[MXNET-860] Avoid implicit double conversions

This avoids some implicit conversions to double and also should unify behaviour between C++11 compliant compilers.
apache · Sep 14, 2018 · e3ba545 · e3ba545
1 parent 52116d4
commit e3ba545
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Showing 17 changed files with 55 additions and 55 deletions.
diff --git a/src/io/image_det_aug_default.cc b/src/io/image_det_aug_default.cc
@@ -560,7 +560,7 @@ class DefaultImageDetAugmenter : public ImageAugmenter {
         }
         cv::cvtColor(res, res, CV_HLS2BGR);
       }
-      if (fabs(c) > 1e-3) {
+      if (std::fabs(c) > 1e-3) {
         cv::Mat tmp = res;
         tmp.convertTo(res, -1, c + 1.f, 0);
       }

diff --git a/src/operator/contrib/adaptive_avg_pooling.cc b/src/operator/contrib/adaptive_avg_pooling.cc
@@ -26,8 +26,8 @@
 // #include "elemwise_op_common.h"
 #include "../elemwise_op_common.h"
 
-#define START_IND(a, b, c) static_cast<int>(floor(static_cast<float>(a * c) / b))
-#define END_IND(a, b, c) static_cast<int>(ceil(static_cast<float>((a + 1) * c) / b))
+#define START_IND(a, b, c) static_cast<int>(std::floor(static_cast<float>(a * c) / b))
+#define END_IND(a, b, c) static_cast<int>(std::ceil(static_cast<float>((a + 1) * c) / b))
 
 namespace mxnet {
 namespace op {

diff --git a/src/operator/contrib/multi_proposal.cc b/src/operator/contrib/multi_proposal.cc
@@ -67,8 +67,8 @@ inline void BBoxTransformInv(const mshadow::Tensor<cpu, 2>& boxes,
 
     float pred_ctr_x = dx * width + ctr_x;
     float pred_ctr_y = dy * height + ctr_y;
-    float pred_w = exp(dw) * width;
-    float pred_h = exp(dh) * height;
+    float pred_w = std::exp(dw) * width;
+    float pred_h = std::exp(dh) * height;
 
     float pred_x1 = pred_ctr_x - 0.5 * (pred_w - 1.0);
     float pred_y1 = pred_ctr_y - 0.5 * (pred_h - 1.0);

diff --git a/src/operator/contrib/multibox_detection.cc b/src/operator/contrib/multibox_detection.cc
@@ -62,8 +62,8 @@ inline void TransformLocations(DType *out, const DType *anchors,
   DType ph = loc_pred[3];
   DType ox = px * vx * aw + ax;
   DType oy = py * vy * ah + ay;
-  DType ow = exp(pw * vw) * aw / 2;
-  DType oh = exp(ph * vh) * ah / 2;
+  DType ow = std::exp(pw * vw) * aw / 2;
+  DType oh = std::exp(ph * vh) * ah / 2;
   out[0] = clip ? std::max(DType(0), std::min(DType(1), ox - ow)) : (ox - ow);
   out[1] = clip ? std::max(DType(0), std::min(DType(1), oy - oh)) : (oy - oh);
   out[2] = clip ? std::max(DType(0), std::min(DType(1), ox + ow)) : (ox + ow);

diff --git a/src/operator/contrib/proposal.cc b/src/operator/contrib/proposal.cc
@@ -63,8 +63,8 @@ inline void BBoxTransformInv(const mshadow::Tensor<cpu, 2>& boxes,
 
         float pred_ctr_x = dx * width + ctr_x;
         float pred_ctr_y = dy * height + ctr_y;
-        float pred_w = exp(dw) * width;
-        float pred_h = exp(dh) * height;
+        float pred_w = std::exp(dw) * width;
+        float pred_h = std::exp(dh) * height;
 
         float pred_x1 = pred_ctr_x - 0.5 * (pred_w - 1.0);
         float pred_y1 = pred_ctr_y - 0.5 * (pred_h - 1.0);

diff --git a/src/operator/contrib/roi_align.cc b/src/operator/contrib/roi_align.cc
@@ -182,9 +182,9 @@ void ROIAlignForward(
     // We use roi_bin_grid to sample the grid and mimic integral
     int roi_bin_grid_h = (sampling_ratio > 0)
         ? sampling_ratio
-        : ceil(roi_height / pooled_height);  // e.g., = 2
+        : std::ceil(roi_height / pooled_height);  // e.g., = 2
     int roi_bin_grid_w =
-        (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);
+        (sampling_ratio > 0) ? sampling_ratio : std::ceil(roi_width / pooled_width);
 
