【Hackathon 6th Fundable Projects 2 No.22】cppcoreguidelines-pro-type-member-init_3-part

paddle/common/enforce.cc

@@ -95,7 +95,7 @@ std::string GetCurrentTraceBackString(bool for_signal) {
 #if !defined(_WIN32) && !defined(PADDLE_WITH_MUSL)
   static constexpr int TRACE_STACK_LIMIT = 100;
 
-  std::array<void*, TRACE_STACK_LIMIT> call_stack;
+  std::array<void*, TRACE_STACK_LIMIT> call_stack = {};
   auto size = backtrace(call_stack.data(), TRACE_STACK_LIMIT);
   auto symbols = backtrace_symbols(call_stack.data(), size);
   Dl_info info;

paddle/fluid/distributed/ps/table/memory_sparse_table.cc

@@ -148,7 +148,7 @@ int32_t MemorySparseTable::Load(const std::string &path,
   omp_set_num_threads(thread_num);
 #pragma omp parallel for schedule(dynamic)
   for (int i = 0; i < _real_local_shard_num; ++i) {
-    FsChannelConfig channel_config;
+    FsChannelConfig channel_config = {};
     channel_config.path = file_list[file_start_idx + i];
     VLOG(1) << "MemorySparseTable::load begin load " << channel_config.path
             << " into local shard " << i;
@@ -227,7 +227,7 @@ int32_t MemorySparseTable::LoadPatch(const std::vector<std::string> &file_list,
   omp_set_num_threads(thread_num);
 #pragma omp parallel for schedule(dynamic)
   for (int i = start_idx; i < end_idx; ++i) {
-    FsChannelConfig channel_config;
+    FsChannelConfig channel_config = {};
     channel_config.path = file_list[i];
     channel_config.converter = _value_accessor->Converter(load_param).converter;
     channel_config.deconverter =
@@ -351,7 +351,7 @@ int32_t MemorySparseTable::Save(const std::string &dirname,
   omp_set_num_threads(thread_num);
 #pragma omp parallel for schedule(dynamic)
   for (int i = 0; i < _real_local_shard_num; ++i) {
-    FsChannelConfig channel_config;
+    FsChannelConfig channel_config = {};
     if (_config.compress_in_save() && (save_param == 0 || save_param == 3)) {
       channel_config.path =
           ::paddle::string::format_string("%s/part-%03d-%05d.gz",
@@ -495,7 +495,7 @@ int32_t MemorySparseTable::Save_v2(const std::string &dirname,
 
 #pragma omp parallel for schedule(dynamic)
   for (int i = 0; i < _real_local_shard_num; ++i) {
-    FsChannelConfig channel_config;
+    FsChannelConfig channel_config = {};
     FsChannelConfig channel_config_for_slot_feature;
 
     if (_config.compress_in_save() && (save_param == 0 || save_param == 3)) {
@@ -670,7 +670,7 @@ int32_t MemorySparseTable::SavePatch(const std::string &path, int save_param) {
   omp_set_num_threads(thread_num);
 #pragma omp parallel for schedule(dynamic)
   for (int i = 0; i < _m_real_local_shard_num; ++i) {
-    FsChannelConfig channel_config;
+    FsChannelConfig channel_config = {};
     channel_config.path = ::paddle::string::format_string("%s/part-%03d-%05d",
                                                           table_path.c_str(),
                                                           _shard_idx,
@@ -870,7 +870,7 @@ int32_t MemorySparseTable::SaveCache(
   _afs_client.remove(::paddle::string::format_string(
       "%s/part-%03d", table_path.c_str(), _shard_idx));
   uint32_t feasign_size = 0;
-  FsChannelConfig channel_config;
+  FsChannelConfig channel_config = {};
   // not compress cache model
   channel_config.path = ::paddle::string::format_string(
       "%s/part-%03d", table_path.c_str(), _shard_idx);

paddle/fluid/framework/data_feed.cc

@@ -1660,7 +1660,7 @@ bool MultiSlotFileInstantDataFeed::Preprocess(const std::string& filename) {
           "Fail to open file: %s in MultiSlotFileInstantDataFeed.",
           filename.c_str()));
 
