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>
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
index 1a486ce..64bda93 100644
--- a/src/runtime/CL/functions/CLConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp
@@ -42,13 +42,14 @@
{
}
-void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
+ const Size2D &dilation)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info));
+ ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation));
switch(CLConvolutionLayer::get_convolution_method(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info,
- weights_info, CLScheduler::get().target()))
+ weights_info, CLScheduler::get().target(), dilation))
{
case ConvolutionMethod::DIRECT:
{
@@ -60,7 +61,7 @@
case ConvolutionMethod::GEMM:
{
auto f = arm_compute::support::cpp14::make_unique<CLGEMMConvolutionLayer>(_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;
}
@@ -71,14 +72,14 @@
}
Status CLConvolutionLayer::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_RETURN_ERROR_ON_NULLPTR(input, weights, output);
//Configure if the parameters match the direct convolution or the gemm-based
const GPUTarget gpu_target = CLScheduler::get().target();
- switch(CLConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info, gpu_target))
+ switch(CLConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info, gpu_target, dilation))
{
case ConvolutionMethod::DIRECT:
{
@@ -89,7 +90,7 @@
case ConvolutionMethod::GEMM:
{
// Validate gemm-based convolution layer
- CLGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info);
+ CLGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info, dilation);
break;
}
default:
@@ -101,7 +102,7 @@
}
ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
- const WeightsInfo &weights_info, const GPUTarget gpu_target)
+ const WeightsInfo &weights_info, const GPUTarget gpu_target, const Size2D &dilation)
{
ARM_COMPUTE_UNUSED(input);
ARM_COMPUTE_UNUSED(weights);
@@ -110,6 +111,7 @@
ARM_COMPUTE_UNUSED(conv_info);
ARM_COMPUTE_UNUSED(weights_info);
ARM_COMPUTE_UNUSED(gpu_target);
+ ARM_COMPUTE_UNUSED(dilation);
return ConvolutionMethod::GEMM;
}
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index bc339f1..e7ad62f 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -151,7 +151,8 @@
return Status{};
}
-void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
+ const Size2D &dilation)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
@@ -160,7 +161,8 @@
biases != nullptr ? biases->info() : nullptr,
output->info(),
conv_info,
- weights_info));
+ weights_info,
+ dilation));
_is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
@@ -187,7 +189,7 @@
const unsigned int kernel_width = weights->info()->dimension(0);
const unsigned int kernel_height = weights->info()->dimension(1);
std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
- conv_info);
+ conv_info, dilation);
unsigned int mat_weights_cols = weights->info()->dimension(3);
unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
@@ -224,7 +226,7 @@
_memory_group.manage(&_gemm_output);
// Configure im2col
- _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias);
+ _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation);
// Configure GEMM
configure_mm(&_im2col_output, weights, &_gemm_output);
@@ -260,7 +262,7 @@
}
Status CLGEMMConvolutionLayer::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_RETURN_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!");
@@ -282,7 +284,7 @@
const unsigned int kernel_width = weights->dimension(0);
const unsigned int kernel_height = weights->dimension(1);
- std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height, conv_info);
+ std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height, conv_info, dilation);
unsigned int mat_weights_cols = weights->dimension(3);
unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + bias_element;
@@ -298,7 +300,7 @@
shape_im2col.set(2, 1);
TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->fixed_point_position());
im2col_reshaped_info.set_quantization_info(input->quantization_info());
- CLIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias);
+ CLIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation);
// Create GEMM output tensor
TensorShape shape_gemm = im2col_reshaped_info.tensor_shape();