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>

2 files changed, 19 insertions, 17 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;
     }
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index 3b8b4243e5..d9707d95e0 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -170,7 +170,7 @@ Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInf
                                       bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height,
                                       bool &is_fully_connected_convolution, bool &is_interleaved, bool &is_quantized,
                                       unsigned int &mat_weights_cols, unsigned int &mat_weights_rows,
-                                      unsigned int &conv_w, unsigned int &conv_h)
+                                      unsigned int &conv_w, unsigned int &conv_h, const Size2D &dilation)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
@@ -205,7 +205,7 @@ Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInf
     mat_weights_rows     = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
 
     std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
-                                                 conv_info);
+                                                 conv_info, dilation);
 
     // Check if its a "fully connected" convolution
     is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
@@ -246,7 +246,8 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w
     }
 }
 
-void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+void NEGEMMConvolutionLayer::configure(const 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);
@@ -262,7 +263,7 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
     Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _append_bias, _are_weights_reshaped,
                                                    kernel_width, kernel_height,
                                                    _is_fully_connected_convolution, _is_interleaved, _is_quantized,
-                                                   mat_weights_cols, mat_weights_rows, conv_w, conv_h);
+                                                   mat_weights_cols, mat_weights_rows, conv_w, conv_h, dilation);
 
     ARM_COMPUTE_ERROR_THROW_ON(status);
 
@@ -362,7 +363,7 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
 
     // Configure kernels
     // Configure im2col
-    _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias);
+    _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias, false, false, dilation);
 
     // Configure matrix multiply
     if(run_optimised)
@@ -420,7 +421,7 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
 }
 
 Status NEGEMMConvolutionLayer::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)
 {
     ARM_COMPUTE_UNUSED(output);
 
@@ -439,7 +440,7 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
 
     Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, append_bias, are_weights_reshaped, kernel_width, kernel_height,
                                                    is_fully_connected_convolution, is_interleaved, is_quantized, mat_weights_cols, mat_weights_rows,
-                                                   conv_w, conv_h);
+                                                   conv_w, conv_h, dilation);
 
     const Size2D kernel_weights = Size2D(kernel_width, kernel_height);
 
@@ -517,7 +518,7 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
     shape_im2col.set(1, mat_input_rows);
     shape_im2col.set(2, 1);
     TensorInfo im2_col_info = input->clone()->set_tensor_shape(shape_im2col);
-    ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, kernel_weights, conv_info, append_bias, false));
+    ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, kernel_weights, conv_info, append_bias, false, false, dilation));
 
     // Create GEMM output tensor
     TensorShape shape_gemm(im2_col_info.tensor_shape());