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author | Alex Gilday <alexander.gilday@arm.com> | 2018-03-23 14:16:00 +0000 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:49:16 +0000 |
commit | 7da29b6b12ff319ed2b6e2c46588dfa1991556fb (patch) | |
tree | 24e766d916ae8da32deb5cd4fac4d82207cbe6ea /src/runtime/CL/functions/CLConvolutionLayer.cpp | |
parent | f92cb23f06572fe73ec5ab9da0ec5713724c2dde (diff) | |
download | ComputeLibrary-7da29b6b12ff319ed2b6e2c46588dfa1991556fb.tar.gz |
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>
Diffstat (limited to 'src/runtime/CL/functions/CLConvolutionLayer.cpp')
-rw-r--r-- | src/runtime/CL/functions/CLConvolutionLayer.cpp | 18 |
1 files changed, 10 insertions, 8 deletions
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp index 1a486ce5c7..64bda93ff0 100644 --- a/src/runtime/CL/functions/CLConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp @@ -42,13 +42,14 @@ CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_ma { } -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 @@ void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, c 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 @@ void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, c } 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 @@ Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo 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 @@ Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo } 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 @@ ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo * ARM_COMPUTE_UNUSED(conv_info); ARM_COMPUTE_UNUSED(weights_info); ARM_COMPUTE_UNUSED(gpu_target); + ARM_COMPUTE_UNUSED(dilation); return ConvolutionMethod::GEMM; } |