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[MXNET-860] - Avoid implicit double conversions in math operations. #12361

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Sep 18, 2018
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[MXNET-860] - Avoid implicit double conversions in math operations. #12361

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2 changes: 1 addition & 1 deletion src/io/image_det_aug_default.cc
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Expand Up @@ -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);
}
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4 changes: 2 additions & 2 deletions src/operator/contrib/adaptive_avg_pooling.cc
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Expand Up @@ -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 {
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4 changes: 2 additions & 2 deletions src/operator/contrib/multi_proposal.cc
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Expand Up @@ -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);
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4 changes: 2 additions & 2 deletions src/operator/contrib/multibox_detection.cc
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Expand Up @@ -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);
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4 changes: 2 additions & 2 deletions src/operator/contrib/proposal.cc
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Expand Up @@ -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);
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8 changes: 4 additions & 4 deletions src/operator/contrib/roi_align.cc
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Expand Up @@ -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
Expand Down Expand Up @@ -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
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8 changes: 4 additions & 4 deletions src/operator/correlation-inl.h
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Expand Up @@ -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;
Expand Down Expand Up @@ -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;
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8 changes: 4 additions & 4 deletions src/operator/image/image_random-inl.h
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Expand Up @@ -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;
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2 changes: 1 addition & 1 deletion src/operator/nn/batch_norm.cc
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Expand Up @@ -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 {
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6 changes: 3 additions & 3 deletions src/operator/nn/lrn-inl.h
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Expand Up @@ -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

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2 changes: 1 addition & 1 deletion src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
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Expand Up @@ -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 {
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12 changes: 6 additions & 6 deletions src/operator/nn/pooling.cc
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Expand Up @@ -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]));
Expand Down Expand Up @@ -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]));
Expand Down Expand Up @@ -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]));
Expand Down
10 changes: 5 additions & 5 deletions src/operator/pooling_v1-inl.h
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Expand Up @@ -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]));
}
Expand All @@ -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]));
}
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16 changes: 8 additions & 8 deletions src/operator/roi_pooling.cc
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Expand Up @@ -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 */);

Expand Down Expand Up @@ -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);
Expand Down
8 changes: 4 additions & 4 deletions src/operator/spatial_transformer.cc
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Expand Up @@ -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 +
Expand Down Expand Up @@ -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) {
Expand Down
8 changes: 4 additions & 4 deletions src/operator/tensor/elemwise_binary_op-inl.h
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Expand Up @@ -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
}

Expand Down Expand Up @@ -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));
}
}
}
Expand Down
4 changes: 2 additions & 2 deletions src/operator/tensor/elemwise_binary_op.h
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Expand Up @@ -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);
Expand All @@ -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);
Expand Down