COMPMID-1097: Port mobilenet to NHWC

Change-Id: I789065bfa0d4ef133388e1904c5caf31e450f80f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129495
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 17 insertions, 16 deletions

diff --git a/src/graph/nodes/ConvolutionLayerNode.cpp b/src/graph/nodes/ConvolutionLayerNode.cpp
index eb0c6a1c1a..eaf1f7f035 100644
--- a/src/graph/nodes/ConvolutionLayerNode.cpp
+++ b/src/graph/nodes/ConvolutionLayerNode.cpp
@@ -26,6 +26,7 @@
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/graph/Graph.h"
 #include "arm_compute/graph/INodeVisitor.h"
+#include "arm_compute/graph/Utils.h"
 
 namespace arm_compute
 {
@@ -53,18 +54,26 @@ PadStrideInfo ConvolutionLayerNode::convolution_info() const
     return _info;
 }
 
-TensorShape ConvolutionLayerNode::compute_output_shape(TensorShape input_shape, TensorShape weights_shape, PadStrideInfo info)
+TensorDescriptor ConvolutionLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
+                                                                 const TensorDescriptor &weights_descriptor,
+                                                                 const PadStrideInfo    &info)
 {
     unsigned int output_width  = 0;
     unsigned int output_height = 0;
-    std::tie(output_width, output_height) = scaled_dimensions(input_shape.x(), input_shape.y(), weights_shape.x(), weights_shape.y(), info);
 
-    TensorShape output_shape{ input_shape };
-    output_shape.set(0, output_width);
-    output_shape.set(1, output_height);
-    output_shape.set(2, weights_shape[3]);
+    const unsigned int input_width   = get_dimension_size(input_descriptor, DataLayoutDimension::WIDTH);
+    const unsigned int input_height  = get_dimension_size(input_descriptor, DataLayoutDimension::HEIGHT);
+    const unsigned int kernel_width  = get_dimension_size(weights_descriptor, DataLayoutDimension::WIDTH);
+    const unsigned int kernel_height = get_dimension_size(weights_descriptor, DataLayoutDimension::HEIGHT);
 
-    return output_shape;
+    std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
+
+    TensorDescriptor output_descriptor = input_descriptor;
+    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::WIDTH), output_width);
+    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::HEIGHT), output_height);
+    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::CHANNEL), weights_descriptor.shape[3]);
+
+    return output_descriptor;
 }
 
 bool ConvolutionLayerNode::forward_descriptors()
@@ -87,10 +96,7 @@ TensorDescriptor ConvolutionLayerNode::configure_output(size_t idx) const
 
     ARM_COMPUTE_ERROR_ON(src == nullptr || weights == nullptr);
 
-    TensorDescriptor output_info  = src->desc();
-    TensorShape      output_shape = compute_output_shape(src->desc().shape, weights->desc().shape, _info);
-    output_info.shape             = output_shape;
-
+    TensorDescriptor output_info = compute_output_descriptor(src->desc(), weights->desc(), _info);
     if(!_out_quant_info.empty())
     {
         output_info.quant_info = _out_quant_info;
@@ -99,11 +105,6 @@ TensorDescriptor ConvolutionLayerNode::configure_output(size_t idx) const
     return output_info;
 }
 
-Status ConvolutionLayerNode::validate()
-{
-    return Status{};
-}
-
 NodeType ConvolutionLayerNode::type() const
 {
     return NodeType::ConvolutionLayer;