), Str(output_dtype));
+           Str(FDDataType::INT32).c_str(), Str(FDDataType::INT64).c_str(),
+           Str(output_dtype).c_str());
   if (axis < 0) axis += x_rank;
   if (output_dtype == FDDataType::INT32) {
     int64_t all_element_num = 0;
@@ -330,7 +331,7 @@ void ArgMinMax(const FDTensor& x, FDTensor* out, int64_t axis,
     }
     FDASSERT(all_element_num <= std::numeric_limits<int>::max(),
              "The element num of the argmin/argmax input at axis is "
-             "%d, is larger than int32 maximum value:%d, you must "
+             "%lld, is larger than int32 maximum value:%d, you must "
              "set the dtype of argmin/argmax to 'int64'.",
              all_element_num, std::numeric_limits<int>::max());
   }

csrc/fastdeploy/function/softmax.cc

@@ -117,7 +117,7 @@ void Softmax(const FDTensor& x, FDTensor* out, int axis) {
   FDASSERT(
       std::abs(axis) < x.shape.size(),
       "The absolute given axis should be smaller than the input's "
-      "dimension. Expected absolute axis is smaller than %d, but receive %d.",
+      "dimension. Expected absolute axis is smaller than %lu, but receive %d.",
       x.shape.size(), std::abs(axis));
   FD_VISIT_FLOAT_TYPES(x.dtype, "SoftmaxKernel",
                        ([&] { SoftmaxKernel<data_t>(x, out, axis); }));

csrc/fastdeploy/function/transpose.cc

@@ -95,12 +95,12 @@ void Transpose(const FDTensor& x, FDTensor* out,
   size_t dims_size = dims.size();
   FDASSERT(dims_size == x.shape.size(),
            "The input tensor's dimension should be equal to the dims's size. "
-           "Expect dims size is %d, but receive %d.",
+           "Expect dims size is %lu, but receive %lu.",
            x.shape.size(), dims_size);
   std::vector<int> count(dims_size, 0);
   for (size_t i = 0; i < dims_size; i++) {
     FDASSERT(dims[i] >= 0,
-             "The dims should be greater than or equal to 0, but receive %d.",
+             "The dims should be greater than or equal to 0, but receive %lld.",
              dims[i]);
     FDASSERT(dims[i] < static_cast<int>(dims_size) && ++count[dims[i]] == 1,
              "Each element of Attribute axis should be a unique value range "

csrc/fastdeploy/pybind/main.cc

@@ -33,8 +33,8 @@ pybind11::dtype FDDataTypeToNumpyDataType(const FDDataType& fd_dtype) {
   } else if (fd_dtype == FDDataType::UINT8) {
     dt = pybind11::dtype::of<uint8_t>();
   } else {
-    FDASSERT(false, "The function doesn't support data type of " +
-                        Str(fd_dtype) + ".");
+    FDASSERT(false, "The function doesn't support data type of %s.",
+             Str(fd_dtype).c_str());
   }
   return dt;
 }
@@ -73,7 +73,8 @@ void PyArrayToTensor(pybind11::array& pyarray, FDTensor* tensor,
 pybind11::array TensorToPyArray(const FDTensor& tensor) {
   auto numpy_dtype = FDDataTypeToNumpyDataType(tensor.dtype);
   auto out = pybind11::array(numpy_dtype, tensor.shape);
-  memcpy(out.mutable_data(), tensor.Data(), tensor.Numel() * FDDataTypeSize(tensor.dtype));
+  memcpy(out.mutable_data(), tensor.Data(),
+         tensor.Numel() * FDDataTypeSize(tensor.dtype));
   return out;
 }
 

csrc/fastdeploy/pybind/main.cc.in

@@ -33,8 +33,8 @@ pybind11::dtype FDDataTypeToNumpyDataType(const FDDataType& fd_dtype) {
   } else if (fd_dtype == FDDataType::UINT8) {
     dt = pybind11::dtype::of<uint8_t>();
   } else {
-    FDASSERT(false, "The function doesn't support data type of " +
-                        Str(fd_dtype) + ".");
+    FDASSERT(false, "The function doesn't support data type of %s.",
+                        Str(fd_dtype).c_str());
   }
   return dt;
 }

csrc/fastdeploy/utils/utils.h

@@ -86,14 +86,13 @@ FASTDEPLOY_DECL bool ReadBinaryFromFile(const std::string& file,
   FDLogger(true, "[INFO]") << __REL_FILE__ << "(" << __LINE__ \
                            << ")::" << __FUNCTION__ << "\t"
 
