arm_compute v18.02
Change-Id: I7207aa488e5470f235f39b6c188b4678dc38d1a6
diff --git a/tests/validation/reference/DepthwiseConvolutionLayer.cpp b/tests/validation/reference/DepthwiseConvolutionLayer.cpp
index 6ca347f..b2a7067 100644
--- a/tests/validation/reference/DepthwiseConvolutionLayer.cpp
+++ b/tests/validation/reference/DepthwiseConvolutionLayer.cpp
@@ -67,10 +67,10 @@
const int filter_half_width = filter_width / 2;
const int filter_half_height = filter_height / 2;
- const int pad_left = std::min(static_cast<int>(conv_info.pad_left()), filter_half_width);
- const int pad_top = std::min(static_cast<int>(conv_info.pad_top()), filter_half_height);
- const int pad_right = std::min(static_cast<int>(conv_info.pad_right()), filter_half_width);
- const int pad_bottom = std::min(static_cast<int>(conv_info.pad_bottom()), filter_half_height);
+ const int pad_left = conv_info.pad_left();
+ const int pad_top = conv_info.pad_top();
+ const int pad_right = conv_info.pad_right();
+ const int pad_bottom = conv_info.pad_bottom();
const int minimum_x = -pad_left + filter_half_width;
const int minimum_y = -pad_top + filter_half_height;
@@ -140,11 +140,18 @@
const int input_depth = src.shape().z();
const int num_batches = src.shape().total_size() / (input_width * input_height * input_depth);
- const int filter_half_size = filter_width / 2;
- const int pad_x = std::min(filter_half_size, static_cast<int>(conv_info.pad().first));
- const int pad_y = std::min(filter_half_size, static_cast<int>(conv_info.pad().second));
- const int minimum_x = -pad_x + filter_half_size;
- const int minimum_y = -pad_y + filter_half_size;
+ const int filter_half_width = filter_width / 2;
+ const int filter_half_height = filter_height / 2;
+
+ const int pad_left = conv_info.pad_left();
+ const int pad_top = conv_info.pad_top();
+ const int pad_right = conv_info.pad_right();
+ const int pad_bottom = conv_info.pad_bottom();
+
+ const int minimum_x = -pad_left + filter_half_width;
+ const int minimum_y = -pad_top + filter_half_height;
+ const int maximum_x = input_width + pad_left - filter_half_width + pad_right - filter_half_width;
+ const int maximum_y = input_height + pad_top - filter_half_height + pad_bottom - filter_half_height;
int out_pos = 0;
for(int r = 0; r < num_batches; ++r)
@@ -152,17 +159,17 @@
for(int z = 0; z < input_depth; ++z)
{
int32_t bias_val = *static_cast<const int32_t *>(biases(Coordinates(z)));
- for(int y = minimum_y; y < input_height + pad_y - filter_half_size; y += conv_info.stride().second)
+ for(int y = minimum_y; y < minimum_y + maximum_y; y += conv_info.stride().second)
{
- for(int x = minimum_x; x < input_width + pad_x - filter_half_size; x += conv_info.stride().first)
+ for(int x = minimum_x; x < minimum_x + maximum_x; x += conv_info.stride().first)
{
Coordinates coords(x, y, z, r);
int filter_offset = filter_plane * z;
int32_t val = 0;
- for(int j = y - filter_half_size; j <= (y + filter_half_size); ++j)
+ for(int j = y - filter_half_height; j <= (y + filter_half_height); ++j)
{
- for(int i = x - filter_half_size; i <= (x + filter_half_size); ++i)
+ for(int i = x - filter_half_width; i <= (x + filter_half_width); ++i)
{
coords.set(0, i);
coords.set(1, j);