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Scala fix (#7782)
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* Occasional test failure, reduce testing threshold

* Fix signed/unsigned warning

* providing explicit null pointer for the provided_arg_stypes. (#7791)

* fix wrong dist-kvstore push/pull/rsp_pull (#7762)

* rm duplicated and unused code (#7764)

* rm not use variables

* rm duplicated and unused code

* bug

* Fix Moderngpu usages in MXNet for CUDA 9 (#7789)

* Modify ModernGPU for CUDA 9

* Remove unused shfl_up that triggered compiler warning

* adjust types to pass to between, add 'const' where possible

* Add MSHADOW_CINLINE

* lint

* Trigger build
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@cjolivier01 @piiswrong
cjolivier01 authored and piiswrong committed Sep 8, 2017
1 parent 02e805f commit 3f742d2
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Showing 2 changed files with 31 additions and 28 deletions.
2 changes: 1 addition & 1 deletion scala-package/core/src/test/scala/ml/dmlc/mxnet/OperatorSuite.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@ class OperatorSuite extends FunSuite with BeforeAndAfterAll
exec.forward()
val forwardOutput = exec.outputs(0)
val forwardOutputExpected = arr.reduce(_ + _)
assert(reldiff(forwardOutput, forwardOutputExpected) < 2e-6)
assert(reldiff(forwardOutput, forwardOutputExpected) < 5e-5)

// backward
val outGrad = Random.uniform(-10, 10, shape)
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57 changes: 30 additions & 27 deletions src/operator/spatial_transformer.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -27,32 +27,35 @@

namespace mshadow {
template<typename DType>
bool between(DType value, int lowerBound, int upperBound) {
return (value >= lowerBound && value <= upperBound);
static MSHADOW_CINLINE bool between(const DType value,
const DType lowerBound,
const DType upperBound) {
return value >= lowerBound && value <= upperBound;
}

template<typename DType>
inline void BilinearSamplingForward(const Tensor<cpu, 4, DType> &output,
const Tensor<cpu, 4, DType> &input,
const Tensor<cpu, 3, DType> grid_src) {
DType *out = output.dptr_;
const DType *data = input.dptr_;
const DType *grid = grid_src.dptr_;
int o_n = output.size(0), o_c = output.size(1), o_h = output.size(2), o_w = output.size(3);
int i_c = input.size(1), i_h = input.size(2), i_w = input.size(3);
const int o_n = output.size(0), o_c = output.size(1), o_h = output.size(2), o_w = output.size(3);
const int i_c = input.size(1), i_h = input.size(2), i_w = input.size(3);
for (index_t n = 0; n < static_cast<index_t>(o_n); ++n) {
for (index_t c = 0; c < static_cast<index_t>(o_c); ++c) {
for (index_t h = 0; h < static_cast<index_t>(o_h); ++h) {
for (index_t w = 0; w < static_cast<index_t>(o_w); ++w) {
index_t out_index = n * o_c * o_h * o_w + c * o_h * o_w + h * o_w + w;
index_t grid_index = n * o_h * o_w * 2 + h * o_w + w;
DType y_real = (*(grid + grid_index + o_h * o_w) + 1) * (i_h - 1) / 2;
DType x_real = (*(grid + grid_index) + 1) * (i_w - 1) / 2;
int top_left_y = static_cast<int>(floor(y_real));
int top_left_x = static_cast<int>(floor(x_real));
DType top_left_y_w = 1.0 - (y_real - top_left_y);
DType top_left_x_w = 1.0 - (x_real - top_left_x);
int data_index = n * i_c * i_h * i_w + c * i_h * i_w +
top_left_y * i_w + top_left_x;
const index_t out_index = n * o_c * o_h * o_w + c * o_h * o_w + h * o_w + w;
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 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 +
top_left_y * i_w + top_left_x;
DType top_left_v = 0;
DType top_right_v = 0;
DType bottom_left_v = 0;
Expand All @@ -66,9 +69,9 @@ inline void BilinearSamplingForward(const Tensor<cpu, 4, DType> &output,
if (between(top_left_x+1, 0, i_w-1) && between(top_left_y + 1, 0, i_h-1))
bottom_right_v = *(data + data_index + i_w + 1);
*(out+out_index) = top_left_v * top_left_y_w * top_left_x_w +
top_right_v * top_left_y_w * (1.0 - top_left_x_w) +
bottom_left_v * (1.0 - top_left_y_w) * top_left_x_w +
bottom_right_v * (1.0 - top_left_y_w) * (1.0 - top_left_x_w);
top_right_v * top_left_y_w * (1.0 - top_left_x_w) +
bottom_left_v * (1.0 - top_left_y_w) * top_left_x_w +
bottom_right_v * (1.0 - top_left_y_w) * (1.0 - top_left_x_w);
}
}
}
Expand All @@ -84,21 +87,21 @@ inline void BilinearSamplingBackward(const Tensor<cpu, 4, DType> &input_grad,
DType *grid_src = grid_src_data.dptr_;
const DType *grad = output_grad.dptr_;
const DType *data = input_data.dptr_;
int o_n = output_grad.size(0), o_c = output_grad.size(1),
o_h = output_grad.size(2), o_w = output_grad.size(3);
int i_c = input_data.size(1), i_h = input_data.size(2), i_w = input_data.size(3);
const int o_n = output_grad.size(0), o_c = output_grad.size(1),
o_h = output_grad.size(2), o_w = output_grad.size(3);
const int i_c = input_data.size(1), i_h = input_data.size(2), i_w = input_data.size(3);
for (index_t n = 0; n < static_cast<index_t>(o_n); ++n) {
for (index_t h = 0; h < static_cast<index_t>(o_h); ++h) {
for (index_t w = 0; w < static_cast<index_t>(o_w); ++w) {
DType top_left_y_gw = 0.0;
DType top_left_x_gw = 0.0;
index_t grid_src_index = n * o_h * o_w * 2 + h * o_w + w;
DType y_real = (*(grid_src + grid_src_index + o_h * o_w) + 1) * (i_h - 1) / 2;
DType x_real = (*(grid_src + grid_src_index) + 1) * (i_w - 1) / 2;
index_t top_left_y = static_cast<int>(floor(y_real));
index_t top_left_x = static_cast<int>(floor(x_real));
DType top_left_y_w = 1.0 - (y_real - top_left_y);
DType top_left_x_w = 1.0 - (x_real - top_left_x);
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 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) {
index_t grad_index = n * o_c * o_h * o_w + c * o_h * o_w + h * o_w + w;
index_t data_index = n * i_c * i_h * i_w + c * i_h * i_w + top_left_y * i_w
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