COMPMID-970 : Remove QS8 / QS16 support

Removed Fixed point position arguments from test sources

Change-Id: I8343724723b71611fd501ed34de0866d3fb60e7e
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/136382
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele DiGiorgio <michele.digiorgio@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 1 insertions, 125 deletions

diff --git a/tests/validation/reference/PoolingLayer.cpp b/tests/validation/reference/PoolingLayer.cpp
index 69734545c9..e9054b9043 100644
--- a/tests/validation/reference/PoolingLayer.cpp
+++ b/tests/validation/reference/PoolingLayer.cpp
@@ -44,7 +44,7 @@ SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, const PoolingLayerInfo
     ARM_COMPUTE_ERROR_ON(info.is_global_pooling() && (src.shape().x() != src.shape().y()));
 
     // Create reference
-    SimpleTensor<T> dst{ compute_pool_shape(TensorInfo(src.shape(), 1, src.data_type(), src.fixed_point_position()), info), src.data_type(), 1, src.fixed_point_position() };
+    SimpleTensor<T> dst{ compute_pool_shape(TensorInfo(src.shape(), 1, src.data_type(), src.fixed_point_position()), info), src.data_type(), 1 };
 
     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;
@@ -152,128 +152,6 @@ SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, const PoolingLayerInfo
     return dst;
 }
 
-template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type>
-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 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);
-
-    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_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();
-
-    // Create reference
-    SimpleTensor<T> dst{ compute_pool_shape(TensorInfo(src.shape(), 1, src.data_type(), src.fixed_point_position()), 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_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);
-
-                    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_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);
-                    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, const PoolingLayerInfo &info)
 {
@@ -285,8 +163,6 @@ SimpleTensor<uint8_t> pooling_layer<uint8_t>(const SimpleTensor<uint8_t> &src, c
 
 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