-  struct stat sb;
+  struct stat sb = {};
   fstat(fd_, &sb);
   end_ = static_cast<size_t>(sb.st_size);
 

paddle/fluid/framework/io/fs.cc

@@ -147,7 +147,7 @@ std::shared_ptr<FILE> localfs_open_append_write(std::string path,
 }
 
 int64_t localfs_file_size(const std::string& path) {
-  struct stat buf;
+  struct stat buf = {};
   if (0 != stat(path.c_str(), &buf)) {
     PADDLE_THROW(platform::errors::External(
         "Failed to get file status via stat function."));

paddle/fluid/inference/tensorrt/convert/deformable_conv_op.cc

@@ -58,7 +58,7 @@ class DeformableConvOpConverter : public OpConverter {
         PADDLE_GET_CONST(int, op_desc.GetAttr("deformable_groups"));
     auto im2col_step = PADDLE_GET_CONST(int, op_desc.GetAttr("im2col_step"));
 
-    nvinfer1::Weights weights;
+    nvinfer1::Weights weights = {};
     weights.count = filter_tensor->numel();
     // TODO(bukejiyu): deformable_conv currently does not support fp16
     // mode,will be supported in the future.

paddle/fluid/inference/tensorrt/convert/transpose_op.cc

@@ -35,7 +35,7 @@ class TransposeOpConverter : public OpConverter {
         axis[i]--;
       }
     }
-    nvinfer1::Permutation perm;
+    nvinfer1::Permutation perm = {};
     for (int i = 0; i < dims; i++) {
       int j = engine_->with_dynamic_shape() ? i : i + 1;
       perm.order[i] = axis[j];

paddle/fluid/inference/tensorrt/dynamic_shape_infermeta.cc

@@ -41,7 +41,7 @@ class ExprWrapper {
   friend ExprWrapper BinaryOp(const ExprWrapper& a,
                               const ExprWrapper& b,
                               nvinfer1::DimensionOperation op) {
-    ExprWrapper result;
+    ExprWrapper result = {};
     if (a.expr_builder) {
       result.expr_builder = a.expr_builder;
     }
@@ -57,7 +57,7 @@ class ExprWrapper {
                               int b_value,
                               nvinfer1::DimensionOperation op) {
     assert(a.expr_builder);
-    ExprWrapper b;
+    ExprWrapper b = {};
     b.expr_builder = a.expr_builder;
     b.expr = b.expr_builder->constant(b_value);
     return BinaryOp(a, b, op);
@@ -129,7 +129,7 @@ static std::vector<ExprWrapper> DimsExprs2VecExprWrapper(
 
 static nvinfer1::DimsExprs VecExprWrapper2DimsExprs(
     const std::vector<ExprWrapper>& output_dims_wrapper) {
-  nvinfer1::DimsExprs output_dims;
+  nvinfer1::DimsExprs output_dims = {};
   output_dims.nbDims = output_dims_wrapper.size();
   for (int i = 0; i < output_dims.nbDims; i++) {
     output_dims.d[i] = output_dims_wrapper[i].extract_expr();
@@ -163,7 +163,7 @@ nvinfer1::DimsExprs GatherNdInferMeta(
     }
   }
 
-  nvinfer1::DimsExprs output;
+  nvinfer1::DimsExprs output = {};
   output.nbDims = result_dims.size();
   for (int i = 0; i < output.nbDims; i++) {
     output.d[i] = result_dims[i];
@@ -196,7 +196,7 @@ nvinfer1::DimsExprs YoloBoxInferMeta(
           nvinfer1::DimensionOperation::kPROD, *dim_x.d[2], *dim_x.d[3]),
       *expr_builder.constant(anchor_num));
 