-#define FDASSERT(condition, format, ...)                                      \
-  if (!(condition)) {                                                         \
-    std::string format_string(format);                                        \
-    int n = std::snprintf(nullptr, 0, format_string.data(), ##__VA_ARGS__);   \
-    std::vector<char> buffer(n + 1);                                          \
-    std::snprintf(buffer.data(), n + 1, format_string.data(), ##__VA_ARGS__); \
-    FDERROR << buffer.data() << std::endl;                                    \
-    std::abort();                                                             \
+#define FDASSERT(condition, format, ...)                        \
+  if (!(condition)) {                                           \
+    int n = std::snprintf(nullptr, 0, format, ##__VA_ARGS__);   \
+    std::vector<char> buffer(n + 1);                            \
+    std::snprintf(buffer.data(), n + 1, format, ##__VA_ARGS__); \
+    FDERROR << buffer.data() << std::endl;                      \
+    std::abort();                                               \
   }
 
 ///////// Basic Marco ///////////
@@ -128,7 +127,7 @@ FASTDEPLOY_DECL bool ReadBinaryFromFile(const std::string& file,
             false,                                                             \
             "Invalid enum data type. Expect to accept data type BOOL, INT32, " \
             "INT64, FP32, FP64, but receive type %s.",                         \
-            Str(__dtype__));                                                   \
+            Str(__dtype__).c_str());                                           \
     }                                                                          \
   }()
 
@@ -148,7 +147,7 @@ FASTDEPLOY_DECL bool ReadBinaryFromFile(const std::string& file,
         FDASSERT(false,                                                       \
                  "Invalid enum data type. Expect to accept data type INT32, " \
                  "INT64, FP32, FP64, but receive type %s.",                   \
-                 Str(__dtype__));                                             \
+                 Str(__dtype__).c_str());                                     \
     }                                                                         \
   }()
 
@@ -164,7 +163,7 @@ FASTDEPLOY_DECL bool ReadBinaryFromFile(const std::string& file,
         FDASSERT(false,                                                      \
                  "Invalid enum data type. Expect to accept data type FP32, " \
                  "FP64, but receive type %s.",                               \
-                 Str(__dtype__));                                            \
+                 Str(__dtype__).c_str());                                    \
     }                                                                        \
   }()
 
@@ -180,7 +179,7 @@ FASTDEPLOY_DECL bool ReadBinaryFromFile(const std::string& file,
         FDASSERT(false,                                                       \
                  "Invalid enum data type. Expect to accept data type INT32, " \
                  "INT64, but receive type %s.",                               \
-                 Str(__dtype__));                                             \
+                 Str(__dtype__).c_str());                                     \
     }                                                                         \
   }()
 

csrc/fastdeploy/vision/common/processors/mat.cc

@@ -97,8 +97,9 @@ FDDataType Mat::Type() {
   } else if (type == 1) {
     return FDDataType::INT8;
   } else if (type == 2) {
-    FDASSERT(false, "While calling Mat::Type(), get UINT16 type which is not "
-                    "supported now.");
+    FDASSERT(false,
+             "While calling Mat::Type(), get UINT16 type which is not "
+             "supported now.");
   } else if (type == 3) {
     return FDDataType::INT16;
   } else if (type == 4) {
@@ -108,10 +109,12 @@ FDDataType Mat::Type() {
   } else if (type == 6) {
     return FDDataType::FP64;
   } else {
-    FDASSERT(false, "While calling Mat::Type(), get type = " +
-                        std::to_string(type) + ", which is not expected!.");
+    FDASSERT(
+        false,
+        "While calling Mat::Type(), get type = %d, which is not expected!.",
+        type);
   }
 }
 
