COMPMID-556: Rename CPP folder to reference

Change-Id: I147644349547c4e3804a80b564a9ad95131ad2d0
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/111560
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>

1 files changed, 302 insertions, 0 deletions

diff --git a/tests/validation/reference/PoolingLayer.cpp b/tests/validation/reference/PoolingLayer.cpp
new file mode 100644
index 0000000000..1a7dd4cbb7
--- /dev/null
+++ b/tests/validation/reference/PoolingLayer.cpp
@@ -0,0 +1,302 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "PoolingLayer.h"
+
+#include "arm_compute/core/Types.h"
+#include "tests/validation/FixedPoint.h"
+#include "tests/validation/Helpers.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+namespace
+{
+TensorShape calculate_output_shape(TensorShape shape, PoolingLayerInfo info)
+{
+    TensorShape dst_shape = shape;
+    const int   pool_size = info.is_global_pooling() ? shape.x() : info.pool_size();
+    const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(shape.x(),
+                                                                                             shape.y(),
+                                                                                             pool_size,
+                                                                                             pool_size,
+                                                                                             info.pad_stride_info());
+    dst_shape.set(0, scaled_dims.first);
+    dst_shape.set(1, scaled_dims.second);
+
+    return dst_shape;
+}
+} // 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)
+{
+    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();
+    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;
+    bool        exclude_padding = info.exclude_padding();
+
+    const auto w_src      = static_cast<int>(src.shape()[0]);
+    const auto h_src      = static_cast<int>(src.shape()[1]);
+    const int  upper_dims = src.shape().total_size() / (w_src * h_src);
+
+    // Create reference
+    SimpleTensor<T> dst{ calculate_output_shape(src.shape(), info), src.data_type(), 1, src.fixed_point_position() };
+
+    const auto w_dst = static_cast<int>(dst.shape()[0]);
+    const auto h_dst = static_cast<int>(dst.shape()[1]);
+
+    if(type == PoolingType::MAX)
+    {
+        for(int r = 0; r < upper_dims; ++r)
+        {
+            for(int h = 0; h < h_dst; ++h)
+            {
+                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);
+                    wstart     = std::max(wstart, 0);
+                    hstart     = std::max(hstart, 0);
+
+                    T max_val = std::numeric_limits<T>::lowest();
+                    for(int y = hstart; y < hend; ++y)
+                    {
+                        for(int x = wstart; x < wend; ++x)
+                        {
+                            const T val = src[r * h_src * w_src + y * w_src + x];
+                            if(val > max_val)
+                            {
+                                max_val = val;
+                            }
+                        }
+                    }
+
+                    dst[r * h_dst * w_dst + h * w_dst + w] = max_val;
+                }
+            }
+        }
+    }
+    else // Average or l2 pooling
+    {
+        for(int r = 0; r < upper_dims; ++r)
+        {
+            for(int h = 0; h < h_dst; ++h)
+            {
+                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 pool   = (hend - hstart) * (wend - wstart);
+                    wstart     = std::max(wstart, 0);
+                    hstart     = std::max(hstart, 0);
+                    wend       = std::min(wend, w_src);
+                    hend       = std::min(hend, h_src);
+                    // Exclude padding pixels from the average
+                    if(exclude_padding)
+                    {
+                        pool = (hend - hstart) * (wend - wstart);
+                    }
+
+                    if(type == PoolingType::AVG)
+                    {
+                        for(int y = hstart; y < hend; ++y)
+                        {
+                            for(int x = wstart; x < wend; ++x)
+                            {
+                                avg_val += src[r * h_src * w_src + y * w_src + x];
+                            }
+                        }
+                        dst[r * h_dst * w_dst + h * w_dst + w] = avg_val / pool;
+                    }
+                    else
+                    {
+                        for(int y = hstart; y < hend; ++y)
+                        {
+                            for(int x = wstart; x < wend; ++x)
+                            {
+                                const T val = src[r * h_src * w_src + y * w_src + x];
+                                avg_val += val * val;
+                            }
+                        }