     // We do average (integral) pooling inside a bin
     const T count = roi_bin_grid_h * roi_bin_grid_w;  // e.g. = 4
@@ -357,9 +357,9 @@ void ROIAlignBackward(
     // We use roi_bin_grid to sample the grid and mimic integral
     int roi_bin_grid_h = (sampling_ratio > 0)
         ? sampling_ratio
-        : ceil(roi_height / pooled_height);  // e.g., = 2
+        : std::ceil(roi_height / pooled_height);  // e.g., = 2
     int roi_bin_grid_w =
-        (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);
+        (sampling_ratio > 0) ? sampling_ratio : std::ceil(roi_width / pooled_width);
 
     // We do average (integral) pooling inside a bin
     const T count = roi_bin_grid_h * roi_bin_grid_w;  // e.g. = 4

diff --git a/src/operator/correlation-inl.h b/src/operator/correlation-inl.h
@@ -98,9 +98,9 @@ class CorrelationOp : public Operator {
     border_size_ = param_.max_displacement + kernel_radius_;
     stride1 = param_.stride1;
     stride2 = param_.stride2;
-    top_width_ = ceil(static_cast<float>(paddedbottomwidth - border_size_ * 2)\
+    top_width_ = std::ceil(static_cast<float>(paddedbottomwidth - border_size_ * 2)\
      / static_cast<float>(stride1));
-    top_height_ = ceil(static_cast<float>(paddedbottomheight - border_size_ * 2)\
+    top_height_ = std::ceil(static_cast<float>(paddedbottomheight - border_size_ * 2)\
      / static_cast<float>(stride1));
     neighborhood_grid_radius_ = param_.max_displacement / stride2;
     neighborhood_grid_width_ = neighborhood_grid_radius_ * 2 + 1;
@@ -211,9 +211,9 @@ void Init(const std::vector<std::pair<std::string, std::string> >& kwargs) overr
     border_size_ = param_.max_displacement + kernel_radius_;
     stride1 = param_.stride1;
     stride2 = param_.stride2;
-    top_width_ = ceil(static_cast<float>(paddedbottomwidth - border_size_ * 2)\
+    top_width_ = std::ceil(static_cast<float>(paddedbottomwidth - border_size_ * 2)\
      / static_cast<float>(stride1));
-    top_height_ = ceil(static_cast<float>(paddedbottomheight - border_size_ * 2)\
+    top_height_ = std::ceil(static_cast<float>(paddedbottomheight - border_size_ * 2)\
      / static_cast<float>(stride1));
     neighborhood_grid_radius_ = param_.max_displacement / stride2;
     neighborhood_grid_width_ = neighborhood_grid_radius_ * 2 + 1;

diff --git a/src/operator/image/image_random-inl.h b/src/operator/image/image_random-inl.h
@@ -418,10 +418,10 @@ void RGB2HLSConvert(const float& src_r,
   float diff;
 
   vmax = vmin = r;
-  vmax = fmax(vmax, g);
-  vmax = fmax(vmax, b);
-  vmin = fmin(vmin, g);
-  vmin = fmin(vmin, b);
+  vmax = std::fmax(vmax, g);
+  vmax = std::fmax(vmax, b);
+  vmin = std::fmin(vmin, g);
+  vmin = std::fmin(vmin, b);
 
   diff = vmax - vmin;
   l = (vmax + vmin) * 0.5f;

diff --git a/src/operator/nn/batch_norm.cc b/src/operator/nn/batch_norm.cc
@@ -33,7 +33,7 @@
 #endif
 
 /*! \brief inverse standard deviation <-> variance */
-#define VARIANCE_TO_INVSTD(__var$,    __eps$)   (1.0/sqrt((__var$) + DType(__eps$)))
+#define VARIANCE_TO_INVSTD(__var$,    __eps$)   (1.0/std::sqrt((__var$) + DType(__eps$)))
 #define INVSTD_TO_VARIANCE(__invstd$, __eps$)   ((1.0 / ((__invstd$) * (__invstd$))) - (__eps$))
 
 namespace mxnet {

diff --git a/src/operator/nn/lrn-inl.h b/src/operator/nn/lrn-inl.h
@@ -61,9 +61,9 @@ struct LRNParam : public dmlc::Parameter<LRNParam> {
 
   bool operator==(const LRNParam& other) const {
     return (this->nsize == other.nsize &&
-            fabs(this->alpha - other.alpha) < 1e-6 &&
-            fabs(this->beta  - other.beta)  < 1e-6 &&
-            fabs(this->knorm - other.knorm) < 1e-6);
+            std::fabs(this->alpha - other.alpha) < 1e-6 &&
+            std::fabs(this->beta  - other.beta)  < 1e-6 &&
+            std::fabs(this->knorm - other.knorm) < 1e-6);
   }
 };  // struct LRNParam
 

diff --git a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
@@ -34,7 +34,7 @@
 #include "./mkldnn_ops-inl.h"
 #include "./mkldnn_base-inl.h"
 