-  nvinfer1::DimsExprs output;
+  nvinfer1::DimsExprs output = {};
   output.nbDims = 3;
   if (output_index == 0) {
     output.d[0] = dim_x.d[0];
@@ -314,7 +314,7 @@ nvinfer1::DimsExprs UnfoldInferMeta(
       nvinfer1::DimensionOperation::kPROD, *output_height, *output_width);
 
   out_dims.push_back(output_col_length);
-  nvinfer1::DimsExprs output;
+  nvinfer1::DimsExprs output = {};
   output.nbDims = out_dims.size();
   for (size_t i = 0; i < out_dims.size(); i++) output.d[i] = out_dims[i];
   return output;
@@ -368,7 +368,7 @@ nvinfer1::DimsExprs Pad3dInferMeta(
     const framework::OpDesc& op_desc) {
   const nvinfer1::DimsExprs x_dim = inputs[0];
 
-  nvinfer1::DimsExprs out_dims;
+  nvinfer1::DimsExprs out_dims = {};
   out_dims.nbDims = x_dim.nbDims;
 
   out_dims.d[0] = x_dim.d[0];
@@ -496,7 +496,7 @@ nvinfer1::DimsExprs PNormInferMeta(
   }
   x_dim.d[axis] = expr_builder.constant(1);
 
-  nvinfer1::DimsExprs output;
+  nvinfer1::DimsExprs output = {};
   if (keepdim) {
     output = x_dim;
   } else {
@@ -515,7 +515,7 @@ nvinfer1::DimsExprs GridSamplerInferMeta(
   const nvinfer1::DimsExprs x_dims = inputs[0];
   const nvinfer1::DimsExprs grid_dims = inputs[1];
 
-  nvinfer1::DimsExprs output;
+  nvinfer1::DimsExprs output = {};
   if (grid_dims.nbDims == 4) {
     output.nbDims = 4;
     output.d[0] = x_dims.d[0];
@@ -684,7 +684,7 @@ nvinfer1::DimsExprs LookupTableV2InferMeta(
   const auto x_dims = inputs[0];
   const auto weight_dims = inputs[1];
 
-  nvinfer1::DimsExprs output;
+  nvinfer1::DimsExprs output = {};
   output.nbDims = x_dims.nbDims + 1;
   for (int i = 0; i < x_dims.nbDims; ++i) {
     output.d[i] = x_dims.d[i];
@@ -714,13 +714,13 @@ nvinfer1::DimsExprs MemoryEfficientAttentionInferMeta(
   if (output_index == 0) {
     return inputs[0];
   } else if (output_index == 1) {
-    nvinfer1::DimsExprs output;
+    nvinfer1::DimsExprs output = {};
     output.nbDims = 2;
     output.d[0] = inputs[0].d[0];
     output.d[1] = inputs[0].d[2];
     return output;
   } else {
-    nvinfer1::DimsExprs output;
+    nvinfer1::DimsExprs output = {};
     output.nbDims = 1;
     output.d[0] = expr_builder.constant(2);
     return output;
@@ -815,7 +815,7 @@ nvinfer1::DimsExprs PadInferMeta(
   auto paddings =
       PADDLE_GET_CONST(std::vector<int>, op_desc.GetAttr("paddings"));
 
-  nvinfer1::DimsExprs output;
+  nvinfer1::DimsExprs output = {};
   output.nbDims = x_dims.nbDims;
   for (int i = 0; i < x_dims.nbDims; ++i) {
     output.d[i] = expr_builder.operation(
@@ -852,7 +852,7 @@ nvinfer1::DimsExprs ArgsortInferMeta(
     nvinfer1::IExprBuilder& expr_builder,  // NOLINT
     const framework::OpDesc& op_desc) {
   const nvinfer1::DimsExprs input_dims = inputs[0];
-  nvinfer1::DimsExprs output;
+  nvinfer1::DimsExprs output = {};
   output.nbDims = input_dims.nbDims;
   for (int i = 0; i < input_dims.nbDims; ++i) {
     output.d[i] = input_dims.d[i];

paddle/fluid/inference/utils/table_printer.cc

@@ -65,7 +65,7 @@ TablePrinter::TablePrinter(const std::vector<std::string>& header) {
     terminal_width = csbi.dwSize.X;
   }
 #else
-  struct winsize terminal_size;
+  struct winsize terminal_size = {};
   int status = ioctl(STDOUT_FILENO, TIOCGWINSZ, &terminal_size);
   if (status == 0 && terminal_size.ws_col != 0) {
     terminal_width = terminal_size.ws_col;

paddle/fluid/platform/gen_comm_id_helper.cc

@@ -163,7 +163,7 @@ int CreateListenSocket(const std::string& ep) {
   int opt = 1;
 
