fix more op errors

paddle/fluid/inference/api/api_impl.cc

@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <algorithm>
-#include <chrono>  // NOLINT
 #include <map>
 #include <set>
 #include <sstream>
@@ -22,30 +21,15 @@ limitations under the License. */
 #include <vector>
 
 #include "paddle/fluid/inference/api/api_impl.h"
+#include "paddle/fluid/inference/api/timer.h"
 #include "paddle/fluid/platform/profiler.h"
 
 DEFINE_bool(profile, false, "Turn on profiler for fluid");
+using Timer = paddle::inference::Timer;
 
 namespace paddle {
 namespace {
 
-// Timer for timer
-class Timer {
- public:
-  std::chrono::high_resolution_clock::time_point start;
-  std::chrono::high_resolution_clock::time_point startu;
-
-  void tic() { start = std::chrono::high_resolution_clock::now(); }
-  double toc() {
-    startu = std::chrono::high_resolution_clock::now();
-    std::chrono::duration<double> time_span =
-        std::chrono::duration_cast<std::chrono::duration<double>>(startu -
-                                                                  start);
-    double used_time_ms = static_cast<double>(time_span.count()) * 1000.0;
-    return used_time_ms;
-  }
-};
-
 template <class T>
 std::string num2str(T a) {
   std::stringstream istr;

paddle/fluid/inference/api/helper.h

@@ -20,30 +20,11 @@
 #include <string>
 #include <vector>
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/api/timer.h"
 
 namespace paddle {
 namespace inference {
 
-// Timer for timer
-class Timer {
- public:
-  double start;
-  double startu;
-  void tic() {
-    struct timeval tp;
-    gettimeofday(&tp, NULL);
-    start = tp.tv_sec;
-    startu = tp.tv_usec;
-  }
-  double toc() {
-    struct timeval tp;
-    gettimeofday(&tp, NULL);
-    double used_time_ms =
-        (tp.tv_sec - start) * 1000.0 + (tp.tv_usec - startu) / 1000.0;
-    return used_time_ms;
-  }
-};
-
 void split(const std::string &str, char sep, std::vector<std::string> *pieces) {
   pieces->clear();
   if (str.empty()) {
@@ -95,14 +76,18 @@ std::string to_string<std::vector<std::vector<float>>>(
   }
   return ss.str();
 }
-// clang-format off
-void TensorAssignData(PaddleTensor *tensor, const std::vector<std::vector<float>> &data) {
+
+void TensorAssignData(PaddleTensor *tensor,
+                      const std::vector<std::vector<float>> &data) {
   // Assign buffer
-  int dim = std::accumulate(tensor->shape.begin(), tensor->shape.end(), 1, [](int a, int b) { return a * b; });
+  int dim = std::accumulate(tensor->shape.begin(), tensor->shape.end(), 1,
+                            [](int a, int b) { return a * b; });
   tensor->data.Resize(sizeof(float) * dim);
   int c = 0;
   for (const auto &f : data) {
-    for (float v : f) { static_cast<float *>(tensor->data.data())[c++] = v; }
+    for (float v : f) {
+      static_cast<float *>(tensor->data.data())[c++] = v;
+    }
   }
 }
 

paddle/fluid/inference/api/timer.h

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// 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.
+#pragma once
+
+#include <chrono>  // NOLINT
+
+namespace paddle {
+namespace inference {
+
+// Timer for timer
+class Timer {
+ public:
+  std::chrono::high_resolution_clock::time_point start;
+  std::chrono::high_resolution_clock::time_point startu;
+
+  void tic() { start = std::chrono::high_resolution_clock::now(); }
+  double toc() {
+    startu = std::chrono::high_resolution_clock::now();
+    std::chrono::duration<double> time_span =
+        std::chrono::duration_cast<std::chrono::duration<double>>(startu -
+                                                                  start);
+    double used_time_ms = static_cast<double>(time_span.count()) * 1000.0;
+    return used_time_ms;
+  }
+};
+
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/operators/gru_unit_op.h

@@ -92,12 +92,12 @@ class GRUUnitKernel : public framework::OpKernel<T> {
               gate_data, frame_size * 3);
 
     // calculate activited gate
-    Eigen::array<int, 2> extents({{batch_size, frame_size}});
-    Eigen::array<int, 2> u_offsets({{0, 0}});
+    Eigen::array<int, 2> extents = {batch_size, frame_size};
+    Eigen::array<int, 2> u_offsets = {0, 0};
     ActCompute(context.Attr<int>("gate_activation"), place,
                g.slice(u_offsets, extents), g.slice(u_offsets, extents));
     auto u = g.slice(u_offsets, extents);  // update gate
-    Eigen::array<int, 2> r_offsets({{0, frame_size}});
+    Eigen::array<int, 2> r_offsets = {0, frame_size};
     ActCompute(context.Attr<int>("gate_activation"), place,
                g.slice(r_offsets, extents), g.slice(r_offsets, extents));
     auto r = g.slice(r_offsets, extents);  // reset gate
@@ -107,7 +107,7 @@ class GRUUnitKernel : public framework::OpKernel<T> {
               weight_data + frame_size * frame_size * 2, frame_size, 1,
               gate_data + frame_size * 2, frame_size * 3);
 
-    Eigen::array<int, 2> c_offsets({{0, frame_size * 2}});
+    Eigen::array<int, 2> c_offsets = {0, frame_size * 2};
     ActCompute(context.Attr<int>("activation"), place,
                g.slice(c_offsets, extents), g.slice(c_offsets, extents));
     auto c = g.slice(c_offsets, extents);  // output candidate
@@ -171,12 +171,12 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
     int batch_size = input->dims()[0];
     int frame_size = hidden_prev->dims()[1];
 
-    Eigen::array<int, 2> extents({{batch_size, frame_size}});
-    Eigen::array<int, 2> u_offsets({{0, 0}});
+    Eigen::array<int, 2> extents = {batch_size, frame_size};
+    Eigen::array<int, 2> u_offsets = {0, 0};
     auto u = g.slice(u_offsets, extents);  // update gate
-    Eigen::array<int, 2> r_offsets({{0, frame_size}});
+    Eigen::array<int, 2> r_offsets = {0, frame_size};
     auto r = g.slice(r_offsets, extents);  // reset gate
-    Eigen::array<int, 2> c_offsets({{0, frame_size * 2}});
+    Eigen::array<int, 2> c_offsets = {0, frame_size * 2};
     auto c = g.slice(c_offsets, extents);  // output candidate
 
     // backward for unactivated update gate

paddle/fluid/operators/label_smooth_op.h

@@ -38,7 +38,8 @@ class LabelSmoothKernel : public framework::OpKernel<T> {
       auto dist = framework::EigenVector<T>::Flatten(*dist_t);
       out.device(dev) =
           static_cast<T>(1 - epsilon) * in +
-          epsilon * dist.broadcast(Eigen::DSizes<int, 1>(in_t->numel()));
+          static_cast<T>(epsilon) *
+              dist.broadcast(Eigen::DSizes<int, 1>(in_t->numel()));
     } else {
       out.device(dev) = static_cast<T>(1 - epsilon) * in +
                         static_cast<T>(epsilon / label_dim);

paddle/fluid/operators/lod_tensor_to_array_op.cc

@@ -16,6 +16,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/detail/safe_ref.h"
 #include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/port.h"
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/split_lod_tensor_op.cc

@@ -15,6 +15,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/memory/memcpy.h"
 #include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/port.h"
 
 namespace paddle {
 namespace operators {

paddle/fluid/platform/port.h

@@ -24,12 +24,16 @@
 
 #if !defined(_WIN32)
 #define UNUSED __attribute__((unused))
-#include <dlfcn.h>     // for dladdr
-#include <execinfo.h>  // for backtrace
+#include <dlfcn.h>     //  dladdr
+#include <execinfo.h>  // backtrace
 #include <sys/stat.h>
+#include <algorithm>  // std::accumulate
 #else
 #include <io.h>  // _popen, _pclose
 #include <windows.h>
+#if defined(_WIN32)
+#include <numeric>  // std::accumulate in msvc
+#endif
 // windows version of __attribute__((unused))
 #define UNUSED __pragma(warning(suppress : 4100))