COMPMID-1017: Implement dilated convolution in NEON, OpenCL, and GC

Change-Id: If4626ec9e215e14dffe22e80812da5bac84a52e2
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125734
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

1 files changed, 10 insertions, 9 deletions

diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
index d4421e8429..e659495b7c 100644
--- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
@@ -40,14 +40,15 @@ NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr<IMemoryManager> memory_ma
 {
 }
 
-void NEConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+void NEConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
+                                   const Size2D &dilation)
 {
     // Perform validate step
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
-    ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info));
+    ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation));
 
     switch(NEConvolutionLayer::get_convolution_method(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info,
-                                                      weights_info))
+                                                      weights_info, dilation))
     {
         case ConvolutionMethod::WINOGRAD:
         {
@@ -59,7 +60,7 @@ void NEConvolutionLayer::configure(ITensor *input, const ITensor *weights, const
         case ConvolutionMethod::GEMM:
         {
             auto f = arm_compute::support::cpp14::make_unique<NEGEMMConvolutionLayer>(_memory_manager);
-            f->configure(input, weights, biases, output, conv_info, weights_info);
+            f->configure(input, weights, biases, output, conv_info, weights_info, dilation);
             _function = std::move(f);
             break;
         }
@@ -77,9 +78,9 @@ void NEConvolutionLayer::configure(ITensor *input, const ITensor *weights, const
 }
 
 Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
-                                    const WeightsInfo &weights_info)
+                                    const WeightsInfo &weights_info, const Size2D &dilation)
 {
-    switch(NEConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info))
+    switch(NEConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info, dilation))
     {
         case ConvolutionMethod::WINOGRAD:
             //Validate Winograd
@@ -87,7 +88,7 @@ Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo
             break;
         case ConvolutionMethod::GEMM:
             //Validate Gemm-based Convolution
-            NEGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info);
+            NEGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info, dilation);
             break;
         case ConvolutionMethod::DIRECT:
             //Validate Gemm-based Convolution
@@ -101,12 +102,12 @@ Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo
 }
 
 ConvolutionMethod NEConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
-                                                             const WeightsInfo &weights_info)
+                                                             const WeightsInfo &weights_info, const Size2D &dilation)
 {
     ARM_COMPUTE_UNUSED(output);
     ARM_COMPUTE_UNUSED(weights_info);
     if((input->data_type() == DataType::F32) && (weights->dimension(0) == 3) && (weights->dimension(1) == 3) && (weights->num_dimensions() <= 4) && (conv_info.stride().first == 1)
-       && (conv_info.stride().second == 1) && (biases != nullptr))
+       && (conv_info.stride().second == 1) && (biases != nullptr) && (dilation == Size2D(1U, 1U)))
     {
         return ConvolutionMethod::WINOGRAD;
     }