COMPMID-828 - Add support for non square pool size - Part1
Change-Id: Ib8100e7c659c49694c746fa3f36ce20f44f6929f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/117804
Reviewed-by: Michele DiGiorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/core/Size2D.h b/arm_compute/core/Size2D.h
index cb053ea..3840771 100644
--- a/arm_compute/core/Size2D.h
+++ b/arm_compute/core/Size2D.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -25,6 +25,7 @@
#define __ARM_COMPUTE_SIZE2D_H__
#include <cstddef>
+#include <utility>
namespace arm_compute
{
@@ -33,10 +34,7 @@
{
public:
/** Default constructor */
- Size2D()
- : width(0), height(0)
- {
- }
+ Size2D() = default;
/** Constructor. Initializes "width" and "height" respectively with "w" and "h"
*
* @param[in] w Width of the image or rectangle
@@ -46,26 +44,6 @@
: width(w), height(h)
{
}
- /** Constructor. Initializes "width" and "height" with the dimensions of "size"
- *
- * @param[in] size Size data object
- */
- Size2D(const Size2D &size)
- : width(size.width), height(size.height)
- {
- }
- /** Copy assignment
- *
- * @param[in] size Constant reference input "Size2D" data object to copy
- *
- * @return Reference to the newly altered left hand side "Size2D" data object
- */
- Size2D &operator=(const Size2D &size)
- {
- width = size.width;
- height = size.height;
- return *this;
- }
/** The area of the image or rectangle calculated as (width * height)
*
* @return Area (width * height)
@@ -77,8 +55,8 @@
}
public:
- size_t width; /**< Width of the image region or rectangle */
- size_t height; /**< Height of the image region or rectangle */
+ size_t width = {}; /**< Width of the image region or rectangle */
+ size_t height = {}; /**< Height of the image region or rectangle */
};
}
#endif /*__ARM_COMPUTE_SIZE2D_H__ */
diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h
index aa415ac..72be5cb 100644
--- a/arm_compute/core/Types.h
+++ b/arm_compute/core/Types.h
@@ -27,6 +27,7 @@
#include "arm_compute/core/Coordinates.h"
#include "arm_compute/core/QAsymm8.h"
#include "arm_compute/core/Rounding.h"
+#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/Strides.h"
#include "arm_compute/core/TensorShape.h"
#include "support/Half.h"
@@ -578,7 +579,7 @@
public:
/** Default Constructor */
PoolingLayerInfo()
- : _pool_type(PoolingType::MAX), _pool_size(0), _pad_stride_info(PadStrideInfo()), _exclude_padding(false), _is_global_pooling(false)
+ : _pool_type(PoolingType::MAX), _pool_size(Size2D()), _pad_stride_info(PadStrideInfo()), _exclude_padding(false), _is_global_pooling(false)
{
}
/** Default Constructor
@@ -594,6 +595,22 @@
unsigned int pool_size,
PadStrideInfo pad_stride_info = PadStrideInfo(),
bool exclude_padding = false)
+ : _pool_type(pool_type), _pool_size(Size2D(pool_size, pool_size)), _pad_stride_info(pad_stride_info), _exclude_padding(exclude_padding), _is_global_pooling(false)
+ {
+ }
+ /** Default Constructor
+ *
+ * @param[in] pool_type Pooling type @ref PoolingType.
+ * @param[in] pool_size Pooling size, in elements, across x and y.
+ * @param[in] pad_stride_info (Optional) Padding and stride information @ref PadStrideInfo
+ * @param[in] exclude_padding (Optional) Strategy when accounting padding in calculations.
+ * True will exclude padding while false will not (Used in AVG/L2 pooling to determine the pooling area).
+ * Defaults to false;
+ */
+ explicit PoolingLayerInfo(PoolingType pool_type,
+ Size2D pool_size,
+ PadStrideInfo pad_stride_info = PadStrideInfo(),
+ bool exclude_padding = false)
: _pool_type(pool_type), _pool_size(pool_size), _pad_stride_info(pad_stride_info), _exclude_padding(exclude_padding), _is_global_pooling(false)
{
}
@@ -604,14 +621,14 @@
* @param[in] pool_type Pooling type @ref PoolingType.
*/
explicit PoolingLayerInfo(PoolingType pool_type)
- : _pool_type(pool_type), _pool_size(0), _pad_stride_info(PadStrideInfo(1, 1, 0, 0)), _exclude_padding(false), _is_global_pooling(true)
+ : _pool_type(pool_type), _pool_size(Size2D()), _pad_stride_info(PadStrideInfo(1, 1, 0, 0)), _exclude_padding(false), _is_global_pooling(true)
{
}
PoolingType pool_type() const
{
return _pool_type;
}
- unsigned int pool_size() const
+ const Size2D &pool_size() const
{
return _pool_size;
}
@@ -630,7 +647,7 @@
private:
PoolingType _pool_type;
- unsigned int _pool_size;
+ Size2D _pool_size;
PadStrideInfo _pad_stride_info;
bool _exclude_padding;
bool _is_global_pooling;
diff --git a/src/core/CL/kernels/CLPoolingLayerKernel.cpp b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
index 860cc92..043a4bd 100644
--- a/src/core/CL/kernels/CLPoolingLayerKernel.cpp
+++ b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
@@ -63,12 +63,13 @@
"Unsupported combination of parameters!");
const bool is_global_pooling = pool_info.is_global_pooling();
- const unsigned int pool_size = is_global_pooling ? input->tensor_shape().x() : pool_info.pool_size();
+ const unsigned int pool_size = is_global_pooling ? input->tensor_shape().x() : pool_info.pool_size().width;
ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_global_pooling && (input->tensor_shape().x() != input->tensor_shape().y()),
"Global pooling is supported only with rectangular inputs!");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_global_pooling && ((pool_info.pad_stride_info().pad().first >= pool_size) || (pool_info.pad_stride_info().pad().second >= pool_size)),
"Invalid pool size and pool pad combination!");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_info.pool_size().width != pool_info.pool_size().height, "Invalid Pool size, width not equal to height!");
// Checks performed when output is configured
if(output->total_size() != 0)
@@ -98,7 +99,7 @@
int pool_stride_y = 0;
unsigned int pooled_w = 0;
unsigned int pooled_h = 0;
- int pool_size = pool_info.pool_size();
+ int pool_size = pool_info.pool_size().width;
const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad();
std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
@@ -171,7 +172,7 @@
unsigned int pooled_w = 0;
unsigned int pooled_h = 0;
const PoolingType pool_type = pool_info.pool_type();
- int pool_size = pool_info.pool_size();
+ int pool_size = pool_info.pool_size().width;
const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
const bool exclude_padding = pool_info.exclude_padding();
std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad();
diff --git a/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp
index 6451db7..64b94c0 100644
--- a/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -62,12 +62,13 @@
"Unsupported combination of parameters!");
const bool is_global_pooling = pool_info.is_global_pooling();
- const unsigned int pool_size = is_global_pooling ? input->tensor_shape().x() : pool_info.pool_size();
+ const unsigned int pool_size = is_global_pooling ? input->tensor_shape().x() : pool_info.pool_size().width;
ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_global_pooling && (input->tensor_shape().x() != input->tensor_shape().y()),
"Global pooling is supported only with rectangular inputs!");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_global_pooling && ((pool_info.pad_stride_info().pad().first >= pool_size) || (pool_info.pad_stride_info().pad().second >= pool_size)),
"Invalid pool size and pool pad combination!");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_info.pool_size().width != pool_info.pool_size().height, "Invalid Pool size, width not equal to height!");
// Checks performed when output is configured
if(output->total_size() != 0)
@@ -97,7 +98,7 @@
int pool_stride_y = 0;
unsigned int pooled_w = 0;
unsigned int pooled_h = 0;
- int pool_size = pool_info.pool_size();
+ int pool_size = pool_info.pool_size().width;
const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad();
std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
@@ -229,7 +230,7 @@
unsigned int pooled_w = 0;
unsigned int pooled_h = 0;
const PoolingType pool_type = pool_info.pool_type();
- int pool_size = pool_info.pool_size();
+ int pool_size = pool_info.pool_size().width;
const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
const bool exclude_padding = pool_info.exclude_padding();
std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad();
diff --git a/src/core/NEON/kernels/NEPoolingLayerKernel.cpp b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp
index be5fa4c..a3ab8a3 100644
--- a/src/core/NEON/kernels/NEPoolingLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp
@@ -151,7 +151,7 @@
v = vsetq_lane_u16(elems[7], v, 7);
}
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, unsigned int &pooled_w, unsigned int pooled_h, int pool_size)
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, unsigned int &pooled_w, unsigned int pooled_h, int pool_size_x, int pool_size_y)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
@@ -166,10 +166,11 @@
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON(pool_type == PoolingType::L2 && is_data_type_quantized(input->data_type()));
- ARM_COMPUTE_RETURN_ERROR_ON((supported_pool_sizes.find(pool_size) == supported_pool_sizes.end()) && ((input->data_type() != DataType::F32) && (input->data_type() != DataType::QASYMM8)));
+ ARM_COMPUTE_RETURN_ERROR_ON((supported_pool_sizes.find(pool_size_x) == supported_pool_sizes.end()) && ((input->data_type() != DataType::F32) && (input->data_type() != DataType::QASYMM8)));
ARM_COMPUTE_RETURN_ERROR_ON(is_global_pooling && (input->tensor_shape().x() != input->tensor_shape().y()));
ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_fixed_point(input->data_type()) && pool_stride_x > 2);
ARM_COMPUTE_RETURN_ERROR_ON(exclude_padding && is_data_type_fixed_point(input->data_type()));
+ ARM_COMPUTE_RETURN_ERROR_ON(pool_size_x != pool_size_y);
if(output->total_size() != 0)
{
@@ -370,7 +371,7 @@
const int pool_stride_x = pad_stride_info.stride().first;
// Update pool size in case of global pooling
- const int pool_size = is_global_pooling ? input->info()->dimension(0) : pool_info.pool_size();
+ const int pool_size = is_global_pooling ? input->info()->dimension(0) : pool_info.pool_size().width;
// Validate pool info before calling scaled_dimensions
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_pool_info(input->info(), pool_info, pool_size));
@@ -387,7 +388,7 @@
auto_init(input->info(), output->info(), pooled_w, pooled_h);
// Perform validation step
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info, pooled_w, pooled_h, pool_size));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info, pooled_w, pooled_h, pool_size, pool_size));
// Set instance variables
_input = input;
@@ -1491,7 +1492,7 @@
Iterator input(_input, window_input);
Iterator output(_output, window);
- const int pool_size = _pool_info.is_global_pooling() ? _input->info()->tensor_shape().x() : _pool_info.pool_size();
+ const int pool_size = _pool_info.is_global_pooling() ? _input->info()->tensor_shape().x() : _pool_info.pool_size().width;
const int pool_pad_right = _pool_info.pad_stride_info().pad_right();
const int pool_pad_top = _pool_info.pad_stride_info().pad_top();
const int pool_pad_left = _pool_info.pad_stride_info().pad_left();
@@ -1613,7 +1614,7 @@
Iterator input(_input, window_input);
Iterator output(_output, window);
- const int pool_size = _pool_info.is_global_pooling() ? _input->info()->tensor_shape().x() : _pool_info.pool_size();
+ const int pool_size = _pool_info.is_global_pooling() ? _input->info()->tensor_shape().x() : _pool_info.pool_size().width;
const int pool_pad_right = _pool_info.pad_stride_info().pad_right();
const int pool_pad_top = _pool_info.pad_stride_info().pad_top();
const int pool_pad_left = _pool_info.pad_stride_info().pad_left();
@@ -1712,7 +1713,7 @@
BorderSize border_size(0);
const bool is_global_pooling = pool_info.is_global_pooling();
- const unsigned int pool_size = is_global_pooling ? input->tensor_shape().x() : pool_info.pool_size();
+ const unsigned int pool_size = is_global_pooling ? input->tensor_shape().x() : pool_info.pool_size().width;
// Validate pool info befor calling scaled_dimensions
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_pool_info(input, pool_info, pool_size));
@@ -1724,7 +1725,7 @@
pool_size,
pool_info.pad_stride_info());
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info, pooled_w, pooled_h, pool_size));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info, pooled_w, pooled_h, pool_size, pool_size));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), pool_info, num_elems_processed_per_iteration, border_size, pooled_w, pooled_h, pool_size).first);
return Status{};
@@ -1739,7 +1740,7 @@
const unsigned int pool_stride_x = _pool_info.pad_stride_info().stride().first;
const unsigned int pool_stride_y = _pool_info.pad_stride_info().stride().second;
- const unsigned int pool_size = _pool_info.pool_size();
+ const unsigned int pool_size = _pool_info.pool_size().width;
// Set step for input in x and y direction for the input
Window window_input(window);
diff --git a/src/runtime/NEON/functions/NEPoolingLayer.cpp b/src/runtime/NEON/functions/NEPoolingLayer.cpp
index 8a32507..bc0b6f8 100644
--- a/src/runtime/NEON/functions/NEPoolingLayer.cpp
+++ b/src/runtime/NEON/functions/NEPoolingLayer.cpp
@@ -38,7 +38,7 @@
void NEPoolingLayer::configure(ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info)
{
// Check if we have Global Pooling Layer
- _is_global_pooling_layer = (input->info()->dimension(0) == pool_info.pool_size()) && (input->info()->dimension(1) == pool_info.pool_size());
+ _is_global_pooling_layer = (input->info()->dimension(0) == pool_info.pool_size().width) && (input->info()->dimension(1) == pool_info.pool_size().height);
// Configure pooling kernel
_pooling_layer_kernel.configure(input, output, pool_info);
diff --git a/tests/validation/CL/PoolingLayer.cpp b/tests/validation/CL/PoolingLayer.cpp
index fdb2a8d..a830e08 100644
--- a/tests/validation/CL/PoolingLayer.cpp
+++ b/tests/validation/CL/PoolingLayer.cpp
@@ -46,21 +46,21 @@
/** Failing data set */
const auto PoolingLayerDatasetSpecial = ((((framework::dataset::make("Shape", TensorShape{ 60U, 52U, 3U, 5U })
* framework::dataset::make("PoolType", PoolingType::AVG))
- * framework::dataset::make("PoolingSize", 100))
+ * framework::dataset::make("PoolingSize", Size2D(100, 100)))
* framework::dataset::make("PadStride", PadStrideInfo(5, 5, 50, 50)))
* framework::dataset::make("ExcludePadding", true));
/** Input data set for floating-point data types */
-const auto PoolingLayerDatasetFP = combine(combine(combine(datasets::PoolingTypes(), framework::dataset::make("PoolingSize", { 2, 3, 4, 7, 9 })),
+const auto PoolingLayerDatasetFP = combine(combine(combine(datasets::PoolingTypes(), framework::dataset::make("PoolingSize", { Size2D(2, 2), Size2D(3, 3), Size2D(7, 7), Size2D(9, 9) })),
framework::dataset::make("PadStride", { PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 1, 0, 0), PadStrideInfo(1, 2, 1, 1), PadStrideInfo(2, 2, 1, 0) })),
framework::dataset::make("ExcludePadding", { true, false }));
/** Input data set for fixed-point data types */
-const auto PoolingLayerDatasetQS = combine(combine(combine(framework::dataset::make("PoolingType", { PoolingType::MAX, PoolingType::AVG }), framework::dataset::make("PoolingSize", { 2, 3 })),
+const auto PoolingLayerDatasetQS = combine(combine(combine(framework::dataset::make("PoolingType", { PoolingType::MAX, PoolingType::AVG }), framework::dataset::make("PoolingSize", { Size2D(2, 2), Size2D(3, 3) })),
framework::dataset::make("PadStride", { PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 1, 0, 0), PadStrideInfo(1, 2, 1, 1), PadStrideInfo(2, 2, 1, 0) })),
framework::dataset::make("ExcludePadding", { true, false }));
/** Input data set for asymmetric data type */
-const auto PoolingLayerDatasetQASYMM8 = combine(combine(combine(framework::dataset::make("PoolingType", { PoolingType::MAX, PoolingType::AVG }), framework::dataset::make("PoolingSize", { 2, 3 })),
