diff options
Diffstat (limited to 'src/runtime/CL/functions/CLConvolutionLayer.cpp')
| -rw-r--r-- | src/runtime/CL/functions/CLConvolutionLayer.cpp | 37 |
1 files changed, 28 insertions, 9 deletions
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp index bcb5424aab..643e24d638 100644 --- a/src/runtime/CL/functions/CLConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp @@ -48,9 +48,16 @@ void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, c 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, dilation, act_info)); - switch(CLConvolutionLayer::get_convolution_method(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, + switch(CLConvolutionLayer::get_convolution_method(input->info(), weights->info(), output->info(), conv_info, weights_info, act_info, CLScheduler::get().target(), dilation)) { + case ConvolutionMethod::WINOGRAD: + { + auto f = arm_compute::support::cpp14::make_unique<CLWinogradConvolutionLayer>(); + f->configure(input, weights, biases, output, conv_info); + _function = std::move(f); + break; + } case ConvolutionMethod::DIRECT: { auto f = arm_compute::support::cpp14::make_unique<CLDirectConvolutionLayer>(); @@ -79,8 +86,14 @@ Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo //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, act_info, gpu_target, dilation)) + switch(CLConvolutionLayer::get_convolution_method(input, weights, output, conv_info, weights_info, act_info, gpu_target, dilation)) { + case ConvolutionMethod::WINOGRAD: + { + //Validate Winograd + CLWinogradConvolutionLayer::validate(input, weights, biases, output, conv_info); + break; + } case ConvolutionMethod::DIRECT: { // Validate direct convolution layer @@ -101,19 +114,25 @@ Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo return Status{}; } -ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, +ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation) { - ARM_COMPUTE_UNUSED(input); - ARM_COMPUTE_UNUSED(weights); - ARM_COMPUTE_UNUSED(biases); + ARM_COMPUTE_ERROR_ON_NULLPTR(input); + ARM_COMPUTE_ERROR_ON_NULLPTR(output); + ARM_COMPUTE_ERROR_ON_NULLPTR(weights); ARM_COMPUTE_UNUSED(output); - ARM_COMPUTE_UNUSED(conv_info); ARM_COMPUTE_UNUSED(weights_info); ARM_COMPUTE_UNUSED(gpu_target); - ARM_COMPUTE_UNUSED(dilation); - ARM_COMPUTE_UNUSED(act_info); + const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); + const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); + const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL); + + if((input->data_type() == DataType::F32) && (input->data_layout() == DataLayout::NCHW) && (input->dimension(idx_c) > 3) && (weights->dimension(idx_w) == 3) && (weights->dimension(idx_h) == 3) + && (weights->num_dimensions() <= 4) && (conv_info.stride().first == 1) && (conv_info.stride().second == 1) && (dilation == Size2D(1U, 1U)) && (!act_info.enabled())) + { + return ConvolutionMethod::WINOGRAD; + } return ConvolutionMethod::GEMM; } |