COMPMID-2449: Implement NEUnPoolLayer

Change-Id: I5677c87bba97dd395a3e13dbce34a3dd2c437033
Signed-off-by: morgolock <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3289
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 42 insertions, 36 deletions

diff --git a/tests/validation/reference/PoolingLayer.cpp b/tests/validation/reference/PoolingLayer.cpp
index 778e28d7c1..c110a67842 100644
--- a/tests/validation/reference/PoolingLayer.cpp
+++ b/tests/validation/reference/PoolingLayer.cpp
@@ -43,9 +43,10 @@ SimpleTensor<T> pooling_layer_internal(const SimpleTensor<T> &src, const Pooling
     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()), info), src.data_type(), 1 };
+    auto            pooled_shape = compute_pool_shape(TensorInfo(src.shape(), 1, src.data_type()), info);
     if(indices)
     {
-        *indices = SimpleTensor<uint32_t> { compute_pool_shape(TensorInfo(src.shape(), 1, src.data_type()), info), DataType::U32, 1 };
+        *indices = SimpleTensor<uint32_t> { pooled_shape, DataType::U32, 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;
@@ -58,56 +59,62 @@ SimpleTensor<T> pooling_layer_internal(const SimpleTensor<T> &src, const Pooling
     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]);
-    const auto h_src      = static_cast<int>(src.shape()[1]);
-    const int  upper_dims = src.shape().total_size() / (w_src * h_src);
+    const auto w_src = static_cast<int>(src.shape()[0]);
+    const auto h_src = static_cast<int>(src.shape()[1]);
+    const auto z_src = static_cast<int>(src.shape()[2]);
+    const auto b_src = static_cast<int>(src.shape()[3]);
+
+    const int upper_dims = src.shape().total_size() / (w_src * h_src);
+
+    const auto w_dst = static_cast<int>(dst.shape()[0]);
+    const auto h_dst = static_cast<int>(dst.shape()[1]);
+    const auto z_dst = static_cast<int>(dst.shape()[2]);
 
-    const auto  w_dst = static_cast<int>(dst.shape()[0]);
-    const auto  h_dst = static_cast<int>(dst.shape()[1]);
     TensorShape shape_nhwc(src.shape());
     permute(shape_nhwc, PermutationVector(2U, 0U, 1U));
-
     if(type == PoolingType::MAX)
     {
-        for(int r = 0; r < upper_dims; ++r)
+        for(int b = 0; b < b_src; ++b)
         {
-            for(int h = 0; h < h_dst; ++h)
+            for(int r = 0; r < z_src; ++r)
             {
-                for(int w = 0; w < w_dst; ++w)
+                for(int h = 0; h < h_dst; ++h)
                 {
-                    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);
-
-                    auto max_val = std::numeric_limits<ACC_T>::lowest();
-                    int  max_index{ 0 };
-                    for(int y = hstart; y < hend; ++y)
+                    for(int w = 0; w < w_dst; ++w)
                     {
-                        for(int x = wstart; x < wend; ++x)
+                        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);
+                        auto max_val = std::numeric_limits<ACC_T>::lowest();
+                        int  max_index{ 0 };
+                        for(int y = hstart; y < hend; ++y)
                         {
-                            const auto val = static_cast<ACC_T>(src[r * h_src * w_src + y * w_src + x]);
-                            if(val > max_val)
+                            for(int x = wstart; x < wend; ++x)
                             {
-                                max_val = val;
-                                if(data_layout == DataLayout::NCHW)
+                                const auto val = static_cast<ACC_T>(src[b * z_src * h_src * w_src + r * h_src * w_src + y * w_src + x]);
+                                if(val > max_val)
                                 {
-                                    max_index = coord2index(src.shape(), Coordinates(x, y, r));
-                                }
-                                else
-                                {
-                                    max_index = coord2index(shape_nhwc, Coordinates(r, x, y));
+                                    max_val = val;
+                                    if(data_layout == DataLayout::NCHW)
+                                    {
+                                        max_index = coord2index(src.shape(), Coordinates(x, y, r, 0));
+                                    }
+                                    else
+                                    {
+                                        max_index = coord2index(shape_nhwc, Coordinates(r, x, y, 0));
+                                    }
                                 }
                             }
                         }
-                    }
 
-                    dst[r * h_dst * w_dst + h * w_dst + w] = static_cast<T>(max_val);
-                    if(indices)
-                    {
-                        (*indices)[r * h_dst * w_dst + h * w_dst + w] = max_index;
+                        dst[b * z_dst * h_dst * w_dst + r * h_dst * w_dst + h * w_dst + w] = static_cast<T>(max_val);
+                        if(indices)
+                        {
+                            (*indices)[b * z_dst * h_dst * w_dst + r * h_dst * w_dst + h * w_dst + w] = max_index;
+                        }
                     }
                 }
             }
@@ -164,7 +171,6 @@ SimpleTensor<T> pooling_layer_internal(const SimpleTensor<T> &src, const Pooling
             }
         }
     }
-
     return dst;
 }