+const auto PoolingLayerDatasetQASYMM8 = combine(combine(combine(framework::dataset::make("PoolingType", { PoolingType::MAX, PoolingType::AVG }), framework::dataset::make("PoolingSize", { Size2D(2, 2), Size2D(3, 3) })),
framework::dataset::make("PadStride", { PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 1, 0, 0), PadStrideInfo(1, 2, 1, 1), PadStrideInfo(2, 2, 1, 0) })),
framework::dataset::make("ExcludePadding", { true, false }));
diff --git a/tests/validation/GLES_COMPUTE/PoolingLayer.cpp b/tests/validation/GLES_COMPUTE/PoolingLayer.cpp
index e789dba..1496cee 100644
--- a/tests/validation/GLES_COMPUTE/PoolingLayer.cpp
+++ b/tests/validation/GLES_COMPUTE/PoolingLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -44,7 +44,7 @@
namespace
{
/** Input data set for floating-point data types */
-const auto PoolingLayerDatasetFP = combine(combine(combine(datasets::PoolingTypes(), framework::dataset::make("PoolingSize", { 2, 3, 4, 7, 9 })),
+const auto PoolingLayerDatasetFP = combine(combine(combine(datasets::PoolingTypes(), framework::dataset::make("PoolingSize", { Size2D(2, 2), Size2D(3, 3), Size2D(4, 4), Size2D(7, 7), Size2D(9, 9) })),
framework::dataset::make("PadStride", { PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 1, 0, 0), PadStrideInfo(1, 2, 1, 1), PadStrideInfo(2, 2, 1, 0) })),
framework::dataset::make("ExcludePadding", { true, false }));
diff --git a/tests/validation/NEON/PoolingLayer.cpp b/tests/validation/NEON/PoolingLayer.cpp
index e526613..eace8d7 100644
--- a/tests/validation/NEON/PoolingLayer.cpp
+++ b/tests/validation/NEON/PoolingLayer.cpp
@@ -44,17 +44,17 @@
namespace
{
/** Input data set for float data types */
-const auto PoolingLayerDatasetFP = combine(combine(combine(datasets::PoolingTypes(), framework::dataset::make("PoolingSize", { 2, 3, 7, 9 })),
+const auto PoolingLayerDatasetFP = combine(combine(combine(datasets::PoolingTypes(), framework::dataset::make("PoolingSize", { Size2D(2, 2), Size2D(3, 3), Size2D(7, 7), Size2D(9, 9) })),
framework::dataset::make("PadStride", { PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 1, 0, 0), PadStrideInfo(1, 2, 1, 1), PadStrideInfo(2, 2, 1, 0) })),
framework::dataset::make("ExcludePadding", { true, false }));
/** Input data set for quantized data types */
-const auto PoolingLayerDatasetQS = combine(combine(combine(framework::dataset::make("PoolingType", { PoolingType::MAX, PoolingType::AVG }), framework::dataset::make("PoolingSize", { 2, 3 })),
+const auto PoolingLayerDatasetQS = combine(combine(combine(framework::dataset::make("PoolingType", { PoolingType::MAX, PoolingType::AVG }), framework::dataset::make("PoolingSize", { Size2D(2, 2), Size2D(3, 3) })),
framework::dataset::make("PadStride", { PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 1, 0, 0), PadStrideInfo(1, 2, 1, 1), PadStrideInfo(2, 2, 1, 0) })),
framework::dataset::make("ExcludePadding", { false }));
/** Input data set for asymmetric data type */
-const auto PoolingLayerDatasetQASYMM8 = combine(combine(combine(framework::dataset::make("PoolingType", { PoolingType::MAX, PoolingType::AVG }), framework::dataset::make("PoolingSize", { 2, 3, 9 })),
+const auto PoolingLayerDatasetQASYMM8 = combine(combine(combine(framework::dataset::make("PoolingType", { PoolingType::MAX, PoolingType::AVG }), framework::dataset::make("PoolingSize", { Size2D(2, 2), Size2D(3, 3), Size2D(9, 9) })),
framework::dataset::make("PadStride", { PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 1, 0, 0), PadStrideInfo(1, 2, 1, 1), PadStrideInfo(2, 2, 1, 0) })),
framework::dataset::make("ExcludePadding", { true, false }));
diff --git a/tests/validation/fixtures/PoolingLayerFixture.h b/tests/validation/fixtures/PoolingLayerFixture.h
index 890eef2..f101199 100644
--- a/tests/validation/fixtures/PoolingLayerFixture.h
+++ b/tests/validation/fixtures/PoolingLayerFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -132,7 +132,7 @@
{
public:
template <typename...>
- void setup(TensorShape shape, PoolingType pool_type, int pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type)
+ void setup(TensorShape shape, PoolingType pool_type, Size2D pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type)
