COMPMID-415: Rename and move tests

The boost validation is now "standalone" in validation_old and builds as
arm_compute_validation_old. The new validation builds now as
arm_compute_validation.

Change-Id: Ib93ba848a25680ac60afb92b461d574a0757150d
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/86187
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 0 insertions, 243 deletions

diff --git a/tests/validation_new/CPP/PoolingLayer.cpp b/tests/validation_new/CPP/PoolingLayer.cpp
deleted file mode 100644
index 5464885dc4..0000000000
--- a/tests/validation_new/CPP/PoolingLayer.cpp
+++ /dev/null
@@ -1,243 +0,0 @@
-/*
- * 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 "tests/validation_new/FixedPoint.h"
-#include "tests/validation_new/half.h"
-
-namespace arm_compute
-{
-namespace test
-{
-namespace validation
-{
-namespace reference
-{
-namespace
-{
-TensorShape calculate_output_shape(TensorShape shape, PoolingLayerInfo info)
-{
-    TensorShape dst_shape = shape;
-    const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(shape.x(),
-                                                                                             shape.y(),
-                                                                                             info.pool_size(),
-                                                                                             info.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)
-{
-    const int   pool_size     = 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;
-
-    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 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);
-
-                    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;
-                }
-            }
-        }
-    }
-
-    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)
-{
-    const int   pool_size     = 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;
-
-    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 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);
-
-                    using namespace fixed_point_arithmetic;
-
-                    const int            fixed_point_position = src.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);
-
-                    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();
-                }
-            }
-        }
-    }
-
-    return dst;
-}
-
-template SimpleTensor<float> pooling_layer(const SimpleTensor<float> &src, PoolingLayerInfo info);
-template SimpleTensor<half_float::half> pooling_layer(const SimpleTensor<half_float::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