-#define VARIANCE_TO_INVSTD(__var$,    __eps$)   (1.0/sqrt((__var$) + DType(__eps$)))
+#define VARIANCE_TO_INVSTD(__var$,    __eps$)   (1.0/std::sqrt((__var$) + DType(__eps$)))
 #define INVSTD_TO_VARIANCE(__invstd$, __eps$)   ((1.0 / ((__invstd$) * (__invstd$))) - (__eps$))
 namespace mxnet {
 namespace op {

diff --git a/src/operator/nn/pooling.cc b/src/operator/nn/pooling.cc
@@ -127,7 +127,7 @@ static bool PoolingShape(const nnvm::NodeAttrs &attrs,
                   (dshape[2] + 2 * param.pad[0] - param.kernel[0]) /
                       param.stride[0];
     } else {
-      oshape[2] = 1 + static_cast<int>(ceil(
+      oshape[2] = 1 + static_cast<int>(std::ceil(
                           static_cast<float>(dshape[2] + 2 * param.pad[0] -
                                              param.kernel[0]) /
                           param.stride[0]));
@@ -157,11 +157,11 @@ static bool PoolingShape(const nnvm::NodeAttrs &attrs,
                   (dshape[3] + 2 * param.pad[1] - param.kernel[1]) /
                       param.stride[1];
     } else {
-      oshape[2] = 1 + static_cast<int>(ceil(
+      oshape[2] = 1 + static_cast<int>(std::ceil(
                           static_cast<float>(dshape[2] + 2 * param.pad[0] -
                                              param.kernel[0]) /
                           param.stride[0]));
-      oshape[3] = 1 + static_cast<int>(ceil(
+      oshape[3] = 1 + static_cast<int>(std::ceil(
                           static_cast<float>(dshape[3] + 2 * param.pad[1] -
                                              param.kernel[1]) /
                           param.stride[1]));
@@ -192,15 +192,15 @@ static bool PoolingShape(const nnvm::NodeAttrs &attrs,
                   (dshape[4] + 2 * param.pad[2] - param.kernel[2]) /
                       param.stride[2];
     } else {
-      oshape[2] = 1 + static_cast<int>(ceil(
+      oshape[2] = 1 + static_cast<int>(std::ceil(
                           static_cast<float>(dshape[2] + 2 * param.pad[0] -
                                              param.kernel[0]) /
                           param.stride[0]));
-      oshape[3] = 1 + static_cast<int>(ceil(
+      oshape[3] = 1 + static_cast<int>(std::ceil(
                           static_cast<float>(dshape[3] + 2 * param.pad[1] -
                                              param.kernel[1]) /
                           param.stride[1]));
-      oshape[4] = 1 + static_cast<int>(ceil(
+      oshape[4] = 1 + static_cast<int>(std::ceil(
                           static_cast<float>(dshape[4] + 2 * param.pad[2] -
                                              param.kernel[2]) /
                           param.stride[2]));

