COMPMID-417 - Adding support for rectangular kernels

Change-Id: I4dde0929bc689c83582b95856dd0253228125df2
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/78994
Reviewed-by: Moritz Pflanzer <moritz.pflanzer@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

4 files changed, 14 insertions, 17 deletions

diff --git a/tests/model_objects/AlexNet.h b/tests/model_objects/AlexNet.h
index d49ef0645a..8443e9c97e 100644
--- a/tests/model_objects/AlexNet.h
+++ b/tests/model_objects/AlexNet.h
@@ -250,26 +250,26 @@ public:
         // Configure Layers
         {
             // Layer 1
-            conv1->configure(&input, w[0].get(), b[0].get(), &conv1_out, PadStrideInfo(4, 4, 0, 0), WeightsInfo(_reshaped_weights, 11U));
+            conv1->configure(&input, w[0].get(), b[0].get(), &conv1_out, PadStrideInfo(4, 4, 0, 0), WeightsInfo(_reshaped_weights, 11U, 11U));
             act1->configure(&conv1_out, &act1_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
             norm1->configure(&act1_out, &norm1_out, NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f));
             pool1->configure(&norm1_out, &pool1_out, PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)));
             // Layer 2
-            conv21->configure(pool11_out.get(), w21.get(), b21.get(), conv21_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U));
-            conv22->configure(pool12_out.get(), w22.get(), b22.get(), conv22_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U));
+            conv21->configure(pool11_out.get(), w21.get(), b21.get(), conv21_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U, 5U));
+            conv22->configure(pool12_out.get(), w22.get(), b22.get(), conv22_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U, 5U));
             act2->configure(&conv2_out, &act2_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
             norm2->configure(&act2_out, &norm2_out, NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f));
             pool2->configure(&norm2_out, &pool2_out, PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)));
             // Layer 3
-            conv3->configure(&pool2_out, w[2].get(), b[2].get(), &conv3_out, PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U));
+            conv3->configure(&pool2_out, w[2].get(), b[2].get(), &conv3_out, PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U));
             act3->configure(&conv3_out, &act3_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
             // Layer 4
-            conv41->configure(act31_out.get(), w41.get(), b41.get(), conv41_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U));
-            conv42->configure(act32_out.get(), w42.get(), b42.get(), conv42_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U));
+            conv41->configure(act31_out.get(), w41.get(), b41.get(), conv41_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U));
+            conv42->configure(act32_out.get(), w42.get(), b42.get(), conv42_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U));
             act4->configure(&conv4_out, &act4_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
             // Layer 5
-            conv51->configure(act41_out.get(), w51.get(), b51.get(), conv51_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U));
-            conv52->configure(act42_out.get(), w52.get(), b52.get(), conv52_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U));
+            conv51->configure(act41_out.get(), w51.get(), b51.get(), conv51_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U));
+            conv52->configure(act42_out.get(), w52.get(), b52.get(), conv52_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U));
             act5->configure(&conv5_out, &act5_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
             pool5->configure(&act5_out, &pool5_out, PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)));
             // Layer 6
diff --git a/tests/validation/CL/PoolingLayer.cpp b/tests/validation/CL/PoolingLayer.cpp
index 1d0e745088..dabc21eaa8 100644
--- a/tests/validation/CL/PoolingLayer.cpp
+++ b/tests/validation/CL/PoolingLayer.cpp
@@ -82,9 +82,8 @@ TensorShape get_output_shape(TensorShape in_shape, const PoolingLayerInfo &pool_
     const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(in_shape.x(),
                                                                                              in_shape.y(),
                                                                                              pool_info.pool_size(),
-                                                                                             pool_info.pad_stride_info().stride().first, pool_info.pad_stride_info().stride().second,
-                                                                                             pool_info.pad_stride_info().pad().first, pool_info.pad_stride_info().pad().second,
-                                                                                             pool_info.pad_stride_info().round());
+                                                                                             pool_info.pool_size(),
+                                                                                             pool_info.pad_stride_info());
     out_shape.set(0, scaled_dims.first);
     out_shape.set(1, scaled_dims.second);
     return out_shape;
diff --git a/tests/validation/NEON/ConvolutionLayerDirect.cpp b/tests/validation/NEON/ConvolutionLayerDirect.cpp
index 4e36e331bd..3ddb010e74 100644
--- a/tests/validation/NEON/ConvolutionLayerDirect.cpp
+++ b/tests/validation/NEON/ConvolutionLayerDirect.cpp
@@ -116,9 +116,8 @@ TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, con
     const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(in_shape.x(),
                                                                                              in_shape.y(),
                                                                                              kernel_shape.x(),
-                                                                                             conv_info.stride().first, conv_info.stride().second,
-                                                                                             conv_info.pad().first, conv_info.pad().second,
-                                                                                             conv_info.round());
+                                                                                             kernel_shape.y(),
+                                                                                             conv_info);
     out_shape.set(0, scaled_dims.first);
     out_shape.set(1, scaled_dims.second);
     out_shape.set(2, kernel_shape[3]);
diff --git a/tests/validation/NEON/PoolingLayer.cpp b/tests/validation/NEON/PoolingLayer.cpp
index 489c5b668b..b29cd3e2e8 100644
--- a/tests/validation/NEON/PoolingLayer.cpp
+++ b/tests/validation/NEON/PoolingLayer.cpp
@@ -99,9 +99,8 @@ TensorShape get_output_shape(TensorShape in_shape, const PoolingLayerInfo &pool_
     const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(in_shape.x(),
                                                                                              in_shape.y(),
                                                                                              pool_info.pool_size(),
-                                                                                             pool_info.pad_stride_info().stride().first, pool_info.pad_stride_info().stride().second,
-                                                                                             pool_info.pad_stride_info().pad().first, pool_info.pad_stride_info().pad().second,
-                                                                                             pool_info.pad_stride_info().round());
+                                                                                             pool_info.pool_size(),
+                                                                                             pool_info.pad_stride_info());
     out_shape.set(0, scaled_dims.first);
     out_shape.set(1, scaled_dims.second);
     return out_shape;