   // NOTE. The linger is used for skipping TIME-WAIT status forcefully.
-  linger ling;
+  linger ling = {};
   ling.l_onoff = 1;
   ling.l_linger = 0;
 
@@ -185,7 +185,7 @@ int CreateListenSocket(const std::string& ep) {
       "setsockopt");
 #endif
 
-  struct sockaddr_in address;
+  struct sockaddr_in address = {};
   address.sin_family = AF_INET;
   address.sin_addr.s_addr = INADDR_ANY;
   address.sin_port = htons(port);
@@ -219,7 +219,7 @@ static int SocketAccept(int server_fd, const CommHead head) {
   static_assert(sizeof(CommHead) <= 1024,
                 "sizeof(CommHead) must <= buffer size");
 
-  struct sockaddr_in client_addr;
+  struct sockaddr_in client_addr = {};
   socklen_t addr_length = sizeof(client_addr);
   std::array<char, 1024> buffer{0};
   int conn = -1;
@@ -255,7 +255,7 @@ static int ConnectAddr(const std::string& ep, const CommHead head) {
   std::string host = addr[0];
   int port = std::stoi(addr[1]);
 
-  struct sockaddr_in server_addr;
+  struct sockaddr_in server_addr = {};
   memset(&server_addr, 0, sizeof(server_addr));
   server_addr.sin_family = AF_INET;
   server_addr.sin_port = htons(port);

paddle/fluid/platform/init.cc

@@ -342,7 +342,7 @@ void DisableSignalHandler() {
 #ifndef _WIN32
   for (const auto &SignalErrorString : SignalErrorStrings) {
     int signal_number = SignalErrorString.signal_number;
-    struct sigaction sig_action;
+    struct sigaction sig_action = {};
     memset(&sig_action, 0, sizeof(sig_action));
     sigemptyset(&sig_action.sa_mask);
     sig_action.sa_handler = SIG_DFL;

paddle/phi/backends/device_code.cc

@@ -177,7 +177,7 @@ static std::string FindCUDAIncludePath() {
     return pos != std::string::npos && pos == (str.length() - substr.length());
   };
 
-  struct stat st;
+  struct stat st = {};
   std::string cuda_include_path;
   if (!FLAGS_cuda_dir.empty()) {
     cuda_include_path = FLAGS_cuda_dir;

paddle/phi/common/port.cc

@@ -75,7 +75,7 @@ int gettimeofday(struct timeval *tp, void *tzp) {
 #endif              // !_WIN32
 
 void ExecShellCommand(const std::string &cmd, std::string *message) {
-  std::array<char, 128> buffer;
+  std::array<char, 128> buffer = {};
 #if !defined(_WIN32)
   std::shared_ptr<FILE> pipe(popen(cmd.c_str(), "r"), pclose);
 #else
@@ -94,7 +94,7 @@ void ExecShellCommand(const std::string &cmd, std::string *message) {
 
 bool PathExists(const std::string &path) {
 #if !defined(_WIN32)
-  struct stat statbuf;
+  struct stat statbuf = {};
   if (stat(path.c_str(), &statbuf) != -1) {
     if (S_ISDIR(statbuf.st_mode)) {
       return true;
@@ -119,7 +119,7 @@ constexpr char kSEP = '\\';
 
 bool FileExists(const std::string &filepath) {
 #if !defined(_WIN32)
-  struct stat buffer;
+  struct stat buffer = {};
   return (stat(filepath.c_str(), &buffer) == 0);
 #else
   struct _stat buffer;

paddle/phi/core/distributed/store/socket.cpp

@@ -34,7 +34,7 @@ static int _get_sockname_of_win(int sock, char* out, int out_len) {
 }
 #else
 static int _get_sockname(int sock, char *out, int out_len) {
-  struct sockaddr_in addr;
+  struct sockaddr_in addr = {};
   socklen_t s_len = sizeof(addr);
 
   if (::getpeername(sock, reinterpret_cast<sockaddr *>(&addr), &s_len)) {
@@ -43,7 +43,7 @@ static int _get_sockname(int sock, char *out, int out_len) {
     return -1;
   }
 