diff --git a/src/operator/pooling_v1-inl.h b/src/operator/pooling_v1-inl.h
@@ -273,10 +273,10 @@ class PoolingV1Prop : public OperatorProperty {
           oshape[3] = 1 + (dshape[3] + 2 * param_.pad[1] - param_.kernel[1]) /
                               param_.stride[1];
       } else {
-          oshape[2] = 1 + static_cast<int>(ceil(static_cast<float>(
+          oshape[2] = 1 + static_cast<int>(std::ceil(static_cast<float>(
                               dshape[2] + 2 * param_.pad[0] -
                               param_.kernel[0]) / param_.stride[0]));
-          oshape[3] = 1 + static_cast<int>(ceil(static_cast<float>(
+          oshape[3] = 1 + static_cast<int>(std::ceil(static_cast<float>(
                               dshape[3] + 2 * param_.pad[1] -
                               param_.kernel[1]) / param_.stride[1]));
       }
@@ -296,13 +296,13 @@ class PoolingV1Prop : public OperatorProperty {
           oshape[4] = 1 + (dshape[4] + 2 * param_.pad[2] - param_.kernel[2]) /
                               param_.stride[2];
       } else {
-          oshape[2] = 1 + static_cast<int>(ceil(static_cast<float>(
+          oshape[2] = 1 + static_cast<int>(std::ceil(static_cast<float>(
                               dshape[2] + 2 * param_.pad[0] -
                               param_.kernel[0]) / param_.stride[0]));
-          oshape[3] = 1 + static_cast<int>(ceil(static_cast<float>(
+          oshape[3] = 1 + static_cast<int>(std::ceil(static_cast<float>(
                               dshape[3] + 2 * param_.pad[1] -
                               param_.kernel[1]) / param_.stride[1]));
-          oshape[4] = 1 + static_cast<int>(ceil(static_cast<float>(
+          oshape[4] = 1 + static_cast<int>(std::ceil(static_cast<float>(
                               dshape[4] + 2 * param_.pad[2] -
                               param_.kernel[2]) / param_.stride[2]));
       }

diff --git a/src/operator/roi_pooling.cc b/src/operator/roi_pooling.cc
@@ -66,10 +66,10 @@ inline void ROIPoolForward(const Tensor<cpu, 4, Dtype> &out,
     Dtype *top_data_n = top_data + n * out_size;
     Dtype *argmax_data_n = argmax_data + n * max_idx_size;
     int roi_batch_ind = bottom_rois_n[0];
-    int roi_start_w = round(bottom_rois_n[1] * spatial_scale_);
-    int roi_start_h = round(bottom_rois_n[2] * spatial_scale_);
-    int roi_end_w = round(bottom_rois_n[3] * spatial_scale_);
-    int roi_end_h = round(bottom_rois_n[4] * spatial_scale_);
+    int roi_start_w = std::round(bottom_rois_n[1] * spatial_scale_);
+    int roi_start_h = std::round(bottom_rois_n[2] * spatial_scale_);
+    int roi_end_w = std::round(bottom_rois_n[3] * spatial_scale_);
+    int roi_end_h = std::round(bottom_rois_n[4] * spatial_scale_);
     assert(roi_batch_ind >= 0);
     assert(static_cast<index_t>(roi_batch_ind) < data.size(0) /* batch size */);
 
@@ -171,10 +171,10 @@ inline void ROIPoolBackwardAcc(const Tensor<cpu, 4, Dtype> &in_grad,
               continue;
             }
 
-            int roi_start_w = round(offset_bottom_rois[1] * spatial_scale_);
-            int roi_start_h = round(offset_bottom_rois[2] * spatial_scale_);
-            int roi_end_w = round(offset_bottom_rois[3] * spatial_scale_);
-            int roi_end_h = round(offset_bottom_rois[4] * spatial_scale_);
+            int roi_start_w = std::round(offset_bottom_rois[1] * spatial_scale_);
+            int roi_start_h = std::round(offset_bottom_rois[2] * spatial_scale_);
+            int roi_end_w = std::round(offset_bottom_rois[3] * spatial_scale_);
+            int roi_end_h = std::round(offset_bottom_rois[4] * spatial_scale_);
 
             bool in_roi = (w >= roi_start_w && w <= roi_end_w &&
                            h >= roi_start_h && h <= roi_end_h);

diff --git a/src/operator/spatial_transformer.cc b/src/operator/spatial_transformer.cc
@@ -51,8 +51,8 @@ inline void BilinearSamplingForward(const Tensor<cpu, 4, DType> &output,
           const index_t grid_index = n * o_h * o_w * 2 + h * o_w + w;
           const DType y_real = (*(grid + grid_index + o_h * o_w) + 1) * (i_h - 1) / 2;
           const DType x_real = (*(grid + grid_index) + 1) * (i_w - 1) / 2;
-          const auto top_left_y = static_cast<int>(floor(y_real));
-          const auto top_left_x = static_cast<int>(floor(x_real));
+          const auto top_left_y = static_cast<int>(std::floor(y_real));
+          const auto top_left_x = static_cast<int>(std::floor(x_real));
           const DType top_left_y_w = 1.0 - (y_real - top_left_y);
           const DType top_left_x_w = 1.0 - (x_real - top_left_x);
           const int data_index = n * i_c * i_h * i_w + c * i_h * i_w +
@@ -99,8 +99,8 @@ inline void BilinearSamplingBackward(const Tensor<cpu, 4, DType> &input_grad,
           const index_t grid_src_index = n * o_h * o_w * 2 + h * o_w + w;
           const DType y_real = (*(grid_src + grid_src_index + o_h * o_w) + 1) * (i_h - 1) / 2;
           const DType x_real = (*(grid_src + grid_src_index) + 1) * (i_w - 1) / 2;
-          const auto top_left_y = static_cast<int>(floor(y_real));
-          const auto top_left_x = static_cast<int>(floor(x_real));
+          const auto top_left_y = static_cast<int>(std::floor(y_real));
+          const auto top_left_x = static_cast<int>(std::floor(x_real));
           const DType top_left_y_w = 1.0 - (y_real - top_left_y);
           const DType top_left_x_w = 1.0 - (x_real - top_left_x);
           for (index_t c = 0; c < static_cast<index_t>(o_c); ++c) {

