arm_compute v18.02
Change-Id: I7207aa488e5470f235f39b6c188b4678dc38d1a6
diff --git a/tests/validation/reference/PoolingLayer.cpp b/tests/validation/reference/PoolingLayer.cpp
index 1a7dd4c..c14ab98 100644
--- a/tests/validation/reference/PoolingLayer.cpp
+++ b/tests/validation/reference/PoolingLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -37,14 +37,15 @@
{
namespace
{
-TensorShape calculate_output_shape(TensorShape shape, PoolingLayerInfo info)
+TensorShape calculate_output_shape(TensorShape shape, const PoolingLayerInfo &info)
{
- TensorShape dst_shape = shape;
- const int pool_size = info.is_global_pooling() ? shape.x() : info.pool_size();
+ TensorShape dst_shape = shape;
+ const int pool_size_x = info.is_global_pooling() ? shape.x() : info.pool_size().width;
+ const int pool_size_y = info.is_global_pooling() ? shape.y() : info.pool_size().height;
const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(shape.x(),
shape.y(),
- pool_size,
- pool_size,
+ pool_size_x,
+ pool_size_y,
info.pad_stride_info());
dst_shape.set(0, scaled_dims.first);
dst_shape.set(1, scaled_dims.second);
@@ -54,16 +55,19 @@
} // namespace
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
-SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, PoolingLayerInfo info)
+SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, const PoolingLayerInfo &info)
{
ARM_COMPUTE_ERROR_ON(info.is_global_pooling() && (src.shape().x() != src.shape().y()));
- const int pool_size = info.is_global_pooling() ? src.shape().x() : info.pool_size();
+ const int pool_size_x = info.is_global_pooling() ? src.shape().x() : info.pool_size().width;
+ const int pool_size_y = info.is_global_pooling() ? src.shape().y() : info.pool_size().height;
PoolingType type = info.pool_type();
int pool_stride_x = info.pad_stride_info().stride().first;
int pool_stride_y = info.pad_stride_info().stride().second;
- int pad_x = info.pad_stride_info().pad().first;
- int pad_y = info.pad_stride_info().pad().second;
+ int pad_left = info.pad_stride_info().pad_left();
+ int pad_top = info.pad_stride_info().pad_top();
+ int pad_right = info.pad_stride_info().pad_right();
+ int pad_bottom = info.pad_stride_info().pad_bottom();
bool exclude_padding = info.exclude_padding();
const auto w_src = static_cast<int>(src.shape()[0]);
@@ -84,10 +88,10 @@
{
for(int w = 0; w < w_dst; ++w)
{
- int wstart = w * pool_stride_x - pad_x;
- int hstart = h * pool_stride_y - pad_y;
- int wend = std::min(wstart + pool_size, w_src);
- int hend = std::min(hstart + pool_size, h_src);
+ int wstart = w * pool_stride_x - pad_left;
+ int hstart = h * pool_stride_y - pad_top;
+ int wend = std::min(wstart + pool_size_x, w_src);
+ int hend = std::min(hstart + pool_size_y, h_src);
wstart = std::max(wstart, 0);
hstart = std::max(hstart, 0);
@@ -118,10 +122,10 @@
for(int w = 0; w < w_dst; ++w)
{
T avg_val(0);
- int wstart = w * pool_stride_x - pad_x;
- int hstart = h * pool_stride_y - pad_y;
- int wend = std::min(wstart + pool_size, w_src + pad_x);
- int hend = std::min(hstart + pool_size, h_src + pad_y);
+ int wstart = w * pool_stride_x - pad_left;
+ int hstart = h * pool_stride_y - pad_top;
+ int wend = std::min(wstart + pool_size_x, w_src + pad_right);
+ int hend = std::min(hstart + pool_size_y, h_src + pad_bottom);
int pool = (hend - hstart) * (wend - wstart);
wstart = std::max(wstart, 0);
hstart = std::max(hstart, 0);
@@ -165,16 +169,19 @@
}
template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type>
-SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, PoolingLayerInfo info)
+SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, const PoolingLayerInfo &info)
{
ARM_COMPUTE_ERROR_ON(info.is_global_pooling() && (src.shape().x() != src.shape().y()));
- const int pool_size = info.is_global_pooling() ? src.shape().x() : info.pool_size();
+ const int pool_size_x = info.is_global_pooling() ? src.shape().x() : info.pool_size().width;
+ const int pool_size_y = info.is_global_pooling() ? src.shape().y() : info.pool_size().height;
PoolingType type = info.pool_type();
int pool_stride_x = info.pad_stride_info().stride().first;
int pool_stride_y = info.pad_stride_info().stride().second;
- int pad_x = info.pad_stride_info().pad().first;
- int pad_y = info.pad_stride_info().pad().second;
+ int pad_left = info.pad_stride_info().pad_left();
+ int pad_top = info.pad_stride_info().pad_top();
+ int pad_right = info.pad_stride_info().pad_right();
+ int pad_bottom = info.pad_stride_info().pad_bottom();
bool exclude_padding = info.exclude_padding();
const auto w_src = static_cast<int>(src.shape()[0]);
@@ -195,10 +202,10 @@
{
for(int w = 0; w < w_dst; ++w)
{
- int wstart = w * pool_stride_x - pad_x;
- int hstart = h * pool_stride_y - pad_y;
- int wend = std::min(wstart + pool_size, w_src);
- int hend = std::min(hstart + pool_size, h_src);
+ int wstart = w * pool_stride_x - pad_left;
+ int hstart = h * pool_stride_y - pad_top;
+ int wend = std::min(wstart + pool_size_x, w_src);
+ int hend = std::min(hstart + pool_size_y, h_src);
wstart = std::max(wstart, 0);
hstart = std::max(hstart, 0);
@@ -228,10 +235,10 @@
{
for(int w = 0; w < w_dst; ++w)
{
- int wstart = w * pool_stride_x - pad_x;
- int hstart = h * pool_stride_y - pad_y;
- int wend = std::min(wstart + pool_size, w_src + pad_x);
- int hend = std::min(hstart + pool_size, h_src + pad_y);
+ int wstart = w * pool_stride_x - pad_left;
+ int hstart = h * pool_stride_y - pad_top;
+ int wend = std::min(wstart + pool_size_x, w_src + pad_right);
+ int hend = std::min(hstart + pool_size_y, h_src + pad_bottom);
int pool = (hend - hstart) * (wend - wstart);
wstart = std::max(wstart, 0);
hstart = std::max(hstart, 0);
@@ -284,7 +291,7 @@
}
template <>
-SimpleTensor<uint8_t> pooling_layer<uint8_t>(const SimpleTensor<uint8_t> &src, PoolingLayerInfo info)
+SimpleTensor<uint8_t> pooling_layer<uint8_t>(const SimpleTensor<uint8_t> &src, const PoolingLayerInfo &info)
{
SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
SimpleTensor<float> dst_tmp = pooling_layer<float>(src_tmp, info);
@@ -292,10 +299,10 @@
return dst;
}
-template SimpleTensor<float> pooling_layer(const SimpleTensor<float> &src, PoolingLayerInfo info);
-template SimpleTensor<half> pooling_layer(const SimpleTensor<half> &src, PoolingLayerInfo info);
-template SimpleTensor<qint8_t> pooling_layer(const SimpleTensor<qint8_t> &src, PoolingLayerInfo info);
-template SimpleTensor<qint16_t> pooling_layer(const SimpleTensor<qint16_t> &src, PoolingLayerInfo info);
+template SimpleTensor<float> pooling_layer(const SimpleTensor<float> &src, const PoolingLayerInfo &info);
+template SimpleTensor<half> pooling_layer(const SimpleTensor<half> &src, const PoolingLayerInfo &info);
+template SimpleTensor<qint8_t> pooling_layer(const SimpleTensor<qint8_t> &src, const PoolingLayerInfo &info);
+template SimpleTensor<qint16_t> pooling_layer(const SimpleTensor<qint16_t> &src, const PoolingLayerInfo &info);
} // namespace reference
} // namespace validation
} // namespace test