-} // namespace vision
-} // namespace fastdeploy
+}  // namespace vision
+}  // namespace fastdeploy

csrc/fastdeploy/vision/detection/ppdet/ppyoloe.cc

@@ -99,8 +99,8 @@ bool PPYOLOE::BuildPreprocessPipelineFromConfig() {
       auto target_size = op["target_size"].as<std::vector<int>>();
       int interp = op["interp"].as<int>();
       FDASSERT(target_size.size(),
-               "Require size of target_size be 2, but now it's " +
-                   std::to_string(target_size.size()) + ".");
+               "Require size of target_size be 2, but now it's %lu.",
+               target_size.size());
       if (!keep_ratio) {
         int width = target_size[1];
         int height = target_size[0];

csrc/fastdeploy/vision/segmentation/ppseg/model.cc

@@ -127,8 +127,8 @@ bool PaddleSegModel::Postprocess(
   //     3. shape: 2-D HW
   FDASSERT(infer_result.dtype == FDDataType::INT64 ||
                infer_result.dtype == FDDataType::FP32,
-           "Require the data type of output is int64 or fp32, but now it's " +
-               Str(infer_result.dtype) + ".");
+           "Require the data type of output is int64 or fp32, but now it's %s.",
+           Str(infer_result.dtype).c_str());
   result->Clear();
 
   if (infer_result.shape.size() == 4) {

docs/compile/prebuilt_libraries.md

@@ -14,15 +14,15 @@ FastDeploy提供了在Windows/Linux/Mac上的预先编译CPP部署库，开发
 
 | 部署库下载地址 | 硬件 | 说明 |
 | :------------- | :--- | :--- |
-| [fastdeploy-linux-x64-0.2.0](https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-0.2.0.tgz) | CPU | g++ 8.2编译产出 |
-| [fastdeploy-linux-x64-gpu-0.2.0](https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz) | CPU/GPU | g++ 8.2, cuda 11.2, cudnn 8.2编译产出 |
+| [fastdeploy-linux-x64-0.2.0.tgz](https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-0.2.0.tgz) | CPU | g++ 8.2编译产出 |
+| [fastdeploy-linux-x64-gpu-0.2.0.tgz](https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz) | CPU/GPU | g++ 8.2, cuda 11.2, cudnn 8.2编译产出 |
 
 ### Windows 10 x64平台
 
 | 部署库下载地址 | 硬件 | 说明 |
 | :------------- | :--- | :--- |
-| [fastdeploy-win-x64-0.2.0](https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-win-x64-0.2.0.zip) | CPU | Visual Studio 16 2019 编译产出 |
-| [fastdeploy-win-x64-gpu-0.2.0](https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-win-x64-gpu-0.2.0.zip) | CPU/GPU | Visual Studio 16 2019，cuda 11.2, cudnn 8.2编译产出 |
+| [fastdeploy-win-x64-0.2.0.zip](https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-win-x64-0.2.0.zip) | CPU | Visual Studio 16 2019 编译产出 |
+| [fastdeploy-win-x64-gpu-0.2.0.zip](https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-win-x64-gpu-0.2.0.zip) | CPU/GPU | Visual Studio 16 2019，cuda 11.2, cudnn 8.2编译产出 |
 
 ### Linux aarch64平台
 
@@ -35,8 +35,8 @@ FastDeploy提供了在Windows/Linux/Mac上的预先编译CPP部署库，开发
 
 | 部署库下载地址 | 架构 |硬件 |
 | :----  | :-- | :------ |
-| [comming...] | x86 | CPU |
-| [comming...] | arm64 | CPU |
+| [fastdeploy-osx-x86_64-0.2.0.tgz](https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-osx-x86_64-0.2.0.tgz) | x86 | CPU |
+| [fastdeploy-osx-arm64-0.2.0.tgz](https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-osx-arm64-0.2.0.tgz) | arm64 | CPU |
 
 ## 其它文档