diff --git a/src/operator/tensor/elemwise_binary_op-inl.h b/src/operator/tensor/elemwise_binary_op-inl.h
@@ -68,13 +68,13 @@ void ElemwiseBinaryOp::RspRspOp(mshadow::Stream<cpu> *s,
       if (rhs_is_dense) {
         // For right-side dense, in order to have sparse output, lhs input zero should
         // always output zero
-        CHECK(fabs(static_cast<float>(OP::Map(DType(0), DType(99)))) < 1e-4f);
+        CHECK(std::fabs(static_cast<float>(OP::Map(DType(0), DType(99)))) < 1e-4f);
         CHECK(!is_dense_result);  // Currently not handled
       }
       if (lhs_is_dense) {
         // For left-side dense, in order to have sparse output, lhs input zero should
         // always output zero
-        CHECK(fabs(static_cast<float>(OP::Map(DType(99), DType(0)))) < 1e-4f);
+        CHECK(std::fabs(static_cast<float>(OP::Map(DType(99), DType(0)))) < 1e-4f);
         CHECK(!is_dense_result);  // Currently not handled
       }
 
@@ -102,10 +102,10 @@ void ElemwiseBinaryOp::RspRspOp(mshadow::Stream<cpu> *s,
             CHECK_EQ(is_dense_result, false);
             if (lhs_in_place) {
               // For in-place, zero L-value must always be zero output
-              DCHECK(fabs(static_cast<float>(OP::Map(DType(0), DType(99)))) < DType(1e-3));
+              DCHECK(std::fabs(static_cast<float>(OP::Map(DType(0), DType(99)))) < DType(1e-3));
             } else {
               // For in-place, zero R-value must always be zero output
-              DCHECK(fabs(static_cast<float>(OP::Map(DType(99), DType(0)))) < DType(1e-3));
+              DCHECK(std::fabs(static_cast<float>(OP::Map(DType(99), DType(0)))) < DType(1e-3));
             }
           }
         }

diff --git a/src/operator/tensor/elemwise_binary_op.h b/src/operator/tensor/elemwise_binary_op.h
@@ -646,7 +646,7 @@ class ElemwiseBinaryOp : public OpBase {
       if (in_stype == lhs_stype && (in_stype == kRowSparseStorage || in_stype == kCSRStorage)) {
         CHECK_EQ(outputs[0].storage_type(), in_stype);
         // rsp -> rsp, _. op requires 0-input returns 0-output
-        DCHECK_LT(fabs(static_cast<float>(LOP::Map(0))), 1e-5f);
+        DCHECK_LT(std::fabs(static_cast<float>(LOP::Map(0))), 1e-5f);
         UnaryOp::ComputeEx<xpu, LOP>(attrs, ctx, inputs, req, {outputs[0]});
       } else {
         LogUnimplementedOp(attrs, ctx, inputs, req, outputs);
@@ -657,7 +657,7 @@ class ElemwiseBinaryOp : public OpBase {
       if (in_stype == rhs_stype && (in_stype == kRowSparseStorage || in_stype == kCSRStorage)) {
         CHECK_EQ(outputs[0].storage_type(), in_stype);
         // rsp -> _, rsp. op requires 0-input returns 0-output
-        DCHECK_LT(fabs(static_cast<float>(ROP::Map(0))), 1e-5f);
+        DCHECK_LT(std::fabs(static_cast<float>(ROP::Map(0))), 1e-5f);
         UnaryOp::ComputeEx<xpu, ROP>(attrs, ctx, inputs, req, {outputs[1]});
       } else {
         LogUnimplementedOp(attrs, ctx, inputs, req, outputs);