-  std::array<char, 128> ip;
+  std::array<char, 128> ip = {};
   int port = 0;
 
   // deal with both IPv4 and IPv6:
@@ -71,7 +71,7 @@ int GetSockName(int sock, char* out, int out_len) {
 }
 
 std::string GetSockName(int fd) {
-  std::array<char, 256> out;
+  std::array<char, 256> out = {};
   GetSockName(fd, out.data(), sizeof(out));
   return std::string(out.data());
 }

paddle/phi/kernels/cpu/eigvals_kernel.cc

@@ -215,7 +215,7 @@ void EigvalsKernel(const Context& ctx, const DenseTensor& x, DenseTensor* out) {
   std::vector<DenseTensor> out_vectors = out->Split(1, 0);
 
   // query workspace size
-  T qwork;
+  T qwork = T();
   int info = 0;
   funcs::lapackEig<T, dtype::Real<T>>('N',
                                       'N',

paddle/phi/kernels/cpu/fill_diagonal_tensor_kernel.cc

@@ -88,8 +88,8 @@ void FillDiagonalTensorKernel(const Context &ctx,
   auto matdims = y.dims();
   auto fill_dims = common::flatten_to_2d(matdims, matdims.size() - 1);
 
-  std::array<int64_t, 2> new_dims;
-  std::array<int64_t, 2> strides;
+  std::array<int64_t, 2> new_dims = {};
+  std::array<int64_t, 2> strides = {};
   std::vector<int64_t> matdim;
   matdim.resize(fill_dims[0]);
   CalMatDims(out_dims,

paddle/phi/kernels/cpu/index_put_grad_kernel.cc

@@ -79,7 +79,7 @@ void LaunchIndexPutGradKernel(const Context& dev_ctx,
                               bool accumulate,
                               DenseTensor* value_grad,
                               DenseTensor* x_grad) {
-  std::array<const int64_t*, 7> pd_indices;
+  std::array<const int64_t*, 7> pd_indices = {};
   for (size_t i = 0; i < indices.size(); ++i) {
     pd_indices[i] = indices[i]->data<int64_t>();
   }

paddle/phi/kernels/cpu/yolo_box_kernel.cc

@@ -67,7 +67,7 @@ void YoloBoxKernel(const Context& dev_ctx,
 
   memset(scores_data, 0, scores->numel() * sizeof(T));
 
-  std::array<T, 4> box;
+  std::array<T, 4> box = {};
   for (int i = 0; i < n; i++) {
     int img_height = imgsize_data[2 * i];
     int img_width = imgsize_data[2 * i + 1];

paddle/phi/kernels/cpu/yolo_loss_kernel.cc

@@ -60,7 +60,7 @@ static inline Box<T> GetYoloBox(const T* x,
                                 int stride,
                                 float scale,
                                 float bias) {
-  Box<T> b;
+  Box<T> b = {};
   b.x = (i + sigmoid<T>(x[index]) * scale + bias) / grid_size;
   b.y = (j + sigmoid<T>(x[index + stride]) * scale + bias) / grid_size;
   b.w = std::exp(x[index + 2 * stride]) * anchors[2 * an_idx] / input_size;
@@ -307,7 +307,7 @@ void YoloLossKernel(const Context& dev_ctx,
       // for positive sample, all losses should be calculated, and for
       // other samples, only objectness loss is required.
       for (int an_idx = 0; an_idx < an_num; an_idx++) {
-        Box<T> an_box;
+        Box<T> an_box = {};
         an_box.x = 0.0;
         an_box.y = 0.0;
         an_box.w = anchors[2 * an_idx] / static_cast<T>(input_size);