{
PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, pad_stride_info, exclude_padding),
data_type, 0, QuantizationInfo());
@@ -144,7 +144,7 @@
{
public:
template <typename...>
- void setup(TensorShape shape, PoolingType pool_type, int pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type, int fractional_bits)
+ void setup(TensorShape shape, PoolingType pool_type, Size2D pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type, int fractional_bits)
{
PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, pad_stride_info, exclude_padding),
data_type, fractional_bits, QuantizationInfo());
@@ -156,7 +156,7 @@
{
public:
template <typename...>
- void setup(TensorShape shape, PoolingType pool_type, int pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type, QuantizationInfo quantization_info)
+ void setup(TensorShape shape, PoolingType pool_type, Size2D pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type, QuantizationInfo quantization_info)
{
PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, pad_stride_info, exclude_padding),
data_type, 0, quantization_info);
diff --git a/tests/validation/reference/PoolingLayer.cpp b/tests/validation/reference/PoolingLayer.cpp
index d05c040..c14ab98 100644
--- a/tests/validation/reference/PoolingLayer.cpp
+++ b/tests/validation/reference/PoolingLayer.cpp
@@ -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);
@@ -54,11 +55,12 @@
} // 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;
@@ -88,8 +90,8 @@
{
int wstart = w * pool_stride_x - pad_left;
int hstart = h * pool_stride_y - pad_top;
- int wend = std::min(wstart + pool_size, w_src);
- int hend = std::min(hstart + pool_size, h_src);
+ 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);
@@ -122,8 +124,8 @@
T avg_val(0);
int wstart = w * pool_stride_x - pad_left;
int hstart = h * pool_stride_y - pad_top;
- int wend = std::min(wstart + pool_size, w_src + pad_right);
- int hend = std::min(hstart + pool_size, h_src + pad_bottom);
+ 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);
@@ -167,11 +169,12 @@
}
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;
@@ -201,8 +204,8 @@
{
int wstart = w * pool_stride_x - pad_left;
int hstart = h * pool_stride_y - pad_top;
- int wend = std::min(wstart + pool_size, w_src);
- int hend = std::min(hstart + pool_size, h_src);
+ 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);
@@ -234,8 +237,8 @@
{
int wstart = w * pool_stride_x - pad_left;
int hstart = h * pool_stride_y - pad_top;
- int wend = std::min(wstart + pool_size, w_src + pad_right);
- int hend = std::min(hstart + pool_size, h_src + pad_bottom);
+ 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);
@@ -288,7 +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);
@@ -296,10 +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
diff --git a/tests/validation/reference/PoolingLayer.h b/tests/validation/reference/PoolingLayer.h
index 334054a..b0d30af 100644
--- a/tests/validation/reference/PoolingLayer.h
+++ b/tests/validation/reference/PoolingLayer.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -36,10 +36,10 @@
namespace reference
{
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
-SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, PoolingLayerInfo info);
+SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, const PoolingLayerInfo &info);
template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
-SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, PoolingLayerInfo info);
+SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, const PoolingLayerInfo &info);
} // namespace reference
} // namespace validation
} // namespace test
diff --git a/utils/TypePrinter.h b/utils/TypePrinter.h
index cab8029..048df43 100644
--- a/utils/TypePrinter.h
+++ b/utils/TypePrinter.h
@@ -760,7 +760,7 @@
if(!info.is_global_pooling())
{
str << ","
- << "PoolSize=" << info.pool_size() << ","
+ << "PoolSize=" << info.pool_size().width << "," << info.pool_size().height << ","
<< "PadStride=" << info.pad_stride_info();
}
str << "}";