+                        dst[r * h_dst * w_dst + h * w_dst + w] = std::sqrt(avg_val / pool);
+                    }
+                }
+            }
+        }
+    }
+
+    return dst;
+}
+
+template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type>
+SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, 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();
+    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;
+    bool        exclude_padding = info.exclude_padding();
+
+    const auto w_src      = static_cast<int>(src.shape()[0]);
+    const auto h_src      = static_cast<int>(src.shape()[1]);
+    const int  upper_dims = src.shape().total_size() / (w_src * h_src);
+
+    // Create reference
+    SimpleTensor<T> dst{ calculate_output_shape(src.shape(), info), src.data_type(), 1, src.fixed_point_position() };
+
+    const auto w_dst = static_cast<int>(dst.shape()[0]);
+    const auto h_dst = static_cast<int>(dst.shape()[1]);
+
+    if(type == PoolingType::MAX)
+    {
+        for(int r = 0; r < upper_dims; ++r)
+        {
+            for(int h = 0; h < h_dst; ++h)
+            {
+                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);
+                    wstart     = std::max(wstart, 0);
+                    hstart     = std::max(hstart, 0);
+
+                    T max_val = std::numeric_limits<T>::lowest();
+                    for(int y = hstart; y < hend; ++y)
+                    {
+                        for(int x = wstart; x < wend; ++x)
+                        {
+                            const T val = src[r * h_src * w_src + y * w_src + x];
+                            if(val > max_val)
+                            {
+                                max_val = val;
+                            }
+                        }
+                    }
+
+                    dst[r * h_dst * w_dst + h * w_dst + w] = max_val;
+                }
+            }
+        }
+    }
+    else // Average or l2 pooling
+    {
+        for(int r = 0; r < upper_dims; ++r)
+        {
+            for(int h = 0; h < h_dst; ++h)
+            {
+                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 pool   = (hend - hstart) * (wend - wstart);
+                    wstart     = std::max(wstart, 0);
+                    hstart     = std::max(hstart, 0);
+                    wend       = std::min(wend, w_src);
+                    hend       = std::min(hend, h_src);
+                    // Exclude padding pixels from the average
+                    if(exclude_padding)
+                    {
+                        pool = (hend - hstart) * (wend - wstart);
+                    }
+
+                    using namespace fixed_point_arithmetic;
+
+                    const int            fixed_point_position = src.fixed_point_position();
+                    const fixed_point<T> const_1(1, fixed_point_position);
+                    const fixed_point<T> invpool_fp(1.f / static_cast<float>(pool), fixed_point_position);
+                    fixed_point<T>       avg_val(0, fixed_point_position, true);
+
+                    if(type == PoolingType::AVG)
+                    {
+                        for(int y = hstart; y < hend; ++y)
+                        {
+                            for(int x = wstart; x < wend; ++x)
+                            {
+                                const fixed_point<T> in_fp(src[r * h_src * w_src + y * w_src + x], fixed_point_position, true);
+                                avg_val = add(avg_val, in_fp);
+                            }
+                        }
+                        dst[r * h_dst * w_dst + h * w_dst + w] = mul(avg_val, invpool_fp).raw();
+                    }
+                    else
+                    {
+                        for(int y = hstart; y < hend; ++y)
+                        {
+                            for(int x = wstart; x < wend; ++x)
+                            {
+                                const fixed_point<T> in_fp(src[r * h_src * w_src + y * w_src + x], fixed_point_position, true);
+                                avg_val = add(avg_val, mul(in_fp, in_fp));
+                            }
+                        }
+                        auto res                               = div(const_1, (inv_sqrt(mul(avg_val, invpool_fp))));
+                        dst[r * h_dst * w_dst + h * w_dst + w] = res.raw();
+                    }
+                }
+            }
+        }
+    }
+
+    return dst;
+}
+
+template <>
+SimpleTensor<uint8_t> pooling_layer<uint8_t>(const SimpleTensor<uint8_t> &src, PoolingLayerInfo info)
+{
+    SimpleTensor<float>   src_tmp = convert_from_asymmetric(src);
+    SimpleTensor<float>   dst_tmp = pooling_layer<float>(src_tmp, info);
+    SimpleTensor<uint8_t> dst     = convert_to_asymmetric(dst_tmp, src.quantization_info());
+    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);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute